Connecting to Google Analytics

For information on the available WHL files for supported environments, and how to install the appropriate file for your Python distribution, see Package Installation.

For information on the module to import, and how to configure the necessary connection properties in a connection string, see Establishing a Connection.

Other available connection properties can be used to configure other aspects of the connector capabilities.

Python Version Support

The CData Python Connector for Google Analytics can be installed and used in Python 3.10 or newer.

Google Analytics Version Support

The connector supports Google Analytics 4 APIs. The connector provides a relational view of the Google Analytics profiles in your Google account or across your Google Apps domain. The connector includes tables that contain often-used dimensions and metrics as columns; additionally, you can customize the table schemas or write your own to combine any valid set of dimensions and metrics. The connector exposes the columns available through the Data API (Google Analytics 4) and the Admin API (Google Analytics 4). You must enable these APIs by creating a project in the Google Developers Console. See "Connecting to Google Analytics" for a guide to creating a project and authenticating to the APIs.

See Also

• Using the Connector: Establish connections and query Google Analytics through Python code.
• From SQLAlchemy: Use SQLAlchemy to establish a connection with dialect URL, and interact with Google Analytics data using mapped classes and Sessions.

Dependencies

The Python connectors require that Python 3.10 or newer be installed.

Installation

The CData Python Connector for Google Analytics is available as a WHL file for Windows, Linux, and Mac. Each connector is built using the Python 3.10 Stable ABI (indicated by the abi3 tag in the filename), so a single wheel supports any Python 3.10 or newer installation — there is no need to match your exact Python minor version. Use the "pip install" command with the appropriate WHL file for your platform.

Windows:

pip install cdata_googleanalytics_connector-26.0.9657-cp310-abi3-win_amd64.whl

Linux:

pip install cdata_googleanalytics_connector-26.0.9657-cp310-abi3-linux_x86_64.whl

macOS:

pip install cdata_googleanalytics_connector-26.0.9657-cp310-abi3-macosx_12_0_arm64.whl

The macOS wheel supports arm64 (Apple Silicon) architectures only on macOS 12 and newer.

Regardless of the environment, certain distributions might require that the "pip3 install" command be used instead, to differentiate from a Python 2 distribution that might exist already. After installation, confirm whether the connector is successfully installed by running the "pip list" command. If "cdata_googleanalytics_connector" is present in the list output by the command, then the installation was successful.

Upgrading

When upgrading, "pip install" does not automatically clean up old JRE files. To avoid leftover files that could cause JVM errors, uninstall the previous version before installing the new one.

Licensing

After the installation is complete, a separate step is needed to activate a license for the connector. Among the CData assets in the distribution's site packages, there is an install-license tool that activates this license. From within the distribution's site-packages folder, after navigating to the "cdata/installlic_googleanalytics" folder, simply use a command like the below to activate the license. Omitting the <key> argument activates a trial license:

• Windows:
  

  ./install-license.exe <key>

• Linux / Mac:
  

  ./install-license.sh <key>

Sometimes, file access issues may cause pip to install the connector in a fallback file path that is not the python distribution's main or primary site-packages location. This can make it difficult to find where the connector was installed, and from there, the license activator. In that event, this python script below will print out the full file path of the connector's native file. This file will be stored in the mentioned cdata folder, from which the installlic_googleanalytics folder is trivial to find:

import os
import cdata.googleanalytics
path = os.path.abspath(cdata.googleanalytics.__file__)
print(path)

Uninstallation

If the connector needs to be uninstalled for any reason, do so by running the pip uninstall command, as in the example below:

pip uninstall cdata-googleanalytics-connector

The objects available within our connector are accessible from the "cdata.googleanalytics" module. To use the module's objects directly:

1. Import the module as follows:
   

   import cdata.googleanalytics as mod

2. To establish a connection string, call the connect() method from the connector object using an appropriate connection string, such as:
   

   mod.connect("InitiateOAuth=GETANDREFRESH;")

Connecting to Google Analytics

Google Analytics 4

Provide the following connection properties before adding the authentication properties.

• Schema: Set this to GoogleAnalytics4.
• PropertyId: Set this to the Google Analytics profile or view you want to connect to. This value can be retrieved from the Properties table. If this is not specified, the first Profile returned will be used.

Authenticating to Google Analytics

The connector supports using user accounts, service accounts and GCP instance accounts for authentication.

The following sections discuss the available authentication schemes for Google Analytics:

• User Accounts (OAuth)
• Service Account (OAuthJWT)
• GCP Instance Account

User Accounts (OAuth)

AuthScheme must be set to OAuth in all user account flows.

Desktop Applications

CData provides an embedded OAuth application that simplifies OAuth desktop Authentication. Alternatively, you can create a custom OAuth application. See Creating a Custom OAuth App for information about creating custom applications and reasons for doing so.

For authentication, the only difference between the two methods is that you must set two additional connection properties when using custom OAuth applications.

After setting the following connection properties, you are ready to connect:

• InitiateOAuth: Set this to GETANDREFRESH, which instructs the connector to automatically attempt to get and refresh the OAuth access token.
• OAuthClientId: (custom applications only) Set this to the Client Id in your custom OAuth application settings.
• OAuthClientSecret: (custom applications only) Set this to the Client Secret in the custom OAuth application settings.

When you connect the connector opens the OAuth endpoint in your default browser. Log in and grant permissions to the application. The connector then completes the OAuth process as follows:

• Extracts the access token from the callback URL.
• Obtains a new access token when the old one expires.
• Saves OAuth values in OAuthSettingsLocation that persist across connections.

Web Applications

When connecting via a Web application, you need to create and register a custom OAuth application with Google Analytics. You can then use the connector to acquire and manage the OAuth token values. See Creating a Custom OAuth App for more information about custom applications.

Get an OAuth Access Token

Set the following connection properties to obtain the OAuthAccessToken:

• OAuthClientId: Set this to the Client Id in your application settings.
• OAuthClientSecret: Set this to the Client Secret in your application settings.

Then call stored procedures to complete the OAuth exchange:

1. Call the GetOAuthAuthorizationURL stored procedure. Set the CallbackURL input to the Callback URL you specified in your application settings. The stored procedure returns the URL to the OAuth endpoint.
2. Navigate to the URL that the stored procedure returned in Step 1. Log in to the custom OAuth application and authorize the web application. Once authenticated, the browser redirects you to the callback URL.
3. Call the GetOAuthAccessToken stored procedure. Set AuthMode to WEB and the Verifier input to the "code" parameter in the query string of the callback URL.

Once you have obtained the access and refresh tokens, you can connect to data and refresh the OAuth access token either automatically or manually.

Automatic Refresh of the OAuth Access Token

To have the driver automatically refresh the OAuth access token, set the following on the first data connection:

• InitiateOAuth: Set this to REFRESH.
• OAuthClientId: Set this to the Client Id in your application settings.
• OAuthClientSecret: Set this to the Client Secret in your application settings.
• OAuthAccessToken: Set this to the access token returned by GetOAuthAccessToken.
• OAuthRefreshToken: Set this to the refresh token returned by GetOAuthAccessToken.
• OAuthSettingsLocation: Set this to the location where the connector saves the OAuth token values, which persist across connections.

On subsequent data connections, the values for OAuthAccessToken and OAuthRefreshToken are taken from OAuthSettingsLocation.

Manual Refresh of the OAuth Access Token

The only value needed to manually refresh the OAuth access token when connecting to data is the OAuth refresh token.

Use the RefreshOAuthAccessToken stored procedure to manually refresh the OAuthAccessToken after the ExpiresIn parameter value returned by GetOAuthAccessToken has elapsed, then set the following connection properties:

• OAuthClientId: Set this to the Client Id in your application settings.
• OAuthClientSecret: Set this to the Client Secret in your application settings.

Then call RefreshOAuthAccessToken with OAuthRefreshToken set to the OAuth refresh token returned by GetOAuthAccessToken. After the new tokens have been retrieved, open a new connection by setting the OAuthAccessToken property to the value returned by RefreshOAuthAccessToken.

Finally, store the OAuth refresh token so that you can use it to manually refresh the OAuth access token after it has expired.

Headless Machines

To configure the driver to use OAuth with a user account on a headless machine, you need to authenticate on another device that has an internet browser.

1. Choose one of two options:
   • Option 1: Obtain the OAuthVerifier value as described in "Obtain and Exchange a Verifier Code" below.
   • Option 2: Install the connector on a machine with an internet browser and transfer the OAuth authentication values after you authenticate through the usual browser-based flow, as described in "Transfer OAuth Settings" below.
2. Then configure the connector to automatically refresh the access token on the headless machine.

Option 1: Obtain and Exchange a Verifier Code

To obtain a verifier code, you must authenticate at the OAuth authorization URL.

Follow the steps below to authenticate from the machine with an internet browser and obtain the OAuthVerifier connection property.

1. Choose one of these options:
   • If you are using the Embedded OAuth Application click Google Analytics OAuth endpoint to open the endpoint in your browser.
   • If you are using a custom OAuth application, create the Authorization URL by setting the following properties:
     • InitiateOAuth: Set to OFF.
     • OAuthClientId: Set to the client Id assigned when you registered your application.
     • OAuthClientSecret: Set to the client secret assigned when you registered your application.
     Then call the GetOAuthAuthorizationURL stored procedure with the appropriate CallbackURL. Open the URL returned by the stored procedure in a browser.
2. Log in and grant permissions to the connector. You are then redirected to the callback URL, which contains the verifier code.
3. Save the value of the verifier code. Later you will set this in the OAuthVerifier connection property.

Next, you need to exchange the OAuth verifier code for OAuth refresh and access tokens. Set the following properties:

On the headless machine, set the following connection properties to obtain the OAuth authentication values:

• InitiateOAuth: Set this to REFRESH.
• OAuthVerifier: Set this to the verifier code.
• OAuthClientId: (custom applications only) Set this to the Client Id in your custom OAuth application settings.
• OAuthClientSecret: (custom applications only) Set this to the Client Secret in the custom OAuth application settings.
• OAuthSettingsLocation: Set this to persist the encrypted OAuth authentication values to the specified location.

After the OAuth settings file is generated, you need to re-set the following properties to connect:

• InitiateOAuth: Set this to REFRESH.
• OAuthClientId: (custom applications only) Set this to the client Id assigned when you registered your application.
• OAuthClientSecret: (custom applications only) Set this to the client secret assigned when you registered your application.
• OAuthSettingsLocation: Set this to the location containing the encrypted OAuth authentication values. Make sure this location gives read and write permissions to the connector to enable the automatic refreshing of the access token.

Option 2: Transfer OAuth Settings

Prior to connecting on a headless machine, you need to create and install a connection with the driver on a device that supports an internet browser. Set the connection properties as described in "Desktop Applications" above.

After completing the instructions in "Desktop Applications", the resulting authentication values are encrypted and written to the location specified by OAuthSettingsLocation. The default filename is OAuthSettings.txt.

Once you have successfully tested the connection, copy the OAuth settings file to your headless machine.

On the headless machine, set the following connection properties to connect to data:

• InitiateOAuth: Set this to REFRESH.
• OAuthClientId: (custom applications only) Set this to the client Id assigned when you registered your application.
• OAuthClientSecret: (custom applications only) Set this to the client secret assigned when you registered your application.
• OAuthSettingsLocation: Set this to the location of your OAuth settings file. Make sure this location gives read and write permissions to the connector to enable the automatic refreshing of the access token.

Service Accounts (OAuthJWT)

To authenticate using a service account, you must create a new service account and have a copy of the accounts certificate. If you do not already have a service account, you can create one by following the procedure in Creating a Custom OAuth App.

For a JSON file, set these properties:

• AuthScheme: Set this to OAuthJWT.
• InitiateOAuth: Set this to GETANDREFRESH.
• OAuthJWTCertType: Set this to GOOGLEJSON.
• OAuthJWTCert: Set this to the path to the .json file provided by Google.
• OAuthJWTSubject: (optional) Only set this value if the service account is part of a GSuite domain and you want to enable delegation. The value of this property should be the email address of the user whose data you want to access.

For a PFX file, set these properties instead:

• AuthScheme: Set this to OAuthJWT.
• InitiateOAuth: Set this to GETANDREFRESH.
• OAuthJWTCertType: Set this to PFXFILE.
• OAuthJWTCert: Set this to the path to the .pfx file provided by Google.
• OAuthJWTCertPassword: (optional) Set this to the .pfx file password. In most cases you must provide this since Google encrypts PFX certificates.
• OAuthJWTCertSubject: (optional) Set this only if you are using a OAuthJWTCertType which stores multiple certificates. Should not be set for PFX certificates generated by Google.
• OAuthJWTIssuer: Set this to the email address of the service account. This address will usually include the domain iam.gserviceaccount.com.
• OAuthJWTSubject: (optional) Only set this value if the service account is part of a GSuite domain and you want to enable delegation. The value of this property should be the email address of the user whose data you want to access.

GCP Instance Accounts

When running on a GCP virtual machine, the connector can authenticate using a service account tied to the virtual machine. To use this mode, set AuthScheme to GCPInstanceAccount.

Java Native Interface (JNI) is a standard programming interface for writing Java native methods and embedding the Java virtual machine into native applications.

The connector leverages the JNI for improved performance on Mac and Linux.

Configure the Config INI File

The Linux and Mac editions of the Google Analytics python connector are configured with an ini file. This file is used to set several parameters, including JNI behavior. This file is to be located in:

{path_to_distribution_site-packages}/cdata/config.ini

Ensure that any configuration properties you set in the ini file fall under the following section name (adjust the 311 number if you are using an different python version from 3.11):

• For Linux:
  

  [googleanalytics.cpython-311-x86_64-linux-gnu.so]

• For Mac:
  

  [googleanalytics.cpython-311-darwin.so]

Configure the JNI connector's behavior by editing the properties in the connector's config.ini file. The connector can be configured as follows:

• LOGFILE: Set this the same way as the CDATA_LOGFILE envrionment variable below.
• JAVA_HOME: Configure the path to the JVM library location used to launch the JVM.
• CLASS_PATH: Use a colon-separated list to configure the paths to the third-party jar libraries.

Configure Environment Variables

Additionally, set the following environment variables:

• CDATA_JAVA_HOME: Configure the path to the JVM library location used to launch the JVM.
• CDATA_JVM_OPTIONS: Place JVM options here.
• CDATA_LOGFILE: Set this in the following scheme: <SCHEME>://<TAG>[|<LEVEL>]
  • SCHEME: The options are STDOUT, FILE.
    • STDOUT: Both the native wrapper and odbc core log into stdout. The Logfile and Verbosity properties can override the behavior of ODBC core.
    • FILE: The native wrapper logs into <FILENAME> while the odbc core logs into <FILENAME>.driver.log. The Logfile and Verbosity properties can override the behavior of ODBC core.
  • TAG
    • For STDOUT, set this to 1. For FILE, set this to the filename.
  • LEVEL
    • Set to one of: FATAL | ERROR | WARNING | INFO | DEBUG

The following are some examples of this syntax:

• STDOUT://1|DEBUG
• FILE:///tmp/my_py.log|DEBUG

Custom Logger

The Python connector supports a custom logging mechanism for redirecting log output to any destination, such as a cloud storage service or logging framework. Use setCustomLoggerFactory() to register a factory function that creates a logger instance for each connection.

The factory function receives the context string from the Logfile connection property (the portion after CUSTOM://) and must return an object with a writeLog(verbosity, message) method.

To enable custom logging:

1. Call setCustomLoggerFactory() with your factory function before opening connections.
2. Set Logfile to CUSTOM:// followed by a context string to identify the connection.
3. Set Verbosity to the desired log level.

The following example demonstrates a custom logger factory that creates a separate logger instance per connection:

import cdata.googleanalytics as mod
import time

class MyLogger:
    def __init__(self, loggerId):
        self.loggerId = loggerId
    def writeLog(self, verbosity, message):
        print("[MyLogger " + self.loggerId + "] " + message)

def createLogger(context):
    return MyLogger(context[len("MyLoggerId="):])

mod.setCustomLoggerFactory(createLogger)

conn1 = mod.connect("...;Logfile=CUSTOM://MyLoggerId=1;Verbosity=2;")
# do something with conn1
time.sleep(1)  # Wait for logs to flush from conn1

conn2 = mod.connect("...;Logfile=CUSTOM://MyLoggerId=2;Verbosity=2;")
# do something with conn2
time.sleep(1)  # Wait for logs to flush from conn2

Creating a Custom OAuth Application

CData embeds OAuth Application Credentials with CData branding that can be used when connecting to Google Analytics via a desktop application or a headless machine.

(For information on getting and setting the OAuthAccessToken and other configuration parameters, see the Desktop Authentication section of "Connecting to Google Analytics".)

However, you must create a custom OAuth application to connect to Google Analytics via the Web. And since custom OAuth applications seamlessly support all three commonly-used auth flows, you might want to create custom OAuth applications (use your own OAuth Application Credentials) for those auth flows anyway.

Custom OAuth applications are useful if you want to:

• control branding of the authentication dialog
• control the redirect URI that the application redirects the user to after the user authenticates
• customize the permissions that you are requesting from the user

The following sections describe how to enable the Directory API and create custom OAuth applications for user accounts (OAuth) and Service Accounts (OAuth/JWT).

Enable the Google Analytics API

Follow these steps to enable the Google Analytics API:

1. Navigate to the Google Cloud Console.
2. Select Library from the left-hand navigation menu. This opens the Library page.
3. In the search field, enter "Google Analytics API" and select Google Analytics API and Google Analytics Admin API from the search results.
4. On the Google Analytics API page, ENABLE both APIs.

Adding Scopes to Your Custom OAuth App

1. In the Google Cloud Console, go to OAuth consent screen > Data Access.
2. Manually add the required scopes and click Update.
3. Click Add or Remove Scopes.
4. Specify the required google analytics scope Google APIs and the newly added scope in your OAuth app in the Scope connection property separated by space.

Create an OAuth Application

To create custom OAuth applications that retrieve the necessary OAuth connection properties, follow these procedures.

User Accounts (OAuth)

For users whose AuthScheme is OAuth and who need to authenticate over a web application, you must always create a custom OAuth application. (For desktop and headless flows, creating a custom OAuth application is optional.)

Do the following:

1. Navigate to the Google Cloud Console.
2. Create a new project or select an existing project.
3. At the left-hand navigation menu, select Credentials.
4. If this project does not already have a consent screen configured, click CONFIGURE CONSENT SCREEN to create one. If you are not using a Google Workspace account, you are restricted to creating an External-type Consent Screen, which requires specifying a support email and developer contact email. Additional info is optional.
5. On the Credentials page, select Create Credentials > OAuth Client ID.
6. In the Application Type menu, select Web application.
7. Specify a name for your custom OAuth application.
8. Under Authorized redirect URIs, click ADD URI and enter a redirect URI.
9. Click Enter, then CREATE. The Cloud Console returns you to the Credentials page.
   A window opens that displays your client Id and client secret.
10. Record the client Id and Client Secret for later use as the OAuthClientId and OAuthClientSecret connection properties.

Note: The client secret remains accessible from from the Google Cloud Console.

Service Accounts (OAuthJWT)

Service accounts (AuthScheme OAuthJWT) can be used in an OAuth flow to access Google APIs on behalf of users in a domain. A domain administrator can delegate domain-wide access to the service account.

To create a new service account:

1. Navigate to the Google Cloud Console.
2. Create a new project or select an existing project.
3. At the left-hand navigation menu, select Credentials.
4. Select Create Credentials > Service account.
5. On the Create service account page, enter the service account name, ID, and an optional description.
6. Click DONE. The Cloud Console redisplays the Credentials page.
7. In the Service Accounts section, select the service account you just created.
8. Click the Advanced Settings section and enable Domain-Wide Delegation.
9. Record the Client ID shown for domain-wide delegation. You'll use this in the Admin Console.
10. In a new tab, navigate to the Google Admin Console.
11. Go to Security > API Controls > Domain-Wide Delegation.
12. Click Manage Domain-Wide Delegation, then Add new.
13. Enter the recorded Client ID and the list of required scopes. See OAuth Scopes and Endpoints for more details.
14. Back in the Cloud Console, select the KEYS tab for the service account.
15. Click ADD KEY > Create new key.
16. Select a supported key type (see OAuthJWTCert and OAuthJWTCertType).
17. Click CREATE. The key is automatically downloaded to your device.
18. Record the additional information for later use.

In the service account flow, the connector exchanges a JSON Web Token (JWT) for the OAuthAccessToken. The private key downloaded in the steps above is used to sign the JWT. The connector inherits the permissions granted to the service account, including any scopes configured through domain-wide delegation.

Google Analytics data is organized into various metrics (Sessions, Impressions, AdClicks, etc.), which can be queried over various dimensions (Country, Month, etc.). There are many valid combinations of metrics and dimensions. The connector surfaces some of the most commonly used combinations as tables for ease of use.

Additionally, the connector allows you to query all valid combinations, even those not included in the predefined tables, using two methods: by using the Dimensions and Metrics columns and by defining custom schemas. Refer to Advanced Queries for more information. Below is a guide to getting started with the default tables.

Selecting Dimensions and Measures

The dimensions and metrics are clearly defined for each table and can be seen in the Data Model. Simply select the metrics and dimensions you are interested in. For example, to find the number of sessions in each month, query the Session metric over the Month dimension. This would return 12 rows: one for each month.

SELECT Sessions, Month FROM Traffic 

To separate out the months in each year, include both the month and the year dimensions in the query:

SELECT Sessions, Month, Year FROM Traffic 

Date Ranges

All Google Analytics reports cover a specific date range. The default behavior is to pull the last month of data if the StartDate and EndDate inputs are left unset. To override this behavior, the values can be set directly in the query. For example:

SELECT Sessions, Month, Year FROM Traffic WHERE StartDate = '90daysAgo' AND EndDate = 'Today'

The supported inputs for StartDate and EndDate in the Google Analytics API are 'today', 'yesterday', 'NdaysAgo' (where N is some number), and an exact date. Starting with the v4 API, up to two separate date ranges can be set in the filter.

SELECT * Query

Unlike most database tables, it is not very helpful to select all metrics and dimensions in a given table.

In some cases, it is not possible to do this since Google Analytics allows a maximum of nine dimensions and 10 metrics in a single query.

The connector interprets the SELECT * query as a request for a default set of metrics and dimensions.

This includes queries that explicitly select all columns. For schemas with fewer than 10 metrics, all metrics are returned.

Refer to the individual view's documentation in Data Model to see which fields are the default for each schema.

Google Analytics has a very large number of metrics and dimensions that would clutter table definitions, so the table definitions included with the product only list the most commonly used combinations. We offer two alternatives to this design choice: You can use the Dimensions and Metrics columns to request fields that are not in the default table, or you can define your own table.

Using the Dimensions and Metrics Columns

To request additional dimensions or metrics for any existing table, the recommended approach is to define custom schemas; however, you can also set the Dimensions and Metrics inputs in the WHERE clause. Both inputs take a comma-separated list so that you can specify multiple fields at once. The values will be returned in the corresponding Dimensions and Metrics column in the same order that you submitted them. For example, the following query will query the Traffic table for Sessions, the Goal 1 Conversion Rate, and Goal 1 Completions and group these metrics together by the User Age Bracket dimension:

SELECT Sessions, Dimensions, Metrics FROM Traffic WHERE Dimensions='UserAgeBracket' AND Metrics='Goal1ConversionRate,Goal1Completions'

In the results from the query above, the value for UserAgeBracket will be returned in the Dimensions field for each row. The Metrics field will contain a comma-separated value containing the requested metrics for Goal 1.

Defining Custom Schemas

If you routinely need to request fields that are not available on the standard tables that ship with the connector, you may want to define your own custom schema so that you can more easily query for the data you need. The CreateCustomSchema stored procedure can be used to define entirely new tables, or you can modify existing schemas to add the columns you need.

The stored procedure outputs schema files, which have a simple format that makes them easy to edit directly.

Using the CreateCustomSchema Stored Procedure

The connector also offers the CreateCustomSchema stored procedure for creating new table definitions. The stored procedure takes a table name, a comma-separated list of metrics, a comma-separated list of dimensions, and an output folder as inputs. Calling the procedure creates a new schema file, which exposes the content as though it were any other queryable table.

Set the Location connection property to a preferred path. A folder (bearing the name of the schema this procedure is run from) will be created inside the Location, if it doesn't exist already. The schema files will be placed in that child folder. If the aforementioned child folder already exists inside the Location, the schema files will be placed there.

The example stored procedure call below persists the columns of the SELECT query in Using the Dimensions and Metrics Columns:

EXEC CreateCustomSchema TableName='Traffic', Dimensions='UserAgeBracket', Metrics='Sessions,Goal1ConversionRate,Goal1Completions',OutputFolder='C:\Users\Administrator\Desktop'

Edit an Existing Schema Manually

To add fields to an existing schema, open the corresponding .rsd file in the installation directory for the connector and follow the steps below:

1. Add an attr tag in the <rsb:info> section to add a column.
2. Add the following attributes. Any of the existing fields can serve as an example.

   Attribute Name	Attribute Value
   name	Set this to a dimension or metric as defined in the API documentation.
   xs:type	Set this to the data type.
   other:dimension	If you want to define a column for a dimension, set this to "true".
   other:metric	If you want to define a column for a metric, set this to "true".

3. To use the new files, set the Location connection property to the folder containing the script files.

Required Scopes and Endpoint Domains for Google Analytics

When integrating with Google Analytics, your application needs specific permissions to interact with the API.

These permissions are defined by access scopes, which determine what data your application can access and what actions it can perform.

This topic provides information about the required access scopes and endpoint domains for the Google Analytics connector.

Understanding Scopes

Scopes are a way to limit an application's access to a user's data. They define the specific actions that an application can perform on behalf of the user.

For example, a read-only scope might allow an application to view data, while a full access scope might allow it to modify data.

Required Scopes for Google Analytics

Scope	Description
googleapis.com/auth/analytics.readonly	Read-only access to the Google Analytics API. Accepted values are NONE, CONSENT, and the default SELECT ACCOUNT.

Understanding Endpoint Domains

Endpoint domains are the specific URLs that the application needs to communicate with in order to authenticate, retrieve records, and perform other essential operations.

Allowlisting these domains ensures that the network traffic between your application and the API is not blocked by firewalls or security settings.

Note: Most users do not need to make any special configurations. Allowlisting is typically only necessary for environments with strict security measures, such as restricted outbound network traffic.

Required Endpoint Domains for Google Analytics

Domain	Always Required?	Description
analyticsadmin.googleapis.com	TRUE
analyticsdata.googleapis.com	TRUE
accounts.google.com	FALSE	This domain is required when the AuthScheme connection property is set to OAuth.
www.googleapis.com and iamcredentials.googleapis.com	FALSE	This domain is required when the AuthScheme connection property is set to OAuthJWT. In the case of "www.googleapis.com", the "www." part is part of the domain name and must be added in your application's egress policy.
metadata.google.internal	FALSE	This domain is required when the AuthScheme connection property is set to GCPInstanceAccount.
sts.googleapis.com	FALSE	This domain is required when the AuthScheme connection property is set to AWSWorkloadIdentity.

General Changes

Date	Version	Source	Category	Type	Description
2026-06-04	26.0.9651	Google Analytics	Data Model	Changed	Renamed the ReportyType parameter to ReportType for the CreateCustomSchema stored procedure. Renamed the Expressions column to Expression for the Metadata view.
2026-05-27	26.0.9643	General	Connection	Removed	Removed the deprecated ReplaceInvalidTypesWithNull connection property. Use the ReplaceInvalidValuesWithNull property instead.
2026-05-22	26.0.9638	Python		Removed	Remove support for Intel x64 architecture on macOS
2026-05-07	26.0.9623	General	Data Model	Added	Added the ColumnCapabilities column to the sys_tablecolumns system table. This column is a bit mask denoting the column's write capabilities.
2026-05-07	26.0.9623	Python		Changed	Updated embedded JRE to jre-17.0.19+10 (Linux x64 / MacOs x64).
2026-04-15	26.0.9601	General	Query Exec	Changed	String comparisons using GREATER, LESS, and CONTAINS operators are now case-insensitive by default.
2026-04-08	26.0.9594	Google Analytics	Security	Changed	TLS 1.3 is now supported by default for HTTP connections.
2026-04-01	26.0.9587	Google Analytics		Removed	Removed the BatchSize connection property.
2026-02-17	25.0.9544	Google Analytics		Added	Added the CurrencyCode pseudocolumn to the Acquisitions, AcquisitionsFirstUserCampaignReport, AcquisitionsFirstUserGoogleAdsAdGroupNameReport, AcquisitionsFirstUserGoogleAdsNetworkTypeReport, AcquisitionsFirstUserMediumReport, AcquisitionsFirstUserSourceMediumReport, AcquisitionsFirstUserSourcePlatformReport, AcquisitionsFirstUserSourceReport, AcquisitionsSessionCampaignReport, AcquisitionsSessionDefaultChannelGroupingReport, AcquisitionsSessionMediumReport, AcquisitionsSessionSourceMediumReport, AcquisitionsSessionSourcePlatformReport, AcquisitionsSessionSourceReport, DemographicAgeReport, DemographicCityReport, DemographicCountryReport, DemographicGenderReport, DemographicInterestsReport, DemographicLanguageReport, DemographicRegionReport, Demographics, EcommPurchasesItemBrandReport, EcommPurchasesItemCategory2Report, EcommPurchasesItemCategory3Report, EcommPurchasesItemCategory4Report, EcommPurchasesItemCategory5Report, EcommPurchasesItemCategoryReport, EcommPurchasesItemCategoryReportCombined, EcommPurchasesItemIdReport, EcommPurchasesItemNameReport, Engagement, EngagementContentGroupReport, EngagementConversionsReport, EngagementEventsReport, EngagementPagesPathReport, EngagementPagesTitleAndScreenClassReport, EngagementPagesTitleAndScreenNameReport, GamesReporting, Monetization, MonetizationPublisherAdsAdFormatReport, MonetizationPublisherAdsAdSourceReport, MonetizationPublisherAdsAdUnitReport, MonetizationPublisherAdsPagePathReport, Tech, TechAppVersionReport, TechBrowserReport, TechDeviceCategoryReport, TechDeviceModelReport, TechOSSystemReport, TechOSVersionReport, TechPlatformDeviceCategoryReport, TechPlatformReport, and TechScreenResolutionReport views.
2026-01-13	25.0.9509	General		Added	Added support for the REGEXP_REPLACE() string function.
2025-12-21	25.0.9486	Python		Added	Added support for custom loggers in Python connectors on Linux and macOS.
2025-12-12	25.0.9477	Google Analytics		Added	Added the DataAnnotations report view. Added the Name column and support for comparison data in the Metadata view.
2025-12-05	25.0.9470	General		Added	Added support for the INSERT INTO SELECT statement, with driver-side execution for providers that do not support the operation natively.
2025-11-07	25.0.9442	Google Analytics		Changed	Changed the datatype of the TotalRevenue field from integer to decimal for Acquisition, Demographic, Engagement, and Tech categories of views.= Changed the datatype of the TotalAdRevenue from integer to decimal for the Monetization category of views.
2025-10-30	25.0.9434	Python		Changed	Updated embedded JRE to jre-17.0.17+10 (Linux x64 / MacOs x64).
2025-10-06	25.0.9410	General		Added	Support for parsing datetime formats using ".S" and ",S" for milliseconds and nanoseconds.
2025-09-12	25.0.9386	General		Added	Added the IsInsertable, IsUpdateable, and IsDeleteable columns to the sys_tables table.
2025-09-10	25.0.9384	General		Changed	All columns in statically defined Views are now reported as read-only.
2025-09-03	25.0.9377	General		Changed	Corrected the behavior when IN criteria with NULL values are used in the projection part. It now returns NULL instead of 0. For example, "NULL IN (1,2)" returns "NULL".
2025-09-01	25.0.9375	General		Added	Added support for using the CAST function with infinity values. This function can cast "inf" and "-inf" to DOUBLE, FLOAT, or REAL.
2025-08-21	25.0.9364	General		Changed	Report behavior change: Fixed inconsistent string value comparisons in non-table queries. For example, "SELECT 'A' = 'a'" previously returned false, but it now returns true.
2025-08-13	25.0.9356	General		Changed	Changed the maximum number of pages held in memory from 15 to 5 for the page providers to decrease heap usage.
2025-07-11	25.0.9323	Google Analytics		Added	Added the Scope connection property.
2025-07-07	25.0.9319	Python		Removed	Removed the 32-bit version of Windows Python.
2025-07-07	25.0.9319	Google Analytics		Changed	In the Data API renamed the Conversions column to KeyEvents in the AcquisitionsFirstUserGoogleAdsAdGroupNameReport, AcquisitionsFirstUserGoogleAdsNetworkTypeReport, DemographicAgeReport, DemographicCityReport, DemographicCountryReport, DemographicGenderReport, DemographicInterestsReport, DemographicLanguageReport, DemographicRegionReport, Demographics, Engagement, EngagementContentGroupReport, EngagementConversionsReport, EngagementPagesPathReport, EngagementPagesTitleAndScreenClassReport, EngagementPagesTitleAndScreenNameReport, Events, Tech, TechAppVersionReport, TechBrowserReport, TechDeviceCategoryReport, TechDeviceModelReport, TechOSSystemReport, TechOSVersionReport, TechPlatformDeviceCategoryReport, TechScreenResolutionReport, Acquisitions, AcquisitionsFirstUserSourceReport, AcquisitionsFirstUserSourcePlatformReport, AcquisitionsFirstUserSourceMediumReport, AcquisitionsFirstUserMediumReport, AcquisitionsFirstUserCampaignReport, AcquisitionsSessionCampaignReport, AcquisitionsSessionDefaultChannelGroupingReport, AcquisitionsSessionMediumReport, AcquisitionsSessionSourceMediumReport, AcquisitionsSessionSourcePlatformReport, and AcquisitionsSessionSourceReport views.
2025-07-07	25.0.9319	Google Analytics		Removed	Removed the MaximumUserAccess column from the PropertiesFireBaseLinks table in the Admin API.
2025-07-02	25.0.9314	Python		Removed	Removed support for Python 3.9.
2025-06-25	25.0.9307	General		Removed	Removed the "ADLS Gen 1" value from the ConnectionType property.
2025-06-25	25.0.9307	Python		Added	Added support for Python 3.13 in Windows, Linux, and Mac editions.
2025-06-25	25.0.9307	Python		Removed	Removed support for Python 3.8 as it is no longer supported.
2025-06-20	25.0.9302	General		Added	Created the following functions: TEXT_ENCODE: encodes a string into a different charset (UTF8 → UTF7 and returns a binary array as the result). TEXT_DECODE: takes a binary array and decodes it back into a string when provided the charset. BASE64_ENCODE: takes a binary array and encodes it as a base64 string (varchar). BASE64_DECODE: takes a base 64-encoded string and decodes it into a binary array.
2025-06-19	25.0.9301	Google Analytics		Changed	The values in the PropertySummaries column are now displayed in aggregate, as raw values. Previously, we displayed the ropertySummaries column values as 'key:value, key:value'.
2025-06-18	25.0.9300	General		Changed	The internal code for exception handling has been refactored. Exception messages returned during certain error conditions may now have different wording or formatting.
2025-05-27	25.0.9278	General		Removed	Removed the "Proprietary" enum option from ProxyAuthscheme.
2025-05-12	25.0.9263	Python		Changed	Updated embedded JRE to jre-17.0.15+6 (Linux x64 / MacOS x64) and jre-17.0.15+6 (MacOS aarch64).
2025-02-15	24.0.9177	General		Added	Added support for converting unsigned integer types to the nearest signed data type that has enough precision to hold the unsigned value.This is done for JDBC only because it does not have support for unsigned data types.
2025-01-31	24.0.9162	Google Analytics		Added	Added the PropertiesAccessBindings, PropertiesAudiences, and PropertiesKeyEvents views to the GoogleAnalytics4 data model.
2025-01-31	24.0.9162	Google Analytics		Added	Added the IncludeDeleted connection property.
2025-01-24	24.0.9155	Google Analytics		Added	Added the Deleted column to the Accounts view. Added the DeleteTime column to the Properties view.
2025-01-15	24.0.9146	Google Analytics		Added	Added two new report views: KeyEvents and ScreenPageViews.
2025-01-10	24.0.9141	Google Analytics		Added	Added the following columns to the MetaData view: DimensionDeprecatedAPINames, MetricDeprecatedAPINames, Expressions, BlockedReasons, and Category. Added the folllwing dimensions to the ActiveUsers view: AudienceId, AudienceResourceName, CityId, CountryId, EventName, MinutesAgo, StreamId, and StreamName. Added the following dimensions to the Events view: AudienceId, AudienceResourceName, CityId, CountryId, MinutesAgo, StreamId, and StreamName.
2024-11-27	24.0.9097	General		Added	Added ThreadId to LogModule output. Logfile lines now include the Thread ID associated with the action being performed.
2024-10-17	24.0.9056	Google Analytics		Added	Added support for Workload Identity Federation using AWS accounts.
2024-09-19	24.0.9028	Google Analytics		Added	Added the IncludeEmptyRows connection property. When this property is set to True, the driver includes rows for which all retrieved metrics are equal to zero. If set to False, these rows are not included.
2024-09-06	24.0.9015	Google Analytics		Added	Added the DefaultStartDate and DefaultEndDate connection properties, which specify default start and end dates for all queries.
2024-07-30	24.0.8977	Google Analytics		Added	Added the UINames attribute to the CreateCustomSchema stored procedure.
2024-07-18	24.0.8965	Google Analytics		Removed	Removed the DefaultFilter property.
2024-07-15	24.0.8962	Google Analytics		Removed	Removed the UniversalAnalytics schema and its associated tables, views, and stored procedures. Removed the Profile, ApiVersion, IncludeEmptyRows, UseResourceQuotas, and SamplingLevel connection properties.
2024-07-04	24.0.8951	Google Analytics		Removed	Removed the Dimensions and Metrics columns from 6 views: Acquisitions, Demographics, Engagement, GamesReporting, Monetization, and Tech.
2024-07-02	24.0.8949	Google Analytics		Changed	Changed the datatype of the Conversions column from integer to decimal in various views in the GoogleAnalyticsV4 schema. Changed the datatype of the CartToViewRate column from integer to decimal in various views in the GoogleAnalyticsV4 schema. Changed the datatype of the ItemsPurchased column from decimal to integer in various views in the GoogleAnalyticsV4 schema. Changed the name of the FirstUserCreativeId column in the Acquisitions view to FirstUserGoogleAdsCreativeId. Changed the name of the SessionDefaultChannelGrouping column in the Acquisitions view SessionDefaultChannelGroup. Changed the name of the FirstUserCreativeId column in the GamesReporting view to FirstUserGoogleAdsCreativeId. Changed the name of the SessionDefaultChannelGrouping column in the AcquisitionsSessionDefaultChannelGroupingReport view to SessionDefaultChannelGroup.
2024-06-05	24.0.8922	Python		Added	Added support for Python 3.12.
2024-05-09	24.0.8895	General		Changed	The ROUND function previously did not accept negative precision values. That feature has now been restored.
2024-05-02	23.0.8888	Google Analytics		Changed	Changed the datatype of the "userEngagementDuration" column from Int to BigInt in the Acquisitions, AcquisitionsFirstUserCampaignReport, AcquisitionsFirstUserGoogleAdsAdGroupNameReport, AcquisitionsFirstUserGoogleAdsNetworkTypeReport, AcquisitionsFirstUserMediumReport, AcquisitionsFirstUserSourceMediumReport, AcquisitionsFirstUserSourcePlatformReport, AcquisitionsFirstUserSourceReport,AcquisitionsSessionCampaignReport, AcquisitionsSessionDefaultChannelGroupingReport, AcquisitionsSessionMediumReport, AcquisitionsSessionSourceMediumReport, AcquisitionsSessionSourcePlatformReport, AcquisitionsSessionSourceReport, Engagement, EngagementContentGroupReport, EngagementPagesPathReport, EngagementPagesTitleAndScreenClassReport, EngagementPagesTitleAndScreenNameReport, and GlobalAccessObject views.
2024-03-15	23.0.8840	General		Added	Created a new SQL function called STRING_COMPARE that provides java's String.compare() ability to SQL queries. Returns a number representative of the compared value of two strings
2024-01-09	23.0.8774	Google Analytics		Added	Added columns DayOfWeekName, IsoWeek, IsoYear, IsoYearIsoWeek, YearMonth, YearWeek in Acquisitions, Demographics, GamesReporting, Engagement, Tech, Monetization views.
2023-11-29	23.0.8733	General		Changed	The ROUND function doesn't accept the negative precision values anymore.
2023-11-29	23.0.8733	General		Changed	The returning types of the FDMonth, FDQuarter, FDWeek, LDMonth, LDQuarter, LDWeek functions are changed from Timestamp to Date. The return type of the ABS function will be consistent with the parameter value type.
2023-11-28	23.0.8732	General		Added	Added the HMACSHA256 formatter to allow for secrets to be decoded if it is in base64 format
2023-11-17	23.0.8721	Google Analytics		Added	Added column SessionsPerUser as metric for engagement and acquisitions views.
2023-08-29	23.0.8641	Python		Added	Added support for SQLAlchemy 2.0.
2023-07-31	23.0.8612	Google Analytics		Added	Added support for CustomChannelGroups for AcquisitionsFirstUserCampaignReport, AcquisitionsSessionCampaignReport, AcquisitionsSessionDefaultChannelGroupingReport, AcquisitionsSessionMediumReport, AcquisitionsSessionSourceMediumReport, AcquisitionsSessionSourcePlatformReport, AcquisitionsSessionSourceReport, AcquisitionsFirstUserMediumReport, AcquisitionsFirstUserSourceMediumReport, AcquisitionsFirstUserSourcePlatformReport, AcquisitionsFirstUserSourceReport, Acquisitions views for GoogleAnalytics4 schema.
2023-07-07	23.0.8588	Google Analytics		Added	Added GlobalAccessObject view for GoogleAnalytics4 schema.
2023-06-20	23.0.8571	General		Added	Added the new sys_lastresultinfo system table.
2023-05-19	23.0.8539	Python		Added	Added support for Python 3.11 on Windows, Linux and Mac.
2023-05-16	23.0.8536	Python		Removed	Removed support for Python 3.7 on Windows and Linux
2023-05-05	23.0.8525	Google Analytics		Changed	Added UserType as defaultdimensions in Global_Access_Object view. Added Users, Sessions, AdxImpressions, AdxCoverage, AdxClicks, AdxCTR, AdxRevenue, AdxRevenuePer1000Sessions as defaultmetrics in Global_Access_Object view.
2023-04-25	23.0.8515	General		Removed	Removed support for the SELECT INTO CSV statement. The core code doesn't support it anymore.
2023-04-25	23.0.8515	Google Analytics		Changed	Changed the metrics name from AddToCarts to ItemsAddedToCart, and ItemViews to ItemsViewed in EcommPurchasesItemCategoryReport, EcommPurchasesItemCategory2Report, EcommPurchasesItemCategory3Report, EcommPurchasesItemCategory4Report, EcommPurchasesItemCategory5Report, EcommPurchasesItemCategoryReportCombined, EcommPurchasesItemBrandReport views. Changed the metric name from AddToCarts to ItemsAddedToCart in EcommPurchasesItemIdReport, EcommPurchasesItemNameReport views. Changed the metrics name from AddToCarts to ItemsAddedToCart, Checkouts to ItemsCheckedOut, ItemListClicks to ItemsClickedInList, ItemListViews to ItemsViewedInList, ItemPromotionClicks to PromotionClicks, ItemPromotionViews to PromotionViews, ItemPurchaseQuantity to ItemsPurchased, and ItemViews to ItemsViewed in Monetization view.
2023-04-03	22.0.8493	Google Analytics		Added	Added PagePath and PageTitle columns in Engagements and Events table.
2023-01-05	22.0.8405	Google Analytics		Added	AcquisitionsFirstUserGoogleAdsAdGroupNameReport, AcquisitionsFirstUserCampaignReport, AcquisitionsFirstUserGoogleAdsNetworkTypeReport, AcquisitionsFirstUserMediumReport, AcquisitionsFirstUserSourceMediumReport, AcquisitionsFirstUserSourcePlatformReport, AcquisitionsFirstUserSourceReport, AcquisitionsSessionCampaignReport, AcquisitionsSessionDefaultChannelGroupingReport, AcquisitionsSessionMediumReport, AcquisitionsSessionSourceMediumReport, AcquisitionsSessionSourcePlatformReport, AcquisitionsSessionSourceReport, EcommPurchasesItemBrandReport, EcommPurchasesItemCategory2Report, EcommPurchasesItemCategory3Report, EcommPurchasesItemCategory4Report, EcommPurchasesItemCategory5Report, EcommPurchasesItemCategoryReport, EcommPurchasesItemCategoryReportCombined, EcommPurchasesItemIdReport, EcommPurchasesItemNameReport, EngagementContentGroupReport, EngagementConversionsReport, EngagementEventsReport, EngagementPagesPathReport, EngagementPagesTitleAndScreenClassReport, EngagementPagesTitleAndScreenNameReport, MonetizationPublisherAdsAdFormatReport,MonetizationPublisherAdsAdSourceReport ,MonetizationPublisherAdsAdUnitReport ,MonetizationPublisherAdsPagePathReport , TechAppVersionReport ,TechBrowserReport,TechDeviceCategoryReport,TechDeviceModelReport, TechOSSystemReport, TechOSVersionReport, TechPlatformDeviceCategoryReport, TechPlatformReport and TechScreenResolutionReport views.
2023-01-03	22.0.8403	Google Analytics		Added	Added DemographicCountryReport, DemographicRegionReport, DemographicCityReport, DemographicLanguageReport, DemographicAgeReport, DemographicGenderReport, DemographicInterestsReport view.
2022-12-27	22.0.8396	Google Analytics		Added	Added TechAppVersionReport,TechBrowserReport,TechDeviceCategoryReport,TechDeviceModelReport,TechOSSystemReport,TechOSVersionReport,TechPlatformDeviceCategoryReport,TechPlatformReport and TechScreenResolutionReport view.
2022-12-22	22.0.8391	Google Analytics		Added	Added PropertyId as a pseudocolumn for the PropertiesDatastream, PropertiesFireBaseLinks, and PropertiesGoogleAdsLinks views.
2022-12-20	22.0.8389	Google Analytics		Added	Added ReportType connection property to distinguish between runRealtimeReport and runReports for the Events and ActiveUsers view.
2022-12-19	22.0.8388	Google Analytics		Added	Added following Dimensions EventName,BrandingInterest,Country,City,Language,UserAgeBracket,UserGender,Region,UnifiedScreenClass and PagePathand to Acquistions view. Added following Metrics UserEngagementDuration,ScreenPageViewsand and EventCountPerUser to Acquistions view.
2022-12-14	22.0.8383	General		Changed	Added the Default column to the sys_procedureparameters table.
2022-12-09	22.0.8378	Google Analytics		Added	Added the WriteToFile parameter for CreateSchema stored procedure. This defaults to true and must be disabled to write the schema to FileStream or FileData.
2022-12-09	22.0.8378	Google Analytics		Removed	Removed OutputFolder parameter from CreateSchema stored procedure. Instead, the Location connection property path will be used to create the schema.
2022-11-15	22.0.8354	Python		Changed	Updated embedded JRE to jre8u345-b01(Linux x64 / MacOS x64) and jre-17.0.5+8(MacOS aarch64).
2022-10-26	22.0.8334	Google Analytics		Added	Added view PropertiesDataStreams .
2022-10-26	22.0.8334	Google Analytics		Removed	Removed the view PropertiesAndroidAppDataStreams, PropertiesAppDataStreams and PropertiesWebDataStreams as the underlying API got deprecated.
2022-10-14	22.0.8322	Google Analytics		Added	Added FileData output attribute to print the response in CreateCustomSchema Stored Procedures for both GoogleAnalytics4 and UniversalAnalytics Schema.
2022-09-30	22.0.8308	General		Changed	Added the IsPath column to the sys_procedureparameters table.
2022-09-29	22.0.8307	Google Analytics		Added	Added FileStream input attribute to add output streams in the CreateCustomSchema stored procedure.
2022-07-26	22.0.8242	Google Analytics		Changed	Changed the default Schema to GoogleAnalytics4 since the Universal Analytics API is deprecated.
2022-07-13	22.0.8229	Google Analytics		Added	Added support for Google Analytics fields in the CustomRedirects table.
2022-05-18	22.0.8173	Python		Added	Added support for Python 3.10 on Windows, Linux, and Mac Added support for Python 3.9 on Mac Added support for Mac M1
2022-05-18	22.0.8173	Python		Removed	Removed support for Python 3.6 on Windows and Linux
2022-03-03	21.0.8097	Google Analytics		Removed	The columns marked in UniversalAnalytics.Global_Access_Object with XX are removed. The XX columns worked incredibly awkwardly, requiring the user to instead explicitly select a specific index of the column, such as LandingContentGroup1 instead of LandingContentGroupXX.
2022-03-03	21.0.8097	Google Analytics		Replacements	Columns in Global_Access_Object formerly marked with XX are replaced by individually indexed columns numbered 1-20. For example, LandingContentGroupXX is replaced with LandingContentGroup1, LandingContentGroup2, etc.
2021-12-22	21.0.8026	Google Analytics		Added	Added Profile as a column for all views (Reports). This column can be used with the IN operator to query data from all the available profiles. This change is only for UniversalAnalytics schema.
2021-09-27	21.0.7940	Google Analytics		Added	Added extra columns for Dimension, Hierarchy and OLAPType to sys_tablecolumns for OLAP properties.
2021-09-02	21.0.7915	General		Added	Added support for the STRING_SPLIT table-valued function in the CROSS APPLY clause.
2021-08-07	21.0.7889	General		Changed	Added the KeySeq column to the sys_foreignkeys table.
2021-08-06	21.0.7888	General		Changed	Added the new sys_primarykeys system table.
2021-07-23	21.0.7874	General		Changed	Updated the Literal Function Names for relative date/datetime functions. Previously, relative date/datetime functions resolved to a different value when used in the projection as opposed to the predicate. For example: SELECT LAST_MONTH() AS lm, Col FROM Table WHERE Col > LAST_MONTH(). Formerly, the two LAST_MONTH() methods would resolve to different datetimes. Now, they will match. As a replacement for the previous behavior, the relative date/datetime functions in the criteria may have an 'L' appended to them. For example: WHERE col > L_LAST_MONTH(). This will continue to resolve to the same values that were previously calculated in the criteria. Note that the "L_" prefix will only work in the predicate - it not available for the projection.
2021-06-18	21.0.7839	Google Analytics		Added	Added support for the GOOGLEJSONBLOB JWT certificate type. This works like the existing GOOGLEJSON certificate type except that the certificate is provided as JSON text instead of as a file path.
2021-05-26	21.0.7816	Google Analytics		Added	Support for Google Analytics Data API (GA4) and Admin API.
2021-04-23	21.0.7783	General		Changed	Updated how display sizes are determined for varchar primary key and foreign key columns so they will match the reported length of the column.
2021-04-16	21.0.7776	General		Changed	Reduced the length to 255 for varchar primary key and foreign key columns.
2021-04-16	21.0.7776	General		Changed	Updated implicit and metadata caching to improve performance and support for multiple connections. Old metadata caches are not compatible - you need to generate new metadata caches if you are currently using CacheMetadata.
2021-04-16	21.0.7776	General		Changed	Updated index naming convention to avoid duplicates.
2020-10-15	20.0.7593	Google Analytics		Added	Convert Traffic.Segments into a mirror to output column to use in JOINS with Segments table.

This section provides a walk-through for writing Google Analytics data access code in Python script.

For more information on the available data source entities and how to query them with SQL, see Data Model. For the SQL syntax, see SQL Compliance.

Connecting from Code

For information on how to deploy the connector and configure the connection to Google Analytics, see Package Installation and Establishing a Connection.

For information on how to connect with the googleanalytics.connector module and its related classes, see Connecting.

Executing SQL

The connection's cursor object is used to directly execute SQL queries. For information on how to execute SELECT statements and process the returned result sets, see Querying Data.

Executing Stored Procedures

You can call stored procedures by using the EXECUTE statement. For further information, see Calling Stored Procedures.

Connecting with the cdata.googleanalytics Module:

The connector's module is used directly to establish a connection with the data source. It does this by using a connection string as its argument. For example:

import cdata.googleanalytics as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")

Once the connection is created, you can use it to execute subsequent SQL queries.

After connecting as described in Connecting, you can use the open connection to execute SQL statements.

Executing Queries

To execute SQL statements that return data, use the execute() method. Once a query is executed, the result set is fetched from the cursor. This result set can then be iterated over to process the records individually.

For example:

cur = conn.execute("SELECT Browser, DeviceCategory FROM Traffic")
rs = cur.fetchall()
for row in rs:
	print(row)

Parameterized Queries

Various Python collections, such as arrays and tuples, can act as additional arguments for the execute() method. This enables you to parameterize the queries executed and help to prevent SQL Injection.

For example:

cmd = "SELECT Browser, DeviceCategory FROM Traffic WHERE Transactions > ?"
params = ["0"]
cur = conn.execute(cmd, params)
rs = cur.fetchall()
for row in rs:
	print(row)

You can execute stored procedures using either the execute() or callproc() method of the connection.

Calling Stored Procedures Using Execute()

When you call stored procedures by issuing EXECUTE commands, the stored procedure arguments are parameterized. For example:

cmd = "EXECUTE CreateCustomSchema TableName = ?"
params = ["MyCustomSchema"]
conn.execute(cmd, params)

Calling Stored Procedures Using Callproc()

When you call stored procedured by issuing the callproc() method, the stored procedure arguments are a procedure name and a list of parameters. For example:

cur = conn.cursor()
params = ["MyCustomSchema"]
cur.callproc("CreateCustomSchema", params)

The connector is integrated with other tools and packages within Python.

Python Integration Guides

The following sections show how to create and use connections with the connector in common packages in Python:

• From SQLAlchemy
• From Pandas
• From Matplotlib
• From Petl

Complete List of Google Analytics Integration Quickstarts

For information on connecting from other applications, see Google Analytics integration guides.

The CData Python Connector for Google Analytics includes a Dialect class that enables integration with SQLAlchemy. Bear in mind that several aspects of connector functionality are not currently supported in SQLAlchemy 2.0 or above. If necessary, downgrade SQLAlchemy to version 1.4 or 1.3 before using this connector.

The following sections detail various aspects of this integration:

Connecting From SQLAlchemy

To construct a URL with which SQLAlchemy loads and uses the appropriate connector automatically, see Connecting

Reflecting Metadata With SQLAlchemy

To learn how to model Google Analytics tables with mapped classes, see Reflecting Metadata.

Querying Data From SQLAlchemy

To learn how to use mapped classes to query the associated tables, see Querying Data.

Connecting With a Dialect URL

Establishing a connection using SQLAlchemy requires a specific URL format.

from sqlalchemy import create_engine
engine = create_engine("googleanalytics:///?InitiateOAuth=GETANDREFRESH;")

For SQLAlchemy 2.0, the dialect name is googleanalytics_2. To establish a connection, use the following URL format:

from sqlalchemy import create_engine
engine = create_engine("googleanalytics_2:///?InitiateOAuth=GETANDREFRESH;")

SQLAlchemy can act as an Object-relational Map (ORM). This enables you to treat records of a database table as instantiable records. To leverage this functionality, you must reflect the underlying metadata in one of the following ways.

Note: The following examples employ SQLAlchemy 1.4.

Modeling Data Using a Mapping Class

Use "sqlalchemy.ext.declarative.declarative_base" to declare a mapping class for the table you wish to model in the ORM. A known table in the data model is modeled either partially or completely, as shown in the following example:

from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class Traffic(Base):
	__tablename__ = "Traffic"
	Id = Column(String, primary_key=True)
	Browser = Column(String)
	DeviceCategory = Column(String)

Automatically Reflecting Metadata

Rather than mapping tables manually, SQLAlchemy can discover the metadata for one or more tables automatically. To accomplish this across the entire data model, use automap_base:

from sqlalchemy import MetaData
from sqlalchemy.ext.automap import automap_base
meta = MetaData()
abase = automap_base(metadata=meta)
abase.prepare(autoload_with=engine)
Traffic = abase.classes.Traffic

You can also reflect a single table with an inspector. When reflecting this way, providing a list of specific columns to map is optional:

from sqlalchemy import MetaData, Table
from sqlalchemy import inspect
meta = MetaData()
insp = inspect(engine)
Traffic_table = Table("Traffic", meta)
insp.reflect_table(Traffic_table, ["Id","DeviceCategory"])

After you use the steps in Connecting to connect, and use one of the methods in Reflecting Metadata to reflect some of the metadata, you can use a session object to query data.

Querying Data Using the Query Method

If the mapping class has been prepared, use it with a session object to query the data source. After binding the engine to the session, provide the mapping class to the session's query method.

For example:

engine = create_engine("googleanalytics:///?InitiateOAuth=GETANDREFRESH;")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Traffic).filter_by(Transactions="0"):
	print("Id: ", instance.Id)
	print("Browser: ", instance.Browser)
	print("DeviceCategory: ", instance.DeviceCategory)
	print("---------")

Querying Data Using the Execute Method

The session object can also run the query with the execute() method alongside the appropriate Table object. Assuming you have an active session, the following is just as viable:

Traffic_table = Traffic.metadata.tables["Traffic"]
for instance in session.execute(Traffic_table.select().where(Traffic_table.c.Transactions == "0")):
	print("Id: ", instance.Id)
	print("FullName: ", instance.Name)
	print("City: ", instance.BillingCity)
	print("---------")

Implicit Joining

If mapped classes of related Google Analytics objects have a singular foreign key relationship, the classes are implicitly joined. After importing the necessary objects, a relationship is established between your two mapped classes, as in the example below:

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, String, Integer, DateTime, ForeignKey
from sqlalchemy.orm import sessionmaker, relationship

Base = declarative_base()
class Contact(Base):
	__tablename__ = "Contact"
	Id = Column(Integer, primary_key=True)
	Name = Column(String)
	Email = Column(String)
	BirthDate = Column(DateTime)
	AccountId = Column(String, ForeignKey("Account.Id"))
	Account_Link = relationship("Account", back_populates="Contact_Link")

class Account(Base):
	__tablename__ = "Account"
	Id = Column(String, primary_key=True)
	Name = Column(String)
	BillingCity = Column(String)
	NumberOfEmployees = Column(Integer)
	Contact_Link = relationship("Contact", order_by=Contact.Id, back_populates="Account_Link")

Once the relationship is established, the tables are queried simultaneously using the session's query() method. For example:

rs = session.query(Account, Contact).filter(Account.Id == Contact.AccountId)
for Ac, Ct in rs:
  print("AccountId: ", Ac.Id)
  print("AccountName: ", Ac.Name)
  print("ContactId: ", Ct.Id)
  print("ContactName: ", Ct.Name)

Other Join Forms

In situations where mapped classes have either no foreign keys or multiple foreign keys, you may need different forms of the JOIN query to accommodate them. Using the earlier classes as examples, the following JOIN queries are possible as well:

• Explicit condition (necessary if there are no foreign keys in your mapped classes):
  

  rs = session.query(Account, Contact).join(Contact, Account.Id == Contact.AccountId)
  for Ac, Ct in rs:

• Left-to-right relationship:
  

  rs = session.query(Account, Contact).join(Account.Contact_Link)
  for Ac, Ct in rs:

• Left-to-right relationship with explicit target:
  

  rs = session.query(Account, Contact).join(Contact, Account.Contact_Link)
  for Ac, Ct in rs:

• String form of a left-to-right relationship:
  

  rs = session.query(Account, Contact).join("Contact_Link")
  for Ac, Ct in rs:

SQLAlchemy ORM also exposes support for other clauses in SQL, such as ORDER BY, GROUP BY, LIMIT, and OFFSET. All of these are supported by this connector:

ORDER BY

The following example sorts by a specified column using the session object's query() method:

rs = session.query(Traffic).order_by(Traffic.AnnualRevenue)
for instance in rs:
	print("Id: ", instance.Id)
	print("Browser: ", instance.Browser)
	print("DeviceCategory: ", instance.DeviceCategory)
	print("---------")

You can also use the session object's execute() method perform an ORDER BY. For example:

rs = session.execute(Traffic_table.select().order_by(Traffic_table.c.AnnualRevenue))
for instance in rs:

GROUP BY

The following example uses the session object's query() method to group records with a specified column:

rs = session.query(func.count(Traffic.Id).label("CustomCount"), Traffic.Browser).group_by(Traffic.Browser)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Browser: ", instance.Browser)
	print("---------")

You can also use the session object's execute() method to perform a GROUP BY:

rs = session.execute(Traffic_table.select().with_only_columns([func.count(Traffic_table.c.Id).label("CustomCount"), Traffic_table.c.Browser]).group_by(Traffic_table.c.Browser))
for instance in rs:

LIMIT and OFFSET

The following example uses the session object's query() method to skip the first 100 records and fetch the following 25:

rs = session.query(Traffic).limit(25).offset(100)
for instance in rs:
	print("Id: ", instance.Id)
	print("Browser: ", instance.Browser)
	print("DeviceCategory: ", instance.DeviceCategory)
	print("---------")

You can also use the session object's execute() method to set a LIMIT or OFFSET:

rs = session.execute(Traffic_table.select().limit(25).offset(100))
for instance in rs:

Certain aggregate functions can also be used within SQLAlchemy by using the func module.

To import this module, execute:

from sqlalchemy.sql import func

Once func is imported, the following aggregate functions are available:

COUNT

The following example counts the number of records in a set of groups using the session object's query() method.

rs = session.query(func.count(Traffic.Id).label("CustomCount"), Traffic.Browser).group_by(Traffic.Browser)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Browser: ", instance.Browser)
	print("---------")

You can also execute COUNT using the session object's execute() method:

rs = session.execute(Traffic_table.select().with_only_columns([func.count(Traffic_table.c.Id).label("CustomCount"), Traffic_table.c.Browser])group_by(Traffic_table.c.Browser))
for instance in rs:

SUM

This example calculates the cumulative amount of a numeric column in a set of groups.

rs = session.query(func.sum(Traffic.AnnualRevenue).label("CustomSum"), Traffic.Browser).group_by(Traffic.Browser)
for instance in rs:
	print("Sum: ", instance.CustomSum)
	print("Browser: ", instance.Browser)
	print("---------")

You can also invoke SUM using the session object's execute() method.

rs = session.execute(Traffic_table.select().with_only_columns([func.sum(Traffic_table.c.AnnualRevenue).label("CustomSum"), Traffic_table.c.Browser]).group_by(Traffic_table.c.Browser))
for instance in rs:

AVG

This example uses the session object's query() method to calculate the average amount of a numeric column in a set of groups:

rs = session.query(func.avg(Traffic.AnnualRevenue).label("CustomAvg"), Traffic.Browser).group_by(Traffic.Browser)
for instance in rs:
	print("Avg: ", instance.CustomAvg)
	print("Browser: ", instance.Browser)
	print("---------")

You can also use the session object's execute() method to invoke AVG:

rs = session.execute(Traffic_table.select().with_only_columns([func.avg(Traffic_table.c.AnnualRevenue).label("CustomAvg"), Traffic_table.c.Browser]).group_by(Traffic_table.c.Browser))
for instance in rs:

MAX and MIN

This example finds the maximum value and minimum value of a numeric column in a set of groups.

rs = session.query(func.max(Traffic.AnnualRevenue).label("CustomMax"), func.min(Traffic.AnnualRevenue).label("CustomMin"), Traffic.Browser).group_by(Traffic.Browser)
for instance in rs:
	print("Max: ", instance.CustomMax)
	print("Min: ", instance.CustomMin)
	print("Browser: ", instance.Browser)
	print("---------")

You can also use the session object's execute() method to invoke MAX and MIN:

rs = session.execute(Traffic_table.select().with_only_columns([func.max(Traffic_table.c.AnnualRevenue).label("CustomMax"), func.min(Traffic_table.c.AnnualRevenue).label("CustomMin"), Traffic_table.c.Browser]).group_by(Traffic_table.c.Browser))
for instance in rs:

When combined with the connector, Pandas can be used to generate data frames that contain your Google Analytics data. Once created, a data frame can be passed to various other Python packages.

Connecting

Pandas relies on an SQLAlchemy engine to execute queries. Before you can use Pandas you must import it:

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine("googleanalytics:///?InitiateOAuth=GETANDREFRESH;")

Querying Data

In Pandas, SELECT queries are provided in a call to the read_sql() method, alongside a relevant connection object. Pandas executes the query on that connection, and returns the results in the form of a data frame, which can be used for a variety of purposes.

df = pd.read_sql("""
	SELECT
	   Browser,
	   DeviceCategory,
     $exNumericCol;
	FROM Traffic;""", engine)
print(df)

Matplotlib contains a number of tools that can graphically model Google Analytics data after being fed a data frame From Pandas.

Using PyPlot

Before any Matplotlib tool, such as pyplot, can be used, it must be imported:

from matplotlib import pyplot as plt

Once a Pandas data frame is obtained, it can be used to create a plot visualizing Google Analytics data. For example, the following plot generates and displays a bar graph relating Browser and AnnualRevenue values:

df.plot(kind="bar", x="Browser", y=["AnnualRevenue"])
plt.show()

The connector can be used to create ETL applications and pipelines for CSV data in Python using Petl.

Install Required Modules

Install the Petl modules using the pip utility.

pip install petl

Connecting

After you import the modules, including the CData Python Connector for Google Analytics, you can use the connector's connect function to create a connection using a valid Google Analytics connection string. If you prefer not to use a direct connection, you can use a SQLAlchemy engine.

import petl as etl
import cdata.googleanalytics as mod
cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;")

Extract, Transform, and Load the Google Analytics Data

Create a SQL query string and store the query results in a DataFrame.

sql = "SELECT	Browser, DeviceCategory FROM Traffic "
table1 = etl.fromdb(cnxn,sql)

Loading Data

With the query results stored in a DataFrame, you can load your data into any supported Petl destination. The following example loads the data into a CSV file.

etl.tocsv(table1,'output.csv')

The extension supports schema discovery by using SQL queries to available System Tables.

Using SQL

The following sections describe the discovery of metadata through several System Tables:

• Tables and Views
• Columns
• Procedures

The connector possesses system tables that are used to discover the tables and views available in the data model. Of these system tables, "sys_tables" and "sys_views" are used to fetch information about the available tables and views respectively:

Tables

import cdata.googleanalytics as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tables"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Views

import cdata.googleanalytics as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_views"
cur.execute(cmd, params)
rs = cur.fetchall()
for row in rs:
	print(row)

The available columns for any given table are fetched from a system table called "sys_tablecolumns". A specific table name is provided in the WHERE criteria to restrict the table from which the column information is fetched:

import cdata.googleanalytics as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tablecolumns WHERE TableName = 'Traffic'"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Procedures

A system table called "sys_procedures" is queried to obtain the available stored procedures that are executed:

import cdata.googleanalytics as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedures"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Parameters

The input parameters of any stored procedure are similarly obtained from the "sys_procedureparameters" system table:

import cdata.googleanalytics as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'CreateCustomSchema'"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

This section details a selection of advanced features of the Google Analytics connector.

User Defined Views

The connector supports the use of user defined views, virtual tables whose contents are decided by a pre-configured user defined query. These views are useful when you cannot directly control queries being issued to the drivers. For an overview of creating and configuring custom views, see User Defined Views .

SSL Configuration

Use SSL Configuration to adjust how connector handles TLS/SSL certificate negotiations. You can choose from various certificate formats;. For further information, see the SSLServerCert property under "Connection String Options" .

Firewall and Proxy

Configure the connector for compliance with Firewall and Proxy, including Windows proxies and HTTP proxies. You can also set up tunnel connections.

Caching Data

Caching Data enables faster access to data and reduces the number of API calls, improving performance. The connector supports a simple caching model where multiple connections can also share the cache over time. When configuring the cache connection, you can specify automatic or explicit data caching.

Query Processing

The connector offloads as much of the SELECT statement processing as possible to Google Analytics and then processes the rest of the query in memory (client-side).

For further information, see Query Processing.

Logging

For an overview of configuration settings that can be used to refine CData logging, see Logging. Only two connection properties are required for basic logging, but there are numerous features that support more refined logging, which enables you to use the LogModules connection property to specify subsets of information to be logged.

Exception Handling

For an overview of how exceptions are reported and the components of an exception, see Exception Handling.

The CData Python Connector for Google Analytics supports the use of user defined views: user-defined virtual tables whose contents are decided by a preconfigured query. User defined views are useful in situations where you cannot directly control the query being issued to the driver; for example, when using the driver from a tool.

Use a user defined view to define predicates that are always applied. If you specify additional predicates in the query to the view, they are combined with the query already defined as part of the view.

There are two ways to create user defined views:

• Create a JSON-formatted configuration file defining the views you want.
• DDL statements.

Defining Views Using a Configuration File

User defined views are defined in a JSON-formatted configuration file called UserDefinedViews.json. The connector automatically detects the views specified in this file.

You can also have multiple view definitions and control them using the UserDefinedViews connection property. When you use this property, only the specified views are seen by the connector.

This user defined view configuration file is formatted so that each root element defines the name of a view, and includes a child element, called query, which contains the custom SQL query for the view.

For example:

{
	"MyView": {
		"query": "SELECT * FROM Traffic WHERE MyColumn = 'value'"
	},
	"MyView2": {
		"query": "SELECT * FROM MyTable WHERE Id IN (1,2,3)"
	}
}

Use the UserDefinedViews connection property to specify the location of your JSON configuration file. For example:

"UserDefinedViews", "C:\\Users\\yourusername\\Desktop\\tmp\\UserDefinedViews.json"

Defining Views Using DDL Statements

The connector is also capable of creating and altering the schema via DDL Statements such as CREATE LOCAL VIEW, ALTER LOCAL VIEW, and DROP LOCAL VIEW.

Create a View

To create a new view using DDL statements, provide the view name and query as follows:

CREATE LOCAL VIEW [MyViewName] AS SELECT * FROM Customers LIMIT 20;

If no JSON file exists, the above code creates one. The view is then created in the JSON configuration file and is now discoverable. The JSON file location is specified by the UserDefinedViews connection property.

Alter a View

To alter an existing view, provide the name of an existing view alongside the new query you would like to use instead:

ALTER LOCAL VIEW [MyViewName] AS SELECT * FROM Customers WHERE TimeModified > '3/1/2020';

The view is then updated in the JSON configuration file.

Drop a View

To drop an existing view, provide the name of an existing schema alongside the new query you would like to use instead.

DROP LOCAL VIEW [MyViewName]

This removes the view from the JSON configuration file. It can no longer be queried.

Schema for User Defined Views

In order to avoid a view's name clashing with an actual entity in the data model, user defined views are exposed in the UserViews schema by default. To change the name of the schema used for UserViews, reset the UserViewsSchemaName property.

Working with User Defined Views

For example, a SQL statement with a user defined view called UserViews.RCustomers only lists customers in Raleigh:

SELECT * FROM Customers WHERE City = 'Raleigh';

An example of a query to the driver:

SELECT * FROM UserViews.RCustomers WHERE Status = 'Active';

Resulting in the effective query to the source:

SELECT * FROM Customers WHERE City = 'Raleigh' AND Status = 'Active';

That is a very simple example of a query to a user defined view that is effectively a combination of the view query and the view definition. It is possible to compose these queries in much more complex patterns. All SQL operations are allowed in both queries and are combined when appropriate.

Customizing the SSL Configuration

By default, the connector attempts to negotiate TLS with the server. The server certificate is validated against the default system trusted certificate store. You can override how the certificate gets validated using the SSLServerCert connection property.

To specify another certificate, see the SSLServerCert connection property.

Connecting Through a Firewall or Proxy

HTTP Proxies

Note: The connector uses the system proxy settings by default, without further configuration needed. If you want to connect to other proxies, set ProxyAutoDetect to False and read further.

To authenticate to an HTTP proxy, set the following:

• ProxyServer: the hostname or IP address of the proxy server that you want to route HTTP traffic through.
• ProxyPort: the TCP port that the proxy server is running on.
• ProxyAuthScheme: the authentication method the connector uses when authenticating to the proxy server.
• ProxyUser: the username of a user account registered with the proxy server.
• ProxyPassword: the password associated with the ProxyUser.

Other Proxies

Set the following properties:

• To use a proxy-based firewall, set FirewallType, FirewallServer, and FirewallPort.
• To tunnel the connection, set FirewallType to TUNNEL.
• To authenticate, specify FirewallUser and FirewallPassword.
• To authenticate to a SOCKS proxy, additionally set FirewallType to SOCKS5.

Caching Data

Caching data provides several benefits, including faster access to data and reducing the number of API calls, which improve performance. The connector supports a simple caching model where multiple connections can also share the cache over time. You can enable and configure caching features by setting the necessary connection properties.

Contents

The sections in this chapter detail the connector's caching functionality and link to the corresponding connection properties, as well as SQL statements.

Configuring the Cache Connection

Configuring the Cache Connection describes the properties that you can set when configuring the cache database.

Caching Metadata

Caching Metadata describes the CacheMetadata property. This property determines whether or not to cache the table metadata to a file store.

Automatically Caching Data

Automatically Caching Data describes how the connector automatically refreshes the cache when the AutoCache property is set.

Explicitly Caching Data

Explicitly Caching Data describes how you can decide what data is stored in the cache and when it is updated.

Data Type Mapping

Data Type Mapping shows the mappings between the data types configured in the schema and the data types in the database.

Configuring the Caching Database

This section describes the properties for caching data to the persistent store of your choice.

CacheLocation

The CacheLocation property species the path to a file-system-based database. When caching is enabled, a file-system-based database is used by default. If CacheLocation is not specified, this database is stored at the path in Location. If neither of these connection properties are specified, the connector uses a platform-dependent default location.

CacheConnection

The CacheConnection property specifies a database driver and the connection string to the caching database.

CacheDriver and CacheProvider

Both the CacheDriver and CacheProvider properties are supported. Each specifies a database driver and the connection string to the caching database. CacheDriver is designed for Linux and MacOS; CacheProvider is Windows-based.

This section describes how to enable caching metadata and how to update the metadata cache.

Before being able to query data, the connector requires relevant metadata to be retrieved. By default, metadata is cached in memory and shared across connections. But if you want to persist across processes, or if metadata requests are expensive, the solution is to cache the metadata to disk.

Enable Caching Metadata

To enable caching of metadata, set CacheMetadata = true and see Configuring the Cache Connection for instructions on how to configure your connection string. The connector caches the metadata the first time it is needed and uses the metadata cache for subsequent requests.

Update the Metadata Cache

Because metadata is cached, changes to metadata on the live source, for example, adding or removing a column or attribute, are not automatically reflected in the metadata cache. To get updates to the live metadata, you need to delete or drop the cached data.

Automatically caching data is useful when you do not want to rebuild the cache for each query. When you query data for the first time, the connector automatically initializes and builds a cache in the background. When AutoCache = true, the connector uses the cache for subsequent query executions, resulting in faster response times.

Configuring Automatic Caching

Caching the Traffic Table

The following example caches the Traffic table in the file specified by the CacheLocation property of the connection string.

SELECT Browser, DeviceCategory FROM Traffic WHERE Transactions > '0'

Common Use Case

A common use for automatically caching data is to improve driver performance when making repeated requests to a live data source, such as building a report or creating a visualization. With auto caching enabled, repeated requests to the same data may be executed in a short period of time, but within an allowable tolerance (CacheTolerance) of what is considered "live" data.

With explicit caching (AutoCache = false), you decide exactly what data is cached and when to query the cache instead of the live data. Explicit caching gives you full control over the cache contents by using CACHE Statements. This section describes some strategies to use the caching features offered by the connector.

Creating the Cache

To load data in the cache, issue the following statement.

CACHE SELECT * FROM tableName WHERE ...

Once the statement is issued, any matching data in tableName is loaded into the corresponding table.

Updating the Cache

This section describes two ways to update the cache.

Updating with the SELECT Statement

The following example shows a statement that can update modified rows and add missing rows in the cached table. However, this statement does not delete extra rows that are already in the cache. This statement only merges the new rows or updates the existing rows.

CACHE SELECT * FROM Traffic WHERE Transactions > '0'

Updating with the TRUNCATE Statement

The following example shows a statement that can update modified rows and add missing rows in the cached table. This statement can also delete rows in the cache table that are not present in the live data source.

  CACHE WITH TRUNCATE SELECT * FROM Traffic WHERE Transactions > '0'
  

Query the Data in Online or Offline Mode

This section describes how to query the data in online or offline mode.

Online: Select Cached Tables

You can use the tableName#CACHE syntax to explicitly execute queries to the cache while still online, as shown in the following example.

SELECT * FROM Traffic#CACHE

Offline: Select Cached Tables

With Offline = true, SELECT statements always execute against the local cache database, regardless of whether you explicitly specify the cached table or not. Modification of the cache is disabled in Offline mode to prevent accidentally updating only the cached data. Executing a DELETE/UPDATE/INSERT statement while in Offline mode results in an exception.

The following example selects from the local cache but not the live data source because Offline = true.

SELECT * FROM Traffic WHERE Transactions='0' ORDER BY DeviceCategory ASC

Delete Data from the Cache

You can delete data from the cache by building a direct connection to the database. Note that the connector does not support manually deleting data from the cache.

Common Use Case

A common use for caching is to have an application always query the cached data and only update the cache at set intervals, such as once every day or every two hours. There are two ways in which this can be implemented:

• AutoCache = false and Offline = false. All queries issued by the application explicitly reference the tableName#CACHE table. When the cache needs to be updated, the application executes a tableName#CACHE ... statement to bring the cached data up to date.
• Offline = true. Caching is transparent to the application. All queries are executed against the table as normal, so most application code does not need to be aware that caching is done. To update the cached data, simply create a separate connection with Offline = false and execute a tableName#CACHE ... statement.

The connector maps types from the data source to the corresponding data type available in the chosen cache database. The following table shows the mappings between the data types configured in the schema and the data types in the database. Some schema types have synonyms which are all listed in the Schema column.

Data Type Mapping

Note: String columns can map to different data types depending on their length.

Schema	.NET	JDBC	SQL Server	Derby	MySQL	Oracle	SQLite	Access
int, integer, int32	Int32	int	int	INTEGER	INT	NUMBER	integer	LONG
smallint, short, int16	Int16	short	smallint	SMALLINT	SMALLINT	NUMBER	integer	SHORT
double, float, real	Double	double	float	DOUBLE	DOUBLE	NUMBER	double	DOUBLE
date	DateTime	java.sql.Date	date	DATE	DATE	DATE	date	DATETIME
datetime, timestamp	DateTime	java.sql.Date	datetime	TIMESTAMP	DATETIME	TIMESTAMP	datetime	DATETIME
time, timespan	TimeSpan	java.sql.Time	time	TIME	TIME	TIMESTAMP	datetime	DATETIME
string, varchar	String	java.lang.String	If length > 4000: nvarchar(max), Otherwise: nvarchar(length)	If length > 32672: LONG VARCHAR, Otherwise VARCHAR(length)	If length > 255: LONGTEXT, Otherwise: VARCHAR(length)	If length > 4000: CLOB, Otherwise: VARCHAR2(length)	nvarchar(length)	If length > 255: LONGTEXT, Otherwise: VARCHAR(length)
long, int64, bigint	Int64	long	bigint	BIGINT	BIGINT	NUMBER	bigint	LONG
boolean, bool	Boolean	boolean	tinyint	SMALLINT	BIT	NUMBER	tinyint	BIT
decimal, numeric	Decimal	java.math.BigDecimal	decimal	DECIMAL	DECIMAL	DECIMAL	decimal	CURRENCY
uuid	Guid	java.util.UUID	nvarchar(length)	VARCHAR(length)	VARCHAR(length)	VARCHAR2(length)	nvarchar(length)	VARCHAR(length)
binary, varbinary, longvarbinary	byte[]	byte[]	binary(1000) or varbinary(max) after SQL Server 2000, image otherwise	BLOB	LONGBLOB	BLOB	BLOB	LONGBINARY

Query Processing

CData has a client-side SQL engine built into the connector library. This enables support for the full capabilities that SQL-92 offers, including filters, aggregations, functions, etc.

For sources that do not support SQL-92, the connector offloads as much of SQL statement processing as possible to Google Analytics and then processes the rest of the query in memory (client-side). This results in optimal performance.

For data sources with limited query capabilities, the connector handles transformations of the SQL query to make it simpler for the connector. The goal is to make smart decisions based on the query capabilities of the data source to push down as much of the computation as possible. The Google Analytics Query Evaluation component examines SQL queries and returns information indicating what parts of the query the connector is not capable of executing natively.

The Google Analytics Query Slicer component is used in more specific cases to separate a single query into multiple independent queries. The client-side Query Engine makes decisions about simplifying queries, breaking queries into multiple queries, and pushing down or computing aggregations on the client-side while minimizing the size of the result set.

There's a significant trade-off in evaluating queries, even partially, client-side. There are always queries that are impossible to execute efficiently in this model, and some can be particularly expensive to compute in this manner. CData always pushes down as much of the query as is feasible for the data source to generate the most efficient query possible and provide the most flexible query capabilities.

More Information

For a full discussion of how CData handles query processing, see CData Architecture: Query Execution.

Logging

Capturing connector logging can be very helpful when diagnosing error messages or other unexpected behavior.

Basic Logging

To begin capturing connector logging, set these properties:

• Logfile: A filepath that designates the name and location of the log file.
• Verbosity: A numerical value (1-5) that determines the amount of detail in the log. See the page in the Connection Properties section for an explanation of the five levels.
• MaxLogFileSize: When the limit is hit, a new log is created in the same folder with the date and time appended to the end. The default limit is 100 MB. Values lower than 100 kB will use 100 kB as the value instead.
• MaxLogFileCount: A string specifying the maximum file count of log files. When the limit is hit, a new log is created in the same folder with the date and time appended to the end and the oldest log file will be deleted. Minimum supported value is 2. A value of 0 or a negative value indicates no limit on the count.

Once these properties are set, the connector populates the log file as it carries out various tasks, such as when authentication is performed or queries are executed. If the specified file doesn't already exist, it is created.

Log Verbosity

The verbosity level determines the amount of detail that the connector reports to the Logfile. Supported Verbosity levels range from 1 to 5.

The following list describes each level:

1	Setting Verbosity to 1 logs the query, the number of rows returned by it, the start of execution and the time taken, and any errors.
2	Setting Verbosity to 2 logs everything included in Verbosity 1, cache queries, and additional information about the request.
3	Setting Verbosity to 3 also logs HTTP headers, as well as the body of the request and the response.
4	Setting Verbosity to 4 also logs transport-level communication with the data source. This includes SSL negotiation.
5	Setting Verbosity to 5 also logs communication with the data source and additional details that may be helpful in troubleshooting problems. This includes interface commands.

For normal operations, Verbosity should not be set to greater than 1. At higher verbosities you can log substantial amounts of data, which can delay execution times.

To refine the logged content further by showing/hiding specific categories of information, see LogModules.

Sensitive Data

Verbosity levels of 3 and higher may capture information that you do not want shared outside of your organization. The following lists information of concern for each level:

• Verbosity 3: The full body of the request and the response, which includes all the data returned by the connector
• Verbosity 4: SSL certificates
• Verbosity 5: Any extra transfer data not included at Verbosity 3, such as non human-readable binary transfer data

Note: Although we mask sensitive values, such as passwords, in the connection string and any request in the log, it is always best practice to review the logs for any sensitive information before sharing outside your organization.

Advanced Logging

You may want to refine the exact information that is recorded to the log file. This can be accomplished using the LogModules property. This property allows you to filter the logging using a semicolon-separated list of logging modules.

Example property value:

LogModules=INFO;EXEC;SSL;SQL;META;

Note that the logfile filtering triggered by the Verbosity connection property takes precedence over the filtering imposed by this connection property. This means that operations of a higher verbosity level than the level specified in the Verbosity connection property are not printed in the logfile, even if they belong to one of the modules specified in this connection property.

The available modules and submodules are:

Module Name	Module Description	Submodules
INFO	General Information. Includes the connection string, product version (build number), and initial connection messages.	Connec – Information related to creating or destroying connections. Messag – Generic label for messages pertaining to connections, the connection string, and product version. These messages are typically specific to the connector, rather than being received and passed along directly from the service.
EXEC	Query Execution. Includes execution messages for user-written SQL queries, parsed SQL queries, and normalized SQL queries. Success/failure messages for queries and query pages appear here as well.	Messag – Messages pertaining to query execution. These messages are typically specific to the connector, rather than being received and passed along directly from the service. Normlz – Query normalization steps. Query normalization is when the product takes the user-submitted query and rewrites the query to get the same results with optimal performance. Origin – This label applies to any messages recording a user's original query (the exact, unaltered, non-normalized query executed by the user). Page – Messages related to query paging. Parsed – Query parsing steps. Parsing is the process of converting the user-submitted query into a standardized format for easier processing.
HTTP	HTTP protocol messages. Includes HTTP requests/responses (including POST messages), as well as Kerberos related messages.	KERB – HTTP requests related to Kerberos. Messag – Messages pertaining to HTTP protocols. These messages are typically specific to the connector, rather than being received and passed along directly from the service. Unpack – This label applies to messages about zipped data being returned from the service API and unpacked by the product. Res – Messages containing HTTP responses. Req – Messages containing HTTP requests.
WSDL	Messages pertaining to the generation of WSDL/XSD files.	—
SSL	SSL certificate messages.	Certif – Messages pertaining to SSL certificates.
AUTH	Authentication related failure/success messages.	Messag – Messages pertaining to authentication. These messages are typically specific to the connector, rather than being received and passed along directly from the service. OAuth – Messages related to OAuth authentication. Krbros – Kerberos-related authentication messages.
SQL	Includes SQL transactions, SQL bulk transfer messages, and SQL result set messages.	Bulk – Messages pertaining to bulk query execution. Cache – Messages related to reading row data from and writing row data to the product's cache for better performance. Messag – Messages pertaining to SQL transactions. These messages are typically specific to the connector, rather than being received and passed along directly from the service. ResSet – Query resultsets. Transc – Messages related to handling transactions, including information about the number of jobs executed and backup table handling.
META	Metadata cache and schema messages.	Cache – Messages related to reading from and modifying column and table definitions in the product's cache for better performance. Schema – Messages related to retrieving metadata from or modifying the service schema. MemSto – Messages related to writing to or reading from in-memory metadata cache. Storag – Messages relating to storing metadata on disk or in an external data store, rather than in memory.
FUNC	Information related to executing SQL functions.	Errmsg – Error messages related to executing SQL functions.
TCP	Incoming and outgoing raw bytes on TCP transport layer messages.	Send – Raw data sent via the TCP protocol. Receiv – Raw data received via the TCP protocol.
FTP	Messages pertaining to the File Transfer Protocol.	Info – Status messages related to communication in the FTP protocol. Client – Messages related to actions taken by the FTP client (the product) during FTP communication. Server – Messages related to actions taken by the FTP server during FTP communication.
SFTP	Messages pertaining to the Secure File Transfer Protocol.	Info – Status messages related to communication in the SFTP protocol. To_Server – Messages related to actions taken by the SFTP client (the product) during SFTP communication. From_Server – Messages related to actions taken by the SFTP server during SFTP communication.
POP	Messages pertaining to data transferred via the Post Office Protocol.	Client – Messages related to actions taken by the POP client (the product) during POP communication. Server – Messages related to actions taken by the POP server during POP communication. Status – Status messages related to communication in the POP protocol.
SMTP	Messages pertaining to data transferred via the Simple Mail Transfer Protocol.	Client – Messages related to actions taken by the SMTP client (the product) during SMTP communication. Server – Messages related to actions taken by the SMTP server during SMTP communication. Status – Status messages related to communication in the SMTP protocol.
CORE	Messages relating to various internal product operations not covered by other modules.	—
DEMN	Messages related to SQL remoting.	—
CLJB	Messages about bulk data uploads (cloud job).	Commit – Submissions for bulk data uploads.
SRCE	Miscellaneous messages produced by the product that don't belong in any other module.	—
TRANCE	Advanced messages concerning low-level product operations.	—

Exception Handling

Exceptions can be surfaced from either the API or the CData Python Connector for Google Analytics. Each exception will have an error code, an error message, and a SQL state.

Error Codes

The error code classifies the type of error.

0	NONE	Used for unclassified errors and internally handled errors. This code also covers data source-specific errors that do not fit in any specific category.
65537	TCP_UNKNOWN_HOST	Unable to resolve a hostname (DNS failure).
65538	TCP_CONNECTION_REFUSED	Could not connect to the remote port.
65539	TCP_AUTH_FAILED	Login failed when using a binary authentication protocol. Use this for auth errors when the protocol is not HTTP (LDAP, SASL, Kerberos, ...).
65540	TCP_TIMEOUT	Did not receive a response after sending a request to the server.
65541	TCP_PROTOCOL	For wire protocol drivers. Either the server sent a bad packet that we are unable to process, or we cannot construct a packet to send.
131073	TLS_SERVER_UNTRUSTED	Could not verify SSL server certificate.
131074	TLS_CLIENT_UNTRUSTED	Server did not accept the client certificate we sent.
196609	OAUTH_DECRYPT_FAILED	OAuthEncryptKey did not decrypt the OAuthSettings file.
196610	OAUTH_MISSING_CLIENT_INFO	OAuthClientId / OAuthClientSecret / OAuthJWTCert is missing.
196611	OAUTH_MISSING_PROP	General OAuth property missing. OAUTH_MISSING_CLIENT_INFO is used for missing client ID/secret and JWT cert.
196612	OAUTH_NO_ACCESS_TOKEN	Unable to retrieve access token. Only use this when getting a token in GetOAuthAccessToken / RefreshOAuthAccessToken.
196613	OAUTH_TOKEN_EXPIRED	The access token expired. Normally used with a RefreshOAuth/OAuthException behavior.
196614	OAUTH_INVALID_PROP	OAuth property has an invalid value. OAUTH_MISSING_CLIENT_INFO / OAUTH_MISSING_PROP is used if the value is not set.
262145	HTTP_REQUEST_TIMEOUT	Did not receive a response from the HTTP server.
262146	HTTP_CLIENT_ERROR	Generic HTTP 4xx error. Only use for 4xx errors not covered by other codes.
262147	HTTP_AUTH_FAILED	HTTP 401 error.
262148	HTTP_LIMIT_EXCEEDED	HTTP 429 error.
262149	HTTP_SERVER_ERROR	HTTP 5xx error.
262150	HTTP_NOT_FOUND_ERROR	HTTP 404 error.
327681	CORE_TIMEOUT	General timeout. Not related to a specific network request.
327682	CORE_OP_NOT_ALLOWED	Operation blocked by provider permissions.
327683	CORE_CONNECTION_CONFIG	Connection configuration is not valid.
327684	CORE_SERIALIZE	Failed to encode data into a specific format (XML, JSON, CSV, ...).
327685	CORE_DESERIALIZE	Failed to decode data from a specific format (XML, JSON, CSV, ...).
393217	SQL_SYNTAX_ERROR	Unable to parse a SQL query.
393218	SQL_MISSING_COLUMNS	Query did not include required columns.
393219	SQL_MISSING_PARAMS	Stored procedure call did not include required parameters.
393220	SQL_QUERY_NOT_SUPPORTED	A part of the query is not allowed in the current context.
458753	SSH_SERVER_UNTRUSTED	Could not verify SSH server.
524289	STORAGE_LIST_EXCEPTION	Issue listing storage resources.
524290	STORAGE_RESOURCE_NOT_FOUND	Issue finding storage resources.
524291	STORAGE_ROOT_RESOURCE_NOT_FOUND	The root resource (bucket/share/drive) was not found; cannot create it in flat file drivers.
524292	STORAGE_RESOURCE_NOT_A_DIRECTORY	Storage resource is not a directory.
524293	STORAGE_RESOURCE_NOT_A_FILE	Storage resource is not a file.
524294	STORAGE_PERMISSIONS_DENIED	Storage permissions denied.

SQL State

The SQL state is used when throwing generic provider errors to the wrapper and indicates the success or failure of a call.

Some of the common SQL states are listed below:

07007	REQUIRED_CLAUSE	Class Code 07: Dynamic SQL Error.
08001	OPEN_CONNECTION	Class Code 08: Connection Exception. The connection was unable to be established to the application server or other server.
08004	REJECT_CONNECTION	The application server rejected establishment of the connection.
42501	PRIVILEGE_IDENTIFIED_OBJECT	Class Code 42: Syntax Error or Access Rule Violation. The authorization ID does not have the privilege to perform the specified operation on the identified object.
42506	AUTH_FAILED	Owner authorization failure occurred.
42601	SQL_SYNTAX	A character, token, or clause is invalid or missing.

Error Message

The error message provides more detailed reasoning about why the error occurred. It provides an explanation of the issue, and may include steps on how to resolve it.

SELECT Statements

See SELECT Statements for a syntax reference and examples.

See Data Model for information on the capabilities of the Google Analytics API.

CACHE Statements

CACHE statements allow granular control over the connector's caching functionality. For a syntax reference and examples, see CACHE Statements.

For more information on the caching feature, see Caching Data.

EXECUTE Statements

Use EXECUTE or EXEC statements to execute stored procedures. See EXECUTE Statements for a syntax reference and examples.

Names and Quoting

• Table and column names are considered identifier names; as such, they are restricted to the following characters: [A-Z, a-z, 0-9, _:@].
• To use a table or column name with characters not listed above, the name must be quoted using square brackets ([name]) in any SQL statement.
• Parameter names can optionally start with the @ symbol (e.g., @p1 or @CustomerName) and cannot be quoted.
• Strings must be quoted using single quotes (e.g., 'John Doe').

The connector provides functions that are similar to those that are available with most standard databases. These functions are implemented in the CData provider engine and thus are available across all data sources with the same consistent API. Three categories of functions are available: string, date, and math.

The connector interprets all SQL function inputs as either strings or column identifiers, so you need to escape all literals as strings, with single quotes. For example, contrast the SQL Server syntax and connector syntax for the DATENAME function:

• SQL Server:
  

  SELECT DATENAME(yy,GETDATE())

• connector:
  

  SELECT DATENAME('yy',GETDATE())

String Functions

These functions perform string manipulations and return a string value. See STRING Functions for more details.

Date Functions

These functions perform date and date time manipulations. See DATE Functions for more details.

Math Functions

These functions provide mathematical operations. See MATH Functions for more details.

ASCII(character_expression)

Returns the ASCII code value of the left-most character of the character expression.

• character_expression: The character expression.

                      SELECT ASCII('0');
                      --  Result: 48
                    

BASE64_ENCODE(input_binary)

Returns the Base64-encoded string form of a binary input.

• input_binary: The binary value to encode.

                        SELECT BASE64_ENCODE(BinaryData);
                    -- Result: 'QmFzZTY0RW5jb2RlZA=='
                    

BASE64_DECODE(input_string)

Returns the binary result of decoding a Base64-encoded string.

• input_string: The Base64-encoded string.

                        SELECT BASE64_DECODE('QmFzZTY0RW5jb2RlZA==');
                    -- Result: (binary output)
                    

CHAR(integer_expression)

Converts the integer ASCII code to the corresponding character.

• integer_expression: The integer from 0 through 255.

                      SELECT CHAR(48);
                      -- Result: '0'
                    

CHARINDEX(expressionToFind ,expressionToSearch [,start_location ])

Returns the starting position of the specified expression in the character string.

• expressionToFind: The character expression to find.
• expressionToSearch: The character expression, typically a column, to search.
• start_location: An optional character position to start searching for expressionToFind in expressionToSearch.

                      SELECT CHARINDEX('456', '0123456');
                      -- Result: 4

                      SELECT CHARINDEX('456', '0123456', 5);
                      -- Result: -1
                    

CHAR_LENGTH(character_expression),

Returns the number of UTF-8 characters present in the expression.

• character_expression: The set of characters to be evaluated for length.

				 SELECT CHAR_LENGTH('sample text') FROM Account LIMIT 1
				 -- Result: 11			
				

CONCAT(string_value1, string_value2, ..., string_valueN)

Returns the string that is the concatenation of two or more string values.

• string_value1: The first string to be concatenated.
• string_value2: The second string to be concatenated.
• string_valueN: (optional) Any additional strings to be concatenated.

                      SELECT CONCAT('Hello, ', 'world!');
                      -- Result: 'Hello, world!'
                    

CONTAINS(expressionToSearch, expressionToFind)

Returns 1 if expressionToFind is found within expressionToSearch; otherwise, 0.

• expressionToSearch: The character expression, typically a column, to search.
• expressionToFind: The character expression to find.

                      SELECT CONTAINS('0123456', '456');
                      -- Result: 1

                      SELECT CONTAINS('0123456', 'Not a number');
                      -- Result: 0
                    

ENDSWITH(character_expression, character_suffix)

Returns 1 if character_expression ends with character_suffix; otherwise, 0.

• character_expression: The character expression.
• character_suffix: The character suffix to search for.

                      SELECT ENDSWITH('0123456', '456');
                      -- Result: 1

                      SELECT ENDSWITH('0123456', '012');
                      -- Result: 0
                    

FILESIZE(uri)

Returns the number of bytes present in the file at the specified file path.

• uri: The path of the file from which to read the size.

				SELECT FILESIZE('C:/Users/User1/Desktop/myfile.txt');
				-- Result: 23684
				

FORMAT(value [, parseFormat], format )

Returns the value formatted with the specified format.

• value: The string to format.
• format: The string specifying the output syntax of the date or numeric format.
• parseFormat: The string specifying the input syntax of the date value. Not applicable to numeric types.

                      SELECT FORMAT(12.34, '#');
                      -- Result: 12

                      SELECT FORMAT(12.34, '#.###');
                      -- Result: 12.34

                      SELECT FORMAT(1234, '0.000E0');
                      -- Result: 1.234E3
                      
                      SELECT FORMAT('2019/01/01', 'yyyy-MM-dd');
                      -- Result: 2019-01-01
                      
                      SELECT FORMAT('20190101', 'yyyyMMdd', 'yyyy-MM-dd');
                      -- Result: '2019-01-01'
                    

HASHBYTES(algorithm, value)

Returns the hash of the input value as a byte array using the given algorithm. The supported algorithms are MD5, SHA1, SHA2_256, SHA2_512, SHA3_224, SHA3_256, SHA3_384, and SHA3_512.

• algorithm: The algorithm to use for hashing. Must be one of MD5, SHA1, SHA2_256, SHA2_512, SHA3_224, SHA3_256, SHA3_384, or SHA3_512.
• value: The value to hash. Must be either a string or byte array.

                      SELECT HASHBYTES('MD5', 'Test');
                      -- Result (byte array): 0x0CBC6611F5540BD0809A388DC95A615B
                    

INDEXOF(expressionToSearch, expressionToFind [,start_location ])

Returns the starting position of the specified expression in the character string.

• expressionToSearch: The character expression, typically a column, to search.
• expressionToFind: The character expression to find.
• start_location: An optional character position to start searching for expressionToFind in expressionToSearch.

                      SELECT INDEXOF('0123456', '456');
                      -- Result: 4

                      SELECT INDEXOF('0123456', '456', 5);
                      -- Result: -1
                    

ISALPHABETIC(character_expression)

Returns 1 if the character expression consists only of alphabetic characters; otherwise, 0.

• character_expression: The string expression to evaluate.

                      SELECT ISALPHABETIC('Hello');
                      -- Result: 1

                      SELECT ISALPHABETIC('Hello123');
                      -- Result: 0

                      SELECT ISALPHABETIC('Hello!');
                      -- Result: 0
                    

ISALPHANUMERIC(character_expression)

Returns 1 if the character expression consists only of alphabetic and numeric characters; otherwise, 0.

• character_expression: The string expression to evaluate.

                      SELECT ISALPHANUMERIC('Hello123');
                      -- Result: 1

                      SELECT ISALPHANUMERIC('123');
                      -- Result: 1

                      SELECT ISALPHANUMERIC('Hello.123');
                      -- Result: 0
                    

ISNUMERIC(character_expression)

Returns 1 if the character expression consists only of numeric digits and up to one decimal point; otherwise, 0.

• character_expression: The string expression to evaluate.

                      SELECT ISNUMERIC('123');
                      -- Result: 1

                      SELECT ISNUMERIC('123.45');
                      -- Result: 1

                      SELECT ISNUMERIC('123.45.67');
                      -- Result: 0

                      SELECT ISNUMERIC('12a3');
                      -- Result: 0
                    

JSON_EXTRACT(json, jsonpath)

Selects any value in a JSON array or object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to extract.
• jsonpath: The XPath used to select the nodes. The JSONPath must be a string constant. The values of the nodes selected will be returned in a token-separated list.

                      SELECT JSON_EXTRACT('{"test": {"data": 1}}', '$.test');
                      -- Result: '{"data":1}'

                      SELECT JSON_EXTRACT('{"test": {"data": 1}}', '$.test.data');
                      -- Result: 1

                      SELECT JSON_EXTRACT('{"test": {"data": [1, 2, 3]}}', '$.test.data[1]');
                      -- Result: 2
                    

LEFT ( character_expression , integer_expression )

Returns the specified number of characters counting from the left of the specified string.

• character_expression: The character expression.
• integer_expression: The positive integer that specifies how many characters will be returned counting from the left of character_expression.

                      SELECT LEFT('1234567890', 3);
                      -- Result: '123'
                    

LEN(string_expression)

Returns the number of characters of the specified string expression.

• string_expression: The string expression.

                      SELECT LEN('12345');
                      -- Result: 5
                    

LOCATE(substring,string)

Returns an integer representing how many characters into the string the substring appears.

• substring: The substring to find inside larger string.
• string: The larger string that is searched for the substring.
• start locations: An optional integer that sets the character position (offset) from which to start searching.

				SELECT LOCATE('sample','XXXXXsampleXXXXX');
				-- Result: 6

                SELECT LOCATE('sample', 'XXXXXsampleXXXXX', 7)
                -- Result: 0
				

LOWER ( character_expression )

Returns the character expression with the uppercase character data converted to lowercase.

• character_expression: The character expression.

                      SELECT LOWER('MIXED case');
                      -- Result: 'mixed case'
                    

LTRIM(character_expression)

Returns the character expression with leading blanks removed.

• character_expression: The character expression.

                      SELECT LTRIM('     trimmed');
                      -- Result: 'trimmed'
                    

MASK(string_expression, mask_character [, start_index [, end_index ]])

Replaces the characters between start_index and end_index with the mask_character within the string.

• string_expression: The string expression to be searched.
• mask_character: The character to mask with.
• start_index: The optional number of characters to leave unmasked at beginning of string. Defaults to 0.
• end_index: The optional number of characters to leave unmasked at end of string. Defaults to 0.

                        SELECT MASK('1234567890','*',);
                        -- Result: '**********'
                        SELECT MASK('1234567890','*', 4);
                        -- Result: '1234******'
                        SELECT MASK('1234567890','*', 4, 2);
                        -- Result: '1234****90'  
                    

NCHAR(integer_expression)

Returns the Unicode character with the specified integer code as defined by the Unicode standard.

• integer_expression: The integer from 0 through 65535 (0 through xFFFF).

OCTET_LENGTH(character_expression),

Returns the number of bytes present in the expression.

• character_expression: The set of characters to be be evaluated.

				 SELECT OCTET_LENGTH('text') FROM Account LIMIT 1
				 -- Result: 4
				

PATINDEX(pattern, expression)

Returns the starting position of the first occurrence of the pattern in the expression. Returns 0 if the pattern is not found.

• pattern: The character expression that contains the sequence to be found. The wild-card character % can be used only at the start or end of the expression.
• expression: The expression, typically a column, to search for the pattern.

                      SELECT PATINDEX('123%', '1234567890');
                      -- Result: 1

                      SELECT PATINDEX('%890', '1234567890');
                      -- Result: 8

                      SELECT PATINDEX('%456%', '1234567890');
                      -- Result: 4
                    

POSITION(expressionToFind IN expressionToSearch)

Returns the starting position of the specified expression in the character string.

• expressionToFind: The character expression to find.
• expressionToSearch: The character expression, typically a column, to search.

                      SELECT POSITION('456' IN '123456');
                      -- Result: 4

                      SELECT POSITION('x' IN '123456');
                      -- Result: 0
                    

QUOTENAME(character_string [, quote_character])

Returns a valid SQL Server-delimited identifier by adding the necessary delimiters to the specified Unicode string.

• character_string: The string of Unicode character data. The string is limited to 128 characters. Inputs greater than 128 characters return null.
• quote_character: An optional single character to be used as the delimiter. These include:
  • a single quotation mark (')
  • a left or right bracket ([])
  • a double quotation mark (")
  • a left or right parenthesis ( () )
  • a greater or less than sign (><)
  • a left or right brace ({})
  • a backtick (`)

  If quote_character is not specified brackets are used. If an unacceptable character is supplied, it returns NULL.

                      SELECT QUOTENAME('table_name');
                      -- Result: '[table_name]'

                      SELECT QUOTENAME('table_name', '"');
                      -- Result: '"table_name"'

                      SELECT QUOTENAME('table_name', '[');
                      -- Result: '[table_name]'
                    

REGEXP_REPLACE(expr, pattern [, replacement [, position [, occurrence [, match_type]]]])

Replaces occurrences of a regular expression pattern in the input string with a specified value and returns the resulting string.

• expr: The string expression to be searched.
• pattern: The regular expression pattern to match.
• replacement: (optional) The string to replace each matched occurrence of pattern with. Supports backreferences \1 through \9 and escape sequences \n, \r, \t, and \\. By default, this argument is an empty string, meaning matched portions are removed from the output string.
• position: (optional) The 1-based starting position used when searching for regular expression matches in expr. The default is 1. All characters prior to the starting position are included in the output string unaltered. Skipped characters are ignored when calculating regular expression matches, even if they match pattern.
• occurrence: (optional) Specifies whether all occurrences of pattern,, or only a specific occurrence of pattern are replaced. The default is 0, which means all occurrences of pattern are replaced with replacement. Set to 1 to only replace the first instance of the pattern; 2 to replace the second; etc.
• match_type: (optional) Modifiers used to customize matching behavior. Supported values are: 'c' (case-sensitive, default), 'i' (case-insensitive), 'm' (multiline), 'n' (dot matches newline), 'x' (extended mode). These can be freely combined by including the letters back to back. For example, 'im' applies the functionality of both 'i' and 'm'. The regular expression syntax used is that of the Extended mode ('x') ignores whitespace and allows inline comments. If the pattern needs to match a literal space, it must be explicitly escaped.

                      SELECT REGEXP_REPLACE('abc123def456', '\d+', 'NUM');
                      -- Result: 'abcNUMdefNUM'

                      SELECT REGEXP_REPLACE('Hello\nHELLO\nhello', '^hello', 'X', 1, 0, 'im');
                      -- Result: 'X\nX\nX'
                    

REPLACE(string_expression, string_pattern, string_replacement)

Replaces all occurrences of a string with another string.

• string_expression: The string expression to be searched. This can be a character or binary data type.
• string_pattern: The substring to be found. Cannot be an empty string.
• string_replacement: The replacement string.

                      SELECT REPLACE('1234567890', '456', '|');
                      -- Result: '123|7890'

                      SELECT REPLACE('123123123', '123', '.');
                      -- Result: '...'

                      SELECT REPLACE('1234567890', 'a', 'b');
                      -- Result: '1234567890'
                    

REPLICATE ( string_expression ,integer_expression )

Repeats the string value the specified number of times.

• string_expression: The string to replicate.
• integer_expression: The repeat count.

                      SELECT REPLACE('x', 5);
                      -- Result: 'xxxxx'
                    

REVERSE ( string_expression )

Returns the reverse order of the string expression.

• string_expression: The string.

                      SELECT REVERSE('1234567890');
                      -- Result: '0987654321'
                    

RIGHT ( character_expression , integer_expression )

Returns the right part of the string with the specified number of characters.

• character_expression: The character expression.
• integer_expression: The positive integer that specifies how many characters of the character expression will be returned.

                      SELECT RIGHT('1234567890', 3);
                      -- Result: '890'
                    

RTRIM(character_expression)

Returns the character expression after it removes trailing blanks.

• character_expression: The character expression.

                      SELECT RTRIM('trimmed     ');
                      -- Result: 'trimmed'
                    

SOUNDEX(character_expression)

Returns the four-character Soundex code, based on how the string sounds when spoken.

• character_expression: The alphanumeric expression of character data.

                      SELECT SOUNDEX('smith');
                      -- Result: 'S530'
                    

SPACE(repeatcount)

Returns the string that consists of repeated spaces.

• repeatcount: The number of spaces.

                      SELECT SPACE(5);
                      -- Result: '     '
                    

SPLIT(string, delimiter, offset)

Returns a section of the string between to delimiters.

• string: The string to split.
• delimiter: The character to split the string with.
• offset: The number of the split to return. Positive numbers are treated as offsets from the left, and negative numbers are treated as offsets from the right.

                      SELECT SPLIT('a/b/c/d', '/', 1);
                      -- Result: 'a'
                      SELECT SPLIT('a/b/c/d', '/', -2);
                      -- Result: 'c'
                    

STARTSWITH(character_expression, character_prefix)

Returns 1 if character_expression starts with character_prefix; otherwise, 0.

• character_expression: The character expression.
• character_prefix: The character prefix to search for.

                      SELECT STARTSWITH('0123456', '012');
                      -- Result: 1

                      SELECT STARTSWITH('0123456', '456');
                      -- Result: 0
                    

STR ( float_expression [ , integer_length [ , integer_decimal ] ] )

Returns the character data converted from the numeric data. For example, STR(123.45, 6, 1) returns 123.5.

• float_expression: The float expression.
• length: The optional total length to return. This includes decimal point, sign, digits, and spaces. The default is 10.
• decimal: The optional number of places to the right of the decimal point. The decimal must be less than or equal to 16.

                      SELECT STR('123.456');
                      -- Result: '123'

                      SELECT STR('123.456', 2);
                      -- Result: '**'

                      SELECT STR('123.456', 10, 2);
                      -- Result: '123.46'
                    

STUFF(character_expression , integer_start , integer_length , replaceWith_expression)

Inserts a string into another string. It deletes the specified length of characters in the first string at the start position and then inserts the second string into the first string at the start position.

• character_expression: The string expression.
• start: The integer value that specifies the location to start deletion and insertion. If start or length is negative, null is returned. If start is longer than the string to be modified, character_expression, null is returned.
• length: The integer that specifies the number of characters to delete. If length is longer than character_expression, deletion occurs up to the last character in replaceWith_expression.
• replaceWith_expression: The expression of character data that will replace length characters of character_expression beginning at the start value.

                      SELECT STUFF('1234567890', 3, 2, 'xx');
                      -- Result: '12xx567890'
                    

SUBSTRING(string_value FROM start FOR length)

Returns the part of the string with the specified length; starts at the specified index.

• string_value: The character string.
• start: The positive integer that specifies the start index of characters to return.
• length: Optional. The positive integer that specifies how many characters will be returned.

                      SELECT SUBSTRING('1234567890' FROM 3 FOR 2);
                      -- Result: '34'

                      SELECT SUBSTRING('1234567890' FROM 3);
                      -- Result: '34567890'
                    

You can also drop the FROM and FOR clauses:

                    SELECT SUBSTRING('1234567890', 3, 2)
                    --Result: '34'
                    SELECT SUBSTRING('1234567890', 3)
                    --Result: '34567890'
                    

TEXT_ENCODE(input_string, charset)

Returns binary output by encoding a string using the specified character set.

• input_string: The plain text string.
• charset: The character set to use, such as 'UTF-8', 'ISO-8859-1'.

                    SELECT TEXT_ENCODE('Café', 'UTF-8');
                    -- Result: (binary output)
                    

TEXT_DECODE(input_binary, charset)

Returns a string decoded from binary data using the specified character set.

• input_binary: The binary value to decode.
• charset: The character set used for decoding.

                    SELECT TEXT_DECODE(BinaryData, 'UTF-8');
                    -- Result: 'Café'
                    

TOSTRING(string_value1)

Converts the value of this instance to its equivalent string representation.

• string_value1: The string to be converted.

                      SELECT TOSTRING(123);
                      -- Result: '123'

                      SELECT TOSTRING(123.456);
                      -- Result: '123.456'

                      SELECT TOSTRING(null);
                      -- Result: ''
                    

TRIM(trimspec trimchar FROM string_value)

Returns the character expression with leading and/or trailing blanks removed.

• trimspec: Optional. If included must be one of the keywords BOTH, LEADING or TRAILING.
• trimchar: Optional. If included should be a one-character string value.
• string_value: The string value to trim.

                      SELECT TRIM('     trimmed     ');
                      -- Result: 'trimmed'

                      SELECT TRIM(LEADING FROM '     trimmed     ');
                      -- Result: 'trimmed     '

                      SELECT TRIM('-' FROM '-----trimmed-----');
                      -- Result: 'trimmed'

                      SELECT TRIM(BOTH '-' FROM '-----trimmed-----');
                      -- Result: 'trimmed'

                      SELECT TRIM(TRAILING '-' FROM '-----trimmed-----');
                      -- Result: '-----trimmed'
                    

UNICODE(ncharacter_expression)

Returns the integer value defined by the Unicode standard of the first character of the input expression.

• ncharacter_expression: The Unicode character expression.

UPPER ( character_expression )

Returns the character expression with lowercase character data converted to uppercase.

• character_expression: The character expression.

                      SELECT UPPER('MIXED case');
                      -- Result: 'MIXED CASE'
                    

XML_EXTRACT(xml, xpath [, separator])

Extracts an XML document using the specified XPath to flatten the XML. A comma is used to separate the outputs by default, but this can be changed by specifying the third parameter.

• xml: The XML document to extract.
• xpath: The XPath used to select the nodes. The nodes selected will be returned in a token-separated list.
• separator: The optional token used to separate the items in the flattened response. If this is not specified, the separator will be a comma.

                      SELECT XML_EXTRACT('<vowels><ch>a</ch><ch>e</ch><ch>i</ch><ch>o</ch><ch>u</ch></vowels>', '/vowels/ch');
                      -- Result: 'a,e,i,o,u'

                      SELECT XML_EXTRACT('<vowels><ch>a</ch><ch>e</ch><ch>i</ch><ch>o</ch><ch>u</ch></vowels>', '/vowels/ch', ';');
                      -- Result: 'a;e;i;o;u'
                    

ABS ( numeric_expression )

Returns the absolute (positive) value of the specified numeric expression.

• numeric_expression: The expression of an indeterminate numeric data type except for the bit data type.

                      SELECT ABS(15);
                      -- Result: 15

                      SELECT ABS(-15);
                      -- Result: 15
                    

ACOS ( float_expression )

Returns the arc cosine, the angle in radians whose cosine is the specified float expression.

• float_expression: The float expression that specifies the cosine of the angle to be returned. Values outside the range from -1 to 1 return null.

                      SELECT ACOS(0.5);
                      -- Result: 1.0471975511966
                    

ASIN ( float_expression )

Returns the arc sine, the angle in radians whose sine is the specified float expression.

• float_expression: The float expression that specifies the sine of the angle to be returned. Values outside the range from -1 to 1 return null.

                      SELECT ASIN(0.5);
                      -- Result: 0.523598775598299
                    

ATAN ( float_expression )

Returns the arc tangent, the angle in radians whose tangent is the specified float expression.

• float_expression: The float expression that specifies the tangent of the angle to be returned.

                      SELECT ATAN(10);
                      -- Result: 1.47112767430373
                    

ATN2 ( float_expression1 , float_expression2 )

Returns the angle in radians between the positive x-axis and the ray from the origin to the point (y, x) where x and y are the values of the two specified float expressions.

• float_expression1: The float expression that is the y-coordinate.
• float_expression2: The float expression that is the x-coordinate.

                      SELECT ATN2(1, 1);
                      -- Result: 0.785398163397448
                    

CEILING ( numeric_expression ) or CEIL( numeric_expression )

Returns the smallest integer greater than or equal to the specified numeric expression.

• numeric_expression: The expression of an indeterminate numeric data type except for the bit data type.

                      SELECT CEILING(1.3);
                      -- Result: 2

                      SELECT CEILING(1.5);
                      -- Result: 2

                      SELECT CEILING(1.7);
                      -- Result: 2
                    

COS ( float_expression )

Returns the trigonometric cosine of the specified angle in radians in the specified expression.

• float_expression: The float expression of the specified angle in radians.

                      SELECT COS(1);
                      -- Result: 0.54030230586814
                    

COT ( float_expression )

Returns the trigonometric cotangent of the angle in radians specified by float_expression.

• float_expression: The float expression of the angle in radians.

                      SELECT COT(1);
                      -- Result: 0.642092615934331
                    

DEGREES ( numeric_expression )

Returns the angle in degrees for the angle specified in radians.

• numeric_expression: The angle in radians, an expression of an indeterminate numeric data type except for the bit data type.

                      SELECT DEGREES(3.1415926);
                      -- Result: 179.999996929531
                    

EXP ( float_expression )

Returns the exponential value of the specified float expression. For example, EXP(LOG(20)) is 20.

• float_expression: The float expression.

                      SELECT EXP(2);
                      -- Result: 7.38905609893065
                    

EXPR ( expression )

Evaluates the expression.

• expression: The expression. Operators allowed are +, -, *, /, ==, !=, >, <, >=, and <=.

                      SELECT EXPR('1 + 2 * 3');
                      -- Result: 7

                      SELECT EXPR('1 + 2 * 3 == 7');
                      -- Result: true
                    

FLOOR ( numeric_expression )

Returns the largest integer less than or equal to the numeric expression.

• numeric_expression: The expression of an indeterminate numeric data type except for the bit data type.

                      SELECT FLOOR(1.3);
                      -- Result: 1

                      SELECT FLOOR(1.5);
                      -- Result: 1

                      SELECT FLOOR(1.7);
                      -- Result: 1
                    

GREATEST(int1,int2,....)

Returns the greatest of the supplied integers.

				SELECT GREATEST(3,5,8,10,1)
				-- Result: 10			
				

HEX(value)

Returns a the equivalent hex for the input value.

• value: A string or numerical value to be converted into hex.

				SELECT HEX(866849198);
				-- Result: 33AB11AE
				
				SELECT HEX('Sample Text');
				-- Result: 53616D706C652054657874
				

JSON_AVG(json, jsonpath)

Computes the average value of a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_AVG('[1,2,3,4,5]', '$[x]');
                      -- Result: 3

                      SELECT JSON_AVG('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 3

                      SELECT JSON_AVG('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 4.5
                    

JSON_COUNT(json, jsonpath)

Returns the number of elements in a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_COUNT('[1,2,3,4,5]', '$[x]');
                      -- Result: 5

                      SELECT JSON_COUNT('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 5

                      SELECT JSON_COUNT('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 2
                    

JSON_MAX(json, jsonpath)

Gets the maximum value in a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_MAX('[1,2,3,4,5]', '$[x]');
                      -- Result: 5

                      SELECT JSON_MAX('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 5

                      SELECT JSON_MAX('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[..3]');
                      -- Result: 4
                    

JSON_MIN(json, jsonpath)

Gets the minimum value in a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_MIN('[1,2,3,4,5]', '$[x]');
                      -- Result: 1

                      SELECT JSON_MIN('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 1

                      SELECT JSON_MIN('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 4
                    

JSON_SUM(json, jsonpath)

Computes the summary value in JSON according to the JSONPath expression. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_SUM('[1,2,3,4,5]', '$[x]');
                      -- Result: 15

                      SELECT JSON_SUM('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 15

                      SELECT JSON_SUM('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 9
                    

LEAST(int1,int2,....)

Returns the least of the supplied integers.

				SELECT LEAST(3,5,8,10,1)
				-- Result: 1			
				

LOG ( float_expression [, base ] )

Returns the natural logarithm of the specified float expression.

• float_expression: The float expression.
• base: The optional integer argument that sets the base for the logarithm.

                      SELECT LOG(7.3890560);
                      -- Result: 1.99999998661119
                    

LOG10 ( float_expression )

Returns the base-10 logarithm of the specified float expression.

• float_expression: The expression of type float.

                      SELECT LOG10(10000);
                      -- Result: 4
                    

MOD(dividend,divisor)

Returns the integer value associated with the remainder when dividing the dividend by the divisor.

• dividend: The number to take the modulus of.
• divisor: The number to divide the dividend by when determining the modulus.

				SELECT MOD(10,3);
				-- Result: 1
				

NEGATE(real_number)

Returns the opposite to the real number input.

• real_number: The real number to find the opposite of.

				SELECT NEGATE(10);
				-- Result: -10
				
				SELECT NEGATE(-12.4)
				--Result: 12.4
				

PI ( )

Returns the constant value of pi.

                  SELECT PI()
                  -- Result: 3.14159265358979 
                

POWER ( float_expression , y )

Returns the value of the specified expression raised to the specified power.

• float_expression: The float expression.
• y: The power to raise float_expression to.

                      SELECT POWER(2, 10);
                      -- Result: 1024

                      SELECT POWER(2, -2);
                      -- Result: 0.25
                    

RADIANS ( float_expression )

Returns the angle in radians of the angle in degrees.

• float_expression: The degrees of the angle as a float expression.

                      SELECT RADIANS(180);
                      -- Result: 3.14159265358979
                    

RAND ( [ integer_seed ] )

Returns a pseudorandom float value from 0 through 1, exclusive.

• seed: The optional integer expression that specifies the seed value. If seed is not specified, a seed value at random will be assigned.

                      SELECT RAND();
                      -- This result may be different, since the seed is randomized
                      -- Result: 0.873159630165044

                      SELECT RAND(1);
                      -- This result will always be the same, since the seed is constant
                      -- Result: 0.248668584157093
                    

ROUND ( numeric_expression [ ,integer_length] [ ,function ] )

Returns the numeric value rounded to the specified length or precision.

• numeric_expression: The expression of a numeric data type.
• length: The optional precision to round the numeric expression to. When this is omitted, the default behavior will be to round to the nearest whole number.
• function: The optional type of operation to perform. When the function parameter is omitted or has a value of 0 (default), numeric_expression is rounded. When a value other than 0 is specified, numeric_expression is truncated.

                      SELECT ROUND(1.3, 0);
                      -- Result: 1

                      SELECT ROUND(1.55, 1);
                      -- Result: 1.6

                      SELECT ROUND(1.7, 0, 0);
                      -- Result: 2

                      SELECT ROUND(1.7, 0, 1);
                      -- Result: 1
                      
                      SELECT ROUND (1.24);
                      -- Result: 1.0
                    

SIGN ( numeric_expression )

Returns the positive sign (1), 0, or negative sign (-1) of the specified expression.

• numeric_expression: The expression of an indeterminate data type except for the bit data type.

                      SELECT SIGN(0);
                      -- Result: 0

                      SELECT SIGN(10);
                      -- Result: 1

                      SELECT SIGN(-10);
                      -- Result: -1
                    

SIN ( float_expression )

Returns the trigonometric sine of the angle in radians.

• float_expression: The float expression specifying the angle in radians.

                     SELECT SIN(1);
                     -- Result: 0.841470984807897
                    

SQRT ( float_expression )

Returns the square root of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQRT(100);
                      -- Result: 10
                    

SQUARE ( float_expression )

Returns the square of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQUARE(10);
                      -- Result: 100

                      SELECT SQUARE(-10);
                      -- Result: 100
                    

TAN ( float_expression )

Returns the tangent of the input expression.

• float_expression: The expression of type float.

                      SELECT TAN(1);
                      -- Result: 1.5574077246549
                    

TRUNC(decimal_number,precision)

Returns the supplied decimal number truncated to have the supplied decimal precision.

• decimal_number: The decimal value to truncate.
• precision: The number of decimal places to truncate the decimal number to.

				SELECT TRUNC(10.3423,2);
				-- Result: 10.34
				

_ROW_NUMBER_()

Returns a row index as an additional column.

				SELECT ColumnName, _ROW_NUMBER_() FROM TableName
				-- Result: ColumnData, 0
				ColumnData2, 1
				ColumnData3, 2
				

CURRENT_DATE()

Returns the current date value.

                  SELECT CURRENT_DATE();
                  -- Result: 2018-02-01
                

CURRENT_TIMESTAMP()

Returns the current time stamp of the database system as a datetime value. This value is equal to GETDATE and SYSDATETIME, and is always in the local timezone.

                  SELECT CURRENT_TIMESTAMP();
                  -- Result: 2018-02-01 03:04:05
                

DATEADD (datepart , integer_number , date [, dateformat])

Returns the datetime value that results from adding the specified number (a signed integer) to the specified date part of the date.

• datepart: The part of the date to add the specified number to. The valid values and abbreviations are
  • year (yy, yyyy)
  • quarter (qq, q)
  • month (mm, m)
  • week (wk, ww)
  • weekday (dw)
  • dayofyear (dy, y)
  • day (dd, d)
  • hour (hh)
  • minute (mi, n)
  • second (ss, s)
  • millisecond (ms)
• number: The number to be added.
• date: The expression of the datetime data type.
• dateformat: The optional output date format.

                  SELECT DATEADD('d', 5, '2018-02-01');
                  -- Result: 2018-02-06

                  SELECT DATEADD('hh', 5, '2018-02-01 00:00:00');
                  -- Result: 2018-02-01 05:00:00
                

DATEDIFF ( datepart , startdate , enddate )

Returns the difference (a signed integer) of the specified time interval between the specified start date and end date.

• datepart: The part of the date that is the time interval of the difference between the start date and end date. The valid values and abbreviations are:
  • Year (year, yyyy, yy)
  • Quarter (quarter, qq, q)
  • Month (month, mm, m)
  • Week (week, wk, ww)
  • Weekday (weekday, dw)
  • Dayofyear (dayofyear, dy, y)
  • Day (day, dd, d)
  • Hour (hour, hh)
  • Minute (minute, mi, n)
  • Second (second, ss, s)
  • Millisecond (millisecond, ms)
• startdate: The datetime expression of the start date.
• enddate: The datetime expression of the end date.

                  SELECT DATEDIFF('d', '2018-02-01', '2018-02-10');
                  -- Result: 9

                  SELECT DATEDIFF('hh', '2018-02-01 00:00:00', '2018-02-01 12:00:00');
                  -- Result: 12
                

DATE_FORMAT(date,format)

Returns the date or timestamp in the format specified. This function mirrors the MySQL DATE_FORMAT function.

• date: A date or timestamp string.
• format: The specifier string of the desired output format. The list of supported format specifiers comes from the MySQL DATE_FORMAT function (see link to MySQL documentation above).

					SELECT DATE_FORMAT('9/4/2021 3:11:53 AM','%h')
					-- Result: 03
				  

DATEFROMPARTS(integer_year, integer_month, integer_day)

Returns the datetime value for the specified year, month, and day.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.

                    SELECT DATEFROMPARTS(2018, 2, 1);
                    -- Result: 2018-02-01
                  

DATENAME(datepart , date)

Returns the character string that represents the specified date part of the specified date.

• datepart: The part of the date to return. The valid values and abbreviations are year (yy, yyyy), quarter (qq, q), month (mm, m), dayofyear (dy, y), day (dd, d), week (wk, ww), weekday (dw), hour (hh), minute (mi, n), second (ss, s), millisecond (ms), microsecond (mcs), and nanosecond (ns).
• date: The datetime expression.

                     SELECT DATENAME('yy', '2018-02-01');
                     -- Result: '2018'

                     SELECT DATENAME('dw', '2018-02-01');
                     -- Result: 'Thursday'
                   

DATEPART(datepart, date [,integer_datefirst])

Returns a character string that represents the specified date part of the specified date.

• datepart: The part of the date to return. The valid values and abbreviations are year (yy, yyyy), quarter (qq, q), month (mm, m), dayofyear (dy, y), day (dd, d), week (wk, ww), weekday (dw), hour (hh), minute (mi, n), second (ss, s), millisecond (ms), microsecond (mcs), nanosecond (ns), ISODOW, ISO_WEEK (isoweek, isowk,isoww), and ISOYEAR.
• date: The datetime string.
• datefirst: The optional integer representing the first day of the week. The default is 7, Sunday.

                    SELECT DATEPART('yy', '2018-02-01');
                    -- Result: 2018

                    SELECT DATEPART('dw', '2018-02-01');
                    -- Result: 5
                  

DATETIMEFROMPARTS(integer_year, integer_month, integer_day, integer_hour, integer_minute, integer_seconds, integer_milliseconds)

Returns the datetime value for the specified date parts.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.
• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.
• seconds: The integer expression specifying the seconds.
• milliseconds: The integer expression specifying the milliseconds.

                    SELECT DATETIMEFROMPARTS(2018, 2, 1, 1, 2, 3, 456);
                    -- Result: 2018-02-01 01:02:03.456
                  

DATETIME2FROMPARTS(integer_year, integer_month, integer_day, integer_hour, integer_minute, integer_seconds, integer_fractions, integer_precision)

Returns the datetime value for the specified date parts.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.
• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.
• seconds: The integer expression specifying the seconds.
• fractions: The integer expression specifying the fractions of the second.
• precision: The integer expression specifying the precision of the fraction.

				    SELECT DATETIME2FROMPARTS(2018, 2, 1, 1, 2, 3, 456, 3);
                    -- Result: 2018-02-01 01:02:03.456
                  

DATE_TRUNC(date, datepart)

Truncates the date to the precision of the given date part. Modeled after the Oracle TRUNC function.

• date: The datetime string that specifies the date.
• datepart: Refer to the Oracle documentation for valid datepart syntax.

				    SELECT DATE_TRUNC('05-04-2005', 'YY');
                    -- Result: '1/1/2005'
					
                    SELECT DATE_TRUNC('05-04-2005', 'MM');
                    -- Result: '5/1/2005'                    
                  

DATE_TRUNC2(datepart, date, [weekday])

Truncates the date to the precision of the given date part. Modeled after the PostgreSQL date_trunc function.

• datepart: One of 'millennium', 'century', 'decade', 'year', 'quarter', 'month', 'week', 'day', 'hour', 'minute' or 'second'.
• date: The datetime string that specifies the date.
• weekday: The optional day of the week to use as the first day for 'week'. One of 'sunday', 'monday', etc.

                    SELECT DATE_TRUNC2('year', '2020-02-04');
                    -- Result: '2020-01-01'

                    SELECT DATE_TRUNC2('week', '2020-02-04', 'monday');
                    -- Result: '2020-02-02', which is the previous Monday
                  

DAY(date)

Returns the integer that specifies the day component of the specified date.

• date: The datetime string that specifies the date.

                    SELECT DAY('2018-02-01');
                    -- Result: 1
                  

DAYNAME(date)

Returns the name of the day of the week of the specified date.

• date: The datetime string that specifies the date.

                    SELECT DAYNAME('8/18/2021');
                    -- Result: Wednesday
                  

DAYOFMONTH(date)

Returns the day of the month of the given date part.

• date: The datetime string that specifies the date.

				  SELECT DAYOFMONTH('04/15/2000');
				  -- Result: 15
				  

DAYOFWEEK(date)

Returns the day of the week of the given date part.

• date: The datetime string that specifies the date.

				  SELECT DAYOFWEEK('04/15/2000');
				  -- Result: 7
				  

DAYOFYEAR(date)

Returns the day of the year of the given date part.

• date: The datetime string that specifies the date.

				  SELECT DAYOFYEAR('04/15/2000');
				  -- Result: 106
				  

EOMONTH(date [, integer_month_to_add ]) or LAST_DAY(date)

Returns the last day of the month that contains the specified date with an optional offset.

• date: The datetime expression specifying the date for which to return the last day of the month.
• integer_month_to_add: The optional integer expression specifying the number of months to add to the date before calculating the end of the month.

                  SELECT EOMONTH('2018-02-01');
                  -- Result: 2018-02-28
                  
                  SELECT LAST_DAY('2018-02-01');
                  -- Result: 2018-02-28

                  SELECT EOMONTH('2018-02-01', 2);
                  -- Result: 2018-04-30
                

EXTRACT(date_part FROM date_column_name)

Returns the last day of the month that contains the specified date with an optional offset.

• date_part: One of the following date components: YEAR, MONTH, DAY, HOUR, MINUTE, SECOND.
• date_column_name: The name of a date column in a table.

                  SELECT EXTRACT(YEAR FROM DateColumn)
                  -- Result: 2021
                

FDWEEK(date)

Returns the first day of the week of the given date part.

• date: The datetime string that specifies the date.
• weeks to add: An optional integer expression specifying the number of months to add to the date before calculating the first day of the week.

				  SELECT FDWEEK('02-08-2018');
				  -- Result: 2/4/2018

          SELECT FDWEEK('02-08-2018', 1)
          --Result: 02/11/2018
				  

FDMONTH(date)

Returns the first day of the month of the given date part.

• date: The datetime string that specifies the date.
• month to add: An optional integer expression specifying the number of months to add to the date before calculating the first day of the month.

				  SELECT FDMONTH('02-08-2018');
				  -- Result: 2/1/2018

          SELECT FDMONTH('02-08-2018', 1) 
          --Result: 03/01/2018
				  

FDQUARTER(date)

Returns the first day of the quarter of the given date part.

• date: The datetime string that specifies the date.
• quarters to add: An optional integer expression specifying the number of months to add to the date before calculating the first day of the quarter.

				  SELECT FDQUARTER('05-08-2018');
				  -- Result: 4/1/2018

          SELECT FDQUARTER('05-08-2018',1)
          --Result: 07/01/2018
				  

FILEMODIFIEDTIME(uri)

Returns the time stamp associated with the Date Modified of the relevant file.

• uri: An absolute path pointing to a file on the local file system.

				 SELECT FILEMODIFIEDTIME('C:/Documents/myfile.txt');
				 -- Result: 6/25/2019 10:06:58 AM
				 

FROM_DAYS(datevalue)

Returns a date derived from the number of days after 1582-10-15 (based upon the Gregorian calendar). This will be equivalent to the MYSQL FROM_DAYS function.

• datevalue: A integer value representing the number of days since 1582-10-15.

				SELECT FROM_DAYS(736000);
				-- Result: 2/6/2015
				

FROM_UNIXTIME(time, issecond)

Returns a representation of the unix_timestamp argument as a value in YYYY-MM-DD HH:MM:SS expressed in the current time zone.

• time: The time stamp value from epoch time. Milliseconds are accepted.
• issecond: Indicates the time stamp value is milliseconds to epoch time.

                      SELECT FROM_UNIXTIME(1540495231, 1);
                      -- Result: 2018-10-25 19:20:31

                      SELECT FROM_UNIXTIME(1540495357385, 0);
                      -- Result: 2018-10-25 19:22:37
                    

GETDATE()

Returns the current time stamp of the database system as a datetime value. This value is equal to CURRENT_TIMESTAMP and SYSDATETIME, and is always in the local timezone.

                  SELECT GETDATE();
                  -- Result: 2018-02-01 03:04:05
                

GETUTCDATE()

Returns the current time stamp of the database system formatted as a UTC datetime value. This value is equal to SYSUTCDATETIME.

In addition, GETUTCDATE can take an optional second parameter, a date and time that are converted to UTC.

                  SELECT GETUTCDATE();
                  -- For example, if the local timezone is Eastern European Time (GMT+2)
                  -- Result: 2018-02-01 05:04:05

                  SELECT GETUTCDATE('2020/08/31 13:56:00')
                  --Result: '2020-08-31 17:56:00'
                

HOUR(date)

Returns the hour component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT HOUR('02-02-2020 11:30:00');
				-- Result: 11
				

ISDATE(date, [date_format])

Returns 1 if the value is a valid date, time, or datetime value; otherwise, 0.

• date: The datetime string.
• date_format: The optional datetime format.

                      SELECT ISDATE('2018-02-01', 'yyyy-MM-dd');
                      -- Result: 1

                      SELECT ISDATE('Not a date');
                      -- Result: 0
                    

LAST_WEEK()

Returns a time stamp equivalent to exactly one week before the current date.

				SELECT LAST_WEEK();	//Assume the date is 3/17/2020	
			 -- Result: 3/10/2020 00:00:00
				

LAST_MONTH()

Returns a time stamp equivalent to exactly one month before the current date.

	
				SELECT LAST_MONTH(); //Assume the date is 3/17/2020
				-- Result: 2/17/2020 00:00:00
				

LAST_YEAR()

Returns a time stamp equivalent to exactly one year before the current date.

				SELECT LAST_YEAR();	//Assume the date is 3/17/2020	
				-- Result: 3/10/2019 00:00:00
				

LDWEEK(date)

Returns the last day of the provided week.

• date: The datetime string.
• weeks to add: An optional integer expression specifying the number of months to add to the date before calculating the last day of the week.

				SELECT LDWEEK('02-02-2020');
				-- Result: 2/8/2020
				

LDMONTH(date)

Returns the last day of the provided month.

• date: The datetime string.
• months to add: An optional integer expression specifying the number of months to add to the date before calculating the last day of the month.

				SELECT LDMONTH('02-02-2020');
				-- Result: 2/29/2020

        SELECT LDMONTH('02-08-2020', 1)
        --Result: 03/31/2020
				

LDQUARTER(date)

Returns the last day of the provided quarter.

• date: The datetime string.
• quarters to add: An optional integer expression specifying the number of months to add to the date before calculating the last day of the quarter.

				SELECT LDQUARTER('02-02-2020');
				-- Result: 3/31/2020

        SELECT LDQUARTER('02-02-2020',1)
        --Result: 06/30/2020
				

MAKEDATE(year, days)

Returns a date value from a year and a number of days.

• year: The year
• days: The number of days into the year. Value must be greater than 0.

          SELECT MAKEDATE(2020, 1);
          -- Result: 2020-01-01
        

MINUTE(date)

Returns the minute component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT MINUTE('02-02-2020 11:15:00');
				-- Result: 15
				

MONTH(date)

Returns the month component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT MONTH('02-02-2020');
				-- Result: 2
				

QUARTER(date)

Returns the quarter associated with the provided datetime.

• date: The datetime string that specifies the date.

				SELECT QUARTER('02-02-2020');
				-- Result: 1
				

SECOND(date)

Returns the second component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT SECOND('02-02-2020 11:15:23');
				-- Result: 23
				

SMALLDATETIMEFROMPARTS(integer_year, integer_month, integer_day, integer_hour, integer_minute)

Returns the datetime value for the specified date and time.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.
• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.

                      SELECT SMALLDATETIMEFROMPARTS(2018, 2, 1, 1, 2);
                      -- Result: 2018-02-01 01:02:00
                    

STRTODATE(string,format)

Parses the provided string value and returns the corresponding datetime.

• string: The string value to be converted to datetime format.
• format: A format string which describes how to interpret the first string input. A few special formats are available as well, including UNIX, UNIXMILIS, TICKS, and FILETICKS.

				SELECT STRTODATE('03*04*2020','dd*MM*yyyy');
				-- Result: 4/3/2020
				

SYSDATETIME()

Returns the current time stamp as a datetime value of the database system. It is equal to GETDATE and CURRENT_TIMESTAMP, and is always in the local timezone.

                  SELECT SYSDATETIME();
                  -- Result: 2018-02-01 03:04:05
                

SYSUTCDATETIME()

Returns the current system date and time as a UTC datetime value. It is equal to GETUTCDATE.

                  SELECT SYSUTCDATETIME();
                  -- For example, if the local timezone is Eastern European Time (GMT+2)
                  -- Result: 2018-02-01 05:04:05
                

TIMEFROMPARTS(integer_hour, integer_minute, integer_seconds, integer_fractions, integer_precision)

Returns the time value for the specified time and with the specified precision.

• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.
• seconds: The integer expression specifying the seconds.
• fractions: The integer expression specifying the fractions of the second.
• precision : The integer expression specifying the precision of the fraction.

                      SELECT TIMEFROMPARTS(1, 2, 3, 456, 3);
                      -- Result: 01:02:03.456
                    

TO_DAYS(date)

Returns the number of days since 0000-00-01. This will only return a value for dates on or after 1582-10-15 (based upon the Gregorian calendar). This will be equivalent to the MYSQL TO_DAYS function.

• date: The datetime string that specifies the date.

				SELECT TO_DAYS('02-06-2015');
				-- Result: 736000
				

WEEK(date)

Returns the week (of the year) associated with the provided datetime.

• date: The datetime string that specifies the date.

				SELECT WEEK('02-17-2020 11:15:23');
				-- Result: 8
				

YEAR(date)

Returns the integer that specifies the year of the specified date.

• date: The datetime string.

                      SELECT YEAR('2018-02-01');
                      -- Result: 2018
                    

The following date literal functions can be used to filter date fields using relative intervals. Note that while the <, >, and = operators are supported for these functions, <= and >= are not.

L_TODAY()

The current day.

  SELECT * FROM MyTable WHERE MyDateField = L_TODAY()

L_YESTERDAY()

The previous day.

  SELECT * FROM MyTable WHERE MyDateField = L_YESTERDAY()

L_TOMORROW()

The following day.

  SELECT * FROM MyTable WHERE MyDateField = L_TOMORROW()

L_LAST_WEEK()

Every day in the preceding week.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_WEEK()

L_THIS_WEEK()

Every day in the current week.

  SELECT * FROM MyTable WHERE MyDateField = L_THIS_WEEK()

L_NEXT_WEEK()

Every day in the following week.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_WEEK()

Also available:

• L_LAST/L_THIS/L_NEXT MONTH
• L_LAST/L_THIS/L_NEXT QUARTER
• L_LAST/L_THIS/L_NEXT YEAR

L_LAST_N_DAYS(n)

The previous n days, excluding the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_DAYS(3)

L_NEXT_N_DAYS(n)

The following n days, including the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_DAYS(3)

Also available:

• L_LAST/L_NEXT_90_DAYS

L_LAST_N_WEEKS(n)

Every day in every week, starting n weeks before current week, and ending in the previous week.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_WEEKS(3)

L_NEXT_N_WEEKS(n)

Every day in every week, starting the following week, and ending n weeks in the future.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_WEEKS(3)

Also available:

• L_LAST/L_NEXT_N_MONTHS(n)
• L_LAST/L_NEXT_N_QUARTERS(n)
• L_LAST/L_NEXT_N_YEARS(n)

A SELECT statement can consist of the following basic clauses.

• SELECT
• INTO
• FROM
• JOIN
• WHERE
• GROUP BY
• HAVING
• UNION
• ORDER BY
• LIMIT

SELECT Syntax

The following syntax diagram outlines the syntax supported by the SQL engine of the connector:

SELECT {
  [ TOP <numeric_literal> | DISTINCT ]
  { 
    * 
    | { 
        <expression> [ [ AS ] <column_reference> ] 
        | { <table_name> | <correlation_name> } .* 
      } [ , ... ] 
  }
  { 
    FROM <table_reference> [ [ AS ] <identifier> ] 
  } [ , ... ]
  [ [  
      INNER | { { LEFT | RIGHT | FULL } [ OUTER ] } 
    ] JOIN <table_reference> [ ON <search_condition> ] [ [ AS ] <identifier> ] 
  ] [ ... ] 
  [ WHERE <search_condition> ]
  [ GROUP BY <column_reference> [ , ... ]
  [ HAVING <search_condition> ]
  [ UNION [ ALL ] <select_statement> ]
  [ 
    ORDER BY 
    <column_reference> [ ASC | DESC ] [ NULLS FIRST | NULLS LAST ]
  ]
  [ 
    LIMIT <expression>
    [ 
      { OFFSET | , }
      <expression> 
    ]
  ] 
}

<expression> ::=
  | <column_reference>
  | @ <parameter> 
  | ?
  | COUNT( * | { [ DISTINCT ] <expression> } )
  | { AVG | MAX | MIN | SUM | COUNT } ( <expression> ) 
  | NULLIF ( <expression> , <expression> ) 
  | COALESCE ( <expression> , ... ) 
  | CASE <expression>
      WHEN { <expression> | <search_condition> } THEN { <expression> | NULL } [ ... ]
    [ ELSE { <expression> | NULL } ]
    END 
  | {RANK() | DENSE_RANK()} OVER ([PARTITION BY <column_reference>] {ORDER BY <column_reference>})
  | <literal>
  | <sql_function> 

<search_condition> ::= 
  {
    <expression> { = | > | < | >= | <= | <> | != | LIKE | NOT LIKE | IN | NOT IN | IS NULL | IS NOT NULL | AND | OR | CONTAINS | BETWEEN | IS DISTINCT FROM | IS NOT DISTINCT FROM } [ <expression> ]
  } [ { AND | OR } ... ] 

Examples

1. Return all columns:
   

   SELECT * FROM Traffic

2. Rename a column:
   

   SELECT [DeviceCategory] AS MY_DeviceCategory FROM Traffic

3. Cast a column's data as a different data type:
   

   SELECT CAST(AnnualRevenue AS VARCHAR) AS Str_AnnualRevenue FROM Traffic

4. Search data:
   

   SELECT * FROM Traffic WHERE Transactions > '0'

5. Return the number of items matching the query criteria:
   

   SELECT COUNT(*) AS MyCount FROM Traffic 

6. Return the number of unique items matching the query criteria:
   

   SELECT COUNT(DISTINCT DeviceCategory) FROM Traffic 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT DeviceCategory FROM Traffic 

8. Sort a result set in ascending order:
   

   SELECT Browser, DeviceCategory FROM Traffic  ORDER BY DeviceCategory ASC

9. Restrict a result set to the specified number of rows:
   

   SELECT Browser, DeviceCategory FROM Traffic LIMIT 10 

10. Parameterize a query to pass in inputs at execution time. This enables you to create prepared statements and mitigate SQL injection attacks.
    

    SELECT * FROM Traffic WHERE Transactions = @param

See Explicitly Caching Data for information on using the SELECT statement in offline mode.

Pseudo Columns

Some input-only fields are available in SELECT statements. These fields, called pseudo columns, do not appear as regular columns in the results, yet may be specified as part of the WHERE clause. You can use pseudo columns to access additional features from Google Analytics.

    SELECT * FROM Traffic WHERE Segments = '@Segments'
    

Date Literal Functions

Date Literal Functions contains SELECT examples with date literal functions.

Window Functions

See Window Functions for SELECT examples containing window functions.

Table-Valued Functions

See Table-Valued Functions for SELECT examples with table-valued functions.

Window functions allow you to create computed fields from a group of rows (a window) that return a result for each row, as opposed to one computed result for a set of rows, as is the case with aggregate functions. The connector supports the following window function syntax.

Note: Window function support is an experimental feature of the connector. This functionality extends beyond the connector's core scope of being SQL-92 compliant. As such, performance with window functions may not be optimal.

Window Function Clauses

OVER

The OVER clause defines the window over which window functions are performed.

SELECT A, B, <window function> OVER (<window frame>) FROM TableName

The <window function> refers to any supported window function clause, and the <window frame> refers to one or more clauses that specify the logic by which the window is defined.

PARTITION BY

The PARTITION BY clause subdivides a window into sub-windows called partitions. For each unique value in the column specified in the PARTITION BY clause, every record with that value collectively forms an individual partition.

SELECT A, B, <window function> OVER (PARTITION BY A ORDER BY B) From Traffic

The <window function> refers to any supported window function clause.

Window Functions

The connector supports math, ranking, and analytic window functions.

Math

These window functions perform mathematical operations on the records within the window.

COUNT()

Calculates the number of records in each partition. The calculated column is of the data type "int".

In each partition, every record will display the total number of records in that partition.

SELECT Name, Role, Earnings, COUNT() OVER (PARTITION BY Role) FROM Employees

COUNT_BIG()

Calculates the number of records in each partition. The calculated column is of the data type "bigint".

In each partition, every record will display the total number of records in that partition.

SELECT Name, Role, Earnings, COUNT_BIG() OVER (PARTITION BY Role) FROM Employees

MIN(numeric_column)

Calculates the minimum value of a numerical column per partition.

In each partition, every record will display the minimum value of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, MIN(Earnings) OVER (PARTITION BY Role) FROM Employees

MAX(numeric_column)

Calculates the maximum value of a numerical column per partition.

In each partition, every record will display the maximum value of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, MAX(Earnings) OVER (PARTITION BY Role) FROM Employees

SUM(numeric_column)

Calculates the sum of a numerical column per partition.

In each partition, every record will display the sum of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, SUM(Earnings) OVER (PARTITION BY Role) FROM Employees

AVG(numeric_column)

Calculates the average value of a numerical column per partition.

In each partition, every record will display the average value of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, AVG(Earnings) OVER (PARTITION BY Role) FROM Employees

MEDIAN(numeric_column)

Calculates the median value of a numerical column per partition.

In each partition, every record will display the median value of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, MEDIAN(Earnings) OVER (PARTITION BY Role) FROM Employees

STDEV(numeric_column)

Calculates the standard deviation of a numerical column per partition.

In each partition, every record will display the standard deviation of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, STDEV(Earnings) OVER (PARTITION BY Role) FROM Employees

STDEVP(numeric_column)

Calculates the population standard deviation of a numerical column per partition.

In each partition, every record will display the population standard deviation of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, STDEVP(Earnings) OVER (PARTITION BY Role) FROM Employees

VAR(numeric_column)

Calculates the statistical standard variance of a numerical column per partition.

In each partition, every record will display the statistical standard variance of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, VAR(Earnings) OVER (PARTITION BY Role) FROM Employees

VARP(numeric_column)

Calculates the variance population of a numerical column per partition.

In each partition, every record will display the variance population of numeric_column across the records in that partition.

SELECT Name, Role, Earnings, VARP(Earnings) OVER (PARTITION BY Role) FROM Employees

Ranking

These window functions rank records that fall within the window and its partitions.

RANK()

Assigns a rank number to each record in a window based on the value of the column specified in the required ORDER BY clause.

If two or more records have an equal value in the in ranked column, they all receive the same rank number and the rank count increments internally, skipping ahead one rank number for each record with a duplicate value in the ORDER BY column.

SELECT Browser, DeviceCategory, RANK() OVER (ORDER BY DeviceCategory) AS Rank FROM Traffic

If you add a PARTITION BY clause, a separate set of ranks is calculated for each partition.

SELECT Browser, DeviceCategory, RANK() OVER (PARTITION BY Browser ORDER BY DeviceCategory) AS Rank FROM Traffic

DENSE_RANK()

Operates like the RANK() function, but it doesn't increment the internal rank counter for each record with a duplicate value in the ranked column.

This means that, while records with identical values in the ORDER BY column still share a rank number, the function never skips a rank number.

SELECT Browser, DeviceCategory, DENSE_RANK() OVER (PARTITION BY Browser ORDER BY DeviceCategory) AS Rank FROM Traffic

If you add a PARTITION BY clause, a separate set of ranks is calculated for each partition.

SELECT Browser, DeviceCategory, DENSE_RANK() OVER (PARTITION BY Browser ORDER BY DeviceCategory) AS Rank FROM Traffic

ROW_NUMBER()

Calculates a row number for each record. An ORDER BY clause in the OVER clause is required.

SELECT Name, Role, Earnings, ROW_NUMBER() OVER (ORDER BY Role) FROM Employees

If you define multiple partitions with PARTITION BY, a new set of row numbers are calculated for each partition.

SELECT Name, Role, Earnings, ROW_NUMBER() OVER (PARTITION BY Role ORDER BY Earnings) FROM Employees

NTILE()

Distributes rows of an ordered partition into a specified number of approximately equal groups, or buckets. It assigns each group a bucket number starting from one. For each row in a group, the NTILE() function assigns a bucket number representing the group to which the row belongs.

The syntax of NTILE() is:

NTILE(buckets) OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

The following are paramaters that NTILE() supports:

• buckets: The number of buckets into which the rows are divided. The buckets can be an expression or subquery that evaluates to a positive integer. It cannot be a window function.
• PARTITION BY: distributes rows of a result set into partitions to which the NTILE() function is applied.
• ORDER BY is clause that specifies the logical order of rows in each partition to which the NTILE() is applied.

If the number of rows is not divisible by the buckets, the NTILE() function returns groups of two sizes with the difference by one. The larger groups always precede the smaller group in the order set by ORDER BY in the OVER() clause.

If the total of rows is divisible by the number of buckets, the function divides the rows evenly among buckets. The following statement creates a new table named ntile_demo that stores 10 integers:

CREATE TABLE sales.ntile_demo (
	v INT NOT NULL
);
	
INSERT INTO sales.ntile_demo(v) 
VALUES(1),(2),(3),(4),(5),(6),(7),(8),(9),(10);	
	
SELECT * FROM sales.ntile_demo;

This statement uses the NTILE() function to divide ten rows into three groups:

SELECT 
	v, 
	NTILE (3) OVER (
		ORDER BY v
	) buckets
FROM 
	sales.ntile_demo;

Analytical

These window functions perform analytical operations on the records within the window.

PERCENT_RANK()

Calculates the relative rank SQL Percentile of each row. It returns values greater than zero, but the maximum value is one. It does not count any NULL values. This function is nondeterministic.

The syntax of PERCENT_RANK() is:

PERCENT_RANK() OVER (
      [PARTITION BY partition_expression, ... ]
      ORDER BY sort_expression [ASC | DESC], ...
  )
  

This syntax uses the following parameters.

• PARTITION BY: By default, SQL Server treats the whole data set as a single set. You can specify the PARTITION BY clause to divide data into multiple sets. The Percent_Rank function performs the analytical calculations on each set. This parameter is optional.
• ORDER BY: Sorts the data in either ascending or descending order. This parameter is required.

Table-valued functions are functions that return a table (rowset).

Note: Table-valued function support is an experimental feature of the connector. This functionality extends beyond the connector's core scope of being SQL-92 compliant. As such, performance with these functions may not be optimal.

Table-Valued Function Clauses

CROSS APPLY

The CROSS APPLY operator is used to perform a subquery on each row of a table or resultset produced by a preceding table expression.

<table_expression_1> CROSS APPLY <table_expression_2>

The second table expression can reference results from the first table expression to create derived columns or an altered recordset via a table-valued function.

Each resulting record is an instance of the record it's splitting, with all the same column values, except for the column(s) containing the value split by the function.

WITH

The WITH clause is used alongside certain table-valued functions to match against constructs within the structure being split (keys, element names, attribute names, etc.) and/or to specify metadata for the columns generated from the function.

SELECT A.ColumnName, X.DerivedColumnName FROM TableName A CROSS APPLY <table-valued function> WITH (DerivedColumnName varchar(255)) AS X

Table-Valued Functions

STRING_SPLIT(input_text,delimiter)

Takes each record in the recordset of the preceding table expression, splits the column containing delimiters (input_text) into substrings separated by the delimiter, and returns one record per substring.

• input_text: A column whose value you want to parse.
• delimiter: The character used to split the value of the column specified in input_text.

Suppose there is a column called "SplitColumn" with the following content:

One-Two-Three

To unpack this value across multiple records:

SELECT A.ID, X.Value FROM [TableWithDelimitedStringField] A CROSS APPLY STRING_SPLIT(A.SplitColumn,'-') WITH (Value VARCHAR(255)) AS X

-- Results:
-----------
|ID|Value|
|1|One|
|1|Two|
|1|Three|

JSONTABLE(json_content,[jsonpath])

For each record in the recordset of the preceding table expression, returns one record for each instance of a key in a JSON array (json_content) that matches the key(s) specified in the WITH clause, at the scope specified by the "jsonpath" input.

• json_content: A JSON "table" (array of objects). The contents can nest, but this must be a single JSON array, not any other JSON structure, at the root level.
  • The values of every instance of the key(s) provided in the WITH clause are retrievable only for substructures which are immediate children of the root-level JSON array.
• jsonpath: An optional JSONPath query defining the scope, within the json_content array, that you want to pull content from. The JSON key(s) identified in the WITH clause must exist at the scope defined in this parameter. This defaults to the JSON root ($).

Consider a sample table with a single record, including an ID column and column with JSON content called "JSONColumn" with the following content:

[
	{
		"name": "Samuel",
		"email": "sam@gmail.com",
		"extrainfo": {
			"city": "Seattle"
		}
	},
	{
		"name": "Katherine",
		"email": "kat@gmail.com",
	},
	{
		"name": "George",
		"email": "george23@gmail.com",
	},
	{
		"name": "Carlos",
		"email": "carlos32@gmail.com",
	}
]

To extract all values for a certain key, specify the scope in the JSONTABLE function and provide the desired key(s) in the WITH clause.

SELECT A.ID, X.name FROM [TableWithJSONField] A CROSS APPLY JSONTABLE(A.JSONColumn) WITH (name VARCHAR(255)) AS X

-- Results: 
|ID|name|
---------
|1 |Samuel|
|1 |Katherine|
|1 |George|
|1 |Carlos|

XMLTABLE(xml_content,[xpath,child_type])

For each record in the resultset of the preceding table expression, returns one record for each of the elements and/or attributes in an XML structure (xml_content) that match the tag name(s) and/or attribute name(s) specified in the WITH clause, at the scope specified in the "xpath" input.

• xml_content: A column containing an XML structure.
• xpath: An optional XPath that specifies the scope within the XML structure at which the connector extracts content matching the tag/attribute name(s) specified in the WITH clause.
  • When extracting the content of sub-elements, the connector can retrieve all content from tags at the root level, (depth 0) immediate children of the root (depth 1), and children of those children (depth 2).
  • When extracting element attribute content, the connector can retrieve all content from tags containing the specified attribute at the root level (depth 0) and from immediate children of root-level elements (depth 1).
• child_type: An optional parameter that specifies the part(s) of the parent element (specified in the xpath input) that the column(s) provided in the WITH clause are checked against to identify content.
  • You can supply the following values:
    • 0: The column(s) in the WITH clause are checked for matches against the parent element's attribute names and sub-element tag names.
    • 1: The column(s) in the WITH clause are checked for matches against the parent element's attribute names.
    • 2: The column(s) in the WITH clause are checked for matches against the parent element's sub-element tag names.
  • When not supplied, this defaults to 0.

Extracting Sub-Element Values

Consider a sample table with a single record, including an ID column and a column with XML content called "XMLContent" with the following content:

<shoppingList>
    <item>
        <name>Apples</name>
        <quantity>3</quantity>
        <unit>Kg</unit>
    </item>
    <item>
        <name>Bread</name>
        <quantity>2</quantity>
        <unit>Loaf</unit>
		<extrainfo>
			<Type>Whole-Grain</Type>
		</extrainfo>
    </item>
    <item>
        <name>Milk</name>
        <quantity>1</quantity>
        <unit>Carton</unit>
    </item>
    <item>
        <name>Eggs</name>
        <quantity>12</quantity>
        <unit></unit>
    </item>
</shoppingList>

To extract sub-element content, specify the scope in the XMLTABLE function and provide the desired element name(s) in the WITH clause. Note that this will not work if the XMLTABLE function's child_type input is set to 1.

SELECT A.ID, X.name FROM [TableWithXMLField] A CROSS APPLY XMLTABLE(A.XMLContent,'//*/item') WITH (name VARCHAR(255)) AS X

-- Results: 
|ID|name|
---------
|1|Apples|
|1|Bread|
|1|Milk|
|1|Eggs|

Extracting Values Using Element Tag Attributes

Suppose you have this sample table with a single record, including an ID column and a column with XML content called "XMLContent" with the following content:

<restaurant>
  <dish type="appetizer">
    <name lang="en">Caprese Salad</name>
    <chef>Chef Giovanni</chef>
    <price currency="USD">9.99</price>
  </dish>
  <dish type="main-course">
    <name lang="fr">Boeuf Bourguignon</name>
    <chef>Chef Marie</chef>
    <price currency="EUR">19.99</price>
  </dish>
  <dish type="dessert">
    <name lang="es">Tres Leches Cake</name>
    <chef>Chef Alejandro</chef>
    <price currency="MXN">89.99</price>
  </dish>
</restaurant>

To extract attribute content, specify the scope in the XMLTABLE function and provide the desired attribute name(s) in the WITH clause. Note that this will not work if the XMLTABLE function's child_type input is set to 2.

SELECT A.ID, X.type FROM [TableWithXMLField] A CROSS APPLY XMLTABLE(A.XMLContent,'//*/dish') WITH (type VARCHAR(255)) AS X

-- Results: 
|ID|type|
---------
|1|appetizer|
|1|main-course|
|1|dessert|

CSVTABLE(csv_content,[delimiter])

For each record in the resultset of the preceding table expression, reads from a column that contains a CSV table (csv_content) and for each record in that CSV table, returns one record containing the value of the CSV column(s) specified in the WITH clause.

• csv_content: A column containing a CSV table.
• delimiter: An optional custom delimiter (instead of a comma) which splits the CSV content contained in the csv_content input.

Consider a sample table with a single record, including an ID column and a column containing CSV table called "CSVContent" with the following content:

Name;Category;Price
Apple;Fruit;0.99
Spaghetti;Pasta;5.49
Chicken Breast;Meat;8.99
Broccoli;Vegetable;2.49

To select every value in the "Name" column and account for the custom delimiter (;):

SELECT A.ID, X.Name FROM [TableWithCSVField] A CROSS APPLY CSVTABLE(A.CSVContent,';') WITH (Name VARCHAR(255)) AS X

-- Results:
|ID|Name|
-----------
|1|Apple|
|1|Spaghetti|
|1|Chicken Breast|
|1|Broccoli|

When caching is enabled, CACHE statements provide complete control over the data that is cached and the table to which it is cached. The CACHE statement executes the SELECT statement specified and caches its results to a table with the same name in the cache database or to table specified in <cached_table_name>. The connector updates or inserts rows to the cache depending on whether or not they already exist in the cache, so the primary key, which is used to identify existing rows, must be included in the selected columns.

See Caching Data for more information on different caching strategies.

CACHE Statement Syntax

The cache statement may include the following options that alter its behavior:

CACHE [ <cached_table_name> ] [ WITH TRUNCATE | AUTOCOMMIT | SCHEMA ONLY | DROP EXISTING | ALTER SCHEMA ] <select_statement> 

WITH TRUNCATE

If this option is set, the connector removes existing rows in the cache table before adding the selected rows. Use this option if you want to refresh the entire cache table but keep its existing schema.

AUTOCOMMIT

If this option is set, the connector commits each row individually. Use this option if you want to ignore the rows that could not be cached due to some reason. By default, the entire result set is cached as a single transaction.

DROP EXISTING

If this option is set, the connector drops the existing cache table before caching the new results. Use this option if you want to refresh the entire cache table, including its schema.

SCHEMA ONLY

If this option is set, the connector creates the cache table based on the SELECT statement without executing the query.

ALTER SCHEMA

If this option is set, the connector alters the schema of the existing table in the cache if it does not match the schema of the SELECT statement. This option results in new columns or dropped columns, if the schema of the SELECT statement does not match the cached table.

Common Queries

Use the following cache statement to cache all rows of a table:

CACHE SELECT * FROM Traffic

Use the following cache statement to cache all rows of a table into the cache table CachedTraffic:

CACHE CachedTraffic SELECT * FROM Traffic

Use the following cache statement for incremental caching. The DateModified column may not exist in all tables. The cache statement shows how incremental caching would work if there were such a column. Also, notice that, in this case, the WITH TRUNCATE and DROP EXISTING options are specifically omitted, which would have deleted all existing rows.

CACHE CachedTraffic SELECT * FROM Traffic WHERE DateModified > '2013-04-04'

Use the following cache statements to create a table with all available columns that will then cache only a few of them. The sequence of statements cache only Browser and DeviceCategory even though the cache table CachedTraffic has all the columns in Traffic.

CACHE CachedTraffic SCHEMA ONLY SELECT * FROM Traffic
CACHE CachedTraffic SELECT Browser, DeviceCategory FROM Traffic

To execute stored procedures, you can use EXECUTE or EXEC statements.

EXEC and EXECUTE assign stored procedure inputs, referenced by name, to values or parameter names.

Stored Procedure Syntax

To execute a stored procedure as an SQL statement, use the following syntax:

 
{ EXECUTE | EXEC } <stored_proc_name> 
{
  [ @ ] <input_name> = <expression>
} [ , ... ]

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

Example Statements

Reference stored procedure inputs by name:

EXECUTE my_proc @second = 2, @first = 1, @third = 3;

Execute a parameterized stored procedure statement:

EXECUTE my_proc second = @p1, first = @p2, third = @p3; 

PIVOT and UNPIVOT can be used to change a table-valued expression into another table.

PIVOT

PIVOT rotates a table-value expression by turning unique values from one column into multiple columns in the output. PIVOT can run aggregations where required on any column value.

PIVOT Synax

 
"SELECT 'AverageCost' AS Cost_Sorted_By_Production_Days, [0], [1], [2], [3], [4]
FROM
(
SELECT DaysToManufacture, StandardCost
FROM Production.Product
) AS SourceTable
PIVOT
(
AVG(StandardCost)
FOR DaysToManufacture IN ([0], [1], [2], [3], [4])
) AS PivotTable;"

UNPIVOT

UNPIVOT carries out nearly the opposite to PIVOT by rotating columns of a table-valued expressions into column values.

UNPIVOT Sytax

 
"SELECT VendorID, Employee, Orders
FROM
(SELECT VendorID, Emp1, Emp2, Emp3, Emp4, Emp5
FROM pvt) p
UNPIVOT
(Orders FOR Employee IN
(Emp1, Emp2, Emp3, Emp4, Emp5)
)AS unpvt;"

For further information on PIVOT and UNPIVOT, see FROM clause plus JOIN, APPLY, PIVOT (Transact-SQL)

The CData Python Connector for Google Analytics models Google Analytics entities in relational Tables, Views, and Stored Procedures. The provided tables will give you an overview of your account information and the profiles available for Google Analytics queries. Google Analytics allows for Dimensions and Metrics to be queried in a large number of arrangements. Some sample views are provided based on common Google Analytics reports. You can, however, also create your own custom views based on any combination of Dimensions and Metrics you need.

The connector uses the Google Analytics API to process supported filters. The connector processes other filters client-side within the connector.

Create Additional Schemas

The CreateCustomSchema stored procedure can be used to easily generate new schema files with a custom combination of Dimensions and Metrics. This procedure takes the TableName and a comma-separated lists of Dimensions and Metrics and builds a schema file that can be read by the connector. Each Dimension or Metric in the list takes the name of the value from Google Analytics (without the 'ga:' prefix). For example:

Dimensions=UserType,SessionCount

Metrics=Users,PercentNewSessions

If the Location connection property is set, the file will be output to that folder. Otherwise, the OutputFolder input can be used to specify an output folder. To begin querying these new files, simply set the Location connection property to the folder containing these new schema files.

Using Google Analytics 4 API

See GoogleAnalytics4 Data Model for the available entities in the Google Analytics 4 API.

The CData Python Connector for Google Analytics models the Google Analytics 4 API as relational tables, views, and stored procedures. The provided tables give you an overview of your account information and the profiles available for Google Analytics queries. Google Analytics supports the querying of dimensions and metrics in various arrangements. Some sample views are provided based on common Google Analytics reports.

You can also create your own custom views based on any combination of Dimensions and Metrics you need.

The connector uses the Google Analytics API to process supported filters. The connector processes other filters client-side within the connector. There is a strict limit of nine dimensions and 10 metrics per query, unless otherwise stated in the specific table or view pages.

Views

Views are tables that cannot be modified, such as Accounts, Properties, Events, Acquisitions, and Engagement. Typically, data that is read-only and cannot be updated are shown as views. Two types of views are made available:

• Base Views are statically defined to model Engagements, Acquisitions, Active Users, and more. You can use base views to create your custom reports. By default, these views return data from all time aggregated into a single row. All base views are subsets of GlobalAccessObject, a view that contains all data for all available dimensions and metrics.
• Predefined Report Views are a set of standard reports that mimic exactly what you see in the Google Analytics UI. All Predefined Report Views have "report" appended to their name. By default, these views return data from all time with a daily breakdown.

Date Ranges and Aggregation

Date Ranges You can specify date ranges in the WHERE clause using the "Date" field with these operators: =, <,>

Additional predefined date fields are available:

• week
• month
• quarter

When filtering on these time intervals, you can use the = operator, specifying the date of first day of the time period. If you specify a different date, an error is returned. For example, to specify the month of May in the year 2022, use the following, specifying the first day of that month:

month = '2022-05-01' 

NOTE: The default time interval for views is 30 days.

Aggregation

Aggregation is the process of reducing and summarizing data. You can apply aggregation at multiple levels:

The following examples show the syntax of aggregation queries:

SELECT Date,NewUsers, TotalUsers From Tech where StartDate = '2022-01-01' and EndDate = '2023-05-18'

SELECT Hour,NewUsers, TotalUsers From Tech where StartDate = '2022-01-01' and EndDate = '2023-05-18'

SELECT Day,NewUsers, TotalUsers From Tech where StartDate = '2022-01-01' and EndDate = '2023-05-18'

SELECT Week,NewUsers, TotalUsers From Tech where StartDate = '2022-01-01' and EndDate = '2023-05-18'

SELECT Month,NewUsers, TotalUsers From Tech where StartDate = '2022-01-01' and EndDate = '2023-05-18'

SELECT Year,NewUsers, TotalUsers From Tech where StartDate = '2022-01-01' and EndDate = '2023-05-18' 

Some comments about the aggregation code examples:

• Date: returns daily data in the query results.
• Hour: returns data aggregated by hour across the specified date range. For example, querying two years worth of data and selecting "Hour" returns 24 rows of data (one for each hour) with two years data aggregated for each hour.
• Day: returns data aggregated by day across the specified date range. For example, querying two years worth of data and selecting "Day" returns seven rows of data (one for each day) with two years data aggregated for each day
• Week: returns data aggregated by week across the specified date range. For example, querying two years worth of data and selecting "Week" returns 104 rows of data (one for each week) with data aggregated for each week
• Month: returns data aggregated by month across the specified date range. For example, querying two years worth of data and selecting "Month" returns 24 rows of data (one for each month) with data aggregated for each month.
• Year: returns data aggregated by year across the specified date range. For example, querying two years worth of data and selecting "Year" returns two rows of data (one for each year) with data aggregated by year.

NOTE: choosing "Date" as a metric overrides any other date metric you select. Only daily data is returned.

Stored Procedures

Stored Procedures are function-like interfaces to the data source. You can use these to search, update, and modify information in the data source.

Server-Side Filtering

Fully qualified queries support both the OR and AND operators used together. The OR operator can only be used with the same columns if it is combined with the AND operator for filtering dimensions or metrics. If the OR operator is used with different columns in combination with the AND operator, it may produce unexpected results. For example, the following query may give unexpected results:

 SELECT * FROM [Acquisitions] WHERE [StartDate] = '2023-03-22' AND [EndDate] = '2023-03-22' AND [PagePath] LIKE '%as' OR [Country] LIKE 'US' 

The following are examples of valid queries:

SELECT * FROM [Acquisitions] WHERE [StartDate] = '2023-03-22' AND [EndDate] = '2023-03-22' AND ([Country] LIKE 'US' OR [Country] LIKE '%In')
SELECT * FROM [Acquisitions] WHERE [StartDate] = '2023-03-22' AND [EndDate] = '2023-03-22' OR [PagePath] LIKE '%as'
SELECT * FROM [Acquisitions] WHERE [PagePath] LIKE '%as' OR [PagePath] LIKE 'A' OR [Country] LIKE '%In'
SELECT * FROM [Acquisitions] WHERE [PagePath] LIKE '%as' AND [Country] LIKE '%In'
SELECT * FROM [Acquisitions] WHERE [PagePath] LIKE '%as' AND ([Country] LIKE 'India' OR [Country] LIKE '%US')
SELECT * FROM [Tech] WHERE [StartDate] = '2021-01-01' AND [EndDate] = '2021-05-18' AND [NewUsers] >= 26 AND [NewUsers] < 35 AND ([Browser] IN ('Chrome', 'Edge') OR [Browser] = 'Edge')
SELECT * FROM [TechDeviceModelReport] WHERE [Date] >= '2020-05-13' AND [Date] <= '2023-06-13' AND [DeviceModel] != '(not set)' AND [DeviceModel] != '(test)' AND ([NewUsers] = 15 OR [NewUsers] = 20)
SELECT * FROM [Tech] WHERE [StartDate] = '2017-01-01' AND [EndDate] = '2023-05-02' AND CONTAINS ([Browser], 'ed') OR CONTAINS ([Browser], 'ch')

Views are similar to tables in the way that data is represented; however, views are read-only.

Queries can be executed against a view as if it were a normal table.

CData Python Connector for Google Analytics Views

Name	Description
Accounts	Lists all Accounts to which the user has access.
AccountSummaries	Lists summaries of all Accounts to which the user has access.
Acquisitions	A base view that retrieves Acquisitions data.
AcquisitionsFirstUserCampaignReport	A predefined view that retrieves Acquisitions first user Campaign data.
AcquisitionsFirstUserGoogleAdsAdGroupNameReport	A predefined view that retrieves user Acquisitions first user google ads ad group name data.
AcquisitionsFirstUserGoogleAdsNetworkTypeReport	A predefined view that retrieves Acquisitions first user google ads ad network type platform data.
AcquisitionsFirstUserMediumReport	A predefined view that retrieves Acquisitions first user medium data.
AcquisitionsFirstUserSourceMediumReport	A predefined view that retrieves Acquisitions first user source medium data.
AcquisitionsFirstUserSourcePlatformReport	A predefined view that retrieves Acquisitions first user source platform data.
AcquisitionsFirstUserSourceReport	A predefined view that retrieves Acquisitions first user source data.
AcquisitionsSessionCampaignReport	A predefined view that retrieves Acquisitions session campaign report data.
AcquisitionsSessionDefaultChannelGroupingReport	A predefined view that retrieves Acquisitions session default channel grouping report data.
AcquisitionsSessionMediumReport	A predefined view that retrieves Acquisitions session medium report data.
AcquisitionsSessionSourceMediumReport	A predefined view that retrieves Acquisitions session source medium report data.
AcquisitionsSessionSourcePlatformReport	A predefined view that retrieves Acquisitions session source platform report data.
AcquisitionsSessionSourceReport	A predefined view that retrieves Acquisitions session source report data.
ActiveUsers	A base view that retrieves Active Users data.
DataAnnotations	List all Reporting Data Annotations on a property.
DemographicAgeReport	A predefined view that retrieves Demographics UserAgeBracket data.
DemographicCityReport	A predefined view that retrieves Demographics City data.
DemographicCountryReport	A predefined view that retrieves Demographics Country data.
DemographicGenderReport	A predefined view that retrieves Demographics UserGender data.
DemographicInterestsReport	A predefined view that retrieves Demographics BrandingInterest data.
DemographicLanguageReport	A predefined view that retrieves Demographics Language data.
DemographicRegionReport	A predefined view that retrieves Demographics Region data.
Demographics	A base view that retrieves Demographics data.
EcommPurchasesItemBrandReport	A predefined view that retrieves Ecommerce purchase item brand data.
EcommPurchasesItemCategory2Report	A predefined view that retrieves Ecommerce purchase item category data.
EcommPurchasesItemCategory3Report	A predefined view that retrieves Ecommerce purchase item category data.
EcommPurchasesItemCategory4Report	A predefined view that retrieves Ecommerce purchase item category data.
EcommPurchasesItemCategory5Report	A predefined view that retrieves Ecommerce purchase item category data.
EcommPurchasesItemCategoryReport	A predefined view that retrieves Ecommerce purchase item category data.
EcommPurchasesItemCategoryReportCombined	A predefined view that retrieves Ecommerce purchase item category data.
EcommPurchasesItemIdReport	A predefined view that retrieves Ecommerce purchase item data.
EcommPurchasesItemNameReport	A predefined view that retrieves Ecommerce purchase item data.
Engagement	A base view that retrieves Engagement data
EngagementContentGroupReport	A predefined view that retrieves Engagement Content Group Report data.
EngagementConversionsReport	A predefined view that retrieves Engagement conversions data.
EngagementEventsReport	A predefined view that retrieves Engagement events data.
EngagementPagesPathReport	A predefined view that retrieves Engagement Pages path report data.
EngagementPagesTitleAndScreenClassReport	A predefined view that retrieves Engagement Pages title and screen class data.
EngagementPagesTitleAndScreenNameReport	A predefined view that retrieves Engagement Pages Title And ScreenName data.
Events	A base view that retrieves Event data
GamesReporting	A base view that retrieves Games Reporting data.
GlobalAccessObject	Retrieves data for all the available dimensions and metrics.
KeyEvents	A base view that retrieves KeyEvents data
MetaData	Retrieves metadata information for standard and custom dimensions / metrics.
Monetization	A base view that retrieves Monetization data.
MonetizationPublisherAdsAdFormatReport	A predefined view that retrieves publisher ads page ad format data.
MonetizationPublisherAdsAdSourceReport	A predefined view that retrieves publisher ads ad source data.
MonetizationPublisherAdsAdUnitReport	A predefined view that retrieves publisher ads ad unit data.
MonetizationPublisherAdsPagePathReport	A predefined view that retrieves publisher ads page path data.
Properties	Lists all Properties to which the user has access.
PropertiesAccessBindings	Lists all access bindings on an account or property. Requires one of the following OAuth scopes: https://www.googleapis.com/auth/analytics.manage.users.readonly https://www.googleapis.com/auth/analytics.manage.users
PropertiesAudiences	Lists Audiences on a property.
PropertiesDataStreams	Lists all data streams under a property to which the user has access. Attribute Parent (e.g: 'properties/123') or Name (e.g: 'properties/123/webDataStreams/456') is required to query the table.
PropertiesFireBaseLinks	Lists all FirebaseLinks on a property to which the user has access.
PropertiesGoogleAdsLinks	Lists all GoogleAdsLinks on a property to which the user has access.
PropertiesKeyEvents	Returns a list of Key Events in the specified parent property.
ScreenPageViews	A base view that retrieves ScreenPage data
SubpropertySyncConfigs	List all SubpropertySyncConfig resources for a property.
Tech	A base view that retrieves Tech data.
TechAppVersionReport	A predefined view that retrieves Tech App Version data.
TechBrowserReport	A predefined view that retrieves Tech Browser data.
TechDeviceCategoryReport	A predefined view that retrieves Tech Device Category data.
TechDeviceModelReport	A predefined view that retrieves Tech Device Model data.
TechOSSystemReport	A predefined view that retrieves Tech os system data.
TechOSVersionReport	A predefined view that retrieves Tech Os version data.
TechPlatformDeviceCategoryReport	A predefined view that retrieves Tech platform device category data.
TechPlatformReport	A predefined view that retrieves Tech platform data.
TechScreenResolutionReport	A predefined view that retrieves Tech Screen Resolution data.

Lists all Accounts to which the user has access.

View-Specific Information

Select

The Accounts table exposes every account the user has access to. The provider uses the GoogleAnalytics4 API to process WHERE clause conditions built with the Name column, which supports the = operator.

The following query is processed server-side:

	SELECT * FROM Accounts WHERE Name = 'accounts/54516992'

The rest of the filter is executed client-side within the provider.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
CreateTime	Datetime				Time the account was created.
DisplayName	String				display name for the account.
Name [KEY]	String				Account name.
RegionCode	String				Country for the account.
UpdateTime	Datetime				Time the account was last modified.
Deleted	Boolean				Indicates whether this Account is soft-deleted or not.

Lists summaries of all Accounts to which the user has access.

View-Specific Information

Select

The AccountSummaries table exposes summaries of all accounts accessible by the caller. The following query is processed server-side:

    SELECT * FROM AccountSummaries

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
Account	String				Account name referred to by this account summary.
DisplayName	String				display name for the account referred to by this account summary.
Name	String				Account summary name.
Propertysummaries	String				Summaries for child accounts of the specific account.

A base view that retrieves Acquisitions data.

View-Specific Information

Select

Retrieves data for Acquisitions report. At least one metric must be specified in the query. In the query you can also specify up to 9 dimensions.

The following is an example query:

	SELECT KeyEvents, NewUsers FROM Acquisitions

CustomChannelGroups are added as dimension columns. You can query CustomChannelGroup in the following way:

 SELECT SessionCustomChannelGroupName, FirstGroupCustomChannelGroupName, SessionDefaultChannelGrouping, KeyEvents FROM Acquisitions 

NOTE: CustomChannelGroupName is for reference purposes only. The exact value depends on the ChannelGroupName added.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
Date	Date	True			The date of the session formatted as YYYYMMDD.
Year	Integer	True			The year of the session. A four-digit year from 2005 to the current year.
Month	Integer	True			The month of the session. An integer from 01 to 12.
Week	Integer	True			The week of the session. A number from 01 to 53. Each week starts on Sunday.
Day	Integer	True			The day of the month. A number from 01 to 31.
DayOfWeekName	String	True			The day of the week in English. This dimension has values of Sunday, Monday, etc.
IsoWeek	Integer	True			ISO week number, where each week starts on Monday Example values include 01, 02, 53.
IsoYear	Integer	True			The ISO year of the event. For details, see Example values include 2022 2023.
IsoYearIsoWeek	Integer	True			The combined values of isoWeek and isoYear. Example values include 201652 and 201701.
YearMonth	Integer	True			The combined values of year and month. Example values include 202212 or 202301.
YearWeek	Integer	True			The combined values of year and week. Example values include 202253 or 202301.
Hour	Integer	True			An hour of the day ranging from 00-23 in the timezone configured for the account. This value is also corrected for daylight savings time.
FirstUserGoogleAdsAdGroupName	String	True			The Ad Group Name in Google Ads that first acquired the user.
FirstUserGoogleAdsAdNetworkType	String	True			The advertising network that first acquired the user.
FirstUserCampaignName	String	True			Name of the marketing campaign that first acquired the user.
FirstUserGoogleAdsCreativeId	String	True			The campaign creative ID that first acquired the users.
FirstUserMedium	String	True		True	The medium that first acquired the user to the website or app.
FirstUserSource	String	True			The source that first acquired the user to the website or app.
SessionCampaignName	String	True			Campaign that referred the user's session.
SessionDefaultChannelGroup	String	True			Channel groupings are rule-based definitions of your traffic sources. These default system definitions reflect Analytics' current view of what constitutes each channel.
SessionMedium	String	True			Channel that referred the user's session.
SessionSource	String	True			The source that initiated a session on your website or app.
EventName	String	True			The name of the event.
BrandingInterest	String	True			Interests demonstrated by users who are higher in the shopping funnel. Users can be counted in multiple interest categories.
Country	String	True			Country from which user activity originated.
City	String	True			City from which user activity originated.
Language	String	True			Language setting for the device from which activity originated.
UserAgeBracket	String	True			User age brackets.
UserGender	String	True			User gender.
Region	String	True			Geographic region from which activity originated.
UnifiedScreenClass	String	True			The page title (web) or screen class (app) on which the event was logged.
PagePath	String	True			The portion of the URL between the hostname and query string for web.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EngagedSessionsPerUser	Decimal	False	True		Average number of engaged sessions per user.
EventCount	Integer	False	True		The count of events.
EventsPerSession	Decimal	False	True		The average number of events per session.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
Sessions	Integer	False	True		The number of sessions that began on the site or app.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	False		The total amount of time (in seconds) the website or app was in the foreground of users device.
ScreenPageViews	Integer	False	False		The number of app screens or web pages the users viewed. Repeated views of a single page or screen are counted.
EventCountPerUser	Decimal	False	False		Average number of events triggered by each user.
SessionsPerUser	Decimal	False	False		The average number of sessions per user (Sessions divided by Active Users).
StartDate	String				Start date for fetching Analytics data. Either a date string or a relative date (e.g., today, yesterday, or #daysAgo).
EndDate	String				End date for fetching Analytics data. Either a date string or a relative date (e.g., today, yesterday, or #daysAgo).

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves Acquisitions first user Campaign data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• FirstUserGoogleAdsAdGroupName supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE FirstUserGoogleAdsAdGroupName = 'Test' 
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE Date = '01/05/2023' 
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE PropertyId = 342020667  AND FirstUserGoogleAdsAdGroupName = 'Test' AND Date = '01/05/2023'
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsFirstUserCampaignReport WHERE Date < '01/01/2022'

CustomChannelGroups are added as dimension columns. You can query CustomChannelGroup in the following way:

 SELECT FirstUserCustomChannelGroupName, SessionDefaultChannelGrouping, KeyEvents FROM AcquisitionsFirstUserCampaignReport 

NOTE: CustomChannelGroupName is for reference purposes only. The exact value depends on the ChannelGroupName added. The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
FirstUserCampaignName	String	True		True	Name of the marketing campaign that first acquired the user.
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	False		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves user Acquisitions first user google ads ad group name data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• FirstUserGoogleAdsAdGroupName supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE FirstUserGoogleAdsAdGroupName = 'Test' 
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE Date = '01/05/2023' 
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE PropertyId = 342020667  AND FirstUserGoogleAdsAdGroupName = 'Test' AND Date = '01/05/2023'
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsFirstUserGoogleAdsAdGroupNameReport WHERE Date < '01/01/2022'

The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
FirstUserGoogleAdsAdGroupName	String	True		True	The Ad Group Name in Google Ads that first acquired the user.
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	True		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves Acquisitions first user google ads ad network type platform data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• FirstUserGoogleAdsNetworkType supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE FirstUserGoogleAdsNetworkType = 'test' 
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE Date = '01/05/2023' 
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE PropertyId = 342020667  AND FirstUserGoogleAdsNetworkType = 'test' AND Date = '01/05/2023'
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsFirstUserGoogleAdsNetworkType WHERE Date < '01/01/2022'

The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
FirstUserGoogleAdsAdNetworkType	String	True		True	The source platform that first acquired the user.
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	True		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves Acquisitions first user medium data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• FirstUserMedium supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsFirstUserMediumReport WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE FirstUserMedium = 'test' 
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE Date = '20221115' 
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE PropertyId = 342020667  AND FirstUserMedium = 'test' AND Date = '01/05/2023''
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsFirstUserMediumReport WHERE Date < '01/01/2022'

CustomChannelGroups are added as dimension columns. You can query CustomChannelGroup in the following way:

 SELECT FirstUserCustomChannelGroupName, SessionDefaultChannelGrouping, KeyEvents FROM AcquisitionsFirstUserMediumReport WHERE FirstUserMedium = 'test' 

NOTE: CustomChannelGroupName is for reference purposes only. The exact value depends on the ChannelGroupName added. The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
FirstUserMedium	String	True		True	The medium that first acquired the user to the website or app.
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	True		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves Acquisitions first user source medium data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• FirstUserGoogleAdsAdGroupName supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE FirstUserGoogleAdsAdGroupName = 'Test' 
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE Date = '01/05/2023'' 
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE PropertyId = 342020667  AND FirstUserGoogleAdsAdGroupName = 'Test' AND Date = '01/05/2023'
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourceMediumReport WHERE Date < '01/01/2022'

CustomChannelGroups are added as dimension columns. You can query CustomChannelGroup in the following way:

 SELECT FirstUserCustomChannelGroupName, SessionDefaultChannelGrouping, KeyEvents FROM AcquisitionsFirstUserSourceMediumReport 

NOTE: CustomChannelGroupName is for reference purposes only. The exact value depends on the ChannelGroupName added. The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
FirstUserSource	String	True		True	The source that first acquired the user to the website or app.
FirstUserMedium	String	True		True	The medium that first acquired the user to the website or app.
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	True		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves Acquisitions first user source platform data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• FirstUserSourcePlatform supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE FirstUserSourcePlatform = 'Test' 
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE Date = '01/05/2023' 
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE PropertyId = 342020667  AND FirstUserSourcePlatform = 'Test' AND Date = '01/05/2023'
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourcePlatformReport WHERE Date < '01/01/2022'

CustomChannelGroups are added as dimension columns. You can query CustomChannelGroup in the following way:

 SELECT FirstUserCustomChannelGroupName, SessionDefaultChannelGrouping, KeyEvents FROM AcquisitionsFirstUserSourcePlatformReport WHERE FirstUserSourcePlatform = 'Test'

NOTE: CustomChannelGroupName is for reference purposes only. The exact value depends on the ChannelGroupName added. The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
FirstUserSourcePlatform	String	True		True	The source platform that first acquired the user.
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	True		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves Acquisitions first user source data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• FirstUserSource supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsFirstUserSourceReport WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE FirstUserSource = '(direct)' 
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE Date = '01/05/2023' 
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE PropertyId = 342020667  AND FirstUserSource = '(direct)' AND Date = '01/05/2023'
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsFirstUserSourceReport WHERE Date < '01/01/2022'

CustomChannelGroups are added as dimension columns. You can query CustomChannelGroup in the following way:

 SELECT FirstUserCustomChannelGroupName, SessionDefaultChannelGrouping, KeyEvents FROM AcquisitionsFirstUserSourceReport WHERE FirstUserSource = '(direct)' 

NOTE: CustomChannelGroupName is for reference purposes only. The exact value depends on the ChannelGroupName added. The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
FirstUserSource	String	True		True	The source that first acquired the user to the website or app.
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
NewUsers	Integer	False	True		The number of users who interacted with the site or launched the app for the first time.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	False		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.

A predefined view that retrieves Acquisitions session campaign report data.

Select

The connector uses the Google Analytics API to process WHERE clause conditions built with the following columns and operators:

• PropertyId supports the following operator: =
• SessionDefaultChannelGrouping supports the following operator: =
• Date supports the following operators: =, >=, <=, <, >
• CurrencyCode supports the following operator: =

For example, the following queries are processed server-side:

SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE PropertyId = 342020667
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE SessionDefaultChannelGrouping = 'Direct' 
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE Date = '01/05/2023' 
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE PropertyId = 342020667  AND SessionDefaultChannelGrouping = 'Direct' AND Date = '01/05/2023'
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE Date >= '01/01/2022' AND Date <= '01/05/2023'
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE Date >= '01/01/2022'
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE Date <= '01/01/2022'
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE Date >  '01/01/2022'
SELECT * FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE Date < '01/01/2022'

CustomChannelGroups are added as dimension columns. You can query CustomChannelGroup in the following way:

 SELECT SessionCustomChannelGroupName, SessionDefaultChannelGrouping, KeyEvents FROM AcquisitionsSessionDefaultChannelGroupingReport WHERE SessionDefaultChannelGrouping = 'Direct' 

NOTE: CustomChannelGroupName is for reference purposes only. The exact value depends on the ChannelGroupName added. The rest of the filter is executed client-side in the connector.

Columns

Name	Type	Dimension	DefaultMetric	DefaultDimension	Description
SessionCampaignName	String	True		True	Campaign that referred the user
Date	Date	True		True	The date of the session formatted as YYYYMMDD.
Sessions	Integer	False	True		The number of sessions that began on the site or app.
EventsPerSession	Decimal	False	True		The average number of events per session.
KeyEvents	Decimal	False	True		The count of key events.
EngagementRate	Decimal	False	True		The percentage of engaged sessions.
EngagedSessions	Integer	False	True		The number of sessions that lasted longer than 10 seconds, or had a conversion event, or had 2 or more screen views.
EventCount	Integer	False	True		The count of events.
TotalRevenue	Decimal	False	True		The sum of revenue from purchases, subscriptions, and advertising.
TotalUsers	Integer	False	True		The total number of users.
UserEngagementDuration	Bigint	False	True		The total amount of time (in seconds) the website or app was in the foreground of users

Pseudocolumns

Pseudocolumn fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
PropertyId	String	Property ID value to be used when querying this table.
CurrencyCode	String	A currency code in ISO4217 format, such as AED, USD, JPY. If the field is empty, the report uses the property's default currency.