Connecting to Google Sheets

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 Sheets can be installed and used in Python 3.10 or newer.

Google Sheets Version Support

The connector leverages two drivers: the Google Drive API to enable bidirectional access to Google Sheets and the Google Sheets to retrieve the actual data.

See Also

• Using the Connector: Establish connections and query Google Sheets through Python code.
• From SQLAlchemy: Use SQLAlchemy to establish a connection with dialect URL, and interact with Google Sheets 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 Sheets 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_googlesheets_connector-26.0.9655-cp310-abi3-win_amd64.whl

Linux:

pip install cdata_googlesheets_connector-26.0.9655-cp310-abi3-linux_x86_64.whl

macOS:

pip install cdata_googlesheets_connector-26.0.9655-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_googlesheets_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_googlesheets" 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_googlesheets folder is trivial to find:

import os
import cdata.googlesheets
path = os.path.abspath(cdata.googlesheets.__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-googlesheets-connector

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

1. Import the module as follows:
   

   import cdata.googlesheets 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;Spreadsheet=NorthwindOrders")

Connecting to Google Sheets

The connector supports authentication schemes using user accounts, service accounts, GCP instance accounts, and API keys for authentication. Each of these types of accounts have different AuthSchemes, but all of them use the OAuth standard for authentication.

Access	AuthScheme	Notes
User Accounts	OAuth	Can use embedded credentials if access from a Desktop application. See Desktop Applications, below.
Service Accounts	OAuthJWT	Depends on whether the data will be JSON or PFX files. See Authenticating as a Service, below.
GCP Instance Acct	GCPInstanceAccount	For use on a GCP virtual machine.
API Key	Token	Also, set APIKey to True. To create an API key in the Google Cloud Console, click Create credentials > API Key. To restrict the key before using it in production, select Restrict and choose one of the available restrictions.

The following sections focus on authentication as a User, and authentication as a Service.

Authenticating as a User (OAuth)

The following subsections describe how to authenticate to Google Sheets from a User account (AuthScheme OAuth) via three common authentication flows:

• Desktop: a connection to a server on the user's local machine, frequently used for testing and prototyping. Authenticated via either embedded OAuth or custom OAuth.
• Web: access to data via a shared website. Authenticated via custom OAuth only.
• Headless Server: a dedicated computer that provides services to other computers and their users, which is configured to operate without a monitor and keyboard. Authenticated via embedded OAuth or custom OAuth.

For information about how to create a custom OAuth application, and why you might want to create one even for auth flows that have embedded OAuth credentials, see Creating a Custom OAuth Application. For a complete list of connection string properties available in Google Sheets, see Connection.

Desktop Applications

CData provides an embedded OAuth application that simplifies authentication at the desktop. You can also authenticate from the desktop via a custom OAuth application, which you configure and register at the Google Sheets console. For further information, see Creating a Custom OAuth Application.

Before you connect, set the following variables:

• InitiateOAuth: GETANDREFRESH. Used to automatically get and refresh the OAuthAccessToken.
• Custom OAuth applications only:
  • OAuthClientId: The client Id assigned when you registered your custom OAuth application.
  • OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.
  • CallbackURL: The redirect URI defined when you registered your custom OAuth application.

When you connect, the connector opens Google Sheets's OAuth endpoint in your default browser. Log in and grant permissions to the application.

After you grant permissions to the application, the connector completes the OAuth process:

1. The connector obtains an access token from Google Sheets and uses it to request data.
2. The OAuth values are saved in the path specified in OAuthSettingsLocation. These values persist across connections.

When the access token expires, the connector refreshes it automatically.

Web Applications

Authenticating via the Web requires you to create and register a custom OAuth application with Google Sheets, as described in Creating a Custom OAuth Application. You can then use the connector to get and manage the OAuth token values.

This section describes how to get the OAuth access token, how to have the driver refresh the OAuth access token automatically, and how to refresh the OAuth access token manually.

Get the OAuth access token:

1. Set the following connection properties to obtain the OAuthAccessToken:
   • OAuthClientId: The client Id in your application settings.
   • OAuthClientSecret: The client secret in your application settings.

2. Call stored procedures to complete the OAuth exchange:
   • Call the GetOAuthAuthorizationURL stored procedure. Set the AuthMode input to WEB and the CallbackURL to the Redirect URI you specified in your application settings. The stored procedure returns the URL to the OAuth endpoint.
   • Navigate to the URL that the stored procedure returned in Step 1. Log in and authorize the web application. You are redirected back to the callback URL.
   • Call the GetOAuthAccessToken stored procedure. Set the AuthMode input to WEB. Set the Verifier input to the code parameter in the query string of the redirect URI.

After you obtain the access and refresh tokens, you can connect to data and refresh the OAuth access token automatically.

Automatic refresh of the OAuth access token:

To have the connector automatically refresh the OAuth access token, do the following:

1. The first time you connect to data, set the following connection parameters:
   • InitiateOAuth: REFRESH.
   • OAuthClientId: The client Id in your application settings.
   • OAuthClientSecret: The client secret in your application settings.
   • OAuthAccessToken: The access token returned by GetOAuthAccessToken.
   • OAuthSettingsLocation: The path where you want the connector to save the OAuth values, which persist across connections.
2. On subsequent data connections, set the following:
   • InitiateOAuth
   • OAuthSettingsLocation

Manual refresh of the OAuth access token:

The only value needed to manually refresh the OAUth access token is the OAuth refresh token.

1. To manually refresh the OAuthAccessToken after the ExpiresIn period (returned by GetOAuthAccessToken) has elapsed, call the RefreshOAuthAccessToken stored procedure.
2. Set the following connection properties:
   • OAuthClientId: The Client Id in your application settings.
   • OAuthClientSecret: The Client Secret in your application settings.

3. Call RefreshOAuthAccessToken with OAuthRefreshToken set to the OAuth refresh token returned by GetOAuthAccessToken.
4. After the new tokens have been retrieved, set the OAuthAccessToken property to the value returned by RefreshOAuthAccessToken. This opens a new connection.

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

Headless Machines

If you need to log in to a resource that resides on a headless machine, you must authenticate on another device that has an internet browser. You can do this in either of the following ways:

• Option 1: Obtain the OAuthVerifier value.
• 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.

After you execute either Option 1 or Option 2, configure the driver to automatically refresh the access token on the headless machine.

Option 1: Obtaining and Exchanging a Verifier Code

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

1. Authenticate from the machine with an internet browser, and obtain the OAuthVerifier connection property.

   If you are using the embedded OAuth application, call the GetOAuthAuthorizationURL stored procedure. Open the URL returned by the stored procedure in a browser.

   If you are using a custom OAuth application, set the following properties:

   • InitiateOAuth: OFF.
   • OAuthClientId: The client Id assigned when you registered your application.
   • OAuthClientSecret: The client secret assigned when you registered your application.

2. Call the GetOAuthAuthorizationURL stored procedure. The stored procedure returns the CallbackURL established when the custom OAuth application was registered. (See Creating a Custom OAuth Application.)

   Copy this URL and paste it into a new browser tab.

3. Log in and grant permissions to the connector. The OAuth application redirects you the redirect URI, with a parameter called code appended. Note the value of this parameter; you will need it later, to configure the OAuthVerifier connection property.

4. Exchange the OAuth verifier code for OAuth refresh and access tokens. On the headless machine, set the following connection properties to obtain the OAuth authentication values:

   • InitiateOAuth: REFRESH.
   • OAuthVerifier: The noted verifier code (the value of the code parameter in the redirect URI).
   • OAuthSettingsLocation: persist the encrypted OAuth authentication values to the specified file.
   • Custom OAuth applications only:
     • OAuthClientId: The client Id in your custom OAuth application settings.
     • OAuthClientSecret: The client secret in the custom OAuth application settings.

5. Test the connection to generate the OAuth settings file.

6. After you re-set the following properties, you are ready to connect:

   • InitiateOAuth: REFRESH.
   • OAuthSettingsLocation: The file containing the encrypted OAuth authentication values. To enable the automatic refreshing of the access token, be sure that this file gives read and write permissions to the connector.
   • Custom OAuth applications only:
     • OAuthClientId: The client Id assigned when you registered your application.
     • OAuthClientSecret: The client secret assigned when you registered your application.

Option 2: Transferring OAuth Settings

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

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

Test the connection to generate the OAuth settings file, then copy the OAuth settings file to your headless machine.

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

• InitiateOAuth: REFRESH
• OAuthSettingsLocation: The path to the OAuth settings file you copied from the machine with the browser. To enable automatic refreshing of the access token, ensure that this file gives read and write permissions to the connector.
• Custom OAuth applications only:
  • OAuthClientId: The client Id assigned when you registered your custom OAuth application.
  • OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.

Authenticating as a Service (OAuthJWT)

Authenticating to Google Sheets from a Service account (AuthScheme OAuth) requires:

• The creation of a new service account (see Creating a Custom OAuth Application); and
• A copy of the accounts certificate.

You must also set the following properties, which are specific to the preferred medium:

JSON File Properties

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

PFX File Properties

• AuthScheme: OAuthJWT.
• InitiateOAuth: GETANDREFRESH.
• OAuthJWTCertType: PFXFILE.
• OAuthJWTCert: The path to the Google-provided .pfx file.
• OAuthJWTIssuer: The email address of the service account. This address usually includes the domain iam.gserviceaccount.com.
• OAuthJWTCertPassword (optional): 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.
• OAuthJWTSubject (optional): The email address of the user whose data you want to access. Only set this value if the service account is part of a GSuite domain and you want to enable delegation.

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 Sheets 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:
  

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

• For Mac:
  

  [googlesheets.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.googlesheets 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 Sheets via a desktop application or a headless machine. If you want to use the embedded OAuth application, all you need to do to connect is to:

• set AuthScheme to OAuth,
• get and set the OAuthAccessToken, and
• set the necessary configuration parameters.

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

However, a custom OAuth connection is required for Users who connect via the Web, and for connecting on behalf of users via a Service Account. Custom OAuth applications are also 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; or
• customize the permissions that you are requesting from the user.

Procedure

Before you create a custom OAuth application for web users or Service Accounts, you must enable the Google Sheets and Google Drive APIs:

1. Navigate to the Google Cloud Console.
2. At the left navigation pane, select Library. The console opens the Library page.
3. Use the Search service to find Google Sheets API. Select "Google Sheets API" from the search results.
4. Use the Search service to find Google Drive API. Select "Google Drive API" from the search results.
5. On the Google Sheets API page, click ENABLE to enable both the Google Sheets and Google Drive APIs.

User Accounts

At the Google Cloud Console:

1. Create a new project or select an existing project.
2. At the left navigation pane, select Credentials.
3. If the selected project does not have a consent screen, click CONFIGURE CONSENT SCREEN. 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 information is optional.
4. On the Credentials page, select Create Credentials > OAuth Client ID.
5. In the Application Type menu, select Web application.
6. Specify a name for your OAuth custom web application.
7. Under Authorized redirect URIs, click ADD URI and enter a redirect URI.
8. Click Enter.
9. Click CREATE.

When the application is complete, the Cloud Console returns you to the Credentials page. A window opens that displays your client Id and client secret.

Although the client secret is accessible from from the Google Cloud Console, we recommend you write down the client secret. You need both the client secret and client Id to specify the OAuthClientId and OAuthClientSecret.

Service Accounts

You can use a service account in this 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.

When using AuthScheme=OAuthJWT, you must create a Service account.

At the Google Cloud Console:

• To complete the service account flow, generate a private key in the Google Cloud Console. In the service account flow, the driver exchanges a JSON Web token (JWT) for the OAuthAccessToken. The private key is required to sign the JWT. The driver grants the same permissions to the Service Account.

• Now create a new Service Account:
  
  1. Create a new project or select an existing project.
  2. At the left navigation pane, select Credentials.
  3. Navigate to Create Credentials > Service account. The Cloud Console displays the Create Service Account page.
  4. Enter the Service account name, the Service account ID, and, optionally, a description.
  5. Click DONE. The Cloud Console returns you to the Credentials page.
  6. In the Service Accounts area, select the service accout you just created.
  7. Click the KEYs tab, then click ADD KEY > Create new key.
  8. Select any supported Key type, such as OAuthJWTCert or OAuthJWTCertType.
  9. Click CREATE.

The key is automatically downloaded to your local device, and any additional information specific to the key is displayed.

Required Scopes and Endpoint Domains for Google Sheets

When integrating with Google Sheets, 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 Sheets 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 Sheets

Scope	Description
drive.readonly	Allows you to list all spreadsheets. Required for read access.
drive	Allows you to list all spreadsheets. Required for write access.
spreadsheets.readonly	Allows you to pull data from specific spreadsheets. Required for read access.
spreadsheets	Allows you to pull data from specific spreadsheets. Required for write access.

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 Sheets

Domain	Always Required	Description
accounts.google.com	False	The domain used for performing OAuth authorization for Google Sheets.
sheets.googleapis.com	True	The base URL of the service used to retrieve sheets.
www.googleapis.com	True	Required for various Google Sheets API operations. Note: The www. subdomain is part of the required host in www.googleapis.com and must be included in any allowlist policy.

General Changes

Date	Version	Source	Category	Type	Description
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 Sheets	Security	Changed	TLS 1.3 is now supported by default for HTTP connections.
2026-01-13	25.0.9509	General		Added	Added support for the REGEXP_REPLACE() string function.
2026-01-07	25.0.9503	Google Sheets		Added	Added a ReadAsEmptyOrNull enum value to the NullValueMode connection property, which reports empty strings for string columns and NULL values for other data types.
2025-12-21	25.0.9486	Python		Added	Added support for custom loggers in Python connectors on Linux and macOS.
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.
2025-10-30	25.0.9434	Python		Changed	Updated embedded JRE to jre-17.0.17+10 (Linux x64 / MacOs x64).
2025-10-13	25.0.9417	Google Sheets		Added	Added the IncludePrimaryKey connection property.
2025-10-06	25.0.9410	General		Added	Support for parsing datetime formats using ".S" and ",S" for milliseconds and nanoseconds.
2025-09-24	25.0.9398	Google Sheets		Added	Added support in the AddSheet stored procedure for a new input parameter, HeaderNames, supplied as a comma-separated list. HeaderNames supplies names for column headers to be added as the first row in a new sheet. These headers can be used later for inserting or updating data.
2025-09-23	25.0.9397	Google Sheets		Added	Added the HeaderNames input parameter to the CreateSpreadsheet stored procedure.
2025-09-22	25.0.9396	Google Sheets		Changed	Formerly, if there were two spreadsheets with the same filename under folders with the same name and a user had activated the UseIdAsTableName property, the driver picked only the file with the latest changes, and ignored the other file. Now the names of tables that have duplicate names (in the format spreadsheetName_spreadsheetId) have an "index" marker appended to the end, and UseIdAsTableName returns all relevant files.
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.
2025-08-28	25.0.9371	Google Sheets		Added	Added the Scope connection property.
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.
2025-07-18	25.0.9330	Google Sheets		Removed	Removed ChildIds and ChildLinks columns from the Folders table.
2025-07-07	25.0.9319	Python		Removed	Removed the 32-bit version of Windows Python.
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.
2025-06-18	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.
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 Sheets		Added	Added support for Workload Identity Federation using AWS accounts.
2024-08-28	24.0.9006	Google Sheets		Removed	Removed the IncludeValuesInResponse connection property.
2024-06-05	24.0.8922	Python		Added	Added support for Python 3.12.
2024-05-29	24.0.8915	Google Sheets		Removed	Removed the Auto AuthScheme option. The default value for the AuthScheme connection property is now OAuth.
2024-05-09	24.0.8895	General		Changed	The ROUND function previously did not accept negative precision values. That feature has now been restored.
2024-03-15	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
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
2023-11-22	23.0.8726	Google Sheets		Changed	UseManagedSecurityAPI hidden property is no longer valid for the ADO.NET edition. We will now always use our internal security implementation.
2023-08-29	23.0.8641	Python		Added	Added support for SQLAlchemy 2.0.
2023-08-18	23.0.8630	Google Sheets		Added	Added SkipHiddenSheets connection property.
2023-06-20	23.0.8571	General		Added	Added the new sys_lastresultinfo system table.
2023-06-09	23.0.8560	Google Sheets		Changed	Renamed the TeamDriveSupport connection property to SupportsAllDrives. Renamed the TeamDriveId column to DriveId.
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
2023-04-25	23.0.8515	General		Removed	Removed support for the SELECT INTO CSV statement. The core code doesn't support it anymore.
2023-04-24	23.0.8514	Google Sheets		Changed	Changed datatype for RecurseFolders property form String to Boolean.
2023-02-13	22.0.8444	Google Sheets		Added	Improve data type detection by ignoring the error values ( bad formula cells ). Added a connection property, IgnoreErrorValues, to control the behavior of the driver when reading the data. By default we skip the cells which has error values. Setting the connection property to true will force the driver to throw an exception in case of an error value.
2023-01-23	22.0.8423	Google Sheets		Added	Added the field DataSourceExecutionStatus for the Sheets view indicates the execution status of a DataSource Sheet Added AddDatasource stored procedure for adding a BigQuery datasource to an spreadsheet. The scope bigQuery.readonly is required for this stored procedure. Added AddDatasourceTable stored procedure to add a table from a created datasource to a Sheet. The scope bigQuery.readonly is required for this stored procedure.
2022-12-14	22.0.8383	General		Changed	Added the Default column to the sys_procedureparameters table.
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).
2022-09-30	22.0.8308	General		Changed	Added the IsPath column to the sys_procedureparameters table.
2022-08-31	22.0.8278	Google Sheets		Added	Added binary output and encoded content support for DownloadDocument. Added FileStream as an input.
2022-08-19	22.0.8266	Google Sheets		Added	Added binary input support for UploadDocument. Added Content as an input for UploadDocument.
2022-08-10	22.0.8257	Google Sheets		Added	Added the 'Hidden' column for the Sheets table.
2022-08-10	22.0.8257	Google Sheets		Removed	Removed the 'Hidden' column from the Spreadsheets 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
2022-04-15	21.0.8140	Google Sheets		Added	Added support for listing all Spreadsheets that are inside a folder and its nested folders. This can be done by setting the RecurseFolders connection property to true and setting the list of folder Ids or folder names on the respective FolderId/FolderName property or both.
2022-04-07	21.0.8132	Google Sheets		Added	Added support for querying Sheets by specifying the SpreadsheetId_SheetId instead of SpreadsheetName_SheetName. Just set the UseIdAsTableName connection property to true and use: SpreadsheetId_SheetId in the queries. For ex: `SELECT * FROM 11696gdF5QUL1EnYikYiUeMTHRqA1111KbdYDoINqI_1151117664`
2022-01-12	21.0.8047	Google Sheets		Changed	Changed the support for working with a custom Range in Select, Insert and UPDATE operations. Columns will instead be named appropriately based on the range instead of starting at A,B,C, etc. This only impacts tables when the Header connection property is set to false. For example: `SELECT * FROM Spreadsheet_Sheet1#C1:F5`. The column names are: C, D, E, F. Previously the driver returned columns with names starting from A: A,B,C,D.
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 Sheets		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.
2021-04-25	21.0.7785	General		Added	Added support for handling client side formulas during insert / update. For example: UPDATE Table SET Col1 = CONCAT(Col1, " - ", Col2) WHERE Col2 LIKE 'A%'
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		Added	Non-conditional updates between two columns is now available to all drivers. For example: UPDATE Table SET Col1=Col2
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.
2021-02-02	20.0.7703	Google Sheets		Added	Added a new view: Folders. It can be used to query the folders contained in a user's Google Drive. Added new column ParentIds on the Spreadsheets view. It displays the comma-separated list of the parent folder Id's, if any.

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

For more information on the available data source entities and how to query them with SQL, see Using Spreadsheets as Tables. For the SQL syntax, see SQL Compliance.

Connecting from Code

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

For information on how to connect with the googlesheets.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. For information on to modify the data in Google Sheets with INSERT, UPDATE, and DELETE statements, see Modifying Data .

Executing Stored Procedures

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

Batch Processing

For information about how to modify several rows of Google Sheets data at once using parameterized INSERT, UPDATE, and DELETE statements, see Batch Processing.

Connecting with the cdata.googlesheets 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.googlesheets as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")

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 Id, Column1 FROM Spreadsheet1_Sheet1")
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 Id, Column1 FROM Spreadsheet1_Sheet1 WHERE Column2 = ?"
params = ["Bob"]
cur = conn.execute(cmd, params)
rs = cur.fetchall()
for row in rs:
	print(row)

The connection is also used to issue INSERT, UPDATE, and DELETE commands to the data source. Parameters can be used with these statements if desired.

Note that the connector does not support transactions. As with normal write operations, all SQL statements executed by this connector affect the data source immediately. Call the connection's commit() method following the execution.

Insert

The following example adds a new record to the table:

cmd = "INSERT INTO Spreadsheet1_Sheet1 (Id, Column1) VALUES (?, ?)"
params = ["Jon Doe", "John"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Update

The following example modifies an existing record in the table:

cmd = "UPDATE Spreadsheet1_Sheet1 SET Column1 = ? WHERE Id = ?"
params = ["John", "6"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes an existing record from the table:

cmd = "DELETE FROM Spreadsheet1_Sheet1 WHERE Id = ?"
params = ["6"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

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 DownloadDocument Id = ?"
params = ["7"]
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 = ["7"]
cur.callproc("DownloadDocument", params)

This Python connector also supports writing to the data source via batch processing, using the cursor object's executemany() method. This requires both a SQL statement string and a data frame of values that act as a series of parameters for executing the SQL statement.

Note that the connector does not support transactions. As with normal write operations, all SQL statements executed by this connector affect the data source immediately. Call the connection's commit() method following the execution.

Insert

The following example adds new records to the table:

cur = conn.cursor()
cmd = "INSERT INTO Spreadsheet1_Sheet1 (Id, Column1) VALUES (?, ?)"
params = [["Jon Doe", "John"], ["Jon Doe", "John"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

Update

The following example modifies existing records in the table:

cur = conn.cursor()
cmd = "UPDATE Spreadsheet1_Sheet1 SET Column1 = ? WHERE Id = ?"
params = [["John", "6"], ["John", "6"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes existing records from the table:

cur = conn.cursor()
cmd = "DELETE FROM Spreadsheet1_Sheet1 WHERE Id = ?"
params = [["6"], ["6"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

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 Sheets Integration Quickstarts

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

The CData Python Connector for Google Sheets 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 Sheets 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.

Modifying Data From SQLAlchemy

The connector provides INSERT/UPDATE/DELETE functionality in SQLAlchemy. To learn how to call the session's execute() method to affect the data in the data source, see Modifying Data.

Connecting With a Dialect URL

Establishing a connection using SQLAlchemy requires a specific URL format.

from sqlalchemy import create_engine
engine = create_engine("googlesheets:///?InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")

For SQLAlchemy 2.0, the dialect name is googlesheets_2. To establish a connection, use the following URL format:

from sqlalchemy import create_engine
engine = create_engine("googlesheets_2:///?InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")

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 Spreadsheet1_Sheet1(Base):
	__tablename__ = "Spreadsheet1_Sheet1"
	Id = Column(String, primary_key=True)
	Id = Column(String)
	Column1 = 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)
Spreadsheet1_Sheet1 = abase.classes.Spreadsheet1_Sheet1

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)
Spreadsheet1_Sheet1_table = Table("Spreadsheet1_Sheet1", meta)
insp.reflect_table(Spreadsheet1_Sheet1_table, ["Id","Column1"])

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("googlesheets:///?InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Spreadsheet1_Sheet1).filter_by(Column2="Bob"):
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	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:

Spreadsheet1_Sheet1_table = Spreadsheet1_Sheet1.metadata.tables["Spreadsheet1_Sheet1"]
for instance in session.execute(Spreadsheet1_Sheet1_table.select().where(Spreadsheet1_Sheet1_table.c.Column2 == "Bob")):
	print("Id: ", instance.Id)
	print("FullName: ", instance.Name)
	print("City: ", instance.BillingCity)
	print("---------")

Implicit Joining

If mapped classes of related Google Sheets 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(Spreadsheet1_Sheet1).order_by(Spreadsheet1_Sheet1.AnnualRevenue)
for instance in rs:
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	print("---------")

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

rs = session.execute(Spreadsheet1_Sheet1_table.select().order_by(Spreadsheet1_Sheet1_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(Spreadsheet1_Sheet1.Id).label("CustomCount"), Spreadsheet1_Sheet1.Id).group_by(Spreadsheet1_Sheet1.Id)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(Spreadsheet1_Sheet1_table.select().with_only_columns([func.count(Spreadsheet1_Sheet1_table.c.Id).label("CustomCount"), Spreadsheet1_Sheet1_table.c.Id]).group_by(Spreadsheet1_Sheet1_table.c.Id))
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(Spreadsheet1_Sheet1).limit(25).offset(100)
for instance in rs:
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	print("---------")

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

rs = session.execute(Spreadsheet1_Sheet1_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(Spreadsheet1_Sheet1.Id).label("CustomCount"), Spreadsheet1_Sheet1.Id).group_by(Spreadsheet1_Sheet1.Id)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(Spreadsheet1_Sheet1_table.select().with_only_columns([func.count(Spreadsheet1_Sheet1_table.c.Id).label("CustomCount"), Spreadsheet1_Sheet1_table.c.Id])group_by(Spreadsheet1_Sheet1_table.c.Id))
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(Spreadsheet1_Sheet1.AnnualRevenue).label("CustomSum"), Spreadsheet1_Sheet1.Id).group_by(Spreadsheet1_Sheet1.Id)
for instance in rs:
	print("Sum: ", instance.CustomSum)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(Spreadsheet1_Sheet1_table.select().with_only_columns([func.sum(Spreadsheet1_Sheet1_table.c.AnnualRevenue).label("CustomSum"), Spreadsheet1_Sheet1_table.c.Id]).group_by(Spreadsheet1_Sheet1_table.c.Id))
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(Spreadsheet1_Sheet1.AnnualRevenue).label("CustomAvg"), Spreadsheet1_Sheet1.Id).group_by(Spreadsheet1_Sheet1.Id)
for instance in rs:
	print("Avg: ", instance.CustomAvg)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(Spreadsheet1_Sheet1_table.select().with_only_columns([func.avg(Spreadsheet1_Sheet1_table.c.AnnualRevenue).label("CustomAvg"), Spreadsheet1_Sheet1_table.c.Id]).group_by(Spreadsheet1_Sheet1_table.c.Id))
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(Spreadsheet1_Sheet1.AnnualRevenue).label("CustomMax"), func.min(Spreadsheet1_Sheet1.AnnualRevenue).label("CustomMin"), Spreadsheet1_Sheet1.Id).group_by(Spreadsheet1_Sheet1.Id)
for instance in rs:
	print("Max: ", instance.CustomMax)
	print("Min: ", instance.CustomMin)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(Spreadsheet1_Sheet1_table.select().with_only_columns([func.max(Spreadsheet1_Sheet1_table.c.AnnualRevenue).label("CustomMax"), func.min(Spreadsheet1_Sheet1_table.c.AnnualRevenue).label("CustomMin"), Spreadsheet1_Sheet1_table.c.Id]).group_by(Spreadsheet1_Sheet1_table.c.Id))
for instance in rs:

Commands can be executed individually by the session with a call to "execute()".

Obtaining the Table Object

The query supplied to this method is constructed using the associated Table object of a mapped class. This Table object is obtained from the mapped class's metadata field, as below:

Spreadsheet1_Sheet1_table = Spreadsheet1_Sheet1.metadata.tables["Spreadsheet1_Sheet1"]

Once the table object is obtained, the write operations are executed in the following ways. The queries are executed immediately without the need for a call to "commit()":

Insert

The following example adds a new record to the table:

session.execute(Spreadsheet1_Sheet1_table.insert(), {"Id": "Jon Doe", "Column1": "John"})

Update

The following example modifies an existing record in the table:

session.execute(Spreadsheet1_Sheet1_table.update().where(Spreadsheet1_Sheet1_table.c.Id == "6").values(Id="Jon Doe", Column1="John"))

Delete

The following example removes an existing record from the table:

session.execute(Spreadsheet1_Sheet1_table.delete().where(Spreadsheet1_Sheet1_table.c.Id == "6"))

When combined with the connector, Pandas can be used to generate data frames that contain your Google Sheets 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("googlesheets:///?InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")

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
	   Id,
	   Column1,
     $exNumericCol;
	FROM Spreadsheet1_Sheet1;""", engine)
print(df)

Modifying Data

To insert new records into a table, create a new data frame, and define its fields accordingly. When that is done, call to_sql() on the data frame to perform the INSERT operation with the connector, as shown in the example below. You must set the "if _exists" argument to "append" to prevent Pandas from attempting building the table from scratch. To prevent Pandas from writing the data frame index as a column, set index=False.

df = pd.DataFrame({"Id": ["Jon Doe"], "Column1": ["John"]})
df.to_sql("Spreadsheet1_Sheet1", con=engine, if_exists="append", index=False)

Matplotlib contains a number of tools that can graphically model Google Sheets 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 Sheets data. For example, the following plot generates and displays a bar graph relating Id and AnnualRevenue values:

df.plot(kind="bar", x="Id", 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 Sheets, you can use the connector's connect function to create a connection using a valid Google Sheets connection string. If you prefer not to use a direct connection, you can use a SQLAlchemy engine.

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

Extract, Transform, and Load the Google Sheets Data

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

sql = "SELECT	Id, Column1 FROM Spreadsheet1_Sheet1 "
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')

Modifying Data

Insert new rows into Google Sheets tables using Petl's appenddb function.

table1 = [['Id','Column1'],['Jon Doe','John']]
etl.appenddb(table1,cnxn,'Spreadsheet1_Sheet1')

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

Views

import cdata.googlesheets as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")
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.googlesheets as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tablecolumns WHERE TableName = 'Spreadsheet1_Sheet1'"
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.googlesheets as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")
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.googlesheets as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;Spreadsheet=NorthwindOrders")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'DownloadDocument'"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

This section details a selection of advanced features of the Google Sheets 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 Sheets 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 Sheets 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 Spreadsheet1_Sheet1 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 Spreadsheet1_Sheet1 Table

The following example caches the Spreadsheet1_Sheet1 table in the file specified by the CacheLocation property of the connection string.

SELECT Id, Column1 FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'

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 Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'

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 Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'
  

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 Spreadsheet1_Sheet1#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 Spreadsheet1_Sheet1 WHERE Column2='Bob' ORDER BY Column1 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 Sheets 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 Sheets 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 Sheets 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 Sheets. 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.

The CData Python Connector for Google Sheets supports several operations on data, including querying, deleting, modifying, and inserting.

SELECT Statements

See SELECT Statements for a syntax reference and examples.

INSERT Statements

See INSERT Statements for a syntax reference and examples, as well as retrieving the new records' Ids.

UPDATE Statements

The primary key Id is required to update a record. See UPDATE Statements for a syntax reference and examples.

DELETE Statements

The primary key Id is required to delete a record. See DELETE Statements for a syntax reference and examples.

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> 
    ]
  ] 
} | SCOPE_IDENTITY() 

<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 Spreadsheet1_Sheet1

2. Rename a column:
   

   SELECT [Column1] AS MY_Column1 FROM Spreadsheet1_Sheet1

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

   SELECT CAST(AnnualRevenue AS VARCHAR) AS Str_AnnualRevenue FROM Spreadsheet1_Sheet1

4. Search data:
   

   SELECT * FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'

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

   SELECT COUNT(*) AS MyCount FROM Spreadsheet1_Sheet1 

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

   SELECT COUNT(DISTINCT Column1) FROM Spreadsheet1_Sheet1 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT Column1 FROM Spreadsheet1_Sheet1 

8. Sort a result set in ascending order:
   

   SELECT Id, Column1 FROM Spreadsheet1_Sheet1  ORDER BY Column1 ASC

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

   SELECT Id, Column1 FROM Spreadsheet1_Sheet1 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 Spreadsheet1_Sheet1 WHERE Column2 = @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 Sheets.

    SELECT * FROM Spreadsheet1_Sheet1 WHERE Query = 'Column3 > 100'
    

Aggregate Functions

For SELECT examples using aggregate functions, see Aggregate Functions.

JOIN Queries

See JOIN Queries for SELECT query examples using JOINs.

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.

COUNT

Returns the number of rows matching the query criteria.

SELECT COUNT(*) FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'

COUNT(DISTINCT)

Returns the number of distinct, non-null field values matching the query criteria.

SELECT COUNT(DISTINCT Id) AS DistinctValues FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'

AVG

Returns the average of the column values.

SELECT Column1, AVG(AnnualRevenue) FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'  GROUP BY Column1

MIN

Returns the minimum column value.

SELECT MIN(AnnualRevenue), Column1 FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob' GROUP BY Column1

MAX

Returns the maximum column value.

SELECT Column1, MAX(AnnualRevenue) FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob' GROUP BY Column1

SUM

Returns the total sum of the column values.

SELECT SUM(AnnualRevenue) FROM Spreadsheet1_Sheet1 WHERE Column2 = 'Bob'

The CData Python Connector for Google Sheets supports standard SQL joins like the following examples.

Inner Join

An inner join selects only rows from both tables that match the join condition:

SELECT c.Name, o.ItemCount, o.TotalCost, o.Date FROM Spreadsheet1_Customers c INNER JOIN Spreadsheet1_Orders o ON c.Id = o.CustomerId

Left Join

A left join selects all rows in the FROM table and only matching rows in the JOIN table:

 SELECT c.Name, o.ItemCount, o.TotalCost, o.Date FROM Spreadsheet1_Customers c LEFT JOIN Spreadsheet1_Orders o ON c.Id = o.CustomerId

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 Spreadsheet1_Sheet1

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 Id, Column1, RANK() OVER (ORDER BY Column1) AS Rank FROM Spreadsheet1_Sheet1

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

SELECT Id, Column1, RANK() OVER (PARTITION BY Id ORDER BY Column1) AS Rank FROM Spreadsheet1_Sheet1

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 Id, Column1, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Column1) AS Rank FROM Spreadsheet1_Sheet1

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

SELECT Id, Column1, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Column1) AS Rank FROM Spreadsheet1_Sheet1

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|

To create new records, use INSERT statements.

INSERT Syntax

The INSERT statement specifies the columns to be inserted and the new column values. You can specify the column values in a comma-separated list in the VALUES clause, as shown in the following example:

INSERT INTO <table_name> 
( <column_reference> [ , ... ] )
VALUES 
( { <expression> | NULL } [ , ... ] ) 
  

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

The following is an example query:

INSERT INTO Spreadsheet1_Sheet1 (Column1) VALUES ('John')

To modify existing records, use UPDATE statements.

Update Syntax

The UPDATE statement takes as input a comma-separated list of columns and new column values as name-value pairs in the SET clause, as shown in the following example:

UPDATE <table_name> SET <select_statement> | {<column_reference> = <expression> [ , ... ]} WHERE { Id = <expression>  } [ { AND | OR } ... ] 

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

The following is an example query:

UPDATE Spreadsheet1_Sheet1 SET Column1='John' WHERE Id = @myId

To delete information from a table, use DELETE statements.

DELETE Syntax

The DELETE statement requires the table name in the FROM clause and the row's primary key in the WHERE clause, as shown in the following example:

<delete_statement> ::= DELETE FROM <table_name> WHERE { Id = <expression> } [ { AND | OR } ... ]

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

The following is an example query:

DELETE FROM Spreadsheet1_Sheet1 WHERE Id = @myId

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 Spreadsheet1_Sheet1

Use the following cache statement to cache all rows of a table into the cache table CachedSpreadsheet1_Sheet1:

CACHE CachedSpreadsheet1_Sheet1 SELECT * FROM Spreadsheet1_Sheet1

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 CachedSpreadsheet1_Sheet1 SELECT * FROM Spreadsheet1_Sheet1 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 Id and Column1 even though the cache table CachedSpreadsheet1_Sheet1 has all the columns in Spreadsheet1_Sheet1.

CACHE CachedSpreadsheet1_Sheet1 SCHEMA ONLY SELECT * FROM Spreadsheet1_Sheet1
CACHE CachedSpreadsheet1_Sheet1 SELECT Id, Column1 FROM Spreadsheet1_Sheet1

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)

Use INSERT INTO SELECT queries to select a list of records from one table and insert those same records into another table as a group. Inserting batches of records in this way may result in improved query performance compared to using many individual INSERT INTO queries.

The table whose records are selected for insertion into another table can be either a real table or a user-defined temporary table.

Inserting Records from Real Tables

To insert a group of records from one real, non-temporary, source table into another destination table, you can use an INSERT INTO SELECT query. This type of query is formatted similarly to a standard INSERT INTO query, except the VALUES clause is substituted with a SELECT query targeting the source table. All records matched by the embedded SELECT query are inserted into the destination table.

If the source table and destination tables have different column names, you must map columns from the source table to the corresponding columns in the destination table you want to insert them into. Perform this mapping by specifying the destination table columns in the same order as the source table columns you want to match them with. For example:

INSERT INTO DestinationTable (A,B,C,D) SELECT Q,R,S,T FROM SourceTable

In this example, the first source column (Q) is inserted into the first destination column (A), the second source column (R) is inserted into the second destination column (B), and so on.

If the source table and destination table both have the same column list with the same names, you can use a streamlined query.

INSERT INTO DestinationTableWithSameColumns SELECT * FROM SourceTable

In this example, there is no need to specify a list of columns for either the source or destination table, because their metadata already matches.

Inserting Records from Temporary Tables

You can manually define and populate temporary tables to hold a list of records for later bulk insertion.

Populate the Temporary Table

To create a temporary table, you must give it a name ending in "#TEMP" and execute an INSERT INTO query using that name, as if that table already existed in the database. After executing the first INSERT INTO, the temporary table exists and can receive subsequent INSERTs. For example:

INSERT INTO Spreadsheet1_Sheet1#TEMP (Column1, MyCustomField__c) VALUES ('New Spreadsheet1_Sheet1', '9000');
INSERT INTO Spreadsheet1_Sheet1#TEMP (Column1, MyCustomField__c) VALUES ('New Spreadsheet1_Sheet1 2', '9001');
INSERT INTO Spreadsheet1_Sheet1#TEMP (Column1, MyCustomField__c) VALUES ('New Spreadsheet1_Sheet1 3', '9002');

This creates a temporary table called Spreadsheet1_Sheet1#TEMP with two columns and three rows of data. Since type cannot be determined on the temporary table itself, all values are considered strings and later converted to the proper type when they are inserted together into the real (non-temporary) table of interest.

Insert Temporary Table Contents into Real Tables

Once your temporary table is populated, execute an INSERT INTO SELECT query targeting the real (non-temporary) table you want to insert the temporary table's records into. This is formatted similarly to a standard INSERT INTO query, except the VALUES clause is substituted with a SELECT query targeting the matching columns in the temporary table. For example:

INSERT INTO Spreadsheet1_Sheet1 (Column1, MyCustomField__c) SELECT Column1, MyCustomField__c FROM Spreadsheet1_Sheet1#TEMP

In this example, the full contents of Spreadsheet1_Sheet1#TEMP are inserted into the Spreadsheet1_Sheet1.

Results

The LastResultInfo#TEMP temporary table contains details about the most recently executed query that uses the contents of a temporary table in an embedded SELECT clause, as is the case for INSERT INTO SELECT queries that use a temporary table as the source of records. This table is cleared and repopulated each time such a query is executed. LastResultInfo#TEMP includes information such as whether the query in question succeeded, and how many rows were affected by the query.

Temporary Table Lifespan

Temporary tables only last as long as the connection remains open. When the connection to Google Sheets is closed, all temporary tables are cleared, including the LastResultInfo#TEMP table.

To perform multiple updates in a single request to Google Sheets,first use the INSERT INTO syntax to insert a temporary table of data into Google Sheets. This works by first populating a temporary table with the data you are going to submit to Google Sheets. Once you have all of the data you want to update, use UPDATE SELECT FROM to pass the temporary table data into the table in Google Sheets.

Populate the Temporary Table

The temporary table you are populating is dynamic and is created at run time the first time you insert to it. Temporary tables are denoted by a # appearing in their name. When using a temporary table to update, the temporary table must be named in the format [TableName]#TEMP, where TableName is the name of the table you are inserting to. For example:

INSERT INTO Spreadsheet1_Sheet1#TEMP (Id, Name, MyCustomField__c) VALUES ('AX1000001', 'New Spreadsheet1_Sheet1', '9000');
INSERT INTO Spreadsheet1_Sheet1#TEMP (Id, Name, MyCustomField__c) VALUES ('AX1000002', 'New Spreadsheet1_Sheet1 2', '9001');
INSERT INTO Spreadsheet1_Sheet1#TEMP (Id, Name, MyCustomField__c) VALUES ('AX1000003', 'New Spreadsheet1_Sheet1 3', '9002');

This creates a temporary table called Spreadsheet1_Sheet1#TEMP with three columns and three rows of data. Since type cannot be determined on the temporary table itself, all values are stored in memory as strings. The values are later converted to the proper type when they are submitted to the Spreadsheet1_Sheet1 table.

Update the Actual Table

Once your temporary table is populated, it is now time to update the actual table in Google Sheets. You can do this by performing an UPDATE to the actual table and selecting the input data from the temporary table. For example:

UPDATE Spreadsheet1_Sheet1 (Id, Column1, MyCustomField__c) SELECT Id, Column1, MyCustomField__c FROM Spreadsheet1_Sheet1#TEMP

In this example, the full contents of the Spreadsheet1_Sheet1#TEMP table are passed into the Spreadsheet1_Sheet1 table. This results in fewer requests being submitted to Google Sheets since multiple updates may be submitted with each request, which is much better for performance if you have many records to update.

Results

The results of the query are stored in the LastResultInfo#TEMP temporary table. This table is cleared and repopulated the next time data is modified by passing in a temporary table. Please be aware that the LastResultInfo#TEMP table has no predefined schema. You need to check its metadata at run time before reading data.

Temporary Table Life Span

Temporary tables only last as long as the connection remains open. When the connection to Google Sheets is closed, all temporary tables are cleared, including the LastResultInfo#TEMP table.

To perform multiple deletes in a single request to Google Sheets, first use the INSERT INTO syntax to create an in-memory temporary table of data to be deleted. Once you have all of the data you want to delete added to temporary table, use DELETE FROM syntax to delete data from the live table in Google Sheets. This functionality is also available via the standard Batch Processing API available in JDBC.

Populate the Temporary Table

The temporary table you are populating is dynamic and is created at run time the first time you insert to it. Temporary tables are denoted by a # appearing in their name. When using a temporary table to delete, the temporary table must be named in the format [TableName]#TEMP, where TableName is the name of the table you are inserting to. For example:

INSERT INTO Spreadsheet1_Sheet1#TEMP (Id) VALUES ('AX1000001');
INSERT INTO Spreadsheet1_Sheet1#TEMP (Id) VALUES ('AX1000002');
INSERT INTO Spreadsheet1_Sheet1#TEMP (Id) VALUES ('AX1000003');

This creates a temporary table called Spreadsheet1_Sheet1#TEMP with one column and three rows of data. Since type cannot be determined on the temporary table itself, all values are stored in memory as strings. They are later converted to the proper type when they are submitted to the Spreadsheet1_Sheet1 table.

Delete from the Actual Table

Once your temporary table is populated, it is now time to insert to the actual table in Google Sheets. You can do this by performing a DELETE from the actual table and selecting the input data from the temporary table. For example:

DELETE FROM Spreadsheet1_Sheet1 WHERE EXISTS SELECT Id FROM Spreadsheet1_Sheet1#TEMP

In this example, the full contents of the Spreadsheet1_Sheet1#TEMP table are passed into the Spreadsheet1_Sheet1 table. This results in fewer requests being submitted to Google Sheets since multiple deletes may be submitted with each request, which is much better for performance if you have many records to delete.

Results

The results of the query are stored in the LastResultInfo#TEMP temporary table. This table is cleared and repopulated the next time data is modified by passing in a temporary table. Please be aware that the LastResultInfo#TEMP table has no predefined schema. You need to check its metadata at run time before reading data.

Temporary Table Life Span

Temporary tables only last as long as the connection remains opened. When the connection to Google Sheets is closed, all temporary tables are cleared, including the LastResultInfo#TEMP table.

Spreadsheets as Tables

The connector models spreadsheets and ranges as relational tables.

Tables

Tables shows various configuration options to reflect your spreadsheets' organization in the tables; for example, you will find guides for working with headers and querying ranges as tables.

Columns provides more information on column discovery.

Stored Procedures

In addition to working with the data in the spreadsheet, you can use the available stored procedures to access functionality in the Google Sheets API that is not modeled as SELECT, INSERT, UPDATE, or DELETE statements.

The connector enables you to represent a top-left-oriented spreadsheet or a user-specified range as a database table. You can control how tables are listed by setting the Header property.

Top-Left Oriented Tables

You can use the connector to start working right away with top-left-oriented tables. The default configuration settings are explained below:

• Top-left-oriented tables are represented with the name of the worksheet.
• The default format requires that the table is top-left-oriented and that the first row of data in the worksheet contains the column names. This means that the default value of true for the Header connection string property is required.
• Headers should not contain special characters.
• By default the connector will return all rows until the first empty row. Note: an empty row between data will prevent further data from being returned.

Due to a limitation of Google's Spreadsheet API, all column headers must be non empty.

User-Specified Range

You can execute SQL commands against a given range as a table by using this format in your query: WORKSHEET#RANGE

Note: Range notation is only available in a SELECT or UPDATE statement. Ranges are not supported for DELETE and INSERT commands.

You can specify column names or generate column names automatically by setting the Header property. This property affects how you use columns in commands.

Header=True (Default)

• Columns are determined by the first row of the Google spreadsheet. If no values are provided for the first row of the spreadsheet, the connector will create unique, alphabetized column names that are available only within the scope of that request.
• The connector also adds an Id column for each row that corresponds to the unique URI of the row on the Google servers. This is used during update and delete operations.

Header=False

• Columns will be dynamically assigned based on either the specified range or the size of the worksheet. The autogenerated column names are alphabetical.
• The Id column for each row will represent the row number from the top of the sheet. For example, if you specify a range A3:E6, rows 3, 4, 5, and 6 will be returned.

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 Sheets Views

Name	Description
Folders	Query the folders contained in a user's Google Drive.
Sheets	Returns a list of a user's sheets and their relevant information.
Spreadsheets	Returns a list of a user's spreadsheets and their relevant information.

Query the folders contained in a user's Google Drive.

Select

The connector will use the Google Sheets API to process WHERE clause conditions built with the server side supported columns and operators. The rest of the filter is executed client side within the connector.

The columns and operators that support server side filtering are:

• Name supports the 'CONTAINS,=,!=' operators.
• Description supports the 'CONTAINS' operator.
• ModifiedTime supports the '<=,<,=,!=,>,>=' operators.
• OwnerEmail supports the 'IN' operator.
• Starred supports the '=,!=' operators.
• Trashed supports the '=,!=' operators.
• ParentIds supports the 'IN' operator.
• DriveId supports the '=' operator. It is used to get all the folders from the specified Drive.

  Note: You must set the connection property SupportsAllDrives to 'true', in order to query from a specific Drive.

All the columns that support server side filtering can be paired with the AND and OR logical operators. For example, the following queries are processed server side:

SELECT * FROM Folders WHERE Name = 'example folder'

SELECT * FROM Folders WHERE OwnerEmail IN ('owner1@email.com', 'owner2@email.com') AND ModifiedTime >= '2020-04-01T05:30:00'

Columns

Name	Type	Description
Id [KEY]	String	The ID of the folder.
Name	String	The name of the folder. This is not necessarily unique within a folder. Note that for immutable items such as the top level folders of Team Drives, My Drive root folder, and Application Data folder the name is constant.
DriveId	String	The Id of the Drive.
Description	String	A short description of the folder or folder.
CreatedTime	Datetime	The creation date of the folder or folder.
ModifiedTime	Datetime	The last modified date of the folder or folder.
Size	Long	The size of the folder in bytes.
OwnerName	String	The name of the resource's owner.
OwnerEmail	String	The email of the resource's owner.
Starred	Boolean	This field sets whether or not the resource is starred.
Trashed	Boolean	This field sets whether or not the resource has been moved to the trash.
Viewed	Boolean	This field sets whether or not the resource has been viewed by the current user.
ParentIds	String	A comma-separated list of parent folder Ids.

Pseudo-Columns

Pseudo column 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
Query	String	This field accepts a valid Google Drive SDK query, which overrides conditionals in the WHERE clause.

Returns a list of a user's sheets and their relevant information.

Columns

Name	Type	Description
SpreadsheetId	String	The assigned Id of the spreadsheet.
SpreadsheetName	String	The name of the spreadsheet.
SheetId	String	The assigned Id of the sheet.
SheetName	String	The name of the sheet.
SheetIndex	Integer	The index of the sheet within the spreadsheet.
SheetType	String	The type of sheet. Defaults to GRID.
Hidden	Boolean	Specifies if the sheet is hidden in the UI or not.
RowCount	Integer	The number of rows in the grid.
ColumnCount	Integer	The number of columns in the grid.
FrozenRowCount	Integer	The number of rows that are frozen in the grid.
FrozenColumnCount	Integer	The number of columns that are frozen in the grid.
DataSourceExecutionStatus	String	The execution status of the DataSource sheet

Returns a list of a user's spreadsheets and their relevant information.

Columns

Name	Type	Description
Id [KEY]	String	The assigned Id of the spreadsheet.
Name	String	The name of the spreadsheet.
Description	String	A short description of the spreadsheet.
OwnerName	String	The name of the resource's owner.
OwnerEmail	String	The email of the resource's owner.
ModifiedTime	Datetime	The last updated date and time of this spreadsheet.
CreatedTime	Datetime	The created date and time of this spreadsheet.
Trashed	Boolean	Whether the spreadsheet has been trashed.
Starred	Boolean	Whether the user has starred the spreadsheet.
Viewed	Boolean	Whether the user has viewed the spreadsheet.
ParentIds	String	A comma-separated list of parent folder Ids.

Stored procedures are function-like interfaces that extend the functionality of the connector beyond simple SELECT/INSERT/UPDATE/DELETE operations with Google Sheets.

Stored procedures accept a list of parameters, perform their intended function, and then return any relevant response data from Google Sheets, along with an indication of whether the procedure succeeded or failed.

CData Python Connector for Google Sheets Stored Procedures

Name	Description
AddDataSource	Add a BigQuery dataSource to the specified spreadsheet. A Custom OAuth app with the scope bigquery.readonly is required to run this stored procedure.
AddDataSourceTable	Add a DataSource Table to a sheet. A Custom OAuth app with the scope bigquery.readonly is required to run this stored procedure.
AddSheet	Add a worksheet to an existing Google spreadsheet.
CopySheet	Copies a sheet from a spreadsheet to another spreadsheet.
CreateSchema	Creates a schema file for the specified table or view.
CreateSpreadsheet	Creates a new spreadsheet in the user's Google Drive. A default sheet is automatically included when the spreadsheet is created.
DeleteSheet	Deletes a worksheet in an existing Google spreadsheet.
DeleteSpreadsheet	Deletes a spreadsheet.
DownloadDocument	Downloads a file from the user's Google Sheets resouces specified by the Id of the spreadsheet and exported in the format specified in the FileFormat property.
FormatRange	Format cells in a specific range
GetOAuthAccessToken	Obtains the OAuth access token to be used for authentication with various Google services.
GetOAuthAuthorizationURL	Obtains the OAuth authorization URL for authentication with various Google services.
RefreshOAuthAccessToken	Obtains the OAuth access token to be used for authentication with various Google services.
UpdateSheet	Updates properties of the sheet for the specified SpreadsheetId and SheetId.
UploadDocument	Uploads a file to the user's Google Drive.

Add a BigQuery dataSource to the specified spreadsheet. A Custom OAuth app with the scope bigquery.readonly is required to run this stored procedure.

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet.
ProjectId	String	The ID of a BigQuery enabled GCP project.
TableProjectId	String	The ID of a BigQuery project the table belongs to. If not specified, the projectId is assumed.
DatasetId	String	The BigQuery dataset id.
TableId	String	The BigQuery table id.
BigQueryQuery	String	The query to use when querying the DataSource

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.
DataSourceId	String	The ID of the created DataSouce
Status	String	The DataExecution Status

Add a DataSource Table to a sheet. A Custom OAuth app with the scope bigquery.readonly is required to run this stored procedure.

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet where the table will be created.
DataSourceId	String	The ID of the DataSource.
SheetId	String	The ID of the sheet where the table will be pulled to. If not specified 0 will be assumed The default value is 0.
RowLimit	String	The limit of rows to return. If not set, a default limit is applied
Range	String	Range where the dataTable will be pulled to. Ex: A1:G10
RefreshDatasourceMode	String	Refreshes the datasource table after creating it. DATASOURCEID - Refreshes all associated data source objects for the datasource. ISALL - Refreshes all existing data source objects in the spreadsheet. NONE - Do not refresh the datasource table The allowed values are DATASOURCEID, ISALL, NONE. The default value is DATASOURCEID.

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.

Add a worksheet to an existing Google spreadsheet.

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet.
SheetId	String	The ID of the sheet. Must be non-negative. This field cannot be changed once set.
Title	String	The name of the sheet.
Index	String	The index of the sheet within the spreadsheet.
SheetType	String	The type of sheet. Defaults to GRID. This field cannot be changed once set. The allowed values are GRID, OBJECT. The default value is GRID.
RowCount	String	The number of rows in the grid.
ColumnCount	String	The number of columns in the grid.
HeaderNames	String	A comma-separated list of column headers to be added as the first row in the new sheet. These headers can be used later for inserting or updating data. If a column name contains special characters, enclose it in double quotes.
FrozenRowCount	String	The number of rows that are frozen in the grid.
FrozenColumnCount	String	The number of columns that are frozen in the grid.
HideGridlines	Boolean	True if the grid is not showing gridlines in the UI.
Hidden	Boolean	True if the sheet is hidden in the UI, false if it is visible.
RightToLeft	Boolean	True if the sheet is an RTL sheet instead of an LTR sheet.

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.

Copies a sheet from a spreadsheet to another spreadsheet.

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet containing the sheet to copy.
SheetId	String	The ID of the sheet to copy
DestinationSpreadsheetId	String	The ID of the spreadsheet where it will be copied to.
SheetName	String	Optional parameter. The new name you want to set to the newly created sheet.

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.
SheetId	String	The Id of the newly created sheet

Creates a schema file for the specified table or view.

CreateSchema

Creates a local schema file (.rsd) from an existing table or view in the data model.

The schema file is created in the directory set in the Location connection property when this procedure is executed. You can edit the file to include or exclude columns, rename columns, or adjust column datatypes.

The connector checks the Location to determine if the names of any .rsd files match a table or view in the data model. If there is a duplicate, the schema file will take precedence over the default instance of this table in the data model. If a schema file is present in Location that does not match an existing table or view, a new table or view entry is added to the data model of the connector.

Input

Name	Type	Description
TableName	String	The name of the table or view.
FileName	String	The full file path and name of the schema to generate. Ex : 'C:\\Users\\User\\Desktop\\SmartSheet\\sheet.rsd'

Result Set Columns

Name	Type	Description
Result	String	Returns Success or Failure.
FileData	String	File data that will be outputted encoded in Base64 if the FileName and FileStream inputs are not set.

Creates a new spreadsheet in the user's Google Drive. A default sheet is automatically included when the spreadsheet is created.

Input

Name	Type	Description
Title	String	The title for the spreadsheet.
Description	String	The description for the spreadsheet.
Hidden	Boolean	This parameter sets whether or not the resource is hidden. The default value is FALSE.
Restricted	Boolean	This parameter sets whether or not the resource is restricted. The default value is FALSE.
Starred	Boolean	This parameter sets whether or not the resource is starred. The default value is FALSE.
Parents	String	The Ids of the parent folders for the created spreadsheet.
HeaderNames	String	A comma-separated list of column headers that will be added as the first row of the default sheet created with the new spreadsheet. These headers can be used later for inserting or updating data. If a column name contains special characters, enclose it in double quotes.

Result Set Columns

Name	Type	Description
Success	String	This parameter indicates whether the operation was successful or not.
Id	String	The Id of the new spreadsheet.

Deletes a worksheet in an existing Google spreadsheet.

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet.
SheetId	String	The ID of the sheet.

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.

Deletes a spreadsheet.

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet.

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.

Downloads a file from the user's Google Sheets resouces specified by the Id of the spreadsheet and exported in the format specified in the FileFormat property.

Input

Name	Type	Description
Id	String	The Id of the resource to be downloaded.
FileFormat	String	If this property is not specified, the file will be downloaded as-is. If you need to convert the file before downloading it in one of the formats shown below, use the MIME types shown on the right. MS Excel --> application/vnd.openxmlformats-officedocument.spreadsheetml.sheet Open Office sheet --> application/x-vnd.oasis.opendocument.spreadsheet Web Page (HTML) --> application/zip PDF --> application/pdf CSV --> (first sheet only) text/csv The default value is application/vnd.openxmlformats-officedocument.spreadsheetml.sheet.
LocalFile	String	The local file path including the file name for the location where the file will be saved on disk. Leave empty to keep the file in memory.
Encoding	String	If the LocalFile input is left empty, the data will be output to FileData in the specified encoding. The allowed values are NONE, BASE64. The default value is BASE64.
Overwrite	Boolean	What to do when downloaded file exists. Set true to overwrite. The default value is false.

Result Set Columns

Name	Type	Description
Success	String	This value shows a boolean indication of whether the operation was successful or not.
FileData	String	If the LocalFile and FileStream inputs are empty, file data will be output in the format specified by the Encoding input.

Format cells in a specific range

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet.
SheetId	String	The ID of the sheet. Must be non-negative.
Range	String	The range of the cells to format. The format should be, for example A1:C3.
BackgroundColor	String	Specify background color by providing RGB values for Red, Green, Blue. For example 255,0,0.
Bold	String	Bold the text of the cells. Accepts true or false.
FontSize	String	Set font size by providing an int value.
FontFamily	String	Set the font used by providing its name.

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.

Obtains the OAuth access token to be used for authentication with various Google services.

NOTE: If, after running this stored procedure, the OAuthRefreshToken was not returned as part of the result set, change the Prompt value to CONSENT and run the procedure again. This forces the app to reauthenticate and send new token information.

Input

Name	Type	Description
AuthMode	String	The type of authentication mode to use. The allowed values are APP, WEB. The default value is WEB.
Verifier	String	The verifier code returned by Google after permissions have been granted for the app to connect. WEB AuthMode only.
Scope	String	The scope of access to Google APIs. By default, access to all APIs used by this data provider will be specified.
CallbackURL	String	Determines where the response is sent. The value of this parameter must exactly match one of the values registered in the APIs Console (including the HTTP or HTTPS schemes, capitalization, and trailing '/').
Prompt	String	This field indicates the prompt to present the user. It accepts one of the following values: NONE, CONSENT, SELECT ACCOUNT. The default is SELECT_ACCOUNT, so a given user will be prompted to select the account to connect to. If it is set to CONSENT, the user will see a consent page every time, even if they have previously given consent to the application for a given set of scopes. Lastly, if it is set to NONE, no authentication or consent screens will be displayed to the user. The default value is SELECT_ACCOUNT.
AccessType	String	Indicates if your application needs to access a Google API when the user is not present at the browser. This parameter defaults to OFFLINE. If your application needs to refresh access tokens when the user is not present at the browser, then use OFFLINE. This will result in your application obtaining a refresh token the first time your application exchanges an authorization code for a user. The allowed values are ONLINE, OFFLINE. The default value is OFFLINE.
State	String	Indicates any state which may be useful to your application upon receipt of the response. Your application receives the same value it sent, as this parameter makes a round-trip to the Google authorization server and back. Uses include redirecting the user to the correct resource in your site, using nonces, and mitigating cross-site request forgery.

Result Set Columns

Name	Type	Description
OAuthAccessToken	String	The authentication token returned from Google. This can be used in subsequent calls to other operations for this particular service.
OAuthRefreshToken	String	A token that may be used to obtain a new access token.
ExpiresIn	String	The remaining lifetime on the access token.

Obtains the OAuth authorization URL for authentication with various Google services.

Input

Name	Type	Description
Scope	String	The scope of access to Google APIs. By default, access to all APIs used by this data provider will be specified.
CallbackURL	String	Determines where the response is sent. The value of this parameter must exactly match one of the values registered in the APIs Console (including the HTTP or HTTPS schemes, capitalization, and trailing '/').
Prompt	String	This field indicates the prompt to present the user. It accepts one of the following values: NONE, CONSENT, SELECT ACCOUNT. The default is SELECT_ACCOUNT, so a given user will be prompted to select the account to connect to. If it is set to CONSENT, the user will see a consent page every time, even if they have previously given consent to the application for a given set of scopes. Lastly, if it is set to NONE, no authentication or consent screens will be displayed to the user. The default value is SELECT_ACCOUNT.
AccessType	String	Indicates if your application needs to access a Google API when the user is not present at the browser. This parameter defaults to OFFLINE. If your application needs to refresh access tokens when the user is not present at the browser, then use OFFLINE. This will result in your application obtaining a refresh token the first time your application exchanges an authorization code for a user. The allowed values are ONLINE, OFFLINE. The default value is OFFLINE.
State	String	Indicates any state which may be useful to your application upon receipt of the response. Your application receives the same value it sent, as this parameter makes a round-trip to the Google authorization server and back. Uses include redirecting the user to the correct resource in your site, nonces, and cross-site-request-forgery mitigations.

Result Set Columns

Name	Type	Description
URL	String	The URL to complete user authentication.

Obtains the OAuth access token to be used for authentication with various Google services.

Input

Name	Type	Description
OAuthRefreshToken	String	The refresh token returned from the original authorization code exchange.

Result Set Columns

Name	Type	Description
OAuthAccessToken	String	The authentication token returned from Google. This can be used in subsequent calls to other operations for this particular service.
ExpiresIn	String	The remaining lifetime on the access token.

Updates properties of the sheet for the specified SpreadsheetId and SheetId.

Input

Name	Type	Description
SpreadsheetId	String	The ID of the spreadsheet.
SheetId	String	The ID of the sheet. Must be non-negative.
Title	String	The name of the sheet.
Index	String	The index of the sheet within the spreadsheet.
RowCount	String	The number of rows in the grid.
ColumnCount	String	The number of columns in the grid.
FrozenRowCount	String	The number of rows that are frozen in the grid.
FrozenColumnCount	String	The number of columns that are frozen in the grid.
HideGridlines	Boolean	True if the grid is not showing gridlines in the UI.
Hidden	Boolean	True if the sheet is hidden in the UI, false if it is visible.
RightToLeft	Boolean	True if the sheet is an RTL sheet instead of an LTR sheet.

Result Set Columns

Name	Type	Description
Success	String	This value shows whether the operation was successful or not.

Uploads a file to the user's Google Drive.

Input

Name	Type	Description
Id	String	The Id for the file. Only needs to be set when updating an existing document.
Name	String	The name for the file, including the extension.
Description	String	The description for the file.
Starred	Boolean	This parameter sets whether or not the resource is starred. The default value is FALSE.
ParentIds	String	The Ids of the parent folders for the uploaded document.
MIMEType	String	The MIME type of the file. The default value is application/vnd.google-apps.spreadsheet.
LocalFile	String	The local file path including the file name of the file to be uploaded. A value for this field is required when FileData or Content are both not specified.
FileData	String	If the LocalFile input is empty, the file data will be used in the format specified by the Encoding parameter.
Encoding	String	The FileData input encoding type. The allowed values are NONE, BASE64. The default value is BASE64.

Result Set Columns

Name	Type	Description
Id	String	The id of the file.
Success	String	This parameter sets whether the operation was successful or not.

You can query the system tables described in this section to access schema information, information on data source functionality, and batch operation statistics.

Schema Tables

The following tables return database metadata for Google Sheets:

• sys_catalogs: Lists the available databases.
• sys_schemas: Lists the available schemas.
• sys_tables: Lists the available tables and views.
• sys_tablecolumns: Describes the columns of the available tables and views.
• sys_procedures: Describes the available stored procedures.
• sys_procedureparameters: Describes stored procedure parameters.
• sys_keycolumns: Describes the primary and foreign keys.
• sys_indexes: Describes the available indexes.

Data Source Tables

The following tables return information about how to connect to and query the data source:

• sys_connection_props: Returns information on the available connection properties.
• sys_sqlinfo: Describes the SELECT queries that the connector can offload to the data source.

Query Information Tables

The following table returns query statistics for data modification queries, including batch operations:

• sys_identity: Returns information about batch operations or single updates.

Lists the available databases.

The following query retrieves all databases determined by the connection string:

SELECT * FROM sys_catalogs

Columns

Name	Type	Description
CatalogName	String	The database name.

Lists the available schemas.

The following query retrieves all available schemas:

          SELECT * FROM sys_schemas
          

Columns

Name	Type	Description
CatalogName	String	The database name.
SchemaName	String	The schema name.

Lists the available tables.

The following query retrieves the available tables and views:

          SELECT * FROM sys_tables
          

Columns

Name	Type	Description
CatalogName	String	The database containing the table or view.
SchemaName	String	The schema containing the table or view.
TableName	String	The name of the table or view.
TableType	String	The table type (table or view).
Description	String	A description of the table or view.
IsUpdateable	Boolean	Whether the table can be updated.
IsInsertable	Boolean	Whether the table can be inserted into.
IsDeleteable	Boolean	Whether rows can be deleted from the table.

Describes the columns of the available tables and views.

The following query returns the columns and data types for the Spreadsheet1_Sheet1 table:

SELECT ColumnName, DataTypeName FROM sys_tablecolumns WHERE TableName = 'Spreadsheet1_Sheet1' 

Columns

Name	Type	Description
CatalogName	String	The name of the database containing the table or view.
SchemaName	String	The schema containing the table or view.
TableName	String	The name of the table or view containing the column.
ColumnName	String	The column name.
DataTypeName	String	The data type name.
DataType	Int32	An integer indicating the data type. This value is determined at run time based on the environment.
Length	Int32	The storage size of the column.
DisplaySize	Int32	The designated column's normal maximum width in characters.
NumericPrecision	Int32	The maximum number of digits in numeric data. The column length in characters for character and date-time data.
NumericScale	Int32	The column scale or number of digits to the right of the decimal point.
IsNullable	Boolean	Whether the column can contain null.
Description	String	A brief description of the column.
Ordinal	Int32	The sequence number of the column.
IsAutoIncrement	String	Whether the column value is assigned in fixed increments.
IsGeneratedColumn	String	Whether the column is generated.
IsHidden	Boolean	Whether the column is hidden.
IsArray	Boolean	Whether the column is an array.
IsReadOnly	Boolean	Whether the column is read-only.
IsKey	Boolean	Indicates whether a field returned from sys_tablecolumns is the primary key of the table.
ColumnType	String	The role or classification of the column in the schema. Possible values include SYSTEM, LINKEDCOLUMN, NAVIGATIONKEY, REFERENCECOLUMN, and NAVIGATIONPARENTCOLUMN.
ColumnCapabilities	Int32	A bit mask denoting the column's write capabilities. The value is the sum of the following: 1 if the column is required for INSERTs, 2 if the column is allowed for INSERTs, and 4 if the column is allowed for UPDATEs. A value of 0 indicates that the write capabilities of the column are unknown or that the column is read-only.

Lists the available stored procedures.

The following query retrieves the available stored procedures:

          SELECT * FROM sys_procedures
          

Columns

Name	Type	Description
CatalogName	String	The database containing the stored procedure.
SchemaName	String	The schema containing the stored procedure.
ProcedureName	String	The name of the stored procedure.
Description	String	A description of the stored procedure.
ProcedureType	String	The type of the procedure, such as PROCEDURE or FUNCTION.

Describes stored procedure parameters.

The following query returns information about all of the input parameters for the DownloadDocument stored procedure:

SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'DownloadDocument' AND Direction = 1 OR Direction = 2

To include result set columns in addition to the parameters, set the IncludeResultColumns pseudo column to True:

SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'DownloadDocument' AND IncludeResultColumns='True'

Columns

Name	Type	Description
CatalogName	String	The name of the database containing the stored procedure.
SchemaName	String	The name of the schema containing the stored procedure.
ProcedureName	String	The name of the stored procedure containing the parameter.
ColumnName	String	The name of the stored procedure parameter.
Direction	Int32	An integer corresponding to the type of the parameter: input (1), input/output (2), or output(4). input/output type parameters can be both input and output parameters.
DataType	Int32	An integer indicating the data type. This value is determined at run time based on the environment.
DataTypeName	String	The name of the data type.
NumericPrecision	Int32	The maximum precision for numeric data. The column length in characters for character and date-time data.
Length	Int32	The number of characters allowed for character data. The number of digits allowed for numeric data.
NumericScale	Int32	The number of digits to the right of the decimal point in numeric data.
IsNullable	Boolean	Whether the parameter can contain null.
IsRequired	Boolean	Whether the parameter is required for execution of the procedure.
IsArray	Boolean	Whether the parameter is an array.
Description	String	The description of the parameter.
Ordinal	Int32	The index of the parameter.
Values	String	The values you can set in this parameter are limited to those shown in this column. Possible values are comma-separated.
SupportsStreams	Boolean	Whether the parameter represents a file that you can pass as either a file path or a stream.
IsPath	Boolean	Whether the parameter is a target path for a schema creation operation.
Default	String	The value used for this parameter when no value is specified.
SpecificName	String	A label that, when multiple stored procedures have the same name, uniquely identifies each identically-named stored procedure. If there's only one procedure with a given name, its name is simply reflected here.
IsCDataProvided	Boolean	Whether the procedure is added/implemented by CData, as opposed to being a native Google Sheets procedure.

Pseudo-Columns

Name	Type	Description
IncludeResultColumns	Boolean	Whether the output should include columns from the result set in addition to parameters. Defaults to False.