Connecting to Salesforce CRM Analytics

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

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

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

Python Version Support

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

Salesforce CRM Analytics Version Support

The connector models Salesforce CRM Analytics datasets and lenses as tables in a relational database, using the Salesforce CRM Analytics REST API.

See Also

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

Dependencies

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

Installation

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

Windows:

pip install cdata_tableaucrm_connector-26.0.9655-cp310-abi3-win_amd64.whl

Linux:

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

macOS:

pip install cdata_tableaucrm_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_tableaucrm_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_tableaucrm" 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_tableaucrm folder is trivial to find:

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

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

1. Import the module as follows:
   

   import cdata.tableaucrm 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;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")

Connecting to Salesforce CRM Analytics

OAuthJWT

Salesforce CRM Analytics uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with Salesforce CRM Analytics. For further information, see Creating a Custom OAuth Application.

Set the AuthScheme to OAuthJWT.

Obtaining the OAuthJWT Consumer Key

To obtain the OAuthJWT consumer key:

1. Log in to Salesforce.com.
2. From Setup, enter Apps in the Quick Find box and then click the link to create an app. In the Connected Apps section of the resulting page, click New.
3. Enter a name to be displayed to users when they log in to grant permissions to your app, along with a contact Email address.
4. Click Enable OAuth Settings and enter a value in the Callback URL box. Set this value only to create the Connected App as it is required. It will not actually be needed for this type of authentication. The Callback URL is in the format:
   

   http://localhost:8019/src/oauthCallback.rst

5. Enable Use digital signatures.
6. Upload your certificate.
7. Select the scope of permissions that your app should request from the user.
8. Click your app name to open a page with information about your app. The OAuth consumer key is displayed.

After creating your OAuth Application, set the following connection properties:

• AuthScheme: OAuthJWT.
• InitiateOAuth: GETANDREFRESH.
• OAuthJWTCert: The JWT Certificate store.
• OAuthJWTPassword: The password of the JWT Certificate store.
• OAuthJWTIssuer: The OAuth Client ID.
• OAuthJWTCertType: The type of the certificate store specified by OAuthJWTCert.

Note: This flow never issues a refresh token.

OAuth

Set AuthScheme to OAuth.

The following subsections describe how to authenticate to Salesforce CRM Analytics from these authentication flows:

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

For information about how to create a custom OAuth application, see Creating a Custom OAuth Application. For a complete list of connection string properties available in Salesforce CRM Analytics, see Connection.

Desktop Applications

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

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

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

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

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

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

1. The connector obtains an access token from Salesforce CRM Analytics 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.

Note: To configure the session timeout in Salesforce, navigate to Setup > Administration Setup > Security Controls > Session Settings.

Web Applications

Authenticating via the Web requires you to create and register a custom OAuth application with Salesforce CRM Analytics, 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.
     If you set the Grant_Type to Code, the callback URL contains the verifier code in the query strong parameter named code.
   • 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. Before connecting to data for the first time, 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.

   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.
   • 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.
   • 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.
• OAuthClientId: The client Id assigned when you registered your custom OAuth application.
• OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.

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 Salesforce CRM Analytics python connector are configured with an ini file. This file is used to set several parameters, including JNI behavior. This file is to be located in:

{path_to_distribution_site-packages}/cdata/config.ini

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

• For Linux:
  

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

• For Mac:
  

  [tableaucrm.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.tableaucrm 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

The following procedure registers a custom OAuth application in the admin console and obtains the OAuth client credentials, the consumer key and consumer secret:

1. Access Setup.
   (If you are connecting from a Desktop application, you can access Setup by logging into Salesforce.com and clicking your user name.)
2. Open a new application.
   • If your organization uses the Salesforce Lightning Experience UI:
     1. Search for "App" in the Quick Find box.
     2. Select App ManagerA (not Manage Connected Apps).
     3. Click New Connected App.
   • If your organization uses the Salesforce Classic UI:
     1. Search for "Apps" in the Quick Find box.
     2. Under Build or Create, select Apps.
     3. Under Connected Apps, click New.
3. Enter a name to be displayed to users when they log in to grant permissions to your application. Also supply a contact email address.
4. Select Enable OAuth Settings.
5. Enter a value in the Callback URL box.
   • If this is a desktop application, set the Callback URL to http://localhost:33333 or a different port number of your choice.
   • If this is a Web application, set the Callback URL to a Website page where you want the user to be returned to, after they have authorized your application.
6. Assign the following OAuth scopes:
   • Access and manage your wave data (wave_api)
   • Access and manage your data (api)
   • Perform requests on your behalf at any time (refresh_token, offline_token)

After your application is created, click the new application name. The admin console displays the OAuth client credentials, the consumer key, and consumer secret. Record this information for later use.

The following sections show how to execute SELECT statements to your Tableau CRM datasets or lenses. See Data Model for information on how the connector models datasets as tables and lenses as views.

Selecting Datasets

Reference the table name of a dataset by adding "Dataset_" as a prefix to the dataset name, as in the following query:

SELECT Id, Name, Item, Quantity, Amount FROM DataSet_Test WHERE Amount = 50

The following query assumes that the dataset has the column names Id, Name, Quantity, and Amount. The dataset may have more columns than what were selected and they can be in any order.

Selecting Lenses

*Note: The Lenses that generate a Compact-Form query, are not supported at this time due to some API restrictions and will not be included in table listing. In order to make them work, currently we have this workaround as solution.

1. From your dashboard choose LENSES
2. Open the LENS which is not working and go to "SAQL Mode"
3. Make a minor update (i.e: a white space in the end of the query)
4. Save the LENS and wait some seconds for the changes to by applied
5. After that you should be able to query that LENS

Reference the table name of a lens by adding "Lens_" as a prefix to the lens name, as in the following query.

SELECT Id, Name, Item, Quantity, Amount FROM Lens_Test WHERE Amount = 50

The query above assumes that the lens has the column names Id, Name, Quantity, and Amount. The lens may have more columns than what were selected and they can be in any order.

The connector offers the possibility to insert data into your Tableau CRM datasets.

Getting the metadata file

To insert data into an exisiting dataset, first you need to download the metadata file. You can download the metadata file during the process of creating a new dataset by performing the following steps:

1. In Tableau CRM, go to the home page, click Create, and then choose Dataset.
2. Select CSV file to upload and click Next.
3. From new opened window download the JSON metadata schema file by clicking Download File in dropdown list at Data Schema File section.
4. For more info on Metadata format refer to this guide: https://resources.docs.salesforce.com/200/latest/en-us/sfdc/pdf/bi_dev_guide_ext_data_format.pdf

or for an existing dataset, if you have a csv file with the dataset's data go through the same process as when creating a new dataset to get the metadata json file for it, but don't actually create the dataset.

Inserting data

To insert new data, the MetadataFilesLocation connection property is required. You must set its value to the location of the file where your metadata files are stored.

INSERT INTO Dataset_Example (Col1, Col2, Col3) VALUES ('val1', 'val2', 'val3')

INSERT INTO SELECT (Bulk inserting)

First you need to insert data into a temporary table. Temporary tables are denoted by a # appearing in their name. When using a temporary table to insert, the temporary table must be named in the format [TableName]#TEMP, where TableName is the name of the table you will be inserting to. For instance:

INSERT INTO Dataset_Example#TEMP (Col1, Col2, Col3) VALUES ('val1', 'val2', 'val3')
INSERT INTO Dataset_Example#TEMP (Col1, Col2, Col3) VALUES ('val4', 'val5', 'val6')
INSERT INTO Dataset_Example#TEMP (Col1, Col2, Col3) VALUES ('val7', 'val8', 'val9')

Then you need to insert to the actual table.

INSERT INTO Dataset_Example (Col1, Col2, Col3) SELECT Col1, Col2, Col3 FROM Dataset_Example#TEMP

If the AsyncBulkOperation connection property is set to 'true' the provider will allow Tableau CRM to process the data while your application continues executing. The downside of using asynchronous services is that you will need to check the status of the upload manually to see if Salesforce has finished processing the request and see if there were any issues. You can do this by calling the GetJobStatus stored procedure. You can get the value of the JobId required for the GetJobStatus stored procedure from the LastResultInfo#TEMP table.

SELECT JobId FROM LastResultInfo#TEMP

The connector offers the possibility to upsert data into your Tableau CRM datasets.

Getting the metadata file

To upsert data into an exisiting dataset, first you need to download the metadata file. You can download the metadata file during the process of creating a new dataset by performing the following steps:

1. In Tableau CRM, go to the home page, click Create, and then choose Dataset.
2. Select CSV file to upload and click Next.
3. From new opened window download the JSON metadata schema file by clicking Download File in dropdown list at Data Schema File section.
4. For more info on Metadata format refer to this guide: https://resources.docs.salesforce.com/200/latest/en-us/sfdc/pdf/bi_dev_guide_ext_data_format.pdf

or for an existing dataset, if you have a csv file with the dataset's data go through the same process as when creating a new dataset to get the metadata json file for it, but don't actually create the dataset.

Upserting data

To upsert new data, the MetadataFilesLocation connection property is required. You must set its value to the location of the file where your metadata files are stored.
Note: The metadata file must contain one (and only one) field with a unique identifier.

UPSERT INTO Dataset_Example (Col1, Col2, Col3) Values ('val1', 'val2', 'val3')

UPSERT INTO SELECT (Bulk upserting)

First you need to insert data into a temporary table. Temporary tables are denoted by a # appearing in their name. When using a temporary table to upsert, the temporary table must be named in the format [TableName]#TEMP, where TableName is the name of the table you will be upserting to. For instance:

INSERT INTO Dataset_Example#TEMP (Col1, Col2, Col3) VALUES ('val1', 'val2', 'val3')
INSERT INTO Dataset_Example#TEMP (Col1, Col2, Col3) VALUES ('val4', 'val5', 'val6')
INSERT INTO Dataset_Example#TEMP (Col1, Col2, Col3) VALUES ('val7', 'val8', 'val9')

Then you need to upsert to the actual table.

UPSERT INTO Dataset_Example (Col1, Col2, Col3) SELECT Col1, Col2, Col3 FROM Dataset_Example#TEMP

If the AsyncBulkOperation connection property is set to 'true' the provider will allow Tableau CRM to process the data while your application continues executing. The downside of using asynchronous services is that you will need to check the status of the upload manually to see if Salesforce has finished processing the request and see if there were any issues. You can do this by calling the GetJobStatus stored procedure. You can get the value of the JobId required for the GetJobStatus stored procedure from the LastResultInfo#TEMP table.

SELECT JobId FROM LastResultInfo#TEMP

The connector offers the possibility to delete data from your Tableau CRM datasets.

Getting the metadata file

To delete data from an exisiting dataset, first you need to download the metadata file. You can download the metadata file during the process of creating a new dataset by performing the following steps:

1. In Tableau CRM, go to the home page, click Create, and then choose Dataset.
2. Select CSV file to upload and click Next.
3. From new opened window download the JSON metadata schema file by clicking Download File in dropdown list at Data Schema File section.
4. For more info on Metadata format refer to this guide: https://resources.docs.salesforce.com/200/latest/en-us/sfdc/pdf/bi_dev_guide_ext_data_format.pdf

or for an existing dataset, if you have a csv file with the dataset's data go through the same process as when creating a new dataset to get the metadata json file for it, but don't actually create the dataset.

Deleting data

To delete existing data, the MetadataFilesLocation connection property is required. You must set its value to the location of the file where your metadata files are stored.
Note: The metadata file must contain one (and only one) field with a unique identifier.

DELETE FROM Dataset_Example WHERE Col1='uniqueValue'

DELETE FROM EXISTS SELECT (Bulk deleting)

First you need to insert data into a temporary table. 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 will be deleting from. For instance:

INSERT INTO Dataset_Example#TEMP (Col1) VALUES ('uniqueValue')
INSERT INTO Dataset_Example#TEMP (Col1) VALUES ('uniqueValue')
INSERT INTO Dataset_Example#TEMP (Col1) VALUES ('uniqueValue')

Then you need to delete from the actual table.

DELETE FROM Dataset_Example WHERE EXISTS SELECT Col1 FROM FROM Dataset_Example#TEMP

If the AsyncBulkOperation connection property is set to 'true' the provider will allow Tableau CRM to process the data while your application continues executing. The downside of using asynchronous services is that you will need to check the status of the upload manually to see if Salesforce has finished processing the request and see if there were any issues. You can do this by calling the GetJobStatus stored procedure. You can get the value of the JobId required for the GetJobStatus stored procedure from the LastResultInfo#TEMP table.

SELECT JobId FROM LastResultInfo#TEMP

The connector offers the possibility to to create datasets or insert data into your Tableau CRM datasets by using CSV files.

Uploading CSV files

You can upload CSV files to datasets by using the UploadCSVToDataset stored procedure. To upload a CSV file to a dataset the following parameters are required: DatasetName, CSVFileLocation. If the AsyncBulkInsert connection property is set to 'true' the provider will allow Tableau CRM to process the data while your application continues executing. The downside of using asynchronous services is that you will need to check the status of the upload manually to see if Salesforce has finished processing the request and see if there were any issues. You can do this by calling the GetJobStatus stored procedure.

• DatasetName: The name of dataset you want to create or append data to.
• CSVFileLocation: The location of the CSV file containing the data that will be uploaded.
• Operation: The operation you want to use when you're loading data into the dataset. It is set to 'Append' by default. If set to 'Overwrite', a dataset with the given data will be created, and replaced if it exists.If set to 'Append', the given data will be added to an exisiting dataset or a new dataset will be created if it does not exist. A metadata file is required for 'Append' operations. The data and metadata for the 'Append' operations must exactly match the dataset on which the operation is happening.
• MetadataFileLocation: This parameter is required when Operation is set to 'Append'.
• AppName: The name of the app that contains the dataset. If the name is ommitted for when you're creating a dataset, the name of the user's private app is used. If the name is omitted for an existing dataset, the system resolves the app name. If the name is specified for an existing dataset, the name is required to match the name of the current app that contains the dataset.
• CSVFileCharset: Set this property to the actual charset of the CSV file you want to upload. You do not need to set this parameter if the file's encoding is UTF-8.

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
2026-05-13	26.0.9629	Salesforce CRM Analytics	Compatibility	Changed	Renamed Tableau CRM to Salesforce CRM Analytics.
2026-05-08	26.0.9624	Salesforce CRM Analytics	Connection	Changed	Changed the default value of the AuthScheme connection property to OAuthPKCE.
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.
2026-01-13	25.0.9509	General		Added	Added support for the REGEXP_REPLACE() string function.
2025-12-21	25.0.9486	Python		Added	Added support for custom loggers in Python connectors on Linux and macOS.
2025-12-05	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).
2025-10-06	25.0.9410	General		Added	Support for parsing datetime formats using ".S" and ",S" for milliseconds and nanoseconds.
2025-09-12	25.0.9386	General		Added	Added the IsInsertable, IsUpdateable, and IsDeleteable columns to the sys_tables table.
2025-09-10	25.0.9384	General		Changed	All columns in statically defined Views are now reported as read-only.
2025-09-03	25.0.9377	General		Changed	Corrected the behavior when IN criteria with NULL values are used in the projection part. It now returns NULL instead of 0. For example, "NULL IN (1,2)" returns "NULL".
2025-09-01	25.0.9375	General		Added	Added support for using the CAST function with infinity values. This function can cast "inf" and "-inf" to DOUBLE, FLOAT, or REAL.
2025-08-21	25.0.9364	General		Changed	Report behavior change: Fixed inconsistent string value comparisons in non-table queries. For example, "SELECT 'A' = 'a'" previously returned false, but it now returns true.
2025-08-13	25.0.9356	General		Changed	Changed the maximum number of pages held in memory from 15 to 5 for the page providers to decrease heap usage.
2025-07-08	25.0.9320	Salesforce CRM Analytics		Changed	The default API version has been updated to version 64.0.
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.
2025-06-19	25.0.9301	Salesforce CRM Analytics		Added	Added a new connection property, validationMode, which controls how lenses are validated during schema discovery. Legal values are: None: No validation is performed during schema discovery. Basic: Each lens is validated to ensure it has a valid query; only valid lenses are pushed. Enhanced: Each lens is checked for the presence of data; only lenses with data are pushed.
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).
2025-03-11	25.0.9201	Salesforce CRM Analytics		Changed	The default API version has been updated to version 63.0.
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.
2024-10-24	24.0.9063	Salesforce CRM Analytics		Changed	The default API version has been updated to version 62.0.
2024-08-12	24.0.8990	Salesforce CRM Analytics		Changed	The default API version has been updated to version 61.0.
2024-06-05	24.0.8922	Python		Added	Added support for Python 3.12.
2024-05-29	24.0.8915	Salesforce CRM Analytics		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.
2024-04-03	23.0.8859	Salesforce CRM Analytics		Changed	Changed the default `APIVersion` property value from 59.0 to the latest version, which is 60.0.
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
2023-12-04	23.0.8738	Salesforce CRM Analytics		Added	Added support for using PKCE with the authorization code OAuth grant type.
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
2023-08-29	23.0.8641	Python		Added	Added support for SQLAlchemy 2.0.
2023-06-20	23.0.8571	General		Added	Added the new sys_lastresultinfo system table.
2023-05-19	23.0.8539	Python		Added	Added support for Python 3.11 on Windows, Linux and Mac.
2023-05-16	23.0.8536	Python		Removed	Removed support for Python 3.7 on Windows and Linux
2023-04-25	23.0.8515	General		Removed	Removed support for the SELECT INTO CSV statement. The core code doesn't support it anymore.
2023-04-11	23.0.8501	Salesforce CRM Analytics		Added	Added support for Query Passthrough.
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.
2022-09-02	22.0.8280	Salesforce CRM Analytics		Added	Added streaming output support for CreateMetadataFile. Added FileStream as an input. Added binary and encoded content output support for CreateSchema. Added FileStream as an input and FileData as an output.
2022-08-18	22.0.8265	Salesforce CRM Analytics		Added	Added encoded content output support for CreateMetadataFile. Added FileData as an output. Added binary input support for UploadCSVToDataset. Added Content as an input.
2022-07-26	22.0.8242	Salesforce CRM Analytics		Changed	Changed provider name to Tableau CRM Analytics.
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
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.
2021-06-25	21.0.7846	Salesforce CRM Analytics		Added	Added support for OAuthJWT via Salesforce.
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.

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

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

Connecting from Code

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

For information on how to connect with the tableaucrm.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 Salesforce CRM Analytics 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.

Connecting with the cdata.tableaucrm 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.tableaucrm as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")

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 Name, CloseDate FROM Dataset_Opportunity")
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 Name, CloseDate FROM Dataset_Opportunity WHERE StageName = ?"
params = ["Closed Won"]
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 Dataset_Opportunity (Name, CloseDate) VALUES (?, ?)"
params = ["12-31-2017", "1-1-2018"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Update

The following example modifies an existing record in the table:

cmd = "UPDATE Dataset_Opportunity SET CloseDate = ? WHERE Id = ?"
params = ["1-1-2018", "25"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes an existing record from the table:

cmd = "DELETE FROM Dataset_Opportunity WHERE Id = ?"
params = ["25"]
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 SelectEntries ObjectName = ?"
params = ["Account"]
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 = ["Account"]
cur.callproc("SelectEntries", params)

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

Python Integration Guides

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

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

Complete List of Salesforce CRM Analytics Integration Quickstarts

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

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

The following sections detail various aspects of this integration:

Connecting From SQLAlchemy

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

Reflecting Metadata With SQLAlchemy

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

Querying Data From SQLAlchemy

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

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("tableaucrm:///?InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")

For SQLAlchemy 2.0, the dialect name is tableaucrm_2. To establish a connection, use the following URL format:

from sqlalchemy import create_engine
engine = create_engine("tableaucrm_2:///?InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")

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 Dataset_Opportunity(Base):
	__tablename__ = "Dataset_Opportunity"
	Id = Column(String, primary_key=True)
	Name = Column(String)
	CloseDate = 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)
Dataset_Opportunity = abase.classes.Dataset_Opportunity

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)
Dataset_Opportunity_table = Table("Dataset_Opportunity", meta)
insp.reflect_table(Dataset_Opportunity_table, ["Id","CloseDate"])

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("tableaucrm:///?InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Dataset_Opportunity).filter_by(StageName="Closed Won"):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("CloseDate: ", instance.CloseDate)
	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:

Dataset_Opportunity_table = Dataset_Opportunity.metadata.tables["Dataset_Opportunity"]
for instance in session.execute(Dataset_Opportunity_table.select().where(Dataset_Opportunity_table.c.StageName == "Closed Won")):
	print("Id: ", instance.Id)
	print("FullName: ", instance.Name)
	print("City: ", instance.BillingCity)
	print("---------")

Implicit Joining

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

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

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

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

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

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

Other Join Forms

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

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

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

• Left-to-right relationship:
  

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

• Left-to-right relationship with explicit target:
  

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

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

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

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

ORDER BY

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

rs = session.query(Dataset_Opportunity).order_by(Dataset_Opportunity.Size)
for instance in rs:
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("CloseDate: ", instance.CloseDate)
	print("---------")

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

rs = session.execute(Dataset_Opportunity_table.select().order_by(Dataset_Opportunity_table.c.Size))
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(Dataset_Opportunity.Id).label("CustomCount"), Dataset_Opportunity.Name).group_by(Dataset_Opportunity.Name)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Name: ", instance.Name)
	print("---------")

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

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

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

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

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

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

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

rs = session.execute(Dataset_Opportunity_table.select().with_only_columns([func.sum(Dataset_Opportunity_table.c.Size).label("CustomSum"), Dataset_Opportunity_table.c.Name]).group_by(Dataset_Opportunity_table.c.Name))
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(Dataset_Opportunity.Size).label("CustomAvg"), Dataset_Opportunity.Name).group_by(Dataset_Opportunity.Name)
for instance in rs:
	print("Avg: ", instance.CustomAvg)
	print("Name: ", instance.Name)
	print("---------")

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

rs = session.execute(Dataset_Opportunity_table.select().with_only_columns([func.avg(Dataset_Opportunity_table.c.Size).label("CustomAvg"), Dataset_Opportunity_table.c.Name]).group_by(Dataset_Opportunity_table.c.Name))
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(Dataset_Opportunity.Size).label("CustomMax"), func.min(Dataset_Opportunity.Size).label("CustomMin"), Dataset_Opportunity.Name).group_by(Dataset_Opportunity.Name)
for instance in rs:
	print("Max: ", instance.CustomMax)
	print("Min: ", instance.CustomMin)
	print("Name: ", instance.Name)
	print("---------")

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

rs = session.execute(Dataset_Opportunity_table.select().with_only_columns([func.max(Dataset_Opportunity_table.c.Size).label("CustomMax"), func.min(Dataset_Opportunity_table.c.Size).label("CustomMin"), Dataset_Opportunity_table.c.Name]).group_by(Dataset_Opportunity_table.c.Name))
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:

Dataset_Opportunity_table = Dataset_Opportunity.metadata.tables["Dataset_Opportunity"]

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(Dataset_Opportunity_table.insert(), {"Name": "12-31-2017", "CloseDate": "1-1-2018"})

Update

The following example modifies an existing record in the table:

session.execute(Dataset_Opportunity_table.update().where(Dataset_Opportunity_table.c.Id == "25").values(Name="12-31-2017", CloseDate="1-1-2018"))

Delete

The following example removes an existing record from the table:

session.execute(Dataset_Opportunity_table.delete().where(Dataset_Opportunity_table.c.Id == "25"))

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

Connecting

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

import pandas as pd
from sqlalchemy import create_engine
engine = create_engine("tableaucrm:///?InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")

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
	   Name,
	   CloseDate,
     $exNumericCol;
	FROM Dataset_Opportunity;""", 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({"Name": ["12-31-2017"], "CloseDate": ["1-1-2018"]})
df.to_sql("Dataset_Opportunity", con=engine, if_exists="append", index=False)

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

Using PyPlot

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

from matplotlib import pyplot as plt

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

df.plot(kind="bar", x="Name", y=["Size"])
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 Salesforce CRM Analytics, you can use the connector's connect function to create a connection using a valid Salesforce CRM Analytics connection string. If you prefer not to use a direct connection, you can use a SQLAlchemy engine.

import petl as etl
import cdata.tableaucrm as mod
cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")

Extract, Transform, and Load the Salesforce CRM Analytics Data

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

sql = "SELECT	Name, CloseDate FROM Dataset_Opportunity "
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 Salesforce CRM Analytics tables using Petl's appenddb function.

table1 = [['Name','CloseDate'],['12-31-2017','1-1-2018']]
etl.appenddb(table1,cnxn,'Dataset_Opportunity')

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.tableaucrm as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tables"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Views

import cdata.tableaucrm as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")
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.tableaucrm as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tablecolumns WHERE TableName = 'Dataset_Opportunity'"
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.tableaucrm as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")
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.tableaucrm as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthClientId=MyConsumerKey;OAuthClientSecret=MyConsumerSecret;CallbackURL=http://localhost:portNumber")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'SelectEntries'"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

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

User Defined Views

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

SSL Configuration

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

Firewall and Proxy

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

Caching Data

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

Query Processing

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

For further information, see Query Processing.

Logging

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

Exception Handling

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

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

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

There are two ways to create user defined views:

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

Defining Views Using a Configuration File

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

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

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

For example:

{
	"MyView": {
		"query": "SELECT * FROM Dataset_Opportunity 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 Dataset_Opportunity Table

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

SELECT Name, CloseDate FROM Dataset_Opportunity WHERE StageName = 'Closed Won'

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 Dataset_Opportunity WHERE StageName = 'Closed Won'

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 Dataset_Opportunity WHERE StageName = 'Closed Won'
  

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 Dataset_Opportunity#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 Dataset_Opportunity WHERE StageName='Closed Won' ORDER BY CloseDate 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 Salesforce CRM Analytics and then processes the rest of the query in memory (client-side). This results in optimal performance.

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

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

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

More Information

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

Logging

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

Basic Logging

To begin capturing connector logging, set these properties:

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

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

Log Verbosity

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

The following list describes each level:

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

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

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

Sensitive Data

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

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

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

Advanced Logging

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

Example property value:

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

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

The available modules and submodules are:

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

Exception Handling

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

Error Codes

The error code classifies the type of error.

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

SQL State

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

Some of the common SQL states are listed below:

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

Error Message

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

The CData Python Connector for Salesforce CRM Analytics supports several operations on data, including querying, deleting, modifying, and inserting.

SELECT Statements

See SELECT Statements for a syntax reference and examples.

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

INSERT Statements

See INSERT Statements for a syntax reference and examples.

UPDATE Statements

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

UPSERT Statements

An UPSERT updates a record if it exists and inserts the record if it does not. See UPSERT 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 Dataset_Opportunity

2. Rename a column:
   

   SELECT [CloseDate] AS MY_CloseDate FROM Dataset_Opportunity

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

   SELECT CAST(Size AS VARCHAR) AS Str_Size FROM Dataset_Opportunity

4. Search data:
   

   SELECT * FROM Dataset_Opportunity WHERE StageName = 'Closed Won'

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

   SELECT COUNT(*) AS MyCount FROM Dataset_Opportunity 

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

   SELECT COUNT(DISTINCT CloseDate) FROM Dataset_Opportunity 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT CloseDate FROM Dataset_Opportunity 

8. Sort a result set in ascending order:
   

   SELECT Name, CloseDate FROM Dataset_Opportunity  ORDER BY CloseDate ASC

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

   SELECT Name, CloseDate FROM Dataset_Opportunity 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 Dataset_Opportunity WHERE StageName = @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 Salesforce CRM Analytics.

    SELECT * FROM Dataset_Opportunity WHERE Pseudo = '@Pseudo'
    

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.

COUNT

Returns the number of rows matching the query criteria.

SELECT COUNT(*) FROM Dataset_Opportunity WHERE StageName = 'Closed Won'

COUNT(DISTINCT)

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

SELECT COUNT(DISTINCT Name) AS DistinctValues FROM Dataset_Opportunity WHERE StageName = 'Closed Won'

AVG

Returns the average of the column values.

SELECT CloseDate, AVG(Size) FROM Dataset_Opportunity WHERE StageName = 'Closed Won'  GROUP BY CloseDate

MIN

Returns the minimum column value.

SELECT MIN(Size), CloseDate FROM Dataset_Opportunity WHERE StageName = 'Closed Won' GROUP BY CloseDate

MAX

Returns the maximum column value.

SELECT CloseDate, MAX(Size) FROM Dataset_Opportunity WHERE StageName = 'Closed Won' GROUP BY CloseDate

SUM

Returns the total sum of the column values.

SELECT SUM(Size) FROM Dataset_Opportunity WHERE StageName = 'Closed Won'

The CData Python Connector for Salesforce CRM Analytics 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 Customers.ContactName, Orders.OrderDate FROM Customers, Orders WHERE Customers.CustomerId=Orders.CustomerId

Left Join

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

SELECT Customers.ContactName, Orders.OrderDate FROM Customers LEFT OUTER JOIN Orders ON Customers.CustomerId=Orders.CustomerId

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 Dataset_Opportunity (CloseDate) VALUES ('1-1-2018')

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 Dataset_Opportunity SET CloseDate='1-1-2018' 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 Dataset_Opportunity 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 Dataset_Opportunity

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

CACHE CachedDataset_Opportunity SELECT * FROM Dataset_Opportunity

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 CachedDataset_Opportunity SELECT * FROM Dataset_Opportunity 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 Name and CloseDate even though the cache table CachedDataset_Opportunity has all the columns in Dataset_Opportunity.

CACHE CachedDataset_Opportunity SCHEMA ONLY SELECT * FROM Dataset_Opportunity
CACHE CachedDataset_Opportunity SELECT Name, CloseDate FROM Dataset_Opportunity

To execute stored procedures, you can use EXECUTE or EXEC statements.

EXEC and EXECUTE assign stored procedure inputs, referenced by name, to values or parameter names.

Stored Procedure Syntax

To execute a stored procedure as an SQL statement, use the following syntax:

 
{ EXECUTE | EXEC } <stored_proc_name> 
{
  [ @ ] <input_name> = <expression>
} [ , ... ]

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

Example Statements

Reference stored procedure inputs by name:

EXECUTE my_proc @second = 2, @first = 1, @third = 3;

Execute a parameterized stored procedure statement:

EXECUTE my_proc second = @p1, first = @p2, third = @p3; 

PIVOT and UNPIVOT can be used to change a table-valued expression into another table.

PIVOT

PIVOT rotates a table-value expression by turning unique values from one column into multiple columns in the output. PIVOT can run aggregations where required on any column value.

PIVOT Synax

 
"SELECT 'AverageCost' AS Cost_Sorted_By_Production_Days, [0], [1], [2], [3], [4]
FROM
(
SELECT DaysToManufacture, StandardCost
FROM Production.Product
) AS SourceTable
PIVOT
(
AVG(StandardCost)
FOR DaysToManufacture IN ([0], [1], [2], [3], [4])
) AS PivotTable;"

UNPIVOT

UNPIVOT carries out nearly the opposite to PIVOT by rotating columns of a table-valued expressions into column values.

UNPIVOT Sytax

 
"SELECT VendorID, Employee, Orders
FROM
(SELECT VendorID, Emp1, Emp2, Emp3, Emp4, Emp5
FROM pvt) p
UNPIVOT
(Orders FOR Employee IN
(Emp1, Emp2, Emp3, Emp4, Emp5)
)AS unpvt;"

For further information on PIVOT and UNPIVOT, see FROM clause plus JOIN, APPLY, PIVOT (Transact-SQL)

The connector models Salesforce CRM Analytics datasets and lenses as database views, or read-only tables, using the Salesforce CRM Analytics REST API. Datasets additionally support bidirectional access.

The following sections provide information on the functionality available through the views and stored procedures.

Views

You can use the connector to work with all the datasets and lenses in your account. View schemas are discovered dynamically at run time.

Dataset tables start with "Dataset_", so if you want to get the data of a dataset called "Test", the table name would be "Dataset_Test". Lens tables start with "Lens_", so if you want to get the data of a lens called "Test", then the table name would be "Lens_Test".

See System Tables to obtain schema information and other connector metadata at run time. The Views section shows an example database modeled on datasets and lenses available in the Salesforce CRM Analytics development environment.

Dynamic Schemas

When you connect, the connector gets the list of tables and the metadata for the tables by calling the appropriate Web services. Any changes you make to your datasets or lenses, such as adding new columns or changing the data type of a column, will immediately be reflected when you connect using the connector.

Query Processing

The connector offloads as much of the SELECT statement processing as possible to Salesforce CRM Analytics and then processes the rest of the query within the connector.

Executing SQL to Datasets and Lenses

Datasets support INSERT statements, while lenses support only SELECT; you can also call the UploadCSVToDataSet stored procedure to insert to an existing dataset or to create a new dataset.

See the following sections for examples of executing SQL to datasets and lenses:

• Selecting Datasets
• Inserting Datasets
• Uploading Datasets

Customizing Schemas

You can customize table schemas to do the following:

• Change the columns and data types reported.
• Change the operations that can be executed against a dataset or lense.

To customize a schema, call the CreateSchema stored procedure. The stored procedure saves the dynamic schema to a static configuration file. Schema files have a simple format that makes them easy to customize.

Stored Procedures

Stored Procedures are function-like interfaces to the data source. They can be used to access other API functionality not surfaced through the views.

The connector models the data in Salesforce CRM Analytics as a list of tables in a relational database that can be queried using standard SQL statements.

CData Python Connector for Salesforce CRM Analytics Tables

Name	Description
Dataset_Account	The Tableau CRM dataset Dataset_Account.
Dataset_Activity	The Tableau CRM dataset Dataset_Activity.
Dataset_Analytics	The Tableau CRM dataset Dataset_Analytics.
Dataset_Case	The Tableau CRM dataset Dataset_Case.
Dataset_DTC_Opportunity_SAMPLE	The Tableau CRM dataset Dataset_DTC_Opportunity_SAMPLE.
Dataset_Fashion	The Tableau CRM dataset Dataset_Fashion.
Dataset_OneMillion	The Tableau CRM dataset Dataset_OneMillion.
Dataset_Opportunity	The Tableau CRM dataset Dataset_Opportunity.
Dataset_Opportunity_products	The Tableau CRM dataset Dataset_Opportunity_products.
Dataset_Pipeline_trending	The Tableau CRM dataset Dataset_Pipeline_trending.
Dataset_Plain_quota	The Tableau CRM dataset Dataset_Plain_quota.
Dataset_Product	The Tableau CRM dataset Dataset_Product.
Dataset_Quota	The Tableau CRM dataset Dataset_Quota.
Dataset_User	The Tableau CRM dataset Dataset_User.
Dataset_User_role	The Tableau CRM dataset Dataset_User_role.

The Tableau CRM dataset Dataset_Account.

Columns

Name	Type	ReadOnly	Description
AccountName	String	False	This field is generated dynamically and corresponds to the AccountName field in the dataset.
AccountSource	String	False	This field is generated dynamically and corresponds to the AccountSource field in the dataset.
AnnualRevenue	Double	False	This field is generated dynamically and corresponds to the AnnualRevenue field in the dataset.
BillingCountry	String	False	This field is generated dynamically and corresponds to the BillingCountry field in the dataset.
BillingState	String	False	This field is generated dynamically and corresponds to the BillingState field in the dataset.
CreatedDate	Datetime	False	This field is generated dynamically and corresponds to the CreatedDate field in the dataset.
CreatedDate_Day	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Day field in the dataset.
CreatedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the CreatedDate_day_epoch field in the dataset.
CreatedDate_Hour	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Hour field in the dataset.
CreatedDate_Minute	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Minute field in the dataset.
CreatedDate_Month	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Month field in the dataset.
CreatedDate_Quarter	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Quarter field in the dataset.
CreatedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the CreatedDate_sec_epoch field in the dataset.
CreatedDate_Second	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Second field in the dataset.
CreatedDate_Week	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Week field in the dataset.
CreatedDate_Year	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Year field in the dataset.
CustomFilter	Boolean	False	This field is generated dynamically and corresponds to the CustomFilter field in the dataset.
Id	String	False	This field is generated dynamically and corresponds to the Id field in the dataset.
Industry	String	False	This field is generated dynamically and corresponds to the Industry field in the dataset.
LastModifiedDate	Datetime	False	This field is generated dynamically and corresponds to the LastModifiedDate field in the dataset.
LastModifiedDate_Day	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Day field in the dataset.
LastModifiedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_day_epoch field in the dataset.
LastModifiedDate_Hour	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Hour field in the dataset.
LastModifiedDate_Minute	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Minute field in the dataset.
LastModifiedDate_Month	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Month field in the dataset.
LastModifiedDate_Quarter	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Quarter field in the dataset.
LastModifiedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_sec_epoch field in the dataset.
LastModifiedDate_Second	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Second field in the dataset.
LastModifiedDate_Week	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Week field in the dataset.
LastModifiedDate_Year	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Year field in the dataset.
Name	String	False	This field is generated dynamically and corresponds to the Name field in the dataset.
NumberOfEmployees	Double	False	This field is generated dynamically and corresponds to the NumberOfEmployees field in the dataset.
OwnerFullPhotoUrl	String	False	This field is generated dynamically and corresponds to the OwnerFullPhotoUrl field in the dataset.
OwnerName	String	False	This field is generated dynamically and corresponds to the OwnerName field in the dataset.
OwnerRoleDeveloperName	String	False	This field is generated dynamically and corresponds to the OwnerRoleDeveloperName field in the dataset.
OwnerRoleHierarchy_RoleNames	String	False	This field is generated dynamically and corresponds to the OwnerRoleHierarchy_RoleNames field in the dataset.
OwnerRoleName	String	False	This field is generated dynamically and corresponds to the OwnerRoleName field in the dataset.
OwnerRoleParentRoleDeveloperName	String	False	This field is generated dynamically and corresponds to the OwnerRoleParentRoleDeveloperName field in the dataset.
OwnerRoleParentRoleName	String	False	This field is generated dynamically and corresponds to the OwnerRoleParentRoleName field in the dataset.
OwnerRoleParentRoleId	String	False	This field is generated dynamically and corresponds to the OwnerRoleParentRoleId field in the dataset.
OwnerRoleRoleNames	String	False	This field is generated dynamically and corresponds to the OwnerRoleRoleNames field in the dataset.
OwnerRoleRoleNamesPath	String	False	This field is generated dynamically and corresponds to the OwnerRoleRoleNamesPath field in the dataset.
OwnerRoleRolePath	String	False	This field is generated dynamically and corresponds to the OwnerRoleRolePath field in the dataset.
OwnerRoleRoles	String	False	This field is generated dynamically and corresponds to the OwnerRoleRoles field in the dataset.
OwnerSmallPhotoUrl	String	False	This field is generated dynamically and corresponds to the OwnerSmallPhotoUrl field in the dataset.
OwnerUniqueUserName	String	False	This field is generated dynamically and corresponds to the OwnerUniqueUserName field in the dataset.
OwnerUsername	String	False	This field is generated dynamically and corresponds to the OwnerUsername field in the dataset.
OwnerUserRoleId	String	False	This field is generated dynamically and corresponds to the OwnerUserRoleId field in the dataset.
OwnerId	String	False	This field is generated dynamically and corresponds to the OwnerId field in the dataset.
Type	String	False	This field is generated dynamically and corresponds to the Type field in the dataset.

The Tableau CRM dataset Dataset_Activity.

Columns

Name	Type	ReadOnly	Description
AccountAccountSource	String	False	This field is generated dynamically and corresponds to the AccountAccountSource field in the dataset.
AccountAnnualRevenue	Double	False	This field is generated dynamically and corresponds to the AccountAnnualRevenue field in the dataset.
AccountBillingCountry	String	False	This field is generated dynamically and corresponds to the AccountBillingCountry field in the dataset.
AccountBillingState	String	False	This field is generated dynamically and corresponds to the AccountBillingState field in the dataset.
AccountIndustry	String	False	This field is generated dynamically and corresponds to the AccountIndustry field in the dataset.
AccountName	String	False	This field is generated dynamically and corresponds to the AccountName field in the dataset.
AccountNumberOfEmployees	Double	False	This field is generated dynamically and corresponds to the AccountNumberOfEmployees field in the dataset.
AccountOwnerName	String	False	This field is generated dynamically and corresponds to the AccountOwnerName field in the dataset.
AccountOwnerRoleName	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleName field in the dataset.
AccountOwnerRoleParentRoleId	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleParentRoleId field in the dataset.
AccountOwnerRoleRolePath	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleRolePath field in the dataset.
AccountOwnerRoleRoles	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleRoles field in the dataset.
AccountOwnerUniqueUserName	String	False	This field is generated dynamically and corresponds to the AccountOwnerUniqueUserName field in the dataset.
AccountOwnerId	String	False	This field is generated dynamically and corresponds to the AccountOwnerId field in the dataset.
AccountType	String	False	This field is generated dynamically and corresponds to the AccountType field in the dataset.
AccountId	String	False	This field is generated dynamically and corresponds to the AccountId field in the dataset.
ActivityDate	Datetime	False	This field is generated dynamically and corresponds to the ActivityDate field in the dataset.
ActivityDate_Day	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Day field in the dataset.
ActivityDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the ActivityDate_day_epoch field in the dataset.
ActivityDate_Hour	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Hour field in the dataset.
ActivityDate_Minute	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Minute field in the dataset.
ActivityDate_Month	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Month field in the dataset.
ActivityDate_Quarter	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Quarter field in the dataset.
ActivityDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the ActivityDate_sec_epoch field in the dataset.
ActivityDate_Second	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Second field in the dataset.
ActivityDate_Week	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Week field in the dataset.
ActivityDate_Year	Double	False	This field is generated dynamically and corresponds to the ActivityDate_Year field in the dataset.
AssignedToName	String	False	This field is generated dynamically and corresponds to the AssignedToName field in the dataset.
AssignedToUniqueUserName	String	False	This field is generated dynamically and corresponds to the AssignedToUniqueUserName field in the dataset.
CallDisposition	String	False	This field is generated dynamically and corresponds to the CallDisposition field in the dataset.
CallDurationInSeconds	Double	False	This field is generated dynamically and corresponds to the CallDurationInSeconds field in the dataset.
CallType	String	False	This field is generated dynamically and corresponds to the CallType field in the dataset.
CreatedDate	Datetime	False	This field is generated dynamically and corresponds to the CreatedDate field in the dataset.
CreatedDate_Day	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Day field in the dataset.
CreatedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the CreatedDate_day_epoch field in the dataset.
CreatedDate_Hour	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Hour field in the dataset.
CreatedDate_Minute	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Minute field in the dataset.
CreatedDate_Month	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Month field in the dataset.
CreatedDate_Quarter	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Quarter field in the dataset.
CreatedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the CreatedDate_sec_epoch field in the dataset.
CreatedDate_Second	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Second field in the dataset.
CreatedDate_Week	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Week field in the dataset.
CreatedDate_Year	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Year field in the dataset.
CustomFilter	Boolean	False	This field is generated dynamically and corresponds to the CustomFilter field in the dataset.
DaysOverdue	Double	False	This field is generated dynamically and corresponds to the DaysOverdue field in the dataset.
DurationInMinutes	Double	False	This field is generated dynamically and corresponds to the DurationInMinutes field in the dataset.
EventSubtype	String	False	This field is generated dynamically and corresponds to the EventSubtype field in the dataset.
Id	String	False	This field is generated dynamically and corresponds to the Id field in the dataset.
IsClosed	Boolean	False	This field is generated dynamically and corresponds to the IsClosed field in the dataset.
IsOverdue	Boolean	False	This field is generated dynamically and corresponds to the IsOverdue field in the dataset.
LastModifiedDate	Datetime	False	This field is generated dynamically and corresponds to the LastModifiedDate field in the dataset.
LastModifiedDate_Day	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Day field in the dataset.
LastModifiedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_day_epoch field in the dataset.
LastModifiedDate_Hour	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Hour field in the dataset.
LastModifiedDate_Minute	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Minute field in the dataset.
LastModifiedDate_Month	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Month field in the dataset.
LastModifiedDate_Quarter	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Quarter field in the dataset.
LastModifiedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_sec_epoch field in the dataset.
LastModifiedDate_Second	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Second field in the dataset.
LastModifiedDate_Week	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Week field in the dataset.
LastModifiedDate_Year	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Year field in the dataset.
OpportunityAccountAccountSource	String	False	This field is generated dynamically and corresponds to the OpportunityAccountAccountSource field in the dataset.
OpportunityAccountBillingCountry	String	False	This field is generated dynamically and corresponds to the OpportunityAccountBillingCountry field in the dataset.
OpportunityAccountBillingState	String	False	This field is generated dynamically and corresponds to the OpportunityAccountBillingState field in the dataset.
OpportunityAccountIndustry	String	False	This field is generated dynamically and corresponds to the OpportunityAccountIndustry field in the dataset.
OpportunityAccountName	String	False	This field is generated dynamically and corresponds to the OpportunityAccountName field in the dataset.
OpportunityAccountOwnerId	String	False	This field is generated dynamically and corresponds to the OpportunityAccountOwnerId field in the dataset.
OpportunityAccountType	String	False	This field is generated dynamically and corresponds to the OpportunityAccountType field in the dataset.
OpportunityAccountId	String	False	This field is generated dynamically and corresponds to the OpportunityAccountId field in the dataset.
OpportunityCloseDate	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate field in the dataset.
OpportunityCloseDate_Day	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Day field in the dataset.
OpportunityCloseDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_day_epoch field in the dataset.
OpportunityCloseDate_Hour	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Hour field in the dataset.
OpportunityCloseDate_Minute	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Minute field in the dataset.
OpportunityCloseDate_Month	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Month field in the dataset.
OpportunityCloseDate_Quarter	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Quarter field in the dataset.
OpportunityCloseDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_sec_epoch field in the dataset.
OpportunityCloseDate_Second	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Second field in the dataset.
OpportunityCloseDate_Week	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Week field in the dataset.
OpportunityCloseDate_Year	String	False	This field is generated dynamically and corresponds to the OpportunityCloseDate_Year field in the dataset.
OpportunityCreatedDate	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate field in the dataset.
OpportunityCreatedDate_Day	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Day field in the dataset.
OpportunityCreatedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_day_epoch field in the dataset.
OpportunityCreatedDate_Hour	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Hour field in the dataset.
OpportunityCreatedDate_Minute	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Minute field in the dataset.
OpportunityCreatedDate_Month	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Month field in the dataset.
OpportunityCreatedDate_Quarter	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Quarter field in the dataset.
OpportunityCreatedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_sec_epoch field in the dataset.
OpportunityCreatedDate_Second	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Second field in the dataset.
OpportunityCreatedDate_Week	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Week field in the dataset.
OpportunityCreatedDate_Year	String	False	This field is generated dynamically and corresponds to the OpportunityCreatedDate_Year field in the dataset.
OpportunityDaysPastDue	Double	False	This field is generated dynamically and corresponds to the OpportunityDaysPastDue field in the dataset.
OpportunityDaysSinceLastActivity	Double	False	This field is generated dynamically and corresponds to the OpportunityDaysSinceLastActivity field in the dataset.
OpportunityForecastCategoryName	String	False	This field is generated dynamically and corresponds to the OpportunityForecastCategoryName field in the dataset.
OpportunityId	String	False	This field is generated dynamically and corresponds to the OpportunityId field in the dataset.
OpportunityIsClosed	String	False	This field is generated dynamically and corresponds to the OpportunityIsClosed field in the dataset.
OpportunityIsLost	String	False	This field is generated dynamically and corresponds to the OpportunityIsLost field in the dataset.
OpportunityIsPastDue	String	False	This field is generated dynamically and corresponds to the OpportunityIsPastDue field in the dataset.
OpportunityIsWon	String	False	This field is generated dynamically and corresponds to the OpportunityIsWon field in the dataset.
OpportunityLeadSource	String	False	This field is generated dynamically and corresponds to the OpportunityLeadSource field in the dataset.
OpportunityName	String	False	This field is generated dynamically and corresponds to the OpportunityName field in the dataset.
OpportunityNeglected	String	False	This field is generated dynamically and corresponds to the OpportunityNeglected field in the dataset.
OpportunityOpportunityAge	Double	False	This field is generated dynamically and corresponds to the OpportunityOpportunityAge field in the dataset.
OpportunityOwnerId	String	False	This field is generated dynamically and corresponds to the OpportunityOwnerId field in the dataset.
OpportunityProductFamily	String	False	This field is generated dynamically and corresponds to the OpportunityProductFamily field in the dataset.
OpportunityProductName	String	False	This field is generated dynamically and corresponds to the OpportunityProductName field in the dataset.
OpportunityRecordTypeName	String	False	This field is generated dynamically and corresponds to the OpportunityRecordTypeName field in the dataset.
OpportunityRecordTypeId	String	False	This field is generated dynamically and corresponds to the OpportunityRecordTypeId field in the dataset.
OpportunityStageName	String	False	This field is generated dynamically and corresponds to the OpportunityStageName field in the dataset.
OpportunityType	String	False	This field is generated dynamically and corresponds to the OpportunityType field in the dataset.
OwnerFullPhotoUrl	String	False	This field is generated dynamically and corresponds to the OwnerFullPhotoUrl field in the dataset.
OwnerName	String	False	This field is generated dynamically and corresponds to the OwnerName field in the dataset.
OwnerRoleDeveloperName	String	False	This field is generated dynamically and corresponds to the OwnerRoleDeveloperName field in the dataset.
OwnerRoleHierarchy_RoleNames	String	False	This field is generated dynamically and corresponds to the OwnerRoleHierarchy_RoleNames field in the dataset.
OwnerRoleName	String	False	This field is generated dynamically and corresponds to the OwnerRoleName field in the dataset.
OwnerRoleParentRoleDeveloperName	String	False	This field is generated dynamically and corresponds to the OwnerRoleParentRoleDeveloperName field in the dataset.
OwnerRoleParentRoleName	String	False	This field is generated dynamically and corresponds to the OwnerRoleParentRoleName field in the dataset.
OwnerRoleParentRoleId	String	False	This field is generated dynamically and corresponds to the OwnerRoleParentRoleId field in the dataset.
OwnerRoleRoleNames	String	False	This field is generated dynamically and corresponds to the OwnerRoleRoleNames field in the dataset.
OwnerRoleRoleNamesPath	String	False	This field is generated dynamically and corresponds to the OwnerRoleRoleNamesPath field in the dataset.
OwnerRoleRolePath	String	False	This field is generated dynamically and corresponds to the OwnerRoleRolePath field in the dataset.
OwnerRoleRoles	String	False	This field is generated dynamically and corresponds to the OwnerRoleRoles field in the dataset.
OwnerSmallPhotoUrl	String	False	This field is generated dynamically and corresponds to the OwnerSmallPhotoUrl field in the dataset.
OwnerUniqueUserName	String	False	This field is generated dynamically and corresponds to the OwnerUniqueUserName field in the dataset.
OwnerUsername	String	False	This field is generated dynamically and corresponds to the OwnerUsername field in the dataset.
OwnerUserRoleId	String	False	This field is generated dynamically and corresponds to the OwnerUserRoleId field in the dataset.
OwnerId	String	False	This field is generated dynamically and corresponds to the OwnerId field in the dataset.
Priority	String	False	This field is generated dynamically and corresponds to the Priority field in the dataset.
Status	String	False	This field is generated dynamically and corresponds to the Status field in the dataset.
Subject	String	False	This field is generated dynamically and corresponds to the Subject field in the dataset.
TaskSubtype	String	False	This field is generated dynamically and corresponds to the TaskSubtype field in the dataset.
WhatId	String	False	This field is generated dynamically and corresponds to the WhatId field in the dataset.

The Tableau CRM dataset Dataset_Analytics.

Columns

Name	Type	ReadOnly	Description
Date	Datetime	False	This field is generated dynamically and corresponds to the Date field in the dataset.
Date_Day	Double	False	This field is generated dynamically and corresponds to the Date_Day field in the dataset.
Date_day_epoch	Double	False	This field is generated dynamically and corresponds to the Date_day_epoch field in the dataset.
Date_Hour	Double	False	This field is generated dynamically and corresponds to the Date_Hour field in the dataset.
Date_Minute	Double	False	This field is generated dynamically and corresponds to the Date_Minute field in the dataset.
Date_Month	Double	False	This field is generated dynamically and corresponds to the Date_Month field in the dataset.
Date_Quarter	Double	False	This field is generated dynamically and corresponds to the Date_Quarter field in the dataset.
Date_sec_epoch	Double	False	This field is generated dynamically and corresponds to the Date_sec_epoch field in the dataset.
Date_Second	Double	False	This field is generated dynamically and corresponds to the Date_Second field in the dataset.
Date_Week	Double	False	This field is generated dynamically and corresponds to the Date_Week field in the dataset.
Date_Year	Double	False	This field is generated dynamically and corresponds to the Date_Year field in the dataset.
Name	String	False	This field is generated dynamically and corresponds to the Name field in the dataset.

The Tableau CRM dataset Dataset_Case.

Columns

Name	Type	ReadOnly	Description
AccountAccountSource	String	False	This field is generated dynamically and corresponds to the AccountAccountSource field in the dataset.
AccountAnnualRevenue	Double	False	This field is generated dynamically and corresponds to the AccountAnnualRevenue field in the dataset.
AccountBillingCountry	String	False	This field is generated dynamically and corresponds to the AccountBillingCountry field in the dataset.
AccountBillingState	String	False	This field is generated dynamically and corresponds to the AccountBillingState field in the dataset.
AccountIndustry	String	False	This field is generated dynamically and corresponds to the AccountIndustry field in the dataset.
AccountName	String	False	This field is generated dynamically and corresponds to the AccountName field in the dataset.
AccountNumberOfEmployees	Double	False	This field is generated dynamically and corresponds to the AccountNumberOfEmployees field in the dataset.
AccountOwnerName	String	False	This field is generated dynamically and corresponds to the AccountOwnerName field in the dataset.
AccountOwnerRoleName	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleName field in the dataset.
AccountOwnerRoleParentRoleId	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleParentRoleId field in the dataset.
AccountOwnerRoleRolePath	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleRolePath field in the dataset.
AccountOwnerRoleRoles	String	False	This field is generated dynamically and corresponds to the AccountOwnerRoleRoles field in the dataset.
AccountOwnerUniqueUserName	String	False	This field is generated dynamically and corresponds to the AccountOwnerUniqueUserName field in the dataset.
AccountOwnerId	String	False	This field is generated dynamically and corresponds to the AccountOwnerId field in the dataset.
AccountType	String	False	This field is generated dynamically and corresponds to the AccountType field in the dataset.
AccountId	String	False	This field is generated dynamically and corresponds to the AccountId field in the dataset.
CaseNumber	Double	False	This field is generated dynamically and corresponds to the CaseNumber field in the dataset.
ClosedDate	Datetime	False	This field is generated dynamically and corresponds to the ClosedDate field in the dataset.
ClosedDate_Day	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Day field in the dataset.
ClosedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the ClosedDate_day_epoch field in the dataset.
ClosedDate_Hour	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Hour field in the dataset.
ClosedDate_Minute	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Minute field in the dataset.
ClosedDate_Month	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Month field in the dataset.
ClosedDate_Quarter	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Quarter field in the dataset.
ClosedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the ClosedDate_sec_epoch field in the dataset.
ClosedDate_Second	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Second field in the dataset.
ClosedDate_Week	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Week field in the dataset.
ClosedDate_Year	Double	False	This field is generated dynamically and corresponds to the ClosedDate_Year field in the dataset.
ContactId	String	False	This field is generated dynamically and corresponds to the ContactId field in the dataset.
CreatedDate	Datetime	False	This field is generated dynamically and corresponds to the CreatedDate field in the dataset.
CreatedDate_Day	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Day field in the dataset.
CreatedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the CreatedDate_day_epoch field in the dataset.
CreatedDate_Hour	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Hour field in the dataset.
CreatedDate_Minute	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Minute field in the dataset.
CreatedDate_Month	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Month field in the dataset.
CreatedDate_Quarter	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Quarter field in the dataset.
CreatedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the CreatedDate_sec_epoch field in the dataset.
CreatedDate_Second	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Second field in the dataset.
CreatedDate_Week	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Week field in the dataset.
CreatedDate_Year	Double	False	This field is generated dynamically and corresponds to the CreatedDate_Year field in the dataset.
CurrentDate	Datetime	False	This field is generated dynamically and corresponds to the CurrentDate field in the dataset.
CurrentDate_Day	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Day field in the dataset.
CurrentDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the CurrentDate_day_epoch field in the dataset.
CurrentDate_Hour	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Hour field in the dataset.
CurrentDate_Minute	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Minute field in the dataset.
CurrentDate_Month	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Month field in the dataset.
CurrentDate_Quarter	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Quarter field in the dataset.
CurrentDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the CurrentDate_sec_epoch field in the dataset.
CurrentDate_Second	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Second field in the dataset.
CurrentDate_Week	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Week field in the dataset.
CurrentDate_Year	Double	False	This field is generated dynamically and corresponds to the CurrentDate_Year field in the dataset.
DurationCalculatedField	Double	False	This field is generated dynamically and corresponds to the DurationCalculatedField field in the dataset.
Id	String	False	This field is generated dynamically and corresponds to the Id field in the dataset.
IsClosed	Boolean	False	This field is generated dynamically and corresponds to the IsClosed field in the dataset.
IsEscalated	Boolean	False	This field is generated dynamically and corresponds to the IsEscalated field in the dataset.
LastModifiedDate	Datetime	False	This field is generated dynamically and corresponds to the LastModifiedDate field in the dataset.
LastModifiedDate_Day	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Day field in the dataset.
LastModifiedDate_day_epoch	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_day_epoch field in the dataset.
LastModifiedDate_Hour	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Hour field in the dataset.
LastModifiedDate_Minute	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Minute field in the dataset.
LastModifiedDate_Month	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Month field in the dataset.
LastModifiedDate_Quarter	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Quarter field in the dataset.
LastModifiedDate_sec_epoch	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_sec_epoch field in the dataset.
LastModifiedDate_Second	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Second field in the dataset.
LastModifiedDate_Week	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Week field in the dataset.
LastModifiedDate_Year	Double	False	This field is generated dynamically and corresponds to the LastModifiedDate_Year field in the dataset.
OpportunityAmount	Double	False	This field is generated dynamically and corresponds to the OpportunityAmount field in the dataset.
Origin	String	False	This field is generated dynamically and corresponds to the Origin field in the dataset.
OwnerId	String	False	This field is generated dynamically and corresponds to the OwnerId field in the dataset.
Status	String	False	This field is generated dynamically and corresponds to the Status field in the dataset.
Subject	String	False	This field is generated dynamically and corresponds to the Subject field in the dataset.
Type	String	False	This field is generated dynamically and corresponds to the Type field in the dataset.

The Tableau CRM dataset Dataset_DTC_Opportunity_SAMPLE.

Columns

Name	Type	ReadOnly	Description
Account_Name	String	False	This field is generated dynamically and corresponds to the Account_Name field in the dataset.
Account_Owner	String	False	This field is generated dynamically and corresponds to the Account_Owner field in the dataset.
Account_Type	String	False	This field is generated dynamically and corresponds to the Account_Type field in the dataset.
Amount	Double	False	This field is generated dynamically and corresponds to the Amount field in the dataset.
Billing_Country	String	False	This field is generated dynamically and corresponds to the Billing_Country field in the dataset.
Billing_State_Province	String	False	This field is generated dynamically and corresponds to the Billing_State_Province field in the dataset.
Close_Date	Datetime	False	This field is generated dynamically and corresponds to the Close_Date field in the dataset.
Close_Date_Day	Double	False	This field is generated dynamically and corresponds to the Close_Date_Day field in the dataset.
Close_Date_day_epoch	Double	False	This field is generated dynamically and corresponds to the Close_Date_day_epoch field in the dataset.
Close_Date_Hour	Double	False	This field is generated dynamically and corresponds to the Close_Date_Hour field in the dataset.
Close_Date_Minute	Double	False	This field is generated dynamically and corresponds to the Close_Date_Minute field in the dataset.
Close_Date_Month	Double	False	This field is generated dynamically and corresponds to the Close_Date_Month field in the dataset.
Close_Date_Quarter	Double	False	This field is generated dynamically and corresponds to the Close_Date_Quarter field in the dataset.
Close_Date_sec_epoch	Double	False	This field is generated dynamically and corresponds to the Close_Date_sec_epoch field in the dataset.
Close_Date_Second	Double	False	This field is generated dynamically and corresponds to the Close_Date_Second field in the dataset.
Close_Date_Week	Double	False	This field is generated dynamically and corresponds to the Close_Date_Week field in the dataset.
Close_Date_Year	Double	False	This field is generated dynamically and corresponds to the Close_Date_Year field in the dataset.
Closed	Boolean	False	This field is generated dynamically and corresponds to the Closed field in the dataset.
Column1	Double	False	This field is generated dynamically and corresponds to the Column1 field in the dataset.
Created_Date	Datetime	False	This field is generated dynamically and corresponds to the Created_Date field in the dataset.
Created_Date_Day	Double	False	This field is generated dynamically and corresponds to the Created_Date_Day field in the dataset.
Created_Date_day_epoch	Double	False	This field is generated dynamically and corresponds to the Created_Date_day_epoch field in the dataset.
Created_Date_Hour	Double	False	This field is generated dynamically and corresponds to the Created_Date_Hour field in the dataset.
Created_Date_Minute	Double	False	This field is generated dynamically and corresponds to the Created_Date_Minute field in the dataset.
Created_Date_Month	Double	False	This field is generated dynamically and corresponds to the Created_Date_Month field in the dataset.
Created_Date_Quarter	Double	False	This field is generated dynamically and corresponds to the Created_Date_Quarter field in the dataset.
Created_Date_sec_epoch	Double	False	This field is generated dynamically and corresponds to the Created_Date_sec_epoch field in the dataset.
Created_Date_Second	Double	False	This field is generated dynamically and corresponds to the Created_Date_Second field in the dataset.
Created_Date_Week	Double	False	This field is generated dynamically and corresponds to the Created_Date_Week field in the dataset.
Created_Date_Year	Double	False	This field is generated dynamically and corresponds to the Created_Date_Year field in the dataset.
Forecast_Category	String	False	This field is generated dynamically and corresponds to the Forecast_Category field in the dataset.
Industry	String	False	This field is generated dynamically and corresponds to the Industry field in the dataset.
Opportunity_Name	String	False	This field is generated dynamically and corresponds to the Opportunity_Name field in the dataset.
Opportunity_Owner	String	False	This field is generated dynamically and corresponds to the Opportunity_Owner field in the dataset.
Opportunity_Source	String	False	This field is generated dynamically and corresponds to the Opportunity_Source field in the dataset.
Opportunity_Type	String	False	This field is generated dynamically and corresponds to the Opportunity_Type field in the dataset.
Owner_Role	String	False	This field is generated dynamically and corresponds to the Owner_Role field in the dataset.
Product_Family	String	False	This field is generated dynamically and corresponds to the Product_Family field in the dataset.
Product_Name	String	False	This field is generated dynamically and corresponds to the Product_Name field in the dataset.
Segment	String	False	This field is generated dynamically and corresponds to the Segment field in the dataset.
Stage	String	False	This field is generated dynamically and corresponds to the Stage field in the dataset.
Won	Boolean	False	This field is generated dynamically and corresponds to the Won field in the dataset.

The Tableau CRM dataset Dataset_Fashion.

Columns

Name	Type	ReadOnly	Description
Body_HTML	String	False	This field is generated dynamically and corresponds to the Body_HTML field in the dataset.
Gift_Card	Boolean	False	This field is generated dynamically and corresponds to the Gift_Card field in the dataset.
Google_Shopping_AdWords_Grouping	String	False	This field is generated dynamically and corresponds to the Google_Shopping_AdWords_Grouping field in the dataset.
Google_Shopping_AdWords_Labels	String	False	This field is generated dynamically and corresponds to the Google_Shopping_AdWords_Labels field in the dataset.
Google_Shopping_Age_Group	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Age_Group field in the dataset.
Google_Shopping_Condition	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Condition field in the dataset.
Google_Shopping_Custom_Label_0	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Custom_Label_0 field in the dataset.
Google_Shopping_Custom_Label_1	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Custom_Label_1 field in the dataset.
Google_Shopping_Custom_Label_2	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Custom_Label_2 field in the dataset.
Google_Shopping_Custom_Label_3	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Custom_Label_3 field in the dataset.
Google_Shopping_Custom_Label_4	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Custom_Label_4 field in the dataset.
Google_Shopping_Custom_Product	Boolean	False	This field is generated dynamically and corresponds to the Google_Shopping_Custom_Product field in the dataset.
Google_Shopping_Gender	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Gender field in the dataset.
Google_Shopping_Google_Product_Category	String	False	This field is generated dynamically and corresponds to the Google_Shopping_Google_Product_Category field in the dataset.
Google_Shopping_MPN	String	False	This field is generated dynamically and corresponds to the Google_Shopping_MPN field in the dataset.
Handle	String	False	This field is generated dynamically and corresponds to the Handle field in the dataset.
Image_Alt_Text	String	False	This field is generated dynamically and corresponds to the Image_Alt_Text field in the dataset.
Image_Src	String	False	This field is generated dynamically and corresponds to the Image_Src field in the dataset.
Option1_Name	String	False	This field is generated dynamically and corresponds to the Option1_Name field in the dataset.
Option1_Value	String	False	This field is generated dynamically and corresponds to the Option1_Value field in the dataset.
Option2_Name	String	False	This field is generated dynamically and corresponds to the Option2_Name field in the dataset.
Option2_Value	String	False	This field is generated dynamically and corresponds to the Option2_Value field in the dataset.
Option3_Name	String	False	This field is generated dynamically and corresponds to the Option3_Name field in the dataset.
Option3_Value	String	False	This field is generated dynamically and corresponds to the Option3_Value field in the dataset.
Published	Boolean	False	This field is generated dynamically and corresponds to the Published field in the dataset.
SEO_Description	String	False	This field is generated dynamically and corresponds to the SEO_Description field in the dataset.
SEO_Title	String	False	This field is generated dynamically and corresponds to the SEO_Title field in the dataset.
Tags	String	False	This field is generated dynamically and corresponds to the Tags field in the dataset.
Title	String	False	This field is generated dynamically and corresponds to the Title field in the dataset.
Type	String	False	This field is generated dynamically and corresponds to the Type field in the dataset.
Variant_Barcode	String	False	This field is generated dynamically and corresponds to the Variant_Barcode field in the dataset.
Variant_Compare_At_Price	String	False	This field is generated dynamically and corresponds to the Variant_Compare_At_Price field in the dataset.
Variant_Fulfillment_Service	String	False	This field is generated dynamically and corresponds to the Variant_Fulfillment_Service field in the dataset.
Variant_Grams	Double	False	This field is generated dynamically and corresponds to the Variant_Grams field in the dataset.
Variant_Image	String	False	This field is generated dynamically and corresponds to the Variant_Image field in the dataset.
Variant_Inventory_Policy	String	False	This field is generated dynamically and corresponds to the Variant_Inventory_Policy field in the dataset.
Variant_Inventory_Qty	Double	False	This field is generated dynamically and corresponds to the Variant_Inventory_Qty field in the dataset.
Variant_Inventory_Tracker	String	False	This field is generated dynamically and corresponds to the Variant_Inventory_Tracker field in the dataset.
Variant_Price	Double	False	This field is generated dynamically and corresponds to the Variant_Price field in the dataset.
Variant_Requires_Shipping	Boolean	False	This field is generated dynamically and corresponds to the Variant_Requires_Shipping field in the dataset.
Variant_SKU	String	False	This field is generated dynamically and corresponds to the Variant_SKU field in the dataset.
Variant_Taxable	Boolean	False	This field is generated dynamically and corresponds to the Variant_Taxable field in the dataset.
Variant_Weight_Unit	String	False	This field is generated dynamically and corresponds to the Variant_Weight_Unit field in the dataset.
Vendor	String	False	This field is generated dynamically and corresponds to the Vendor field in the dataset.

The Tableau CRM dataset Dataset_OneMillion.

Columns

Name	Type	ReadOnly	Description
Col_1	String	False	This field is generated dynamically and corresponds to the Col_1 field in the dataset.
Col_10	String	False	This field is generated dynamically and corresponds to the Col_10 field in the dataset.
Col_2	String	False	This field is generated dynamically and corresponds to the Col_2 field in the dataset.
Col_3	String	False	This field is generated dynamically and corresponds to the Col_3 field in the dataset.
Col_4	String	False	This field is generated dynamically and corresponds to the Col_4 field in the dataset.
Col_5	String	False	This field is generated dynamically and corresponds to the Col_5 field in the dataset.
Col_6	String	False	This field is generated dynamically and corresponds to the Col_6 field in the dataset.
Col_7	String	False	This field is generated dynamically and corresponds to the Col_7 field in the dataset.
Col_8	String	False	This field is generated dynamically and corresponds to the Col_8 field in the dataset.
Col_9	String	False	This field is generated dynamically and corresponds to the Col_9 field in the dataset.