Connecting to Smartsheet

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

Smartsheet Version Support

The connector defaults to version 2.0 of the Smartsheet API. Later versions can be specified in the property.

See Also

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

Linux:

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

macOS:

pip install cdata_smartsheet_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_smartsheet_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_smartsheet" 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_smartsheet folder is trivial to find:

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

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

1. Import the module as follows:
   

   import cdata.smartsheet 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("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")

Connecting to Smartsheet

Smartsheet supports connections via the following authentication methods:

• Using the Personal Access Token
• Using OAuth

Personal Access Token

Use the personal token to test and to access your own data. To obtain the personal token, follow the steps below:

1. Log into Smartsheet.
2. Click Account and select Personal Settings.
3. Click API Access and use the form to generate new access tokens or manage existing access tokens.

Set the AuthScheme to PersonalAccessToken. You can then set the PersonalAccessToken to the token you generated.

OAuth

Smartsheet supports OAuth authentication only. Regardless of whether you are authenticating from the Web, a Desktop, or a Headless Server, you must set AuthScheme to OAuth, and you must create a custom OAuth application, as described in Creating a Custom OAuth Application.

The following subsections provide details about authenticating from a desktop application, the web, or a headless machine, via a custom OAuth application.

Desktop Applications

To authenticate with the credentials for a custom OAuth application, you must get and refresh the OAuth access token. After you do that, you are ready to connect.

Get and refresh the OAuth access token:

• OAuthClientId: The client Id assigned when you registered your application.
• OAuthClientSecret: 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 Smartsheet's OAuth endpoint in your default browser.

Log in and grant permissions to the application. The connector then completes the OAuth process:

1. The connector obtains an access token from Smartsheet 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 the access token automatically.

Web Applications

To authenticate from the web with the credentials for a custom OAuth application, you must register a custom OAuth application with Smartsheet. You can then use the connector to get and manage the OAuth token values.

Get the OAuth access token:

To obtain the OAuthAccessToken set the following connection properties:

• OAuthClientId: The client Id in your application settings.
• OAuthClientSecret: The client secret in your application settings.

To complete the OAuth exchange, call stored procedures as follows:

1. 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.
2. 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.
3. 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:

If you want the connector to refresh the OAuth access token automatically, do the following.

The first time you connect to data, set the following connection parameters:

• InitiateOAuth: REFRESH.
• OAuthClientId: The client Id in your application settings.
• OAuthClientSecret: The client secret in your application settings.
• OAuthAccessToken: The access token returned by GetOAuthAccessToken.
• OAuthSettingsLocation: The path where you want the connector to save OAuth values that persist across connections.

On subsequent data connections, set the following connection parameters:

• 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 authenticate via OAuth with a user account 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 as described in "Obtain and Exchange a Verifier Code" below.
• Option 2: Install the connector on a machine with an internet browser and transfer the OAuth authentication values after you authenticate through the usual browser-based flow, as described in "Transfer OAuth Settings" below.

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. 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. Use the appropriate CalllbackURL to call the GetOAuthAuthorizationURL stored procedure.
3. Copy the returned URL into a browser and open the page.
4. Log in and grant permissions to the connector. You are redirected to the redirect URI.
5. Record the code parameter that is appended to the redirect URI. You will use it later, when you set up the OAuthVerifier connection property.
6. To exchange the OAuth verifier code for OAuth refresh and access tokens, set the following connection properties, which provide the OAuth authentication values:
   • InitiateOAuth: REFRESH.
   • OAuthVerifier: The noted verifier code (the value of the code parameter in the redirect URI).
   • OAuthClientId: The client Id in your custom OAuth application settings.
   • OAuthClientSecret: The client secret in the custom OAuth application settings.
   • OAuthSettingsLocation: Persist the encrypted OAuth authentication values to the specified file.
7. Test the connection to generate the OAuth settings file, then re-set the following properties to connect:
   • InitiateOAuth: REFRESH.
   • OAuthClientId: The client Id assigned when you registered your application.
   • OAuthClientSecret: The client secret assigned when you registered your application.
   • OAuthSettingsLocation: The file containing the encrypted OAuth authentication values. Make sure this file gives read and write permissions to the connector to enable the automatic refreshing of the access token.

Option 2: Transfer OAuth Settings

Before 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 previously 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.
• OAuthClientId: The client Id assigned when you registered your application.
• OAuthClientSecret: The client secret assigned when you registered your application.
• 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.

Synch Connections

Before you connect, set the following variables:

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

Click Connect to Smartsheet to open the OAuth endpoint in your default browser. Log in and grant permissions to the application.

The driver then completes the OAuth process as follows:

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

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 Smartsheet 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:
  

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

• For Mac:
  

  [smartsheet.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.smartsheet 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

Note: Creating an OAuth application requires a Smartsheet developer account.

To register an app and obtain the OAuth client credentials, the client Id and client secret:

1. Log into your Smartsheet developer account.
2. Click Account > Developer Tools > Create New App.
3. Enter a name, description, and other information to be displayed to users when they log in to grant permissions to your app.
4. Set other parameters, depending on the type of application you are creating:
   • For desktop applications, set the Redirect URL to http://localhost:33333 or a different port number of your choice.
   • For Web applications, set the Redirect URL to the page where the user should return after they authorize your application.

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.
2026-05-26	26.0.9642	Smartsheet	Connection	Changed	Changed the default value of the Pagesize connection property from 500 to 1000.
2026-05-22	26.0.9638	Python		Removed	Remove support for Intel x64 architecture on macOS
2026-05-19	26.0.9635	Smartsheet	Connection	Added	Added a new AU enum value in the Region connection property.
2026-05-19	26.0.9635	Smartsheet	Connection	Removed	Removed the following connection properties: UseLegacyAPI UseFullFilePathsAsTableNames
2026-05-19	26.0.9635	Smartsheet	Data Model	Added	Added a new Info_GroupMembers child table, depending on the Info_Groups.Id column. This child table supports SELECT, INSERT, and DELETE statements. Added the Info_Objects view. Added the CreatedAt and ModifiedAt columns to the Info_Templates view.
2026-05-19	26.0.9635	Smartsheet	Data Model	Changed	In support of CUD operations on sheet metadata and definitions: Renamed the CreateSheet.SheetName procedure input parameter to Name. Renamed DeleteSheet.SheetId procedure input.parameter to Id. Table column metadata changes: The Info_Favorites.ObjectId column is now reported as a primary key. The Info_Cells.RowId column is now reported as a primary key. This composite key ensures record uniqueness. Procedure output parameter metadata changes: CopySheet.Success datatype has changed from VARCHAR to BOOLEAN. MoveSheet.Success datatype has changed from VARCHAR to BOOLEAN. GetOAuthAccessToken.ExpiresIn datatype has changed from VARCHAR to INT. RefreshOAuthAccessToken.ExpiresIn datatype has changed from VARCHAR to INT. CopyRowsToAnotherSheet procedure changes: Renamed (fixed casing) the CopyRowsToAnotherSheet.ignoreRowsNotFound input parameter to IgnoreRowsNotFound. Changed the data type of the CopyRowsToAnotherSheet.IgnoreRowsNotFound input parameter from VARCHAR to BOOLEAN. Changed the data type of the CopyRowsToAnother Sheet.Success output parameter from VARCHAR to BOOLEAN. Changed the data type of the CopyRowsToAnotherSheet.AffectedRows output parameter from VARCHAR to INT. DownloadAttachment procedure changes: Renamed (fixed casing) DownloadAttachment.SheetID input parameter to SheetId. Changed the data type of the DownloadAttachment.Override input parameter from VARCHAR to BOOLEAN. Changed the data type of the DownloadAttachment.Success output parameter from VARCHAR to BOOLEAN. ImportFile procedure changes: Changed the data type of the ImportFile.Overwrite input parameter from VARCHAR to BOOLEAN. Changed the data type of the ImportFile.Success output parameter from VARCHAR to BOOLEAN. MoveRowsToAnotherSheet procedure changes: Changed the data type of the MoveRowsToAnotherSheet.IgnoreRowsNotFound input parameter from VARCHAR to BOOLEAN. Changed the data type of the MoveRowsToAnotherSheet.Success output parameter from VARCHAR to BOOLEAN. Changed the data type of the MoveRowsToAnotherSheet.AffectedRows output parameter from VARCHAR to INT. Procedure input parameters now reporting as required: CopySheet.SheetId CopySheet.DestinationType CopySheet.DestinationId CopySheet.NewName MoveSheet.DestinationId CopyRowsToAnotherSheet.SheetId CopyRowsToAnotherSheet.DestinationSheetId MoveRowsToAnotherSheet.SheetId MoveRowsToAnotherSheet.DestinationSheetId Foreign key reporting changes: Info_Attachments.Id -- New foreign key: Info_AttachmentVersions.AttachmentId. Info_Columns.id -- New foreign keys: Info_Cells.ColumnId and Info_CellHistory.ColumnId. Info_Comments.Id -- New foreign key: Info_Attachments.CommentId. Info_Dashboards.Id -- New foreign keys: Info_DashboardShares.DashboardId and Info_DashboardPublishSettings.DashboardId. Info_Discussions.Id -- New foreign keys: Info_Attachments.DiscussionId and Info_Comments.DiscussionId. Info_Folders.Id -- New foreign keys: Info_Sheets.FolderId, Info_Dashboards.FolderId, Info_Reports.FolderId, Info_Templates.FolderId, Info_Sheets.RootFolderId, Info_Dashboards.RootFolderId, and Info_Reports.RootFolderId. Info_Groups.Id -- New foreign keys: Info_ReportShares.GroupId, Info_SheetShares.GroupId, Info_Users.GroupId, Info_WorkspaceShares.GroupId, and Info_DashboardShares.GroupId. Info_Reports.Id -- New foreign keys: Info_ReportPublishSettings.ReportId and Info_ReportShares.ReportId. Info_Rows.Id -- New foreign keys: Info_Rows.ParentId, Info_Rows.SiblingId, Info_Cells.RowId, Info_Attachments.RowId, Info_CellHistory.RowId, and Info_Discussions.RowId. Info_Sheets.Id -- New foreign keys: Info_Rows.SheetId, Info_Columns.SheetId, Info_Cells.SheetId, Info_Attachments.SheetId, Info_AttachmentVersions.SheetId, Info_CellHistory.SheetId, Info_Comments.SheetId, Info_Discussions.SheetId, Info_SheetPublishSettings.SheetId, and Info_SheetShares.SheetId. Info_Users.Id -- New foreign keys: Info_Attachments.UserId, Info_DashboardShares.UserId, Info_ReportShares.UserId, Info_SheetShares.UserId, and Info_WorkspaceShares.UserId. Info_Workspaces.Id -- New foreign keys: Info_Sheets.WorkspaceId, Info_Dashboards.WorkspaceId, Info_Folders.WorkspaceId, Info_Reports.WorkspaceId, Info_Templates.WorkspaceId, and Info_WorkspaceShares.WorkspaceId.
2026-05-19	26.0.9635	Smartsheet	Data Model	Removed	Removed the WorkspaceId and FolderId columns and the RootFolderId pseudocolumn from the Info_Sheets, Info_Reports, and Info_Dashboards views. Removed the DeleteSheet.SheetName procedure input parameter. Removed UseSimpleColumnNames connection property and replaced it with the shared UseSimpleNames connection property. Removed Info_Groups.MemberEmails pseudocolumn.
2026-05-08	26.0.9624	Smartsheet	Data Model	Removed	Removed the following columns from the Info_Sheets view: DependenciesEnabled, Favorite, FromId, GanttEnabled, ReadOnly, ResourceManagementEnabled, ShowParentRowsForFilters, TotalRowCount, and Version. Removed the following columns from the Info_Templates view: Blank, Categories, Description, GlobalTemplate, Image, LargeImage, Locale, Scope, Tags, and TemplateType. Removed the Favorite column from the Info_Workspaces table. Removed the Favorite column from the Info_Folders table. Removed the Info_Home view.
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).
2026-05-07	26.0.9623	Smartsheet	Data Model	Changed	The RowId primary key column in dynamic sheet tables and dynamic report views is now correctly reported as read-only. The following columns in the Info_Discussions table are now correctly reported as read-only: Id, Title, AccessLevel, ParentId, ParentType, LastCommentedAt, LastCommentBy, CreatorName, and ReadOnly.
2026-05-07	26.0.9623	Smartsheet	Query Exec	Changed	Specifying the FolderId and the WorkspaceId input parameters is no longer mandatory when executing the ImportFile stored procedure.
2026-05-05	26.0.9621	Smartsheet	Query Exec	Changed	Changed the SELECT statement for the Info_Dashboards view to use the new token-based pagination strategy.
2026-04-15	26.0.9601	General	Query Exec	Changed	String comparisons using GREATER, LESS, and CONTAINS operators are now case-insensitive by default.
2026-04-09	26.0.9595	Smartsheet	Data Model	Changed	The ToTop pseudocolumn's data type is now reported as boolean in dynamic sheet tables.
2026-04-09	26.0.9595	Smartsheet	Data Model	Removed	Removed the ToTop pseudocolumn from dynamic report views.
2026-04-08	26.0.9594	Smartsheet	Security	Changed	TLS 1.3 is now supported by default for HTTP connections.
2026-04-07	26.0.9593	Smartsheet	Data Model	Removed	Removed the Version column from the Info_Sheets table. Removed the CreatedAt and ModifiedAt columns from the following views: InfoSheetShares InfoReportShares InfoDashboardShares InfoWorkspaceShares
2026-03-31	26.0.9586	Smartsheet	Data Model	Changed	Updated the following views to use token-based pagination strategy: Info_DashboardShares, Info_ReportShares, Info_SheetShares, and Info_WorkspaceShares. Updated the following stored procedures to use the new Smartsheet API endpoints: ShareDashboard, ShareReport, ShareSheet, and ShareWorkspace.
2026-03-31	26.0.9586	Smartsheet	Data Model	Removed	Removed OWNER as a possible value for the AccessLevel input parameter in the following stored procedures: ShareDashboard, ShareReport, ShareSheet, ShareWorkspace, UpdateDashboardShare, UpdateReportShare, UpdateSheetShare, and UpdateWorkspaceShare.
2026-03-31	26.0.9586	Smartsheet	Data Model	Added	Added the MultiShareAggregate input parameter to the ShareDashboard, ShareReport, ShareSheet, and ShareWorkspace stored procedures to support sharing objects with multiple members.
2026-03-30	26.0.9585	Smartsheet	Data Model	Deprecated	Deprecated the CreatedAt and UpdatedAt columns in the following views: Info_DashboardShares, Info_ReportShares, Info_SheetShares, and Info_WorkspaceShares.
2026-02-03	25.0.9530	Smartsheet		Added	Added support for UPSERT and bulk UPSERT statements.
2026-01-13	25.0.9509	General		Added	Added support for the REGEXP_REPLACE() string function.
2026-01-13	25.0.9509	Smartsheet		Changed	Changed the Info_Workspaces view to use the token-based pagination strategy, improving performance of queries against the view.
2025-12-21	25.0.9486	Python		Added	Added support for custom loggers in Python connectors on Linux and macOS.
2025-12-19	25.0.9484	Smartsheet		Added	Added support for OAuth flow authentication through embedded credentials.
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.
2025-07-07	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	Smartsheet		Removed	Removed the Info_Shares view.
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.
2025-06-16	25.0.9298	Smartsheet		Added	Added Description column to the Info_Attachments table. Added MemberEmails column to the Info_Groups table. Added support for DELETE statements in the Info_Discussions table. Added support for INSERT, UPDATE, and DELETE statements in the Info_Groups table. Added Info_AttachmentVersions view for reading data on attachment versions. Added UploadAttachmentVersion stored procedure for re-uploading file attachments. Added DeleteAllVersions input to the DeleteAttachment stored procedure for deleting all versions of the
2025-06-16	25.0.9298	Smartsheet		Removed	Removed FilePath and ContentEncoded columns from the Info_Attachments table. Removed support for uploading/creating file attachments through INSERT statements in the Info_Attachments table. Exposed a new UploadAttachment stored procedure for this functionality instead.
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).
2025-05-09	25.0.9260	Smartsheet		Added	Added the blank, categories, globalTemplate, largeImage, tags, and scope columns to the Info_Templates table.
2025-05-07	25.0.9258	Smartsheet		Added	Added support for UPDATE and DELETE statements to the Info_Comments table.
2025-05-06	25.0.9257	Smartsheet		Added	Added to the Info_Folders view two new columns: CreatedAt and ModifiedAt. These columns only display when UseLegacyAPI=False. Added two new views: Info_Dashboards (which represents the dashboards created in the Smartsheet account) and Info_DashboardShares (which represents the sharing operations of the dashboard entities).
2025-05-05	25.0.9256	Smartsheet		Added	Added three new views to expose the publish status details of Dashboards, Reports, and Sheets.
2025-04-29	25.0.9250	Smartsheet		Added	Added a new connection property, UseLegacyAPI. Setting this property enables users to switch between the old metadata retrieval logic (UseLegacyAPI=true, default behavior) which uses the Home API (deprecated) endpoint, and the new metadata retrieval logic, where more recent APIs are used. Added a new column: Info_Folders.ParentFolderId. Added a RootFolderId pseudocolumn to the following tables: Info_Folders, Info_Sheets, Info_Reports and Info_Templates. This pseudocolumn displays when the UseLegacyAPI configuration parameter is set to False.
2025-04-08	25.0.9229	Smartsheet		Added	Added the following views: Info_SheetShares, Info_ReportShares, and Info_WorkspaceShares.
2025-02-15	24.0.9177	General		Added	Added support for converting unsigned integer types to the nearest signed data type that has enough precision to hold the unsigned value.This is done for JDBC only because it does not have support for unsigned data types.
2025-01-27	24.0.9158	Smartsheet		Added	Added validations for data definition language (DDL) statements. A RowId column with the exact definition RowId VARCHAR PRIMARY KEY is required in CREATE TABLE statements. Only the column name and data type are supported for other columns, such as Column1 VARCHAR. Added validation for ALTER TABLE statements so the RowId column cannot be modified in any way.
2024-12-30	24.0.9130	Smartsheet		Added	Added the UseIdAsTableName connection property. This property determines whether the provider uses IDs instead of sheet and report names as table identifiers.
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.
2024-06-05	24.0.8922	Python		Added	Added support for Python 3.12.
2024-05-09	24.0.8895	General		Changed	The ROUND function previously did not accept negative precision values. That feature has now been restored.
2024-03-15	23.0.8840	General		Added	Created a new SQL function called STRING_COMPARE that provides java's String.compare() ability to SQL queries. Returns a number representative of the compared value of two strings
2023-11-29	23.0.8733	General		Changed	The ROUND function doesn't accept the negative precision values anymore.
2023-11-29	23.0.8733	General		Changed	The returning types of the FDMonth, FDQuarter, FDWeek, LDMonth, LDQuarter, LDWeek functions are changed from Timestamp to Date. The return type of the ABS function will be consistent with the parameter value type.
2023-11-28	23.0.8732	General		Added	Added the HMACSHA256 formatter to allow for secrets to be decoded if it is in base64 format
2023-08-29	23.0.8641	Python		Added	Added support for SQLAlchemy 2.0.
2023-07-18	23.0.8599	Smartsheet		Added	Added new columns to the Info_Users view. Due to API limitations, these are only populated when an Id filter is defined. The new columns are the following: Company, Department, MobilePhone, LastLogin, Role, Title, WorkPhone
2023-06-20	23.0.8571	General		Added	Added the new sys_lastresultinfo system table.
2023-06-08	23.0.8559	Smartsheet		Added	Added Region property. The property contain three values: Global the default value, EU and GOV.
2023-05-19	23.0.8539	Python		Added	Added support for Python 3.11 on Windows, Linux and Mac.
2023-05-16	23.0.8536	Python		Removed	Removed support for Python 3.7 on Windows and Linux
2023-05-04	23.0.8524	Smartsheet		Added	Added ColumnFormat TypeDetectionScheme, which determines the datatypes based on the format specified for the column.
2023-04-25	23.0.8515	General		Removed	Removed support for the SELECT INTO CSV statement. The core code doesn't support it anymore.
2022-12-14	22.0.8383	General		Changed	Added the Default column to the sys_procedureparameters table.
2022-11-24	22.0.8363	Smartsheet		Added	Added Overwrite parameter for the ImportFile stored procedure. If the parameter is set to True the stored procedure will delete every sheet that has the same name as the one which will be uploaded.
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).
2022-11-11	22.0.8350	Smartsheet		Added	The ValueSource property was added, allowing users to alter which field would be used by the driver to determine the value of the cell. Auto will use which field is not null. Value will use the "Value" field. DisplayValue will use the "DisplayValue" field.
2022-09-30	22.0.8308	General		Changed	Added the IsPath column to the sys_procedureparameters table.
2022-09-02	22.0.8280	Smartsheet		Added	Added streaming output support for DownloadAttachment. 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	Smartsheet		Added	Added binary input support for ImportFile. Added FileName and Content (InputStream) as inputs.
2022-07-05	22.0.8221	Smartsheet		Added	Added the Header connection property, which indicates whether or not the first row should be used as a column's name.
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
2021-09-20	21.0.7933	Smartsheet		Added	Added the MoveSheet stored procedure, which moves the specified sheet to a new location.
2021-09-02	21.0.7915	General		Added	Added support for the STRING_SPLIT table-valued function in the CROSS APPLY clause.
2021-08-24	21.0.7906	Smartsheet		Changed	The Info_comments table now only requires a SheetId instead of SheetId and DiscussionId. In other words, this table will now return all the comments of a given sheet.
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.
2021-07-16	21.0.7867	Smartsheet		Added	Added the UseFullFilePathsAsTableNames connection property, which sets whether or not to include the full file path in the name of an exposed table or view, corresponding to a sheet or report.
2021-06-30	21.0.7851	Smartsheet		Added	Added support for importing a CSV or XLSX to the top-level "sheets" folder, to a specified folder or to a specified workspace.
2021-05-26	21.0.7816	Smartsheet		Added	Added support for adding a discussion to a sheet or a row. Added support for adding a comment to a discussion. You can find examples at their respective tables in Data Model.
2021-04-25	21.0.7785	General		Added	Added support for handling client side formulas during insert / update. For example: UPDATE Table SET Col1 = CONCAT(Col1, " - ", Col2) WHERE Col2 LIKE 'A%'
2021-04-23	21.0.7783	General		Changed	Updated how display sizes are determined for varchar primary key and foreign key columns so they will match the reported length of the column.
2021-04-16	21.0.7776	General		Added	Non-conditional updates between two columns is now available to all drivers. For example: UPDATE Table SET Col1=Col2
2021-04-16	21.0.7776	General		Changed	Reduced the length to 255 for varchar primary key and foreign key columns.
2021-04-16	21.0.7776	General		Changed	Updated implicit and metadata caching to improve performance and support for multiple connections. Old metadata caches are not compatible - you need to generate new metadata caches if you are currently using CacheMetadata.
2021-04-16	21.0.7776	General		Changed	Updated index naming convention to avoid duplicates.
2021-03-24	21.0.7753	Smartsheet		Changed	Improved the way we were exposing the Personal Access Token. Now we have a new connection property (PersonalAccessToken) and a new value for the AuthScheme property (again PersonalAccessToken).
2020-10-13	20.0.7591	Smartsheet		Added	Changed metadata discovery endpoint to [/home](https://smartsheet-platform.github.io/api-docs/#list-contents) endpoint. Support for folder/workspace traversal. A query to info_folders, info_sheets and info_reports will return the complete list of the objects, not just of the first directory level. Exposing the FolderId and WorkspaceID for each object type in info_folders, info_sheets and info_reports.

This section provides a walk-through for writing Smartsheet 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 Smartsheet, see Package Installation and Establishing a Connection.

For information on how to connect with the smartsheet.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 Smartsheet with INSERT, UPDATE, and DELETE statements, see Modifying Data .

Executing Stored Procedures

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

Batch Processing

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

Connecting with the cdata.smartsheet 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.smartsheet as mod
conn = mod.connect("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")

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

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

Executing Queries

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

For example:

cur = conn.execute("SELECT Id, Name FROM Sheet_Test_Sheet")
rs = cur.fetchall()
for row in rs:
	print(row)

Parameterized Queries

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

For example:

cmd = "SELECT Id, Name FROM Sheet_Test_Sheet WHERE Favorite = ?"
params = ["True"]
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 Sheet_Test_Sheet (Id, Name) VALUES (?, ?)"
params = ["Basic Project with Resource Management", "Basic Agile Project with Gantt Timeline"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Update

The following example modifies an existing record in the table:

cmd = "UPDATE Sheet_Test_Sheet SET Name = ? WHERE Id = ?"
params = ["Basic Agile Project with Gantt Timeline", "123456"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes an existing record from the table:

cmd = "DELETE FROM Sheet_Test_Sheet WHERE Id = ?"
params = ["123456"]
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 RefreshOAuthAccessToken OAuthAccessToken = ?"
params = ["OAuthAccessToken"]
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 = ["OAuthAccessToken"]
cur.callproc("RefreshOAuthAccessToken", params)

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

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

Insert

The following example adds new records to the table:

cur = conn.cursor()
cmd = "INSERT INTO Sheet_Test_Sheet (Id, Name) VALUES (?, ?)"
params = [["Basic Project with Resource Management", "Basic Agile Project with Gantt Timeline"], ["Basic Project with Resource Management", "Basic Agile Project with Gantt Timeline"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

Update

The following example modifies existing records in the table:

cur = conn.cursor()
cmd = "UPDATE Sheet_Test_Sheet SET Name = ? WHERE Id = ?"
params = [["Basic Agile Project with Gantt Timeline", "123456"], ["Basic Agile Project with Gantt Timeline", "123456"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes existing records from the table:

cur = conn.cursor()
cmd = "DELETE FROM Sheet_Test_Sheet WHERE Id = ?"
params = [["123456"], ["123456"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

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

Python Integration Guides

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

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

Complete List of Smartsheet Integration Quickstarts

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

The CData Python Connector for Smartsheet 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 Smartsheet 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("smartsheet:///?AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")

For SQLAlchemy 2.0, the dialect name is smartsheet_2. To establish a connection, use the following URL format:

from sqlalchemy import create_engine
engine = create_engine("smartsheet_2:///?AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")

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

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)
Sheet_Test_Sheet_table = Table("Sheet_Test_Sheet", meta)
insp.reflect_table(Sheet_Test_Sheet_table, ["Id","Name"])

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("smartsheet:///?AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Sheet_Test_Sheet).filter_by(Favorite="True"):
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	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:

Sheet_Test_Sheet_table = Sheet_Test_Sheet.metadata.tables["Sheet_Test_Sheet"]
for instance in session.execute(Sheet_Test_Sheet_table.select().where(Sheet_Test_Sheet_table.c.Favorite == "True")):
	print("Id: ", instance.Id)
	print("FullName: ", instance.Name)
	print("City: ", instance.BillingCity)
	print("---------")

Implicit Joining

If mapped classes of related Smartsheet 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(Sheet_Test_Sheet).order_by(Sheet_Test_Sheet.TotalRowCount)
for instance in rs:
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("---------")

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

rs = session.execute(Sheet_Test_Sheet_table.select().order_by(Sheet_Test_Sheet_table.c.TotalRowCount))
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(Sheet_Test_Sheet.Id).label("CustomCount"), Sheet_Test_Sheet.Id).group_by(Sheet_Test_Sheet.Id)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Id: ", instance.Id)
	print("---------")

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

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

LIMIT and OFFSET

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

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

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

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

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

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

SUM

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

rs = session.query(func.sum(Sheet_Test_Sheet.TotalRowCount).label("CustomSum"), Sheet_Test_Sheet.Id).group_by(Sheet_Test_Sheet.Id)
for instance in rs:
	print("Sum: ", instance.CustomSum)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(Sheet_Test_Sheet_table.select().with_only_columns([func.sum(Sheet_Test_Sheet_table.c.TotalRowCount).label("CustomSum"), Sheet_Test_Sheet_table.c.Id]).group_by(Sheet_Test_Sheet_table.c.Id))
for instance in rs:

AVG

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

rs = session.query(func.avg(Sheet_Test_Sheet.TotalRowCount).label("CustomAvg"), Sheet_Test_Sheet.Id).group_by(Sheet_Test_Sheet.Id)
for instance in rs:
	print("Avg: ", instance.CustomAvg)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(Sheet_Test_Sheet_table.select().with_only_columns([func.avg(Sheet_Test_Sheet_table.c.TotalRowCount).label("CustomAvg"), Sheet_Test_Sheet_table.c.Id]).group_by(Sheet_Test_Sheet_table.c.Id))
for instance in rs:

MAX and MIN

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

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

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

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

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

Obtaining the Table Object

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

Sheet_Test_Sheet_table = Sheet_Test_Sheet.metadata.tables["Sheet_Test_Sheet"]

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(Sheet_Test_Sheet_table.insert(), {"Id": "Basic Project with Resource Management", "Name": "Basic Agile Project with Gantt Timeline"})

Update

The following example modifies an existing record in the table:

session.execute(Sheet_Test_Sheet_table.update().where(Sheet_Test_Sheet_table.c.Id == "123456").values(Id="Basic Project with Resource Management", Name="Basic Agile Project with Gantt Timeline"))

Delete

The following example removes an existing record from the table:

session.execute(Sheet_Test_Sheet_table.delete().where(Sheet_Test_Sheet_table.c.Id == "123456"))

When combined with the connector, Pandas can be used to generate data frames that contain your Smartsheet 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("smartsheet:///?AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")

Querying Data

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

df = pd.read_sql("""
	SELECT
	   Id,
	   Name,
     $exNumericCol;
	FROM Sheet_Test_Sheet;""", engine)
print(df)

Modifying Data

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

df = pd.DataFrame({"Id": ["Basic Project with Resource Management"], "Name": ["Basic Agile Project with Gantt Timeline"]})
df.to_sql("Sheet_Test_Sheet", con=engine, if_exists="append", index=False)

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

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

import petl as etl
import cdata.smartsheet as mod
cnxn = mod.connect("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")

Extract, Transform, and Load the Smartsheet Data

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

sql = "SELECT	Id, Name FROM Sheet_Test_Sheet "
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 Smartsheet tables using Petl's appenddb function.

table1 = [['Id','Name'],['Basic Project with Resource Management','Basic Agile Project with Gantt Timeline']]
etl.appenddb(table1,cnxn,'Sheet_Test_Sheet')

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.smartsheet as mod
conn = mod.connect("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tables"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Views

import cdata.smartsheet as mod
conn = mod.connect("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")
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.smartsheet as mod
conn = mod.connect("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tablecolumns WHERE TableName = 'Sheet_Test_Sheet'"
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.smartsheet as mod
conn = mod.connect("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")
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.smartsheet as mod
conn = mod.connect("AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'RefreshOAuthAccessToken'"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

This section details a selection of advanced features of the Smartsheet 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 Smartsheet 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 Smartsheet 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 Sheet_Test_Sheet 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 Sheet_Test_Sheet Table

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

SELECT Id, Name FROM Sheet_Test_Sheet WHERE Favorite = 'True'

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 Sheet_Test_Sheet WHERE Favorite = 'True'

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 Sheet_Test_Sheet WHERE Favorite = 'True'
  

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 Sheet_Test_Sheet#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 Sheet_Test_Sheet WHERE Favorite='True' ORDER BY Name 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 Smartsheet 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 Smartsheet Query Evaluation component examines SQL queries and returns information indicating what parts of the query the connector is not capable of executing natively.

The Smartsheet 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 Smartsheet. 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 Smartsheet 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 Smartsheet 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 Sheet_Test_Sheet

2. Rename a column:
   

   SELECT [Name] AS MY_Name FROM Sheet_Test_Sheet

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

   SELECT CAST(TotalRowCount AS VARCHAR) AS Str_TotalRowCount FROM Sheet_Test_Sheet

4. Search data:
   

   SELECT * FROM Sheet_Test_Sheet WHERE Favorite = 'True'

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

   SELECT COUNT(*) AS MyCount FROM Sheet_Test_Sheet 

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

   SELECT COUNT(DISTINCT Name) FROM Sheet_Test_Sheet 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT Name FROM Sheet_Test_Sheet 

8. Sort a result set in ascending order:
   

   SELECT Id, Name FROM Sheet_Test_Sheet  ORDER BY Name ASC

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

   SELECT Id, Name FROM Sheet_Test_Sheet 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 Sheet_Test_Sheet WHERE Favorite = @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 Smartsheet.

    SELECT * FROM Sheet_Test_Sheet 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.

Window Functions

See Window Functions for SELECT examples containing window functions.

Table-Valued Functions

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

COUNT

Returns the number of rows matching the query criteria.

SELECT COUNT(*) FROM Sheet_Test_Sheet WHERE Favorite = 'True'

COUNT(DISTINCT)

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

SELECT COUNT(DISTINCT Id) AS DistinctValues FROM Sheet_Test_Sheet WHERE Favorite = 'True'

AVG

Returns the average of the column values.

SELECT Name, AVG(TotalRowCount) FROM Sheet_Test_Sheet WHERE Favorite = 'True'  GROUP BY Name

MIN

Returns the minimum column value.

SELECT MIN(TotalRowCount), Name FROM Sheet_Test_Sheet WHERE Favorite = 'True' GROUP BY Name

MAX

Returns the maximum column value.

SELECT Name, MAX(TotalRowCount) FROM Sheet_Test_Sheet WHERE Favorite = 'True' GROUP BY Name

SUM

Returns the total sum of the column values.

SELECT SUM(TotalRowCount) FROM Sheet_Test_Sheet WHERE Favorite = 'True'

The CData Python Connector for Smartsheet 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 Sheet_Customers.ContactName, Sheet_Orders.OrderDate FROM Sheet_Customers, Sheet_Orders WHERE Sheet_Customers.CustomerId=Sheet_Orders.CustomerId

Left Join

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

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

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

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

Window Function Clauses

OVER

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

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

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

PARTITION BY

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

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

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

Window Functions

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

Math

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

COUNT()

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

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

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

COUNT_BIG()

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

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

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

MIN(numeric_column)

Calculates the minimum value of a numerical column per partition.

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

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

MAX(numeric_column)

Calculates the maximum value of a numerical column per partition.

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

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

SUM(numeric_column)

Calculates the sum of a numerical column per partition.

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

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

AVG(numeric_column)

Calculates the average value of a numerical column per partition.

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

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

MEDIAN(numeric_column)

Calculates the median value of a numerical column per partition.

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

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

STDEV(numeric_column)

Calculates the standard deviation of a numerical column per partition.

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

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

STDEVP(numeric_column)

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

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

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

VAR(numeric_column)

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

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

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

VARP(numeric_column)

Calculates the variance population of a numerical column per partition.

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

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

Ranking

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

RANK()

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

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

SELECT Id, Name, RANK() OVER (ORDER BY Name) AS Rank FROM Sheet_Test_Sheet

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

SELECT Id, Name, RANK() OVER (PARTITION BY Id ORDER BY Name) AS Rank FROM Sheet_Test_Sheet

DENSE_RANK()

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

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

SELECT Id, Name, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Name) AS Rank FROM Sheet_Test_Sheet

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

SELECT Id, Name, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Name) AS Rank FROM Sheet_Test_Sheet

ROW_NUMBER()

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

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

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

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

NTILE()

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

The syntax of NTILE() is:

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

The following are paramaters that NTILE() supports:

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

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

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

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

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

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

Analytical

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

PERCENT_RANK()

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

The syntax of PERCENT_RANK() is:

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

This syntax uses the following parameters.

• PARTITION BY: By default, SQL Server treats the whole data set as a single set. You can specify the PARTITION BY clause to divide data into multiple sets. The Percent_Rank function performs the analytical calculations on each set. This parameter is optional.
• ORDER BY: Sorts the data in either ascending or descending order. This parameter is required.

Table-valued functions are functions that return a table (rowset).

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

Table-Valued Function Clauses

CROSS APPLY

The CROSS APPLY operator is used to perform a subquery on each row of a table or resultset produced by a preceding table expression.

<table_expression_1> CROSS APPLY <table_expression_2>

The second table expression can reference results from the first table expression to create derived columns or an altered recordset via a table-valued function.

Each resulting record is an instance of the record it's splitting, with all the same column values, except for the column(s) containing the value split by the function.

WITH

The WITH clause is used alongside certain table-valued functions to match against constructs within the structure being split (keys, element names, attribute names, etc.) and/or to specify metadata for the columns generated from the function.

SELECT A.ColumnName, X.DerivedColumnName FROM TableName A CROSS APPLY <table-valued function> WITH (DerivedColumnName varchar(255)) AS X

Table-Valued Functions

STRING_SPLIT(input_text,delimiter)

Takes each record in the recordset of the preceding table expression, splits the column containing delimiters (input_text) into substrings separated by the delimiter, and returns one record per substring.

• input_text: A column whose value you want to parse.
• delimiter: The character used to split the value of the column specified in input_text.

Suppose there is a column called "SplitColumn" with the following content:

One-Two-Three

To unpack this value across multiple records:

SELECT A.ID, X.Value FROM [TableWithDelimitedStringField] A CROSS APPLY STRING_SPLIT(A.SplitColumn,'-') WITH (Value VARCHAR(255)) AS X

-- Results:
-----------
|ID|Value|
|1|One|
|1|Two|
|1|Three|

JSONTABLE(json_content,[jsonpath])

For each record in the recordset of the preceding table expression, returns one record for each instance of a key in a JSON array (json_content) that matches the key(s) specified in the WITH clause, at the scope specified by the "jsonpath" input.

• json_content: A JSON "table" (array of objects). The contents can nest, but this must be a single JSON array, not any other JSON structure, at the root level.
  • The values of every instance of the key(s) provided in the WITH clause are retrievable only for substructures which are immediate children of the root-level JSON array.
• jsonpath: An optional JSONPath query defining the scope, within the json_content array, that you want to pull content from. The JSON key(s) identified in the WITH clause must exist at the scope defined in this parameter. This defaults to the JSON root ($).

Consider a sample table with a single record, including an ID column and column with JSON content called "JSONColumn" with the following content:

[
	{
		"name": "Samuel",
		"email": "sam@gmail.com",
		"extrainfo": {
			"city": "Seattle"
		}
	},
	{
		"name": "Katherine",
		"email": "kat@gmail.com",
	},
	{
		"name": "George",
		"email": "george23@gmail.com",
	},
	{
		"name": "Carlos",
		"email": "carlos32@gmail.com",
	}
]

To extract all values for a certain key, specify the scope in the JSONTABLE function and provide the desired key(s) in the WITH clause.

SELECT A.ID, X.name FROM [TableWithJSONField] A CROSS APPLY JSONTABLE(A.JSONColumn) WITH (name VARCHAR(255)) AS X

-- Results: 
|ID|name|
---------
|1 |Samuel|
|1 |Katherine|
|1 |George|
|1 |Carlos|

XMLTABLE(xml_content,[xpath,child_type])

For each record in the resultset of the preceding table expression, returns one record for each of the elements and/or attributes in an XML structure (xml_content) that match the tag name(s) and/or attribute name(s) specified in the WITH clause, at the scope specified in the "xpath" input.

• xml_content: A column containing an XML structure.
• xpath: An optional XPath that specifies the scope within the XML structure at which the connector extracts content matching the tag/attribute name(s) specified in the WITH clause.
  • When extracting the content of sub-elements, the connector can retrieve all content from tags at the root level, (depth 0) immediate children of the root (depth 1), and children of those children (depth 2).
  • When extracting element attribute content, the connector can retrieve all content from tags containing the specified attribute at the root level (depth 0) and from immediate children of root-level elements (depth 1).
• child_type: An optional parameter that specifies the part(s) of the parent element (specified in the xpath input) that the column(s) provided in the WITH clause are checked against to identify content.
  • You can supply the following values:
    • 0: The column(s) in the WITH clause are checked for matches against the parent element's attribute names and sub-element tag names.
    • 1: The column(s) in the WITH clause are checked for matches against the parent element's attribute names.
    • 2: The column(s) in the WITH clause are checked for matches against the parent element's sub-element tag names.
  • When not supplied, this defaults to 0.

Extracting Sub-Element Values

Consider a sample table with a single record, including an ID column and a column with XML content called "XMLContent" with the following content:

<shoppingList>
    <item>
        <name>Apples</name>
        <quantity>3</quantity>
        <unit>Kg</unit>
    </item>
    <item>
        <name>Bread</name>
        <quantity>2</quantity>
        <unit>Loaf</unit>
		<extrainfo>
			<Type>Whole-Grain</Type>
		</extrainfo>
    </item>
    <item>
        <name>Milk</name>
        <quantity>1</quantity>
        <unit>Carton</unit>
    </item>
    <item>
        <name>Eggs</name>
        <quantity>12</quantity>
        <unit></unit>
    </item>
</shoppingList>

To extract sub-element content, specify the scope in the XMLTABLE function and provide the desired element name(s) in the WITH clause. Note that this will not work if the XMLTABLE function's child_type input is set to 1.

SELECT A.ID, X.name FROM [TableWithXMLField] A CROSS APPLY XMLTABLE(A.XMLContent,'//*/item') WITH (name VARCHAR(255)) AS X

-- Results: 
|ID|name|
---------
|1|Apples|
|1|Bread|
|1|Milk|
|1|Eggs|

Extracting Values Using Element Tag Attributes

Suppose you have this sample table with a single record, including an ID column and a column with XML content called "XMLContent" with the following content:

<restaurant>
  <dish type="appetizer">
    <name lang="en">Caprese Salad</name>
    <chef>Chef Giovanni</chef>
    <price currency="USD">9.99</price>
  </dish>
  <dish type="main-course">
    <name lang="fr">Boeuf Bourguignon</name>
    <chef>Chef Marie</chef>
    <price currency="EUR">19.99</price>
  </dish>
  <dish type="dessert">
    <name lang="es">Tres Leches Cake</name>
    <chef>Chef Alejandro</chef>
    <price currency="MXN">89.99</price>
  </dish>
</restaurant>

To extract attribute content, specify the scope in the XMLTABLE function and provide the desired attribute name(s) in the WITH clause. Note that this will not work if the XMLTABLE function's child_type input is set to 2.

SELECT A.ID, X.type FROM [TableWithXMLField] A CROSS APPLY XMLTABLE(A.XMLContent,'//*/dish') WITH (type VARCHAR(255)) AS X

-- Results: 
|ID|type|
---------
|1|appetizer|
|1|main-course|
|1|dessert|

CSVTABLE(csv_content,[delimiter])

For each record in the resultset of the preceding table expression, reads from a column that contains a CSV table (csv_content) and for each record in that CSV table, returns one record containing the value of the CSV column(s) specified in the WITH clause.

• csv_content: A column containing a CSV table.
• delimiter: An optional custom delimiter (instead of a comma) which splits the CSV content contained in the csv_content input.

Consider a sample table with a single record, including an ID column and a column containing CSV table called "CSVContent" with the following content:

Name;Category;Price
Apple;Fruit;0.99
Spaghetti;Pasta;5.49
Chicken Breast;Meat;8.99
Broccoli;Vegetable;2.49

To select every value in the "Name" column and account for the custom delimiter (;):

SELECT A.ID, X.Name FROM [TableWithCSVField] A CROSS APPLY CSVTABLE(A.CSVContent,';') WITH (Name VARCHAR(255)) AS X

-- Results:
|ID|Name|
-----------
|1|Apple|
|1|Spaghetti|
|1|Chicken Breast|
|1|Broccoli|

To create new records, use INSERT statements.

INSERT Syntax

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

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

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

The following is an example query:

INSERT INTO Sheet_Test_Sheet (Name) VALUES ('Basic Agile Project with Gantt Timeline')

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 Sheet_Test_Sheet SET Name='Basic Agile Project with Gantt Timeline' WHERE Id = @myId

An UPSERT statement updates an existing record or creates a new record if no matching record is found.

Upsert and Bulk Upsert Overview

This feature introduces upsert and bulk upsert support for Smartsheet.

Because the Smartsheet API does not natively support upsert operations, this functionality is implemented as an application-level (artificial) upsert. Inserts and updates are sent as separate API requests, with the system determining which operation to perform at runtime.

How It Works

At runtime, each row is evaluated based on the value of the primary column:

• If the value already exists in the target sheet, the row is sent as an update.
• If the value does not exist, the row is sent as an insert.

Requirements

• The sheet must include a user-defined primary column.
• The primary column values must be unique and non-null for each row.
• Note: These constraints are not enforced by Smartsheet and must be guaranteed externally.

Upsert Strategies

The system determines whether a row already exists using one of the following strategies:

• Full sheet scan retrieves and evaluates all rows.
• Search API searches by the primary column value.

The strategy is selected at runtime based on:

• The number of rows being inserted or updated.
• The total number of rows in the sheet (for example, page size considerations).

This approach ensures correct behavior while optimizing performance for different data sizes.

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 Sheet_Test_Sheet 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 Sheet_Test_Sheet

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

CACHE CachedSheet_Test_Sheet SELECT * FROM Sheet_Test_Sheet

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 CachedSheet_Test_Sheet SELECT * FROM Sheet_Test_Sheet WHERE DateModified > '2013-04-04'

Use the following cache statements to create a table with all available columns that will then cache only a few of them. The sequence of statements cache only Id and Name even though the cache table CachedSheet_Test_Sheet has all the columns in Sheet_Test_Sheet.

CACHE CachedSheet_Test_Sheet SCHEMA ONLY SELECT * FROM Sheet_Test_Sheet
CACHE CachedSheet_Test_Sheet SELECT Id, Name FROM Sheet_Test_Sheet

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

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

Stored Procedure Syntax

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

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

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

Example Statements

Reference stored procedure inputs by name:

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

Execute a parameterized stored procedure statement:

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

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

PIVOT

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

PIVOT Synax

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

UNPIVOT

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

UNPIVOT Sytax

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

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

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

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

Inserting Records from Real Tables

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

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

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

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

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

INSERT INTO DestinationTableWithSameColumns SELECT * FROM SourceTable

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

Inserting Records from Temporary Tables

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

Populate the Temporary Table

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

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

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

Insert Temporary Table Contents into Real Tables

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

INSERT INTO Sheet_Test_Sheet (Name, MyCustomField__c) SELECT Name, MyCustomField__c FROM Sheet_Test_Sheet#TEMP

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

Results

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

Temporary Table Lifespan

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

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

Populate the Temporary Table

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

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

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

Update the Actual Table

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

UPDATE Sheet_Test_Sheet (Id, Name, MyCustomField__c) SELECT Id, Name, MyCustomField__c FROM Sheet_Test_Sheet#TEMP

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

Results

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

Temporary Table Life Span

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

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

Populate the Temporary Table

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

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

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

Delete from the Actual Table

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

DELETE FROM Sheet_Test_Sheet WHERE EXISTS SELECT Id FROM Sheet_Test_Sheet#TEMP

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

Results

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

Temporary Table Life Span

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

The CData Python Connector for Smartsheet models the Smartsheet data as an easy-to-use SQL database.

Tables

The data model of the connector is dynamic. This means that, when you connect using the connector, any changes you make in the Smartsheet UI, such as adding a new table, adding new columns, or changing a column's data type, are automatically included in the schema of the connector.

However, some tables and views, such as workspaces and folders, are static. These are defined in schema files, which are simple, text-based configuration files. The names of the static tables and views are prefixed with Info_.

Stored Procedures

Stored Procedures are actions that are invoked via SQL queries. They perform tasks beyond standard CRUD operations, including managing sheets and attachments, and retrieving OAuth credentials.

API Restrictions

The connector uses the Smartsheet API to process supported filters. The connector processes other filters client-side within the connector.

Hierarchy Navigation

By Hierarchy Navigation, we refer to recursively exploring the Smartsheet hierarchy (tree) of objects through the API by doing the following for each workspace (unless the RootFolderId pseudo-column is used).

• Fetching object metadata for objects located in the workspace's root.
• Fetching object metadata for objects located in the folders of the workspace's root.
• Fetching object metadata for objects located in the subfolders of those folders.
• Fetching object metadata for objects for all subfolders, until reaching folders that have no more folder objects.

For the following entities:

• Info_Objects
• Info_Folders
• Info_Templates

the connector will always navigate the hierarchy.

Navigating the hierarchy is very costly in terms of performance because the connector has to make many API calls.

You can add hyperlink columns to table schema files generated by the CreateSchema stored procedure.

After generating a schema file from the desired table, add a new column (attr) as follows:

• Set other:columnid to a unique value (any string that isn't already taken by another column).
• Add an other:hyperlink attribute to the column. Set this attribute to the value in the name attribute of the column you want your new hyperlink column to display hyperlinks for.

  These two values must match exactly.

For example, suppose you have this column in your schema file:

<attr name="MyColumnName" xs:type="string" ... other:columnid="5555555555555555"/>

Your new hyperlink column attached to this column should look like this:

<attr name="nameLink" xs:type="string" ... other:columnid="YourUniqueIDHere" other:hyperlink="MyColumnName"/>

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

CData Python Connector for Smartsheet Tables

Name	Description
Info_Attachments	Read and upload attachments in the sheets of your Smartsheet account.
Info_Columns	Read and write properties of columns in your Smartsheet sheets.
Info_Comments	Read and write comment data on your Smartsheet sheets.
Info_Discussions	Read and write discussions data on your Smartsheet sheets.
Info_Folders	Read and write folders data in your Smartsheet account.
Info_GroupMembers	Read, add, and remove members in groups within your Smartsheet account.
Info_Groups	Read and write groups data in your Smartsheet account.
Info_Users	Read and write users data in your Smartsheet account.
Info_Workspaces	Read and write workspace data in your Smartsheet account.

Read and upload attachments in the sheets of your Smartsheet account.

Table-specific Information

SELECT

Queries on this table may be slow if your account contains many sheets. To improve performance, specify SheetId. You can also use DiscussionId, RowId, or CommentId to retrieve attachments for the related object.

Retrieve all attachments from all sheets.

SELECT * FROM Info_Attachments

Retrieve all attachments in a specific sheet.

SELECT * FROM Info_Attachments WHERE SheetId = '2940085806098308'

Retrieve all attachments in a specific discussion.

SELECT * FROM Info_Attachments WHERE SheetId = '2940085806098308' AND DiscussionId = '8206230771525508'

Retrieve all attachments in a specific row of a sheet.

SELECT * FROM Info_Attachments WHERE SheetId = '2940085806098308' AND RowId = '6773684447799172'

Retrieve all attachments in a specific comment of a sheet.

SELECT * FROM Info_Attachments WHERE SheetId = '2940085806098308' AND CommentId = '1322606759569284'

Retrieve details of a specific attachment in a sheet.

SELECT * FROM Info_Attachments WHERE SheetId = '2940085806098308' AND Id = '3053958945105796'

INSERT

You can create an attachment in:

• A sheet. In this case, the SheetId column is required.
• A row in a sheet. In this case, the SheetId and RowId columns are required.
• A comment in a sheet. In this case, the SheetId and CommentId columns are required.

The Name column is required in every case. Other required columns vary depending on the type of attachment you are creating. Refer to the query examples below.

INSERT INTO Info_Attachments (SheetId, Name, AttachmentType, URL) VALUES ('2940085806098308', 'Link Attachment', 'LINK', 'https://cdata.com')
INSERT INTO Info_Attachments (SheetId, RowId, Name, AttachmentType, AttachmentSubType, URL) VALUES ('2940085806098308', '6773684447799172', 'Sheet Attachment', 'GOOGLE_DRIVE', 'SPREADSHEET', 'https://docs.google.com/spreadsheets/d/xxxx_yyyy/edit?usp=drive_link')

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		The unique identifier of the attachment.
Name	String	False		The name of the attachment.
Url	String	False		The temporary URL of the attachment.
UrlExpiresInMillis	Long	True		The temporary URL time to live for the attachment.
AttachmentType	String	False		The attachment type. Possible values are: 'FILE', 'GOOGLE_DRIVE', 'LINK', 'BOX_COM', 'DROPBOX', 'EVERNOTE', and 'EGNYTE'.
AttachmentSubType	String	False		The attachment subtype, valid only for either 'GOOGLE_DRIVE' attachments or 'EGNYTE' attachments. Possible values for 'GOOGLE_DRIVE' attachments: 'DOCUMENT', 'SPREADSHEET', 'PRESENTATION', 'PDF', 'DRAWING'. Possible values for 'EGNYTE' attachments: 'FOLDER'.
CreatedAt	Timestamp	True		A timestamp of when the attachment was originally added.
UserId	String	True	Info_Users.Id	The unique identifier of the user who created the attachment.
UserEmail	String	True		The email address of the user who created the attachment.
UserName	String	True		The full name of the user who created the attachment.
MimeType	String	True		Attachment MIME type. For example 'PNG'.
ParentType	String	True		The type of the object the attachment belongs to. Possible values: 'SHEET', 'ROW', or 'COMMENT'.
ParentId	String	True		The unique identifier of the object the attachment belongs to.
SizeInKb	Long	True		The size of the file, if the attachment is a file.
SheetId	String	False	Info_Sheets.Id	The Id of the sheet.
RowId	String	False	Info_Rows.Id	The Id of the row.
DiscussionId	String	True	Info_Discussions.Id	The Id of the discussion.
CommentId	String	False	Info_Comments.Id	The Id of the comment.
Description	String	False		The description of the attachment.

Read and write properties of columns in your Smartsheet sheets.

Table-specific Information

SELECT

This table returns details for columns in a specified sheet. The SheetId column is always required.

Retrieve all columns in a sheet.

SELECT * FROM Info_Columns WHERE SheetId = '568679927703428'

Retrieve details of a specific column in the sheet.

SELECT * FROM Info_Columns WHERE SheetId = '568679927703428' AND Id = '1967782344478596'

INSERT

You can create a new column in the sheet by providing the SheetId, Index, Title, and Type properties as shown in the example query below.

INSERT INTO Info_Columns (SheetId, Index, Title, Type) VALUES ('568679927703428', 1, 'Test Column 1', 'TEXT_NUMBER')

UPDATE

You can update the properties of a column in the sheet by specifying the SheetId and Id columns in the criteria as shown in the example query below.

UPDATE Info_Columns SET Index = 3, Title = 'Transportation', Type = 'PICKLIST', OptionsAggregate = '["ship", "motorcycle", "airplane"]', Hidden = false, Width = 30, Locked = false, LockedForUser = false WHERE SheetId = '568679927703428' AND Id = '1967782344478596'

DELETE

You can delete one of the columns in the sheet by specifying the SheetId and Id columns in the criteria as shown in the example query below.

DELETE FROM Info_Columns WHERE SheetId = '568679927703428' AND Id = '1967782344478596'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		The unique identifier for the column, used to reference it programmatically.
SheetId	String	False	Info_Sheets.Id	The unique identifier of the sheet to which this column belongs.
Index	Integer	False		The numeric position of the column in the sheet, starting at 0 for the first column.
Title	String	False		The displayed name of the column, as shown in the Smartsheet interface.
Description	String	False		A text description of the column, providing additional context about its purpose or contents.
Type	String	False		The functional data type of the column, determining how data is stored and validated. The allowed values are TEXT_NUMBER, CHECKBOX, DATE, DATETIME, ABSTRACT_DATETIME, PICKLIST, MULTI_PICKLIST, CONTACT_LIST, MULTI_CONTACT_LIST, DURATION, PREDECESSOR.
Primary	Boolean	True		Indicates whether this column is the primary column, which typically contains key identifiers or names for rows.
ColumnType	String	False		The system-defined column type. The allowed values are AUTO_NUMBER, CREATED_BY, CREATED_DATE, MODIFIED_BY, MODIFIED_DATE.
Version	Integer	True		The column's compatibility level. Indicates whether the column supports advanced features such as multi-contact or multi-picklist data types.
Formula	String	False		The formula for the column, if set. Applies to TEXT_NUMBER, CHECKBOX, CONTACT_LIST, DATE, ABSTRACT_DATETIME, PICKLIST, MULTI_PICKLIST, and Symbol column types.
Symbol	String	False		Represents visual markers or indicators used in the column, with values varying by column type (for example, checkboxes, picklists). The allowed values are ARROWS_3_WAY, ARROWS_4_WAY, ARROWS_5_WAY, DECISION_SHAPES, DECISION_SYMBOLS, DIRECTIONS_3_WAY, DIRECTIONS_4_WAY, EFFORT, HARVEY_BALLS, HEARTS, MONEY, PAIN, PRIORITY, PRIORITY_HML, PROGRESS, RYG, RYGB, RYGG, SIGNAL, SKI, STAR_RATING, VCR, WEATHER.
OptionsAggregate	String	False		A list of selectable options for the column, applicable for columns like dropdowns or picklists.
ContactOptionsAggregate	String	False		Array of the contact options available for the column.
AutoNumberFormatAggregate	String	False		A JSON object specifying the format for an auto-number column.
Width	Integer	False		The pixel width used to display the column in the sheet's user interface.
Hidden	Boolean	False		Indicates whether the column is hidden in the Smartsheet interface.
Format	String	False		The applied formatting settings for the column, such as text alignment or date format.
TagsAggregate	String	True		Defines system tags for the column, used to indicate roles in features like Gantt charts or calendars (for example, 'gantt_duration').
Validation	Boolean	False		Indicates whether validation is enabled for the column values. Writable for CONTACT_LIST, MULTI_PICKLIST, and TEXT_NUMBER column types.
Locked	Boolean	False		Indicates whether the column is locked, preventing edits by all users except the owner or admin.
LockedForUser	Boolean	False		Indicates whether the column is locked for the current user based on their permissions.

Read and write comment data on your Smartsheet sheets.

Table-specific Information

SELECT

This table returns details for comments in a sheet. The SheetId column is always required.

Retrieve all comments from a sheet.

SELECT * FROM Info_Comments WHERE SheetId = '2940085806098308';
SELECT * FROM Info_Comments WHERE SheetId IN ('2940085806098308', '8075134644473732');

Retrieve all comments from a specific discussion in the sheet.

SELECT * FROM Info_Comments WHERE SheetId = '2940085806098308' AND DiscussionId = '8206230771525508'

Retrieve a specific comment in the sheet.

SELECT * FROM Info_Comments WHERE SheetId = '2940085806098308' AND Id = '1322606759569284'

INSERT

You can insert a comment to a discussion by providing the SheetId, DiscussionId and Text columns.

INSERT INTO Info_Comments (SheetId, DiscussionId, Text) VALUES ('568679927703428', '4661021235275652', 'This is a comment 3')

UPDATE

You can update the text of a comment in the sheet by specifying the SheetId and Id columns in the criteria as shown in the example query below.

UPDATE Info_Comments SET Text = 'My Comment.' WHERE SheetId = '2736916857677700' AND Id = '3641651486822276'

DELETE

You can delete a comment in the sheet by specifying the SheetId and Id columns in the criteria as shown in the example query below.

DELETE FROM Info_Comments WHERE SheetId = '2736916857677700' AND Id = '3641651486822276'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		A unique identifier assigned to each comment, ensuring it can be referenced distinctly.
Text	String	False		The main content of the comment, containing the user's input or feedback.
UserName	String	True		The name of the user who authored the comment, useful for identifying contributors.
UserEmail	String	True		The email address of the user who authored the comment, allowing for communication or verification.
CreatedAt	Datetime	True		The timestamp indicating when the comment was originally created.
ModifiedAt	Datetime	True		The timestamp indicating the most recent update or edit made to the comment.
AttachmentsAggregate	String	True		A serialized array of attachment objects associated with the comment, providing access to related files.
DiscussionId	String	False	Info_Discussions.Id	The unique identifier of the discussion thread to which the comment belongs.
SheetId	String	False	Info_Sheets.Id	The unique identifier of the sheet where the comment is located.

Read and write discussions data on your Smartsheet sheets.

Table-specific Information

SELECT

This table returns a list of discussions. The SheetId column is always required.

Retrieve all discussions in a sheet.

SELECT * FROM Info_Discussions WHERE SheetId = '2940085806098308'

Retrieve all discussions in a specific row of a sheet.

SELECT * FROM Info_Discussions WHERE SheetId = '2940085806098308' AND RowId = '6773684447799172'

Retrieve details of a specified discussion.

SELECT * FROM Info_Discussions WHERE SheetId = '2940085806098308' AND Id = '8206230771525508'

INSERT

You can insert a discussion into a sheet by providing the SheetId and FirstDiscussionComment columns. To add a discussion to a row in the sheet instead, provide the RowId, SheetId, and FirstDiscussionComment columns. Refer to the following query examples:

INSERT INTO Info_Discussions (SheetId, FirstDiscussionComment) VALUES ('568679927703428', 'This is a comment 1')
INSERT INTO Info_Discussions (RowId, SheetId, FirstDiscussionComment) VALUES ('1889077216995204', '568679927703428', 'This is a comment 2')

DELETE

You can delete a discussion in the sheet by specifying the SheetId and Id columns in the criteria as shown in the following example query:

DELETE FROM Info_Discussions WHERE SheetId = '8449165781585796' AND Id = '5354407478988676'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		A unique identifier for the discussion within the Smartsheet.
Title	String	True		The subject or headline of the discussion, providing a brief summary of its content.
AccessLevel	String	True		Indicates the user's permission level for accessing the discussion (for example, view, edit).
ParentId	String	True		The unique identifier of the row or sheet directly associated with the discussion.
ParentType	String	True		Specifies whether the discussion is linked to a row or a sheet. Possible values include: SHEET or ROW.
LastCommentedAt	Datetime	True		The timestamp indicating when the most recent comment was added to the discussion.
LastCommentBy	String	True		The full name of the user who made the latest comment in the discussion.
CreatorName	String	True		The full name of the user who initiated the discussion.
ReadOnly	Boolean	True		Indicates if the discussion is in a read-only state, preventing any modifications.
SheetId	String	False	Info_Sheets.Id	The unique identifier of the sheet where the discussion is located.
RowId	String	False	Info_Rows.Id	The unique identifier of the row associated with the discussion.

Pseudo-Columns

Pseudo column fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
FirstDiscussionComment	String	A special input-only field for adding the initial comment to a new discussion.

Read and write folders data in your Smartsheet account.

Table-specific Information

SELECT

Retrieve all folders.

SELECT * FROM Info_Folders

Retrieve details for a specific folder.

SELECT * FROM Info_Folders WHERE Id = '2035256120371076'

Retrieve all folders in a specific workspace.

SELECT * FROM Info_Folders WHERE WorkspaceId = '2940085806098308'

Retrieve all folders which are located beneath the folder specified in the folder hierarchy. Only simple criteria like the ones below can be processed for this pseudo-column, otherwise the condition will just be ignored:

SELECT * FROM Info_Folders WHERE RootFolderId = '993868452784004';
SELECT * FROM Info_Folders WHERE RootFolderId = '993868452784004' AND Name = 'MyFolder';

INSERT

You can create a folder inside a workspace or a parent folder by providing either the WorkspaceId column or the ParentFolderId column, and the Name column as shown in the example queries below.

INSERT INTO Info_Folders (WorkspaceId, Name) VALUES ('609757951223684', 'My Folder 1')
INSERT INTO Info_Folders (ParentFolderId, Name) VALUES ('5465554459879300', 'My Folder 1')

UPDATE

You can update folder data by specifying the Id column in the criteria as shown in the example query below.

UPDATE Info_Folders SET Name = 'My Test Folder 1' WHERE Id = '2035256120371076'

DELETE

You can delete a folder by specifying the Id column in the criteria as shown in the example query below.

DELETE FROM Info_Folders WHERE Id = '2035256120371076'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		The unique identifier of the folder.
Name	String	False		The folder name.
Permalink	String	True		URL that represents a direct link to the folder in Smartsheet.
WorkspaceId	String	False	Info_Workspaces.Id	The unique identifier of the workspace that contains this folder, useful for context within a workspace hierarchy.
ParentFolderId	String	False	Info_Folders.Id	The unique identifier of the parent folder where the folder is stored, indicating its organizational structure.
CreatedAt	Datetime	True		The time when the folder was created.
ModifiedAt	Datetime	True		The time when the folder was last modified.

Pseudo-Columns

Pseudo column fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
RootFolderId	String	The Id of the root folder in which the folder resides. Can be used to query the data from only a specific folder (and its subfolders) in the Smartsheet hierarchy.

Read, add, and remove members in groups within your Smartsheet account.

Table-specific Information

SELECT

Retrieve all members in a specific group.

SELECT * FROM Info_GroupMembers WHERE GroupId = '2035256120371076'

INSERT

You can add a member to a group by providing the GroupId and Email columns as shown in the following example query:

INSERT INTO Info_GroupMembers (GroupId, Email) VALUES ('2035256120371076', 'user1@testing.com')

DELETE

You can remove a member from a group by specifying the GroupId and Id columns in the criteria as shown in the following example query:

DELETE FROM Info_GroupMembers WHERE GroupId = '2035256120371076' AND UserId = '4417793262151556'

Columns

Name	Type	ReadOnly	References	Description
UserId [KEY]	String	True	Info_Users.Id	The unique identifier of the user.
GroupId [KEY]	String	False	Info_Groups.Id	The unique identifier of the group.
Email	String	False		The email address of the user.
FirstName	String	True		The first name of the user.
LastName	String	True		The last name of the user.
Name	String	True		The full name of the user.

Read and write groups data in your Smartsheet account.

Table-specific Information

SELECT

Retrieve all groups.

SELECT * FROM Info_Groups

Retrieve details for a specific group.

SELECT * FROM Info_Groups WHERE Id = '2035256120371076'

INSERT

You can create a group by providing the Name column as shown in the following example query:

INSERT INTO Info_Groups (Name, Description) VALUES ('My Group', 'Testing Group 1.')

To add members to a group, use the Info_GroupMembers table:

INSERT INTO Info_GroupMembers (GroupId, Email) VALUES ('2035256120371076', 'user1@testing.com')

UPDATE

You can update group data by specifying the Id column in the criteria as shown in the following example query:

UPDATE Info_Groups SET Name = 'Testing Group 1', Description = 'My Testing Group 1.', OwnerId = '8428447480473476' WHERE Id = '2035256120371076'

DELETE

You can delete a group by specifying the Id column in the criteria as shown in the following example query:

DELETE FROM Info_Groups WHERE Id = '2035256120371076'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		A unique identifier for the group, used as the primary key.
Name	String	False		The descriptive name of the group, typically used for display and identification.
Description	String	False		A brief summary or details about the purpose or function of the group.
Owner	String	True		The email address of the user who owns or administers the group.
OwnerId	String	False		The unique identifier for the user who owns or manages the group.
CreatedAt	Datetime	True		The timestamp indicating when the group was initially created.
ModifiedAt	Datetime	True		The timestamp indicating the most recent update or change made to the group.

Read and write users data in your Smartsheet account.

Table-specific Information

SELECT

Retrieve all users.

SELECT * FROM Info_Users

Retrieve all users in a specific group.

SELECT * FROM Info_Users WHERE GroupId = '2928085806875091'

INSERT

You can create a user in your Smartsheet account by providing the Email, LicensedSheetCreator and Admin columns as shown in the example query below.

INSERT INTO Info_Users (Email, LicensedSheetCreator, Admin) VALUES ('testUser1@test.com', true, true)

UPDATE

You can update user data by providing the Id column in the criteria as shown in the example query below.

UPDATE Info_Users SET FirstName = 'My Test', LastName = 'User 1', Admin = false, GroupAdmin = false, LicensedSheetCreator = false WHERE Id = '8307802553771908'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		A unique identifier assigned to each user, used to differentiate them within the system.
AccountId	String	True		The unique identifier of the account to which the user belongs, establishing account association.
AccountName	String	True		The name of the account associated with the user, providing a human-readable reference.
Email	String	False		The email address registered for the user, used for communication and login purposes.
Name	String	True		The user's full name as it appears in the system, combining first and last name.
FirstName	String	False		The user's first name, typically used in personal greetings and identification.
LastName	String	False		The user's last name, often used for formal identification.
Admin	Boolean	False		Indicates whether the user is a system administrator with privileges to manage accounts and other users.
Locale	String	True		The language and regional settings associated with the user, impacting date formats and other locale-specific elements.
TimeZone	String	True		The user's timezone, used to localize date and time information across the platform.
LicensedSheetCreator	Boolean	False		Indicates if the user holds a license to create and own sheets within the system.
GroupAdmin	Boolean	False		Specifies whether the user can create and manage groups, granting them group admin privileges.
ResourceViewer	Boolean	False		Indicates if the user has access to view resource management features such as workload views.
Status	String	True		The current status of the user within the system. Possible values include: ACTIVE, PENDING, or DECLINED.
GroupId	String	True	Info_Groups.Id	The unique identifier of the group to which the user belongs, if applicable.
Company	String	True		The name of the company the user is associated with. Populated only when filtering by user Id.
Department	String	True		The department within the company that the user is part of. Populated only when filtering by user Id.
MobilePhone	String	True		The user's mobile phone number. This field is populated only when filtering by user Id.
LastLogin	String	True		The timestamp of the user's last login. Only populated if the user has logged in and an Id filter is applied.
Role	String	True		The role or position of the user within their organization. Populated only when filtering by user Id.
Title	String	True		The professional title of the user within their organization. Populated only when filtering by user Id.
WorkPhone	String	True		The user's work phone number. This field is populated only when filtering by user Id.
SendEmail	Boolean	False		Whether to send an email to the user's email address once they are created (invited in the Smartsheet account). This columns always returns null in SELECT statements, and is supposed to be used in INSERT statements only.

Read and write workspace data in your Smartsheet account.

Table-specific Information

SELECT

Retrieve all workspaces.

SELECT * FROM Info_Workspaces

Retrieve details for a specific workspace.

SELECT * FROM Info_Workspaces WHERE Id = '2928085806875091'

INSERT

You can create a workspace by providing the Name column as shown in the example query below.

INSERT INTO Info_Workspaces (Name) VALUES ('My Workspace 1')

UPDATE

You can update workspace data by specifying the Id column in the criteria as shown in the example query below.

UPDATE Info_Workspaces SET Name = 'My Test Workspace 1' WHERE Id = '2928085806875091'

DELETE

You can delete a workspace by specifying the Id column in the criteria as shown in the example query below.

DELETE FROM Info_Workspaces WHERE Id = '2928085806875091'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		A globally unique identifier (GUID) for the workspace, used to distinguish it from other workspaces.
Name	String	False		The user-defined name of the workspace, used for organization and identification.
AccessLevel	String	True		Specifies the permissions level assigned to the user for this workspace, such as Viewer, Editor, or Admin.
Permalink	String	True		A permanent URL that provides a direct link to access the workspace within Smartsheet.

An example of a dynamic connector table (sheets in Smartsheet).

Columns

Name	Type	ReadOnly	References	Description
RowId [KEY]	String	True		The 'RowId' column.
PrimaryColumn	Int	False		The 'PrimaryColumn' column.
TextColumn	String	False		The 'TextColumn' column.
CheckboxColumn	Boolean	False		The 'CheckboxColumn' column.
NumberColumn	Double	False		The 'NumberColumn' column.
DateColumn	Date	False		The 'DateColumn' column.
ContactListColumn	String	False		The 'ContactListColumn' column.

Pseudo-Columns

Pseudo column fields are used in the WHERE clause of SELECT statements and offer more granular control over the data returned from the data source.

Name	Type	Description
ToTop	Boolean	The 'ToTop' column.

Views are similar to tables in the way that data is represented; however, views are read-only.

Queries can be executed against a view as if it were a normal table.

CData Python Connector for Smartsheet Views

Name	Description
Info_AttachmentVersions	Read data on the versions of the attachments in your Smartsheet sheets.
Info_CellHistory	Query the available CellHistory of a Cell in Smartsheet.
Info_Cells	Query Smartsheet Cells. A collection of Cells comprises each Row in a Sheet.
Info_Contacts	Query Smartsheet Contacts. A Contact is the personal contact of a User in Smartsheet.
Info_DashboardPublishSettings	Provides the publish status details of Smartsheet dashboards
Info_Dashboards	Query Smartsheet dashboards.
Info_DashboardShares	Query the Sharing operations of Dashboards.
Info_Favorites	Query Smartsheet Favorites.
Info_Objects	Returns Smartsheet objects, including sheets, reports, dashboards, templates, and folders.
Info_ReportPublishSettings	Provides publish settings for Smartsheet reports.
Info_Reports	Query Smartsheet reports, providing insights into custom aggregated data across sheets.
Info_ReportShares	Query the Sharing operations of Reports.
Info_Rows	Query Rows in a Sheet or Report. Each Row is composed of a collection of Cells, and may optionally contain Discussions and Attachments.
Info_ServerInformation	Query Smartsheet Server Information including application constants.
Info_SheetPublishSettings	Provides publish settings for Smartsheet sheets.
Info_Sheets	Explore detailed metadata and structure of Smartsheet sheets, including their components like columns, rows, and attachments.
Info_SheetShares	Query the Sharing operations of Sheets.
Info_Templates	Retrieve Smartsheet template information to streamline the creation of standardized sheets.
Info_WorkspaceShares	Query the Sharing operations of Workspaces.

Read data on the versions of the attachments in your Smartsheet sheets.

View-specific Information

SELECT

Retrieve all attachment versions of an attachment in the sheet.

SELECT * FROM Info_AttachmentVersions WHERE SheetId = '8449165781585796' AND AttachmentId = '2879526696423300'

Columns

Name	Type	References	Description
Id [KEY]	String		The unique identifier of the attachment version.
SheetId	String	Info_Sheets.Id	The unique identifier of the sheet in which the attachment is located.
AttachmentId	String	Info_Attachments.Id	The unique identifier of the attachment (Id of the most recent version).
Name	String		The name of the attachment.
MimeType	String		The attachment MIME type (e.g. 'PNG').
CreatedAt	Timestamp		The time when the attachment was created.
ParentId	String		The unique identifier of the object to which the attachment belongs to.
ParentType	String		The type of object to which the attachment belongs to. The allowed values are SHEET, ROW, COMMENT.
UserName	String		The full name of the user who created the attachment.
UserEmail	String		The email address of the user who created the attachment.
URL	String		The temporary URL of the attachment.
SizeInKB	Long		The size of the attachment in kilobytes.
Description	String		The description of the attachment.

Access the historical changes of specific cells in Smartsheet, enabling detailed audit and tracking capabilities.

View-specific Information

SELECT

To get data from this view, the SheetId, RowId, and ColumnId columns are always required.

Retrieve Cell History.

SELECT * FROM Info_CellHistory WHERE SheetId = '2940085806098308' AND RowId = '6773684447799172' AND ColumnId = '7999197812156292'

Columns

Name	Type	References	Description
Value	String		The most recent content in the cell, which could include text, numbers, or the result of a formula. This value represents the cell's current state.
ModifiedAt	Datetime		The date and time when the cell was last updated. This helps in identifying the most recent activity in the cell.
EditorName	String		The full name of the user who made the latest changes to the cell. This provides clarity about who is responsible for updates.
EditorEmail	String		The email address of the user who last modified the cell, allowing for easy communication or audit purposes.
SheetId	String	Info_Sheets.Id	A unique identifier for the sheet containing this cell. This links the cell to its corresponding sheet in Smartsheet.
RowId	String	Info_Rows.Id	A unique identifier for the row in which this cell is located. This helps in pinpointing and managing specific rows.
ColumnId	String	Info_Columns.Id	A unique identifier for this cell's column enables efficient column-based operations or analysis.

Explore individual cell data in Smartsheet rows, offering granular insights into sheet contents.

View-specific Information

SELECT

This view returns details for Cells of a specified Row. The SheetId and RowId columns are always required.

Retrieve Cells.

SELECT * FROM Info_Cells WHERE SheetId = '2940085806098308' AND RowId = '6773684447799172'

Columns

Name	Type	References	Description
Id [KEY]	String		The unique identifier of the column that contains the cell. This is a reference to the parent column in the sheet's schema.
Value	String		The actual content of the cell, which can be a string, number, or boolean, representing the cell's primary data.
DisplayValue	String		The formatted version of the cell's content as displayed to the user in the Smartsheet UI, reflecting applied formats and rules.
ColumnType	String		The type of data or content allowed in the column (for example, text, date, dropdown) as defined in the column's schema.
Formula	String		The formula applied to the cell, if any, used to compute dynamic values based on other cell references.
Format	String		Descriptor specifying the visual formatting of the cell, such as text style, color, or alignment.
ConditionalFormat	String		The formatting applied to the cell based on conditional rules set at the column or sheet level.
Strict	Boolean		Indicates if strict parsing rules are applied to the cell's value. Defaults to true; set to false for more lenient value handling.
SheetId	String	Info_Sheets.Id	The unique identifier of the sheet to which this cell belongs.
RowId [KEY]	String	Info_Rows.Id	The unique identifier of the row containing this cell, representing its position in the sheet.

Query Smartsheet user contact details, facilitating management of personal and shared connections.

View-specific Information

SELECT

This view returns details for Contacts.

Retrieve Contacts.

SELECT * FROM Info_Contacts

Retrieve details of a specified Contact.

SELECT * FROM Info_Contacts WHERE Id = '1322606759569284'

Columns

Name	Type	References	Description
Id [KEY]	String		A unique identifier assigned to each contact, used to differentiate them within the database.
Name	String		The full name of the contact, typically including both first and last names.
Email	String		The primary email address associated with the contact, used for communication purposes.

Provides the publish status details of Smartsheet dashboards

Columns

Name	Type	References	Description
DashboardId [KEY]	String	Info_Dashboards.Id	The unique identifier of the dashboard whose publish status is being retrieved.
ReadOnlyFullEnabled	Boolean		Indicates whether the 'Read-Only Full' version of the dashboard is published, allowing viewers to interact with widgets and use shortcuts.
ReadOnlyFullUrl	String		The URL for accessing the 'Read-Only Full' view of the published dashboard. Only returned if 'ReadOnlyFullEnabled' is true.
ReadOnlyFullAccessibleBy	String		Specifies who can access the 'Read-Only Full' view of the published dashboard. Possible values: ALL (anyone with the link), ORG (members of the owner's organization), SHARED (users shared to the item).