Connecting to Mailchimp

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

Mailchimp Version Support

The connector defaults to version 3 of the Core Mailchimp API.

See Also

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

Linux:

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

macOS:

pip install cdata_mailchimp_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_mailchimp_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_mailchimp" 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_mailchimp folder is trivial to find:

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

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

1. Import the module as follows:
   

   import cdata.mailchimp 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("APIKey=myAPIKey;")

Connecting to Mailchimp Marketing API

Mailchimp Marketing API supports the following authentication methods:

• APIKey
• OAuth

API Key

The easiest way to connect to Mailchimp Marketing API is to use the API Key. The APIKey grants full access to your Mailchimp account. To obtain the APIKey:

1. Log into Mailchimp.
2. Navigate to Account > Extras > API Keys.
3. Note the value of the API Key.

Once you have the value of the API Key:

1. Set APIKey to the value of the API Key.
2. Set AuthScheme to APIKey.

OAuth

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:

• InitiateOAuth: GETANDREFRESH. Used to automatically get and refresh the OAuthAccessToken.
• 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 Mailchimp's OAuth endpoint in your default browser. Log in and grant permissions to the application.

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

1. The connector obtains an access token from Mailchimp 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

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

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

Get the OAuth access token:

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

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

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

Automatic refresh of the OAuth access token:

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

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

Headless Machines

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

• Option 1: Obtain the OAuthVerifier value.
• Option 2: Install the connector on a machine with an internet browser and transfer the OAuth authentication values after you authenticate through the usual browser-based flow.

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

Option 1: Obtaining and Exchanging a Verifier Code

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

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

   Set the following properties:

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

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

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

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

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

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

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

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

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

Option 2: Transferring OAuth Settings

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

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

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

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

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

Connecting to Mailchimp Transactional API

To connect to the Mailchimp Transactional API, you must authenticate using a Transactional API key. To obtain the Transactional API key, you must have the Transactional Email Plan enabled in your account.

Once you have enabled the Transactional Email Plan, follow the steps below to create a Transactional API key:

1. Log into Mailchimp.
2. Navigate to User Menu > Profile > Extras > API keys.
3. Click Create A Mandrill API Key. You are then redirected to the Mandrill website.
4. Click Create API Key. A form displays.
5. You can optionally enter a description, then click Create API Key.
6. Copy and save the value of the Transactional API key.

Once you have the value of the Transactional API key:

1. Set TransactionalAPIKey to the value of the Transactional API key.
2. Set Schema to Transactional.

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 Mailchimp 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:
  

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

• For Mac:
  

  [mailchimp.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.mailchimp as mod
import time

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

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

mod.setCustomLoggerFactory(createLogger)

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

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

Creating a Custom OAuth Application

There are two authentication methods available for connecting to Mailchimp: You can use the APIKey or use OAuth.

OAuth can be used to enable other users to access their own data. It is also useful if you want to:

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

To register a custom OAuth application in Mailchimp and obtain the OAuth client credentials, the OAuthClientId and OAuthClientSecret:

1. Log into your Mailchimp account.
2. Navigate to Account > Extras > API Keys > Register and Manage Your Apps.
3. Enter the information you want to be displayed to users when they are prompted to grant permissions to your application. This information includes your app name, company, and website.
4. If this is a Desktop application, specify a Redirect URI of http://127.0.0.1.
   

   If this is a Web application, specify a Redirect URI where you would like users to be redirected after they grant permissions to your application.

After you have created and registered a custom OAuth app, users can connect to Mailchimp as described in "Connecting to Mailchimp".

General Changes

Date	Version	Source	Category	Type	Description
2026-06-04	26.0.9651	Mailchimp	Data Model	Added	The following columns have been added to the ECommerceOrders table: Customer_EmailAddress (string, customer/email_address) Customer_SmsPhoneNumber (string, customer/sms_phone_number) Customer_OptInStatus (boolean, customer/opt_in_status) Customer_Company (string, customer/company) Customer_FirstName (string, customer/first_name) Customer_LastName (string, customer/last_name) Customer_OrdersCount (integer, customer/orders_count) Customer_TotalSpent (decimal, customer/total_spent) Customer_Address_Address1 (string, customer/address/address1) Customer_Address_Address2 (string, customer/address/address2) Customer_Address_City (string, customer/address/city) Customer_Address_Province (string, customer/address/province) Customer_Address_ProvinceCode (string, customer/address/province_code) Customer_Address_PostalCode (string, customer/address/postal_code) Customer_Address_Country (string, customer/address/country) Customer_Address_CountryCode (string, customer/address/country_code) Customer_CreatedAt (datetime, customer/created_at) Customer_UpdatedAt (datetime, customer/updated_at)
2026-05-27	26.0.9643	General	Connection	Removed	Removed the deprecated ReplaceInvalidTypesWithNull connection property. Use the ReplaceInvalidValuesWithNull property instead.
2026-05-22	26.0.9638	Python		Removed	Remove support for Intel x64 architecture on macOS
2026-05-07	26.0.9623	General	Data Model	Added	Added the ColumnCapabilities column to the sys_tablecolumns system table. This column is a bit mask denoting the column's write capabilities.
2026-05-07	26.0.9623	Python		Changed	Updated embedded JRE to jre-17.0.19+10 (Linux x64 / MacOs x64).
2026-04-30	26.0.9616	Mailchimp	Data Model	Changed	In the MailChimp schema, the following columns have been made read-only: In the Campaigns table: AbSplitOpts_FromNameA, AbSplitOpts_FromNameB, AbSplitOpts_PickWinner, AbSplitOpts_ReplyEmailA, AbSplitOpts_ReplyEmailB, AbSplitOpts_SendTimeA, AbSplitOpts_SendTimeB, AbSplitOpts_SendTimeWinner, AbSplitOpts_SplitSize, AbSplitOpts_SplitTest, AbSplitOpts_SubjectA, AbSplitOpts_SubjectB, AbSplitOpts_WaitTime, AbSplitOpts_WaitUnits, DeliveryStatus, ItemURL, Recipients_SegmentText, and ReportSummary In the EcommerceOrders table: HasOutreach, Outreach_Name, Outreach_PublishedTime, and Outreach_Type In the ListMembers table: InterestCategoryId, InterestIds, InterestMatch, SinceLastCampaign, SmsPhoneNumber, SmsSubscriptionLastUpdated, SmsSubscriptionStatus, StatusIfNew, UnsubscribedSince, and Vip In the Lists table: Visibility
2026-04-21	26.0.9607	Mailchimp	Data Model	Added	Added the following stored procedures to the MailChimp Data Model: PublishLandingPage, UnpublishLandingPage, and VerifyConnectedSiteScript.
2026-04-16	26.0.9602	Mailchimp	Connection	Removed	Removed the IncludeCustomFields connection property.
2026-04-16	26.0.9602	Mailchimp	Data Model	Removed	Removed from the Mailchimp schema: all Transactional\* tables, views, and stored procedures. These tables, views, and stored procedures are available through the Transactional schema.
2026-04-16	26.0.9602	Mailchimp	Metadata	Changed	MailChimp/ListAbuse.rsd: Date datatype changed from string to date. MailChimp/ReportAbuse.rsd: Date datatype changed from string to date. MailChimp/ReportProductActivity.rsd: TotalPurchased datatype changed from integer to decimal; TotalRevenue datatype changed from integer to decimal. Transactional/MessageContent.rsd: Ts datatype changed from string to datetime; renamed Tags# column to Tags and Attachments# column to Attachments. Transactional/Messages.rsd: Ts datatype changed from string to long.
2026-04-15	26.0.9601	General	Query Exec	Changed	String comparisons using GREATER, LESS, and CONTAINS operators are now case-insensitive by default.
2026-04-15	26.0.9601	Mailchimp	Data Model	Changed	The CustomerId pseudocolumn is now a column in the ECommerceOrder table. This column provides the Id of the customer that is associated with the ECommerce Order.
2026-04-10	26.0.9596	Mailchimp	Data Model	Added	Added flattened sub-fields of the address-type merge field as individual columns in list-specific ListMembers tables. The aggregate address-type field is also returned.
2026-04-08	26.0.9594	Mailchimp	Security	Changed	TLS 1.3 is now supported by default for HTTP connections.
2026-03-20	25.0.9575	Mailchimp	Data Model	Added	Added a new view, SurveyResponsesResults.
2026-02-12	25.0.9539	Mailchimp		Changed	Changed the data type of the DisplayOrder and Options_Size columns from integer to long in the ListMergeFields table.
2026-02-05	25.0.9532	Mailchimp		Added	Added the Transactional schema. This data model reflects the contents of the Transactional API. Added the Schema connection property. This property allows you to toggle between data models based on the Marketing and Transactional APIs.
2026-02-04	25.0.9531	Mailchimp		Changed	The data type of the Quantity column in the EcommerceCartLines and EcommerceOrderLines tables has been changed from integer to long. The data type of the InventoryQuantity column in the EcommerceProductVariants table has been changed from integer to long.
2026-01-22	25.0.9518	Mailchimp		Added	Added ListLocations and ListFacebookEcommerceReport views.
2026-01-13	25.0.9509	General		Added	Added support for the REGEXP_REPLACE() string function.
2025-12-21	25.0.9486	Python		Added	Added support for custom loggers in Python connectors on Linux and macOS.
2025-12-05	25.0.9470	General		Added	Added support for the INSERT INTO SELECT statement, with driver-side execution for providers that do not support the operation natively.
2025-11-27	25.0.9462	Mailchimp		Added	Added a new connection property, TransactionalAPIKey. When set, this property enables the user to retrieve results from transactional views.
2025-10-30	25.0.9434	Python		Changed	Updated embedded JRE to jre-17.0.17+10 (Linux x64 / MacOs x64).
2025-10-17	25.0.9421	Mailchimp		Added	Added the Opens_ProxyExcludedOpens, Opens_ProxyExcludedUniqueOpens, Opens_ProxyExcludedOpenRate, and ListStats_ProxyExcludedOpenRate columns to the Reports table.
2025-10-06	25.0.9410	General		Added	Support for parsing datetime formats using ".S" and ",S" for milliseconds and nanoseconds.
2025-10-03	25.0.9407	Mailchimp		Added	Added BatchWebhooks, FacebookAds, FileManagerFolderFiles, LandingPageContents, LandingPages, ListMemberActivityFeeds, ListMemberGoals, ListsTagsSearch, ListSurveys, ReportEepUrls, ReportingFacebookAds, ReportingLandingPages, ReportingSurveyQuestionAnswers, ReportingSurveyQuestions, ReportProductActivity, ReportSubReports, and VerifiedDomains views. Added the ViewTemplatesDefaultContent stored procedure.
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.
2025-09-10	25.0.9384	Mailchimp		Added	Added the SmsPhoneNumber, SmsSubscriptionStatus and SmsSubscriptionLastUpdated columns to the ListMembers table. Added the TotalSent column to the ReportDomainPerformance view. Added the ProxyExcludedOpens column to the ReportLocations view.
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.
2025-08-25	25.0.9368	Mailchimp		Added	Added the MergeFields column to the ListMembers table. This column is present whether the IncludeCustomFields property is true or false. Thus, custom fields no longer appear in ListMembers when IncludeCustomFields=true. To query merge fields as custom fields, use the ListMember_*ListName* table instead.
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.
2025-08-12	25.0.9355	Mailchimp		Added	Added TransactionalCancelScheduledEmail, TransactionalRescheduledEmail, TransactionalSendMessage, and TransactionalSendTemplate stored procedures.
2025-07-29	25.0.9341	Mailchimp		Added	Added TransactionalAllowLists, TransactionalTags, and TransactionalTemplates tables. Added TransactionalScheduledEmails, TransactionalSenders, and TransactionalUserInfos views.
2025-07-22	25.0.9334	Mailchimp		Added	Added Update and Insert functionality for the Templates table. Added Html pseudo-column in the Templates table. This column is required for Update operations.
2025-07-22	25.0.9334	Mailchimp		Changed	The Active column in the Templates table has been changed to be read-only.
2025-07-11	25.0.9323	Mailchimp		Removed	Removed the Tracking_Highrise column from AutomationEmails, Automations, and Campaigns.
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.
2025-06-28	25.0.9310	Mailchimp		Added	Added new views AccountExports, BatchOperations, CampaignsContent, CampaignSendCheckList, CampaignsVariateContents, ChimpChatterActivity, ConnectedSites, EcommerceProductImages, EcommerceStoresPromoCodes, and EcommerceStoresPromoRules. Added DownloadAccountExports stored procedure. Added ListId and MemberId as keys in ListMemberTags view. Added DeleteECommerceCarts and UpdateECommerceCarts Stored Procedures.
2025-06-28	25.0.9310	Mailchimp		Removed	Removed keys from EcommerceCarts table, ListMemberActivity view and ListMemberEvents table. Removed Update and Delete support for ECommerceCarts table.
2025-06-27	25.0.9309	Mailchimp		Added	Added Since pseudo column in the CampaignOpenEmailDetails view.Added ItemURL column in the Campaigns table.Added HasUnreadMessages pseudo column in the Conversations view.Added CustomerId and HasOutreach pseudo columns in the EcommerceOrders table.Added InterestCategoryId, InterestMatch, InterestIds, SinceLastCampaign and UnsubscribedSince pseudo columns in the ListMembers table.Added IncludeCleaned, IncludeTransactional and IncludeUnsubscribed pseudo columns in the ListSegmentMembers table.Added IncludeCleaned, IncludeTransactional and IncludeUnsubscribed pseudo columns in the ListSegments table.Added AnsweredQuestion, ChoseAnswer and RespondentFamiliarityIs pseudo columns in the SurveyResponses view.
2025-06-25	25.0.9307	General		Removed	Removed the "ADLS Gen 1" value from the ConnectionType property.
2025-06-25	25.0.9307	Python		Added	Added support for Python 3.13 in Windows, Linux, and Mac editions.
2025-06-25	25.0.9307	Python		Removed	Removed support for Python 3.8 as it is no longer supported.
2025-06-20	25.0.9302	General		Added	Created the following functions: TEXT_ENCODE: encodes a string into a different charset (UTF8 → UTF7 and returns a binary array as the result). TEXT_DECODE: takes a binary array and decodes it back into a string when provided the charset. BASE64_ENCODE: takes a binary array and encodes it as a base64 string (varchar). BASE64_DECODE: takes a base 64-encoded string and decodes it into a binary array.
2025-06-18	25.0.9300	General		Changed	The internal code for exception handling has been refactored. Exception messages returned during certain error conditions may now have different wording or formatting.
2025-05-28	25.0.9279	Mailchimp		Added	Added Recipients_ListIsActive and Recipients_StoreId columns to the Automations view. Added Trigger_Settings_WorkflowType, Trigger_Settings_WorkflowTitle, Trigger_Settings_WorkflowEmailsCount, WebId, Delay_ActionDescription, Delay_FullDescription, NeedsBlockRefresh, HasLogoMergeTag, Recipients_ListIsActive, Recipients_ListName, Recipients_RecipientCount, Recipients_SegmentText, and Settings_PreviewText columns to the AutomationEmails view. Added DateEdited, EditedBy, and ContentType columns to the Templates table. Added WebId, ParentCampaignId, NeedsBlockRefresh, Resendable, Recipients_ListIsActive, and Settings_PreviewText columns to the Campaigns table. Added IsSyncing, ConnectedSite_SiteForeignId, ConnectedSite_SiteScript_Url, ConnectedSite_SiteScript_Fragment, Automations_AbandondedCart_IsSupported, Automations_AbandondedCart_Id, Automations_AbandondedCart_Status, Automations_AbandondedCart_IsSupported, Automations_AbandondedBrowse_Id, Automations_AbandondedBrowse_Status, and ListIsActive columns to the EcommerceStores view. Added ImageUrl and Discount columns to the EcommerceOrderLines table. Added LandingSite, OrderUrl, DiscountTotal, Promos, Outreach_Id, Outreach_Name, Outreach_Type, Outreach_PublishedTime, TrackingNumber, TrackingCarrier, and TrackingUrl columns to the EcommerceOrders table. Added CurrencyCode and Images columns to the EcommerceProducts table. Added ListId, ListIsActive, ListName, SubjectLine, PreviewText, RssLastSend, and Ecommerce columns to the Reports view. Added ListIsActive, MergeFields, and Vip columns to the ReportAbuse view. Added MergeFields, Vip, ListIsActive, and ContactStatus columns to the ReportClickDetailsMembers view. Added ListIsActive column to the ReportEmailActivity view. Added CountryCode and RegionName columns to the ReportLocations view. Added MergeFields, Vip, and ListIsActive columns to the ReportSentTo view. Added MergeFields, Vip, and ListIsActive columns to the ReportUnsubscribes view. Added WebId, DoubleOptin, HasWelcome, MarketingPermissions, and Stats_TotalContacts columns to the Lists table. Added MergeFields and Vip columns to the ListAbuse view. Added EmailId and ContactId columns to the ListMemberNotes table. Added ContactId, WebId, UnsubscribeReason, ConsentsToOneToOneMessaging, Stats_EcommerceData_TotalRevenue, Stats_EcommerceData_NumberOfOrders, Stats_EcommerceData_CurrencyCode, Location_Region, MarketingPermissions, Source, and TagsCount to the ListMembers table. Added MergeFields column to the ListSegmentMembers table.
2025-05-27	25.0.9278	General		Removed	Removed the "Proprietary" enum option from ProxyAuthscheme.
2025-05-23	25.0.9274	Mailchimp		Added	Added parallel pagination for various tables and views.
2025-05-12	25.0.9263	Python		Changed	Updated embedded JRE to jre-17.0.15+6 (Linux x64 / MacOS x64) and jre-17.0.15+6 (MacOS aarch64).
2025-02-15	24.0.9177	General		Added	Added support for converting unsigned integer types to the nearest signed data type that has enough precision to hold the unsigned value.This is done for JDBC only because it does not have support for unsigned data types.
2024-11-27	24.0.9097	General		Added	Added ThreadId to LogModule output. Logfile lines now include the Thread ID associated with the action being performed.
2024-07-24	24.0.8971	Mailchimp		Removed	The ListId column in the AutomationEmailQueues table is no longer a key column. The ListId column in the AutomationsRemovedSubscribers view is no longer a key column. The ParentId and BlockId columns in the CampaignFeedback table are no longer key columns. The ProductId and ProductVariantId columns in the EcommerceCartLines table are no longer key columns. The CampaignId column in the EcommerceCarts table is no longer a key column. The ProductId and ProductVariantId columns in the EcommerceOrderLines table are no longer key columns. The FolderId column in the FileManagerFiles table is no longer a key column. The CampaignId column in the ReportAdvice view is no longer a key column. The CampaignId column in the ReportDomainPerformance view is no longer a key column. The FolderId column in the Templates table is no longer a key column.
2024-07-24	24.0.8971	Mailchimp		Added	Added Region as a key column in the ReportLocations view.
2024-06-05	24.0.8922	Python		Added	Added support for Python 3.12.
2024-05-10	24.0.8896	Mailchimp		Changed	Converted ListMemberTags, AutomationEmailQueues and AutomationsRemovedSubscribers to views.
2024-05-10	24.0.8896	Mailchimp		Added	Added AddOrRemoveMemberTags, AddSubscriberToWorkflowEmail and RemoveSubscriberFromWorkflow Stored Procedures.
2024-05-09	24.0.8895	General		Changed	The ROUND function previously did not accept negative precision values. That feature has now been restored.
2024-03-21	23.0.8846	Mailchimp		Changed	Converted ConversationMessages from a table to a view since its API endpoint now only supports reads.
2024-03-15	23.0.8840	General		Added	Created a new SQL function called STRING_COMPARE that provides java's String.compare() ability to SQL queries. Returns a number representative of the compared value of two strings
2024-03-07	23.0.8832	Mailchimp		Added	Added Timestamp as composite column in ListMemberActivity view. Added ContactId column in ListMemberActivity view. Removed ListsTwitterLeadGenCards view as it is no longer supported as per API. AuthorizedApps is converted to view as only GET endpoint is supported as per API.
2023-11-29	23.0.8733	General		Changed	The ROUND function doesn't accept the negative precision values anymore.
2023-11-29	23.0.8733	General		Changed	The returning types of the FDMonth, FDQuarter, FDWeek, LDMonth, LDQuarter, LDWeek functions are changed from Timestamp to Date. The return type of the ABS function will be consistent with the parameter value type.
2023-11-28	23.0.8732	General		Added	Added the HMACSHA256 formatter to allow for secrets to be decoded if it is in base64 format
2023-08-29	23.0.8641	Python		Added	Added support for SQLAlchemy 2.0.
2023-06-20	23.0.8571	General		Added	Added the new sys_lastresultinfo system table.
2023-05-19	23.0.8539	Python		Added	Added support for Python 3.11 on Windows, Linux and Mac.
2023-05-16	23.0.8536	Python		Removed	Removed support for Python 3.7 on Windows and Linux
2023-04-25	23.0.8515	General		Removed	Removed support for the SELECT INTO CSV statement. The core code doesn't support it anymore.
2022-12-14	22.0.8383	General		Changed	Added the Default column to the sys_procedureparameters table.
2022-11-15	22.0.8354	Python		Changed	Updated embedded JRE to jre8u345-b01(Linux x64 / MacOS x64) and jre-17.0.5+8(MacOS aarch64).
2022-09-30	22.0.8308	General		Changed	Added the IsPath column to the sys_procedureparameters table.
2022-05-26	22.0.8181	Mailchimp		Added	Added Support for Surveys and SurveyResponses Views
2022-05-24	22.0.8179	Mailchimp		Changed	Changed provider name to Mailchimp.
2022-05-18	22.0.8173	Python		Added	Added support for Python 3.10 on Windows, Linux, and Mac Added support for Python 3.9 on Mac Added support for Mac M1
2022-05-18	22.0.8173	Python		Removed	Removed support for Python 3.6 on Windows and Linux
2022-03-10	21.0.8104	Mailchimp		Changed	Changed the IncludeCustomFields connection property default value from false to true.
2021-10-29	21.0.7972	Mailchimp		Changed	Converted ECommerceStores from a table to view.
2021-09-02	21.0.7915	General		Added	Added support for the STRING_SPLIT table-valued function in the CROSS APPLY clause.
2021-08-07	21.0.7889	General		Changed	Added the KeySeq column to the sys_foreignkeys table.
2021-08-06	21.0.7888	General		Changed	Added the new sys_primarykeys system table.
2021-07-23	21.0.7874	General		Changed	Updated the Literal Function Names for relative date/datetime functions. Previously, relative date/datetime functions resolved to a different value when used in the projection as opposed to the predicate. For example: SELECT LAST_MONTH() AS lm, Col FROM Table WHERE Col > LAST_MONTH(). Formerly, the two LAST_MONTH() methods would resolve to different datetimes. Now, they will match. As a replacement for the previous behavior, the relative date/datetime functions in the criteria may have an 'L' appended to them. For example: WHERE col > L_LAST_MONTH(). This will continue to resolve to the same values that were previously calculated in the criteria. Note that the "L_" prefix will only work in the predicate - it not available for the projection.
2021-04-25	21.0.7785	General		Added	Added support for handling client side formulas during insert / update. For example: UPDATE Table SET Col1 = CONCAT(Col1, " - ", Col2) WHERE Col2 LIKE 'A%'
2021-04-23	21.0.7783	General		Changed	Updated how display sizes are determined for varchar primary key and foreign key columns so they will match the reported length of the column.
2021-04-16	21.0.7776	General		Added	Non-conditional updates between two columns is now available to all drivers. For example: UPDATE Table SET Col1=Col2
2021-04-16	21.0.7776	General		Changed	Reduced the length to 255 for varchar primary key and foreign key columns.
2021-04-16	21.0.7776	General		Changed	Updated implicit and metadata caching to improve performance and support for multiple connections. Old metadata caches are not compatible - you need to generate new metadata caches if you are currently using CacheMetadata.
2021-04-16	21.0.7776	General		Changed	Updated index naming convention to avoid duplicates.

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

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

Executing Stored Procedures

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

Connecting with the cdata.mailchimp 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.mailchimp as mod
conn = mod.connect("APIKey=myAPIKey;")

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

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

Executing Queries

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

For example:

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

Parameterized Queries

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

For example:

cmd = "SELECT Name, Contact_Company FROM Lists WHERE  = ?"
params = [""]
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 Lists (Name, Contact_Company) VALUES (?, ?)"
params = ["", ""]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Update

The following example modifies an existing record in the table:

cmd = "UPDATE Lists SET Contact_Company = ? WHERE Id = ?"
params = ["", "1"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes an existing record from the table:

cmd = "DELETE FROM Lists WHERE Id = ?"
params = ["1"]
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 GetOAuthAccessToken CallbackURL = ?"
params = ["http://127.0.0.1"]
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 = ["http://127.0.0.1"]
cur.callproc("GetOAuthAccessToken", params)

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

Python Integration Guides

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

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

Complete List of Mailchimp Integration Quickstarts

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

The CData Python Connector for Mailchimp 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 Mailchimp 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("mailchimp:///?APIKey=myAPIKey;")

For SQLAlchemy 2.0, the dialect name is mailchimp_2. To establish a connection, use the following URL format:

from sqlalchemy import create_engine
engine = create_engine("mailchimp_2:///?APIKey=myAPIKey;")

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

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)
Lists_table = Table("Lists", meta)
insp.reflect_table(Lists_table, ["Id","Contact_Company"])

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("mailchimp:///?APIKey=myAPIKey;")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Lists).filter_by(=""):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("Contact_Company: ", instance.Contact_Company)
	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:

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

Implicit Joining

If mapped classes of related Mailchimp 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(Lists).order_by(Lists.AnnualRevenue)
for instance in rs:
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	print("Contact_Company: ", instance.Contact_Company)
	print("---------")

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

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

GROUP BY

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

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

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

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

LIMIT and OFFSET

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

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

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

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

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

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

SUM

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

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

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

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

AVG

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

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

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

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

MAX and MIN

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

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

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

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

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

Obtaining the Table Object

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

Lists_table = Lists.metadata.tables["Lists"]

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(Lists_table.insert(), {"Name": "", "Contact_Company": ""})

Update

The following example modifies an existing record in the table:

session.execute(Lists_table.update().where(Lists_table.c.Id == "1").values(Name="", Contact_Company=""))

Delete

The following example removes an existing record from the table:

session.execute(Lists_table.delete().where(Lists_table.c.Id == "1"))

When combined with the connector, Pandas can be used to generate data frames that contain your Mailchimp 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("mailchimp:///?APIKey=myAPIKey;")

Querying Data

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

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

Modifying Data

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

df = pd.DataFrame({"Name": [""], "Contact_Company": [""]})
df.to_sql("Lists", con=engine, if_exists="append", index=False)

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

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

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

Install Required Modules

Install the Petl modules using the pip utility.

pip install petl

Connecting

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

import petl as etl
import cdata.mailchimp as mod
cnxn = mod.connect("APIKey=myAPIKey;")

Extract, Transform, and Load the Mailchimp Data

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

sql = "SELECT	Name, Contact_Company FROM Lists "
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 Mailchimp tables using Petl's appenddb function.

table1 = [['Name','Contact_Company'],['','']]
etl.appenddb(table1,cnxn,'Lists')

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.mailchimp as mod
conn = mod.connect("APIKey=myAPIKey;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tables"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Views

import cdata.mailchimp as mod
conn = mod.connect("APIKey=myAPIKey;")
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.mailchimp as mod
conn = mod.connect("APIKey=myAPIKey;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tablecolumns WHERE TableName = 'Lists'"
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.mailchimp as mod
conn = mod.connect("APIKey=myAPIKey;")
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.mailchimp as mod
conn = mod.connect("APIKey=myAPIKey;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'GetOAuthAccessToken'"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

This section details a selection of advanced features of the Mailchimp 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 Mailchimp 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 Mailchimp 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 Lists 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 Lists Table

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

SELECT Name, Contact_Company FROM Lists WHERE  = ''

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 Lists WHERE  = ''

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 Lists WHERE  = ''
  

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 Lists#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 Lists WHERE ='' ORDER BY Contact_Company 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 Mailchimp 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 Mailchimp Query Evaluation component examines SQL queries and returns information indicating what parts of the query the connector is not capable of executing natively.

The Mailchimp 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 Mailchimp. 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 Mailchimp 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 Mailchimp API.

INSERT Statements

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

UPDATE Statements

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

DELETE Statements

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

CACHE Statements

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

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

EXECUTE Statements

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

Names and Quoting

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

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

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

• SQL Server:
  

  SELECT DATENAME(yy,GETDATE())

• connector:
  

  SELECT DATENAME('yy',GETDATE())

String Functions

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

Date Functions

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

Math Functions

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

ASCII(character_expression)

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

• character_expression: The character expression.

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

BASE64_ENCODE(input_binary)

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

• input_binary: The binary value to encode.

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

BASE64_DECODE(input_string)

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

• input_string: The Base64-encoded string.

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

CHAR(integer_expression)

Converts the integer ASCII code to the corresponding character.

• integer_expression: The integer from 0 through 255.

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

CHARINDEX(expressionToFind ,expressionToSearch [,start_location ])

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

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

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

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

CHAR_LENGTH(character_expression),

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

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

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

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

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

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

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

CONTAINS(expressionToSearch, expressionToFind)

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

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

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

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

ENDSWITH(character_expression, character_suffix)

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

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

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

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

FILESIZE(uri)

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

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

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

FORMAT(value [, parseFormat], format )

Returns the value formatted with the specified format.

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

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

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

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

HASHBYTES(algorithm, value)

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

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

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

INDEXOF(expressionToSearch, expressionToFind [,start_location ])

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

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

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

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

ISALPHABETIC(character_expression)

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

• character_expression: The string expression to evaluate.

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

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

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

ISALPHANUMERIC(character_expression)

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

• character_expression: The string expression to evaluate.

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

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

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

ISNUMERIC(character_expression)

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

• character_expression: The string expression to evaluate.

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

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

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

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

JSON_EXTRACT(json, jsonpath)

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

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

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

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

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

LEFT ( character_expression , integer_expression )

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

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

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

LEN(string_expression)

Returns the number of characters of the specified string expression.

• string_expression: The string expression.

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

LOCATE(substring,string)

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

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

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

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

LOWER ( character_expression )

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

• character_expression: The character expression.

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

LTRIM(character_expression)

Returns the character expression with leading blanks removed.

• character_expression: The character expression.

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

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

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

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

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

NCHAR(integer_expression)

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

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

OCTET_LENGTH(character_expression),

Returns the number of bytes present in the expression.

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

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

PATINDEX(pattern, expression)

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

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

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

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

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

POSITION(expressionToFind IN expressionToSearch)

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

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

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

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

QUOTENAME(character_string [, quote_character])

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

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

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

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

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

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

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

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

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

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

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

REPLACE(string_expression, string_pattern, string_replacement)

Replaces all occurrences of a string with another string.

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

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

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

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

REPLICATE ( string_expression ,integer_expression )

Repeats the string value the specified number of times.

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

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

REVERSE ( string_expression )

Returns the reverse order of the string expression.

• string_expression: The string.

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

RIGHT ( character_expression , integer_expression )

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

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

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

RTRIM(character_expression)

Returns the character expression after it removes trailing blanks.

• character_expression: The character expression.

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

SOUNDEX(character_expression)

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

• character_expression: The alphanumeric expression of character data.

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

SPACE(repeatcount)

Returns the string that consists of repeated spaces.

• repeatcount: The number of spaces.

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

SPLIT(string, delimiter, offset)

Returns a section of the string between to delimiters.

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

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

STARTSWITH(character_expression, character_prefix)

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

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

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

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

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

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

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

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

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

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

STUFF(character_expression , integer_start , integer_length , replaceWith_expression)

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

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

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

SUBSTRING(string_value FROM start FOR length)

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

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

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

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

You can also drop the FROM and FOR clauses:

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

TEXT_ENCODE(input_string, charset)

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

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

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

TEXT_DECODE(input_binary, charset)

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

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

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

TOSTRING(string_value1)

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

• string_value1: The string to be converted.

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

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

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

TRIM(trimspec trimchar FROM string_value)

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

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

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

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

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

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

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

UNICODE(ncharacter_expression)

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

• ncharacter_expression: The Unicode character expression.

UPPER ( character_expression )

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

• character_expression: The character expression.

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

XML_EXTRACT(xml, xpath [, separator])

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

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

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

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

ABS ( numeric_expression )

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

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

                      SELECT ABS(15);
                      -- Result: 15

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

ACOS ( float_expression )

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

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

                      SELECT ACOS(0.5);
                      -- Result: 1.0471975511966
                    

ASIN ( float_expression )

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

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

                      SELECT ASIN(0.5);
                      -- Result: 0.523598775598299
                    

ATAN ( float_expression )

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

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

                      SELECT ATAN(10);
                      -- Result: 1.47112767430373
                    

ATN2 ( float_expression1 , float_expression2 )

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

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

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

CEILING ( numeric_expression ) or CEIL( numeric_expression )

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

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

                      SELECT CEILING(1.3);
                      -- Result: 2

                      SELECT CEILING(1.5);
                      -- Result: 2

                      SELECT CEILING(1.7);
                      -- Result: 2
                    

COS ( float_expression )

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

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

                      SELECT COS(1);
                      -- Result: 0.54030230586814
                    

COT ( float_expression )

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

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

                      SELECT COT(1);
                      -- Result: 0.642092615934331
                    

DEGREES ( numeric_expression )

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

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

                      SELECT DEGREES(3.1415926);
                      -- Result: 179.999996929531
                    

EXP ( float_expression )

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

• float_expression: The float expression.

                      SELECT EXP(2);
                      -- Result: 7.38905609893065
                    

EXPR ( expression )

Evaluates the expression.

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

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

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

FLOOR ( numeric_expression )

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

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

                      SELECT FLOOR(1.3);
                      -- Result: 1

                      SELECT FLOOR(1.5);
                      -- Result: 1

                      SELECT FLOOR(1.7);
                      -- Result: 1
                    

GREATEST(int1,int2,....)

Returns the greatest of the supplied integers.

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

HEX(value)

Returns a the equivalent hex for the input value.

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

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

JSON_AVG(json, jsonpath)

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

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

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

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

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

JSON_COUNT(json, jsonpath)

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

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

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

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

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

JSON_MAX(json, jsonpath)

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

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

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

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

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

JSON_MIN(json, jsonpath)

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

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

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

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

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

JSON_SUM(json, jsonpath)

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

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

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

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

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

LEAST(int1,int2,....)

Returns the least of the supplied integers.

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

LOG ( float_expression [, base ] )

Returns the natural logarithm of the specified float expression.

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

                      SELECT LOG(7.3890560);
                      -- Result: 1.99999998661119
                    

LOG10 ( float_expression )

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

• float_expression: The expression of type float.

                      SELECT LOG10(10000);
                      -- Result: 4
                    

MOD(dividend,divisor)

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

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

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

NEGATE(real_number)

Returns the opposite to the real number input.

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

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

PI ( )

Returns the constant value of pi.

                  SELECT PI()
                  -- Result: 3.14159265358979 
                

POWER ( float_expression , y )

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

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

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

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

RADIANS ( float_expression )

Returns the angle in radians of the angle in degrees.

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

                      SELECT RADIANS(180);
                      -- Result: 3.14159265358979
                    

RAND ( [ integer_seed ] )

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

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

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

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

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

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

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

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

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

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

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

SIGN ( numeric_expression )

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

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

                      SELECT SIGN(0);
                      -- Result: 0

                      SELECT SIGN(10);
                      -- Result: 1

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

SIN ( float_expression )

Returns the trigonometric sine of the angle in radians.

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

                     SELECT SIN(1);
                     -- Result: 0.841470984807897
                    

SQRT ( float_expression )

Returns the square root of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQRT(100);
                      -- Result: 10
                    

SQUARE ( float_expression )

Returns the square of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQUARE(10);
                      -- Result: 100

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

TAN ( float_expression )

Returns the tangent of the input expression.

• float_expression: The expression of type float.

                      SELECT TAN(1);
                      -- Result: 1.5574077246549
                    

TRUNC(decimal_number,precision)

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

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

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

_ROW_NUMBER_()

Returns a row index as an additional column.

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

CURRENT_DATE()

Returns the current date value.

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

CURRENT_TIMESTAMP()

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

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

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

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

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

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

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

DATEDIFF ( datepart , startdate , enddate )

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

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

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

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

DATE_FORMAT(date,format)

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

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

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

DATEFROMPARTS(integer_year, integer_month, integer_day)

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

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

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

DATENAME(datepart , date)

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

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

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

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

DATEPART(datepart, date [,integer_datefirst])

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

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

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

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

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

Returns the datetime value for the specified date parts.

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

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

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

Returns the datetime value for the specified date parts.

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

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

DATE_TRUNC(date, datepart)

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

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

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

DATE_TRUNC2(datepart, date, [weekday])

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

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

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

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

DAY(date)

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

• date: The datetime string that specifies the date.

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

DAYNAME(date)

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

• date: The datetime string that specifies the date.

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

DAYOFMONTH(date)

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

• date: The datetime string that specifies the date.

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

DAYOFWEEK(date)

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

• date: The datetime string that specifies the date.

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

DAYOFYEAR(date)

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

• date: The datetime string that specifies the date.

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

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

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

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

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

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

EXTRACT(date_part FROM date_column_name)

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

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

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

FDWEEK(date)

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

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

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

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

FDMONTH(date)

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

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

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

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

FDQUARTER(date)

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

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

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

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

FILEMODIFIEDTIME(uri)

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

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

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

FROM_DAYS(datevalue)

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

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

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

FROM_UNIXTIME(time, issecond)

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

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

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

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

GETDATE()

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

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

GETUTCDATE()

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

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

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

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

HOUR(date)

Returns the hour component from the provided datetime.

• date: The datetime string that specifies the date.

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

ISDATE(date, [date_format])

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

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

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

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

LAST_WEEK()

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

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

LAST_MONTH()

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

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

LAST_YEAR()

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

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

LDWEEK(date)

Returns the last day of the provided week.

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

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

LDMONTH(date)

Returns the last day of the provided month.

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

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

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

LDQUARTER(date)

Returns the last day of the provided quarter.

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

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

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

MAKEDATE(year, days)

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

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

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

MINUTE(date)

Returns the minute component from the provided datetime.

• date: The datetime string that specifies the date.

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

MONTH(date)

Returns the month component from the provided datetime.

• date: The datetime string that specifies the date.

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

QUARTER(date)

Returns the quarter associated with the provided datetime.

• date: The datetime string that specifies the date.

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

SECOND(date)

Returns the second component from the provided datetime.

• date: The datetime string that specifies the date.

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

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

Returns the datetime value for the specified date and time.

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

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

STRTODATE(string,format)

Parses the provided string value and returns the corresponding datetime.

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

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

SYSDATETIME()

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

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

SYSUTCDATETIME()

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

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

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

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

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

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

TO_DAYS(date)

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

• date: The datetime string that specifies the date.

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

WEEK(date)

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

• date: The datetime string that specifies the date.

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

YEAR(date)

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

• date: The datetime string.

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

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

L_TODAY()

The current day.

  SELECT * FROM MyTable WHERE MyDateField = L_TODAY()

L_YESTERDAY()

The previous day.

  SELECT * FROM MyTable WHERE MyDateField = L_YESTERDAY()

L_TOMORROW()

The following day.

  SELECT * FROM MyTable WHERE MyDateField = L_TOMORROW()

L_LAST_WEEK()

Every day in the preceding week.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_WEEK()

L_THIS_WEEK()

Every day in the current week.

  SELECT * FROM MyTable WHERE MyDateField = L_THIS_WEEK()

L_NEXT_WEEK()

Every day in the following week.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_WEEK()

Also available:

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

L_LAST_N_DAYS(n)

The previous n days, excluding the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_DAYS(3)

L_NEXT_N_DAYS(n)

The following n days, including the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_DAYS(3)

Also available:

• L_LAST/L_NEXT_90_DAYS

L_LAST_N_WEEKS(n)

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

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_WEEKS(3)

L_NEXT_N_WEEKS(n)

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

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_WEEKS(3)

Also available:

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

A SELECT statement can consist of the following basic clauses.

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

SELECT Syntax

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

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

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

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

Examples

1. Return all columns:
   

   SELECT * FROM Lists

2. Rename a column:
   

   SELECT [Contact_Company] AS MY_Contact_Company FROM Lists

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

   SELECT CAST(AnnualRevenue AS VARCHAR) AS Str_AnnualRevenue FROM Lists

4. Search data:
   

   SELECT * FROM Lists WHERE  = ''

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

   SELECT COUNT(*) AS MyCount FROM Lists 

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

   SELECT COUNT(DISTINCT Contact_Company) FROM Lists 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT Contact_Company FROM Lists 

8. Sort a result set in ascending order:
   

   SELECT Name, Contact_Company FROM Lists  ORDER BY Contact_Company ASC

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

   SELECT Name, Contact_Company FROM Lists 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 Lists WHERE  = @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 Mailchimp.

    SELECT * FROM Lists 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 Lists WHERE  = ''

COUNT(DISTINCT)

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

SELECT COUNT(DISTINCT Name) AS DistinctValues FROM Lists WHERE  = ''

AVG

Returns the average of the column values.

SELECT Contact_Company, AVG(AnnualRevenue) FROM Lists WHERE  = ''  GROUP BY Contact_Company

MIN

Returns the minimum column value.

SELECT MIN(AnnualRevenue), Contact_Company FROM Lists WHERE  = '' GROUP BY Contact_Company

MAX

Returns the maximum column value.

SELECT Contact_Company, MAX(AnnualRevenue) FROM Lists WHERE  = '' GROUP BY Contact_Company

SUM

Returns the total sum of the column values.

SELECT SUM(AnnualRevenue) FROM Lists WHERE  = ''

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

Inner Join

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

Left Join

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

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 Lists

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 Name, Contact_Company, RANK() OVER (ORDER BY Contact_Company) AS Rank FROM Lists

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

SELECT Name, Contact_Company, RANK() OVER (PARTITION BY Name ORDER BY Contact_Company) AS Rank FROM Lists

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 Name, Contact_Company, DENSE_RANK() OVER (PARTITION BY Name ORDER BY Contact_Company) AS Rank FROM Lists

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

SELECT Name, Contact_Company, DENSE_RANK() OVER (PARTITION BY Name ORDER BY Contact_Company) AS Rank FROM Lists

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 Lists (Contact_Company) VALUES ('')

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 Lists SET Contact_Company='' WHERE Id = @myId

To delete information from a table, use DELETE statements.

DELETE Syntax

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

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

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

The following is an example query:

DELETE FROM Lists 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 Lists

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

CACHE CachedLists SELECT * FROM Lists

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 CachedLists SELECT * FROM Lists WHERE DateModified > '2013-04-04'

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

CACHE CachedLists SCHEMA ONLY SELECT * FROM Lists
CACHE CachedLists SELECT Name, Contact_Company FROM Lists

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

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

Stored Procedure Syntax

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

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

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

Example Statements

Reference stored procedure inputs by name:

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

Execute a parameterized stored procedure statement:

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

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

PIVOT

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

PIVOT Synax

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

UNPIVOT

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

UNPIVOT Sytax

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

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

The CData Python Connector for Mailchimp models Mailchimp data as an easy-to-use SQL database with tables, views, and stored procedures.

The connector exposes two schemas:

• See MailChimp Data Model for the available entities in the MailChimp Marketing data model.
• See Transactional Data Model for the available entities in the MailChimp Transactional model.

Tables

The Tables section, which details standard SQL tables, and the Views section, which lists read-only SQL tables, contain samples of what you might have access to in your Mailchimp account. The connector uses the Mailchimp API to process supported filters. The connector processes other filters client-side within the connector.

Commonly used tables include:

Table	Description
Automations	A summary of the automations within an account.
AutomationEmails	A summary of the emails in an automation workflow.
CampaignContents	Retrieves the HTML and plain-text content associated with a specific campaign.
CampaignFeedback	Contains feedback comments submitted by recipients regarding a campaign's content or performance.
Campaigns	Provides detailed information on campaigns created within the account, including type, status, and send statistics.
EcommerceCustomers	Stores records of e-commerce customers linked to Mailchimp, used for purchase tracking and segmentation.
EcommerceOrders	Contains details of e-commerce orders tracked through connected stores, including order totals and customer details.
EcommerceProducts	Lists products available through connected e-commerce integrations, including titles, variants, and pricing.
EcommerceStores	A list of an account's ecommerce stores.
ListActivity	Displays up to 180 days of daily aggregated activity statistics for a given audience list, excluding automation events.
ListMembers	Individuals who are currently or have been previously subscribed to this list, including members who have bounced or unsubscribed.
ListMemberTags	Tags assigned to a certain member/members.
Lists	Contains all audience lists in the account, including configuration details, subscriber counts, and opt-in settings.
ListSegmentMembers	Lists subscribers that belong to a specific segment, including historical membership data.
ListSegments	Provides information on available audience segments, including criteria and segment type.
ReportClickDetails	A list of URLs and unique IDs included in HTML and plain-text versions of a campaign.
ReportEmailActivity	A list of member's subscriber activity in a specific campaign.
Reports	A list of reports containing campaigns marked as Sent.
Templates	A list an account's available templates.

Stored Procedures

Stored Procedures are actions that are invoked via SQL queries. They perform tasks beyond standard CRUD operations, including managing campaigns and transactions, and managing OAuth tokens.

The CData Python Connector for Mailchimp models Mailchimp data as an easy-to-use SQL database. There are three parts to the data model: tables, views, and stored procedures.

The connector dynamically retrieves custom fields for the ListMembers tables when you connect. Any changes you make to the custom fields, such as adding a new field or changing a custom field's data type, are reflected when you reconnect.

Dynamic Tables

Along with the default static tables, the connector also allows querying on dynamic tables. These are tables that are created based on the "audiences" (also called "lists") in your Mailchimp account.

For example, suppose you have three audiences in your account: Old Audience, New Audience, and VIP Audience. The connector creates two new tables for each audience: one is prefixed with ListMembers_ and the other with ListMergeFields_. In this example, the following six tables are created:

• ListMembers_OldAudience
• ListMembers_NewAudience
• ListMembers_VIPAudience
• ListMergeFields_OldAudience
• ListMergeFields_NewAudience
• ListMergeFields_VIPAudience

The tables are created by removing spaces from the audience name, then appending the result to ListMembers_ and ListMergeFields_.

• Tables prefixed with ListMembers_ display all members for a specific audience along with custom field values.
• Those starting with ListMergeFields_ display all custom fields names for members in that audience.

CData Python Connector for Mailchimp Tables

Name	Description
CampaignFeedback	Contains feedback comments submitted by recipients regarding a campaign's content or performance.
CampaignFolders	Lists folders used to organize campaigns within the account.
Campaigns	Provides detailed information on campaigns created within the account, including type, status, and send statistics.
EcommerceCartLines	Lists individual items included in an e-commerce cart, including product details and quantities.
EcommerceCarts	Contains data on e-commerce carts associated with the account, including customer and total value information.
EcommerceCustomers	Stores records of e-commerce customers linked to Mailchimp, used for purchase tracking and segmentation.
EcommerceOrderLines	Lists line items included in e-commerce orders, such as product identifiers, quantities, and prices.
EcommerceOrders	Contains details of e-commerce orders tracked through connected stores, including order totals and customer details.
EcommerceProducts	Lists products available through connected e-commerce integrations, including titles, variants, and pricing.
EcommerceProductVariants	Contains information about product variants, such as size or color, linked to e-commerce items.
FileManagerFiles	Provides a catalog of all files and images stored in the account's File Manager, including metadata and size.
FileManagerFolders	Lists folders available in the File Manager for organizing images and files.
ListInterestCategories	Returns the interest categories for a Mailchimp audience list.
ListInterests	Lists individual interests belonging to a specific interest category within a list.
ListMemberEvents	Contains event information related to individual list members, such as sign-ups or profile updates.
ListMemberNotes	Contains notes created for specific list members, showing the most recent entries by date.
ListMembers	Lists individuals who are currently or have been previously subscribed to this list, including members who have bounced or unsubscribed.
ListMergeFields	Returns merge fields for a Mailchimp list. Merge fields map to subscriber profile data, such as first name or address, and were previously referred to as merge vars.
Lists	Returns a collection of subscriber lists associated with this account. Lists contain subscribers who have opted in to receive correspondence from the account holder or their organization.
ListSegmentMembers	Returns the members of a specific list segment, including those who have bounced or unsubscribed.
ListSegments	Returns a list of available segments for a list.
ListsWebhooks	Lists webhooks configured for an audience list, used to trigger updates based on subscriber activity.
TemplateFolders	Lists folders used to organize design templates within the account.
Templates	Returns an account's available templates.

Contains feedback comments submitted by recipients regarding a campaign's content or performance.

Table-Specific Information

SELECT, INSERT, UPDATE, and DELETE are supported for CampaignFeedback.

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following columns and operators. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
FeedbackId	=
CampaignId	=

For example, the following queries are processed server-side:

SELECT * FROM CampaignFeedback WHERE FeedbackId = '1245'
SELECT * FROM CampaignFeedback WHERE CampaignId = '1245'

Insert

The CampaignId and Message columns are required for INSERT operations.

INSERT INTO CampaignFeedback (CampaignId, Message) VALUES ('myCampaignId', 'myMessage')

Columns

Name	Type	ReadOnly	References	Description
FeedbackId [KEY]	Integer	True		The unique identifier of the feedback entry associated with a campaign.
ParentId	Integer	True		If the feedback is a reply, this field stores the identifier of the parent feedback item.
BlockId	Integer	False		The identifier of the editable content block within the campaign that the feedback refers to.
Message	String	False		The text content of the feedback message provided by the user.
IsComplete	Boolean	False		If the value is 'true', the feedback item has been marked as resolved or completed. If the value is 'false', it remains open.
CreatedBy	String	True		The username of the Mailchimp user who submitted the feedback.
CreatedAt	Datetime	True		The date and time when the feedback entry was created.
UpdatedAt	Datetime	True		The date and time when the feedback entry was last modified.
Source	String	True		Indicates the platform or method through which the feedback was submitted, such as email, web, SMS, iOS, Android, or API.
CampaignId [KEY]	String	False	Campaigns.Id	The unique identifier of the campaign to which the feedback relates.

Lists folders used to organize campaigns within the account.

Table-Specific Information

SELECT, INSERT, UPDATE, and DELTE are supported for CampaignFolders.

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following column and operator. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
Id	=

For example, the following query is processed server-side:

SELECT * FROM CampaignFolders WHERE Id = '1245'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		The unique identifier of the campaign folder within the Mailchimp account.
Name	String	False		The display name of the campaign folder as shown in the Mailchimp interface.
Count	Integer	True		The total number of campaigns currently stored in this folder.

Provides detailed information on campaigns created within the account, including type, status, and send statistics.

Table-Specific Information

SELECT, UPDATE, and DELETE are supported for Campaigns.

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following columns and operators. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
Id	=
Type	=
CreateTime	=, <, >, <=, >=
SendTime	=, <, >, <=, >=
Status	=
Recipients_ListId	=
Settings_FolderId	=

For example, the following queries are processed server-side:

SELECT * FROM Campaigns WHERE Id = '1245'
SELECT * FROM Campaigns WHERE Type = '1245'
SELECT * FROM Campaigns WHERE Status = 'save'
SELECT * FROM Campaigns WHERE Recipients_ListId = '12345'
SELECT * FROM Campaigns WHERE Settings_FolderId = '12345'
SELECT * FROM Campaigns WHERE CreateTime = '2024-02-07 00:00:37.0'
SELECT * FROM Campaigns WHERE CreateTime >= '2024-02-07 00:00:37.0'
SELECT * FROM Campaigns WHERE CreateTime <= '2024-02-07 00:00:37.0'
SELECT * FROM Campaigns WHERE CreateTime > '2024-02-07 00:00:37.0'
SELECT * FROM Campaigns WHERE CreateTime < '2024-02-07 00:00:37.0'

Additionally, the CreateTime column can be used in the ORDER BY clause, as follows:

SELECT * FROM Campaigns ORDER BY CreateTime DESC

Update

UPDATE Campaigns SET Settings_Title = 'Test' WHERE Id = '1234'
UPDATE Campaigns SET Recipients_SegmentOpts = '{"match":"any","saved_segment_id":314699}' WHERE Id = 'cfb12c2228'
UPDATE Campaigns SET Settings_Title = 'Test', Recipients_ListId = '1234', RssOpts_FeedUrl = 'exampleUrl', Type = 'rss', RssOpts_Frequency = 'daily' WHERE Id = '1234'

Note: UPDATE operations cannot be performed on already-sent campaigns. Also, the type of a campaign cannot be updated once it is set. Depending on the campaign type, specific options can be updateable only for specific campaign types. For example, if a campaign is of type "rss" then only the RSS Options fields can be updateable for this campaign. Variant and AbSplitOps settings are not updateable in this case.

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True		The unique identifier assigned to the campaign within the Mailchimp account.
Type	String	False		Specifies the type of campaign, such as regular, plaintext, absplit, or rss.
CreateTime	Datetime	True		The date and time when the campaign was created.
ArchiveUrl	String	True		The URL of the campaign's archived version, accessible via the campaign archive.
LongArchiveUrl	String	True		The original full-length URL of the campaign archive version.
Status	String	True		Indicates the current state of the campaign, such as save, paused, schedule, sending, or sent.
EmailsSent	Integer	True		The total number of emails successfully sent for this campaign.
SendTime	Datetime	True		The date and time when the campaign was sent to recipients.
ContentType	String	False		Defines how the campaign content is structured, such as template, drag_and_drop, HTML, or URL.
Recipients_ListId	String	False	Lists.Id	The unique identifier of the audience list targeted by the campaign.
Recipients_ListName	String	True		The display name of the audience list associated with the campaign.
Recipients_SegmentText	String	True		A formatted HTML string describing the audience segment used for this campaign in plain language.
Recipients_RecipientCount	Integer	True		The total number of recipients included in the campaign's target audience.
Recipients_SegmentOpts	String	False		The segmentation parameters that define which subscribers received this campaign.
Settings_SubjectLine	String	False		The subject line displayed in recipients' inboxes for the campaign email.
Settings_Title	String	False		The internal title of the campaign as shown within the Mailchimp interface.
Settings_FromName	String	False		The sender name displayed in recipients' inboxes for the campaign.
Settings_ReplyTo	String	False		The reply-to email address used for recipient responses to the campaign.
Settings_UseConversation	Boolean	False		If the value is 'true', Mailchimp's Conversations feature is enabled to manage replies within the platform.
Settings_ToName	String	False		The personalized 'To' field value used in the campaign, typically populated with a merge tag like the recipient's first name.
Settings_FolderId	String	False		The identifier of the folder in which this campaign is stored, if applicable.
Settings_Authenticate	Boolean	False		If the value is 'true', Mailchimp authenticated the campaign to improve deliverability. The default value is 'true'.
Settings_AutoFooter	Boolean	False		If the value is 'true', Mailchimp automatically appends the default footer to the campaign content.
Settings_InlineCss	Boolean	False		If the value is 'true', CSS styles are automatically inlined within the campaign's HTML for improved compatibility.
Settings_AutoTweet	Boolean	False		If the value is 'true', Mailchimp automatically posts a tweet linking to the campaign archive when the campaign is sent.
Settings_AutoFbPost	String	False		A list of Facebook page IDs where the campaign is automatically shared when sent.
Settings_FbComments	Boolean	False		If the value is 'true', Facebook comments are enabled on the campaign archive, allowing recipients to leave feedback.
Settings_Timewarp	Boolean	True		If the value is 'true', the campaign uses Mailchimp's Timewarp feature to send emails based on recipients' local time zones.
Settings_TemplateId	Integer	False	Templates.Id	The identifier of the email template used to design the campaign.
Settings_DragAndDrop	Boolean	True		If the value is 'true', the campaign was created using Mailchimp's drag-and-drop editor.
VariateSettings_WinningCombinationId	String	True		The identifier of the A/B test combination selected as the winning version.
VariateSettings_WinningCampaignId	String	True		The identifier of the campaign version sent to the remaining recipients after a winner was chosen.
VariateSettings_WinnerCriteria	String	False		Specifies the metric used to determine the winning campaign version, such as opens, clicks, or manual selection.
VariateSettings_WaitTime	Integer	False		The number of minutes Mailchimp waits before selecting the winning campaign variation.
VariateSettings_TestSize	Integer	False		The percentage of the audience used for testing in an A/B split, ranging from 10 to 100.
VariateSettings_SubjectLines	String	False		Lists the subject lines used in different campaign variations for testing.
VariateSettings_SendTimes	String	False		Lists the send times tested across A/B campaign variations.
VariateSettings_FromNames	String	False		Lists the different 'From' names used across the A/B campaign variations.
VariateSettings_ReplyToAddresses	String	False		Lists the reply-to addresses tested across campaign variations.
VariateSettings_Contents	String	True		Describes the content variations used in the A/B test campaigns.
VariateSettings_Combinations	String	True		Lists the specific combinations of variables used to create each campaign variant.
Tracking_Opens	Boolean	False		If the value is 'true', open tracking is enabled for the campaign. The default value is 'true'.
Tracking_HtmlClicks	Boolean	False		If the value is 'true', click tracking is enabled for links in the HTML version of the campaign. The default value is 'true'.
Tracking_TextClicks	Boolean	False		If the value is 'true', click tracking is enabled for links in the plain-text version of the campaign. The default value is 'true'.
Tracking_GoalTracking	Boolean	False		If the value is 'true', Goal tracking is enabled to measure conversions and subscriber activity on linked websites.
Tracking_Ecomm360	Boolean	False		If the value is 'true', eCommerce360 tracking is enabled to associate campaign performance with sales data.
Tracking_GoogleAnalytics	String	False		The custom slug used for Google Analytics tracking, limited to 50 bytes.
Tracking_Clicktale	String	False		The custom slug used for ClickTale Analytics tracking, limited to 50 bytes.
Tracking_Salesforce	String	False		Salesforce tracking options for the campaign, available when using Mailchimp's Salesforce integration.
Tracking_Capsule	String	False		Capsule CRM tracking options for the campaign, available when using Mailchimp's Capsule integration.
RssOpts_FeedUrl	String	False		The RSS feed URL used for generating campaign content in an RSS-to-Email campaign.
RssOpts_Frequency	String	False		The frequency of the RSS campaign, such as daily, weekly, or monthly.
RssOpts_Schedule	String	False		The defined schedule for sending the RSS-to-Email campaign.
RssOpts_LastSent	String	True		The date when the RSS campaign was last sent.
RssOpts_ConstrainRssImg	Boolean	False		If the value is 'true', Mailchimp constrains image widths from RSS feeds within the campaign layout.
AbSplitOpts_SplitTest	String	True		Specifies the type of A/B split used in the campaign, such as subject, from_name, or schedule.
AbSplitOpts_PickWinner	String	True		Defines how the winning version of the A/B test is selected, based on opens, clicks, or manual choice.
AbSplitOpts_WaitUnits	String	True		Specifies the unit of time (hours or days) used to determine when a winner is chosen.
AbSplitOpts_WaitTime	Integer	True		The duration to wait before selecting a winning version after sending test variants.
AbSplitOpts_SplitSize	Integer	True		The percentage of subscribers included in the test groups for A/B campaigns, typically between 1 and 50.
AbSplitOpts_FromNameA	String	True		The 'From' name used for Group A in an A/B test campaign.
AbSplitOpts_FromNameB	String	True		The 'From' name used for Group B in an A/B test campaign.
AbSplitOpts_ReplyEmailA	String	True		The reply-to email address used for Group A in an A/B test.
AbSplitOpts_ReplyEmailB	String	True		The reply-to email address used for Group B in an A/B test.
AbSplitOpts_SubjectA	String	True		The subject line assigned to Group A in an A/B test campaign.
AbSplitOpts_SubjectB	String	True		The subject line assigned to Group B in an A/B test campaign.
AbSplitOpts_SendTimeA	Datetime	True		The date and time when the A/B test campaign for Group A was sent.
AbSplitOpts_SendTimeB	Datetime	True		The date and time when the A/B test campaign for Group B was sent.
AbSplitOpts_SendTimeWinner	Datetime	True		The date and time when the winning version of the campaign was sent to the remaining audience.
SocialCard_ImageUrl	String	False		The URL of the image displayed in social media previews for the campaign.
SocialCard_Description	String	False		A short description of the campaign content shown in social media previews.
SocialCard_Title	String	False		The title displayed in the social preview card, typically matching the campaign's subject line.
ReportSummary	String	True		Summarizes engagement metrics for sent campaigns, including opens, clicks, and unsubscribes.
DeliveryStatus	String	True		Indicates the current delivery progress or any ongoing sending activity for the campaign.
WebId	Integer	True		The internal Mailchimp web application identifier used to access the campaign at https://{dc}.admin.mailchimp.com/campaigns/show/?id={web_id}.
ParentCampaignId	String	True		If this campaign is part of another, identifies the parent campaign, such as for RSS or automation child campaigns.
NeedsBlockRefresh	Boolean	True		If the value is 'true', indicates that the campaign content needs refreshing in the Mailchimp editor. Deprecated; always returns false.
Resendable	Boolean	True		If the value is 'true', the campaign can be resent to subscribers who did not open the original message.
Recipients_ListIsActive	Boolean	True		If the value is 'true', the audience list used for this campaign is active. If the value is 'false', it has been deleted or disabled.
Settings_PreviewText	String	False		The preview text shown alongside the subject line in recipients' inboxes.
ItemURL	String	True		The full URL reference of the campaign item within the Mailchimp interface.

Lists individual items included in an e-commerce cart, including product details and quantities.

Table-Specific Information

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following columns and operators. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
StoreId	=
CartId	=
Id	=

For example, the following queries are processed server-side:

SELECT * FROM EcommerceCartLines WHERE StoreId = 'Test_Store123' AND CartId = '44'

SELECT * FROM EcommerceCartLines WHERE StoreId = 'Test_Store123' AND CartId = '44' AND Id = '88'

Note: To retrieve the StoreId, query the EcommerceStores view.

Delete

The following is an example of a DELETE operation:

DELETE FROM EcommerceCartLines WHERE StoreId = 'Test_Store123' AND CartId = '44' AND Id = '88'

Note: The API throws an error if the cart contains only one line item. Therefore, you must delete the cart to delete all line items.

Columns

Name	Type	ReadOnly	References	Description
StoreId [KEY]	String	False		The unique identifier of the store where the cart line item is recorded. Each store represents an e-commerce integration connected to Mailchimp.
CartId [KEY]	String	False		The unique identifier of the shopping cart that contains this line item. Each cart groups one or more products selected by a customer.
Id [KEY]	String	False		The unique identifier of the specific line item within the cart, used to differentiate it from other items in the same cart.
ProductId	String	False		The unique identifier of the product added to the cart. This links the line item to the product record in the associated store.
ProductTitle	String	True		The display name or title of the product associated with the cart line item.
ProductVariantId	String	False		The unique identifier of the specific product variant included in the cart, such as a size or color variation.
ProductVariantTitle	String	True		The name or description of the product variant, helping identify the specific version of the product being purchased.
Quantity	Long	False		The number of units of this product variant included in the cart line item.
Price	Decimal	False		The unit price of the product variant in the cart, before applying any discounts or taxes.

Contains data on e-commerce carts associated with the account, including customer and total value information.

Table-Specific Information

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following columns and operators. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
StoreId	=
Id	=

For example, the following queries are processed server-side:

SELECT * FROM EcommerceCarts WHERE StoreId = 'Test_Store123'

SELECT * FROM EcommerceCarts WHERE StoreId = 'Test_Store123' AND Id = '44'

Note: To retrieve the StoreId, query the EcommerceStores view.

Columns

Name	Type	ReadOnly	References	Description
StoreId	String	False		The unique identifier of the store associated with this cart. Each store represents an e-commerce integration connected to the Mailchimp account.
Id	String	False		The unique identifier assigned to the specific cart. This value distinguishes the cart record within the store.
Customer	String	False		Details about the customer who created the cart. For existing customers, include only the customer ID to link the cart to their profile.
CampaignId	String	False	Campaigns.Id	The unique identifier of the Mailchimp campaign associated with the cart, allowing tracking of marketing influence on purchases.
CheckoutUrl	String	False		The direct URL where the customer can view and complete their checkout process for this cart.
CurrencyCode	String	False		The three-letter International Organization for Standardization (ISO) 4217 currency code that defines the currency used in the cart.
OrderTotal	Decimal	False		The total monetary value of all items in the cart, including taxes and discounts.
TaxTotal	Decimal	False		The total tax amount applied to the cart based on the products and applicable tax rules.
Lines	String	False		A list of individual line items contained within the cart. Line item details can be modified through the EcommerceCartLines table.
CreatedAt	Datetime	True		The date and time when the cart was initially created in the store system.
UpdatedAt	Datetime	True		The date and time when the cart was most recently updated or modified.

Stores records of e-commerce customers linked to Mailchimp, used for purchase tracking and segmentation.

Table-Specific Information

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following columns and operators. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
StoreId	=
Id	=
EmailAddress	=

For example, the following queries are processed server-side:

SELECT * FROM EcommerceCustomers WHERE StoreId = 'Test_Store123'

SELECT * FROM EcommerceCustomers WHERE EmailAddress = 'abc@abc.com'

SELECT * FROM EcommerceCustomers WHERE StoreId = 'Test_Store123' AND Id = '44'

Note: To retrieve the StoreId, query the EcommerceStores view.

Columns

Name	Type	ReadOnly	References	Description
StoreId [KEY]	String	False		The unique identifier of the store where the customer record resides. Each store represents an e-commerce integration connected to the Mailchimp account.
Id [KEY]	String	False		The unique identifier assigned to the customer within the store. This value links customer records to related orders and carts.
EmailAddress	String	False		The primary email address of the customer, used for communications, segmentation, and marketing automation.
OptInStatus	Boolean	False		If the value is 'true', the customer has opted in to receive marketing emails. This setting never overrides an existing list member's opt-in status but applies to new contacts added via the e-commerce API.
Company	String	False		The company name associated with the customer, if applicable.
FirstName	String	False		The first name of the customer, used for personalization and segmentation.
LastName	String	False		The last name of the customer, used for personalization and segmentation.
OrdersCount	Integer	True		The total number of completed orders associated with the customer across all recorded transactions.
TotalSpent	Decimal	True		The cumulative monetary amount the customer has spent on completed orders.
Address_Address1	String	False		The first line of the customer's billing or shipping address, typically the street address or P.O. box.
Address_Address2	String	False		An additional address line for apartment numbers, suites, or secondary address details.
Address_City	String	False		The city where the customer resides or where their order is billed or shipped.
Address_Province	String	False		The full name of the customer's state or province.
Address_ProvinceCode	String	False		The two-letter code representing the customer's state or province, following regional postal standards.
Address_PostalCode	String	False		The customer's postal or ZIP code for billing or shipping.
Address_Country	String	False		The full name of the customer's country.
Address_CountryCode	String	False		The two-letter ISO 3166-1 code for the customer's country.
CreatedAt	Datetime	True		The date and time when the customer record was first created in the store.
UpdatedAt	Datetime	True		The date and time when the customer's information was last updated.

Lists line items included in e-commerce orders, such as product identifiers, quantities, and prices.

Table-Specific Information

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following columns and operators. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
StoreId	=
OrderId	=
Id	=

For example, the following queries are processed server-side:

SELECT * FROM EcommerceOrderLines WHERE StoreId = 'Test_Store123' AND OrderId = '44'

SELECT * FROM EcommerceOrderLines WHERE StoreId = 'Test_Store123' AND OrderId = '44' AND Id = '88'

Note: To retrieve the StoreId, query the EcommerceStores view.

Delete

The following is an example of a DELETE operation:

DELETE FROM EcommerceOrderLines WHERE StoreId = 'Test_Store123' AND OrderId = '44' AND Id = '88'

Note: The API throws an error if the order contains only one line item. Therefore, you must delete the order to delete all line items.

Columns

Name	Type	ReadOnly	References	Description
StoreId [KEY]	String	False		The unique identifier of the store where the order line item is recorded. Each store represents an e-commerce integration connected to the Mailchimp account.
OrderId [KEY]	String	False		The unique identifier of the order that contains this specific line item. Each order can include one or more products purchased together.
Id [KEY]	String	False		The unique identifier of the line item within the order, used to distinguish it from other items in the same transaction.
ProductId	String	False		The unique identifier of the product associated with the line item. This links the order record to the product catalog.
ProductTitle	String	True		The display name or title of the product purchased in the order.
ProductVariantId	String	False		The unique identifier of the specific variant of the product included in the order, such as a particular size, color, or model.
ProductVariantTitle	String	True		The name or description of the selected product variant purchased in this order line.
Quantity	Long	False		The number of units of the product variant included in the order line item.
Price	Decimal	False		The unit price of the product variant at the time of purchase, before applying discounts or taxes.
Discount	Decimal	False		The total discount amount applied to this line item, including promotional codes or price adjustments.
ImageUrl	String	True		The URL of the product image associated with the order line item, typically used for display in receipts or analytics dashboards.

Contains details of e-commerce orders tracked through connected stores, including order totals and customer details.

Table-Specific Information

Select

The connector uses the Mailchimp API to process WHERE clause conditions built with the following columns and operators. The rest of the filter is executed client-side within the connector.

Column	Supported Operators
StoreId	=
Id	=
CampaignId	=
Outreach_Id	=
CustomerId	=
HasOutreach	=

For example, the following queries are processed server-side:

SELECT * FROM EcommerceOrders WHERE StoreId = 'Test_Store123'
SELECT * FROM EcommerceOrders WHERE StoreId = 'Test_Store123' AND Id = '44'
SELECT * FROM EcommerceOrders WHERE CampaignId = '12144'
SELECT * FROM EcommerceOrders WHERE Outreach_Id = '12144'
SELECT * FROM EcommerceOrders WHERE CustomerId = '12144'
SELECT * FROM EcommerceOrders WHERE HasOutreach = 'true'

Note: To retrieve the StoreId, query the EcommerceStores view.

Columns

Name	Type	ReadOnly	References	Description
StoreId [KEY]	String	False		The unique identifier of the store where the order was placed. Each store represents an e-commerce integration connected to the Mailchimp account.
Id [KEY]	String	False		The unique identifier assigned to the order within the store. This value distinguishes the order from other transactions.
Customer	String	False		Details about the customer who placed the order. For existing customers, include only the customer ID to associate the order with their record.
CampaignId	String	False	Campaigns.Id	The unique identifier of the Mailchimp campaign associated with the order, enabling marketing attribution and performance tracking.
FinancialStatus	String	False		The financial state of the order, such as refunded, processing, or cancelled, indicating its payment status.
FulfillmentStatus	String	False		The fulfillment progress of the order, such as partial or fulfilled, showing whether items have been shipped or completed.
CurrencyCode	String	False		The three-letter International Organization for Standardization (ISO) 4217 currency code that defines the currency used in the order.
OrderTotal	Decimal	False		The total monetary amount of the order, including products, taxes, and shipping costs, after discounts are applied.
TaxTotal	Decimal	False		The total tax amount applied to the order based on the products purchased and the buyer's location.
ShippingTotal	Decimal	False		The total shipping charge applied to the order.
TrackingCode	String	False		The Mailchimp tracking code applied to the order. It uses the 'mc_tc' parameter from eCommerce360-enabled tracking URLs to measure marketing impact. The allowed values are prec.
ProcessedAtForeign	Datetime	False		The date and time when the order was processed in the connected store system.
CancelledAtForeign	Datetime	False		The date and time when the order was canceled, if applicable.
UpdatedAtForeign	Datetime	False		The date and time when the order record was last updated in the store.
ShippingAddress_Name	String	False		The full name of the recipient for the order's shipping address.
ShippingAddress_Address1	String	False		The first line of the shipping address, typically the street address or P.O. box.
ShippingAddress_Address2	String	False		An additional field for apartment, suite, or building details in the shipping address.
ShippingAddress_City	String	False		The city where the order is being shipped.
ShippingAddress_Province	String	False		The state or province listed in the shipping address.
ShippingAddress_ProvinceCode	String	False		The two-letter code representing the state or province in the shipping address.
ShippingAddress_PostalCode	String	False		The postal or ZIP code for the shipping address.
ShippingAddress_Country	String	False		The full name of the country where the order is shipped.
ShippingAddress_CountryCode	String	False		The two-letter ISO 3166-1 code for the shipping country.
ShippingAddress_Longitude	Double	False		The longitude coordinate associated with the shipping address location.
ShippingAddress_Latitude	Double	False		The latitude coordinate associated with the shipping address location.
ShippingAddress_Phone	String	False		The phone number associated with the shipping address, if provided.
ShippingAddress_Company	String	False		The company name associated with the shipping address, if applicable.
BillingAddress_Name	String	False		The full name of the person or company on the billing address.
BillingAddress_Address1	String	False		The first line of the billing address, typically the street address or P.O. box.
BillingAddress_Address2	String	False		An additional field for apartment, suite, or building details in the billing address.
BillingAddress_City	String	False		The city where the billing address is located.
BillingAddress_Province	String	False		The state or province listed in the billing address.
BillingAddress_ProvinceCode	String	False		The two-letter code representing the state or province in the billing address.
BillingAddress_PostalCode	String	False		The postal or ZIP code for the billing address.
BillingAddress_Country	String	False		The full name of the country for the billing address.
BillingAddress_CountryCode	String	False		The two-letter ISO 3166-1 code for the billing country.
BillingAddress_Longitude	Double	False		The longitude coordinate for the billing address location.
BillingAddress_Latitude	Double	False		The latitude coordinate for the billing address location.
BillingAddress_Phone	String	False		The phone number associated with the billing address.
BillingAddress_Company	String	False		The company name associated with the billing address, if applicable.
Lines	String	False		A list of the order's line items, each representing a product or variant purchased. Line items can be updated through the EcommerceOrderLines table.
Outreach_Id	String	False		The unique identifier of the marketing outreach associated with the order, such as an email campaign or ad.
Outreach_Name	String	True		The name of the outreach campaign linked to the order.
Outreach_Type	String	True		The type of marketing outreach, such as email, social, or advertisement.
Outreach_PublishedTime	String	True		The date and time when the outreach campaign was published, in ISO 8601 format.
TrackingNumber	String	False		The tracking number provided by the shipping carrier for the order.
TrackingCarrier	String	False		The name of the shipping carrier handling the order, such as UPS, FedEx, or DHL.
TrackingUrl	String	False		The URL provided by the carrier to track the shipment's delivery status.
LandingSite	String	False		The URL of the page where the buyer first arrived before completing the order, useful for analyzing marketing funnels.
Promos	String	False		A list of promotional or discount codes applied to the order. When updating, this field is fully replaced with new values.
OrderUrl	String	False		The URL of the order record within the e-commerce system, used for quick reference or access.
DiscountTotal	Decimal	False		The total value of discounts applied to the order across all items.
CustomerId	String	True		A unique identifier for the customer associated with the order.
Customer_EmailAddress	String	True		The primary email address of the customer associated with the order, used for communications and marketing.
Customer_SmsPhoneNumber	String	True		The SMS-capable phone number of the customer associated with the order.
Customer_OptInStatus	Boolean	True		If the value is 'true', the customer has opted in to receive marketing emails from the store.
Customer_Company	String	True		The company name associated with the customer who placed the order, if applicable.
Customer_FirstName	String	True		The first name of the customer who placed the order, used for personalization and segmentation.
Customer_LastName	String	True		The last name of the customer who placed the order, used for personalization and segmentation.
Customer_OrdersCount	Integer	True		The total number of completed orders associated with the customer across all recorded transactions.
Customer_TotalSpent	Decimal	True		The cumulative monetary amount the customer has spent on completed orders across the store.
Customer_Address_Address1	String	True		The first line of the customer's address, typically the street address or P.O. box.
Customer_Address_Address2	String	True		An additional address line for apartment numbers, suites, or secondary address details.
Customer_Address_City	String	True		The city where the customer resides.
Customer_Address_Province	String	True		The full name of the customer's state or province.
Customer_Address_ProvinceCode	String	True		The two-letter code representing the customer's state or province, following regional postal standards.
Customer_Address_PostalCode	String	True		The customer's postal or ZIP code for billing or shipping.
Customer_Address_Country	String	True		The full name of the customer's country.
Customer_Address_CountryCode	String	True		The two-letter ISO 3166-1 code for the customer's country.
Customer_CreatedAt	Datetime	True		The date and time when the customer record was first created in the store.
Customer_UpdatedAt	Datetime	True		The date and time when the customer's information was last updated.

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
HasOutreach	Boolean	If the value is 'true', restricts results to orders associated with an outreach campaign, such as an email or ad. This column is only valid for SELECT operations.