Connecting to Klaviyo

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

Klaviyo Version Support

The connector leverages the Klaviyo API to enable bidirectional access to Klaviyo.

See Also

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

Linux:

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

macOS:

pip install cdata_klaviyo_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_klaviyo_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_klaviyo" 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_klaviyo folder is trivial to find:

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

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

1. Import the module as follows:
   

   import cdata.klaviyo as mod

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

   mod.connect("InitiateOAuth=GETANDREFRESH;")

Connecting to Klaviyo

To connect to Klaviyo, first choose an authentication method by setting the following property:

• AuthScheme: The type of authentication to use whe connecting to Klaviyo.

Depending on the selected method, additional connection properties are required. See below for configuration steps for each supported authentication type.

Accessing Custom Fields

By default, the connector surfaces only system fields. To detect and include custom fields set IncludeCustomFields to true. When IncludeCustomFields is set to true the connector scans RowScanDepth records to detect custom fields and determine their datatype. Set RowScanDepth to 0 to scan all records for custom field detection. Note that enabling custom fields and setting RowScanDepth to 0 or a large value may affect performance.

Accessing List_{ListName} and Segment_{SegmentName} as separate views

By default, the connector surfaces only the system tables and Views. To model each List and Segment as a separate view, similar to the Klaviyo interface, set ListDynamicViews to true.

Authenticating to Klaviyo

You can authenticate to Klaviyo using either an API Key or OAuth PKCE.

API Key

First, generate an API Key, if you have not done so already:

1. Log in to your user account.
2. Navigate to the Private API Key creation page.
3. Click Create Private API Key.
4. Under Private API Key Name, assign a name to the key.
5. Under Select Access Level, choose the required access level. At minimum, Accounts: Read access is required for a successful Test Connection.
6. Click Create to generate the API Key. Copy and save the generated API Key, as it is only shown once.

Next, set the following properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to the value of the Private API Key that you generated.

OAuth PKCE

Set AuthScheme to OAuthPKCE.

Desktop Applications

To authenticate using OAuth PKCE you must create a custom OAuth application. See Creating a Custom OAuth App for instructions.

Get and Refresh the OAuth Access Token

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

• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• OAuthClientId Set this to the client Id assigned when you registered your app.
• OAuthClientSecret Set this to the client secret assigned when you registered your app. This is optional if a secret was not generated during app registration.
• CallbackURL Set this to the redirect URI defined when you registered your app. For example: http://localhost:3333

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

1. The connector obtains an access token from Klaviyo and uses it to request data.
2. The OAuth values are saved in the location specified in OAuthSettingsLocation, allowing them to persist across sessions.

The connector refreshes the access token automatically when it expires.

Web Applications

When connecting via a Web application, you need to register a custom OAuth app with Klaviyo. You can then use the connector to get and manage the OAuth token values. See Creating a Custom OAuth App for more information.

Get an OAuth Access Token

Set the following connection properties to obtain the OAuthAccessToken:

• OAuthClientId: Set this to the client Id in your app settings.
• OAuthClientSecret: Set this to the client secret in your app settings. This is optional if a secret was not generated during app registration.

Then call stored procedures to complete the OAuth exchange:

1. Call the GetOAuthAuthorizationURL stored procedure. Set the CallbackURL input to the Redirect URI you specified in your app settings. The procedure returns the URL to the OAuth endpoint and a PKCEVerifier value. Note this value for later use.

2. Navigate to the URL. Log in and authorize the web application. After authenticating, the browser redirects you to the redirect URI.
3. Call the GetOAuthAccessToken stored procedure. Set the AuthMode input to WEB. Set the PKCEVerifier input of this procedure to the corresponding output from the GetOAuthAuthorizationURL procedure from Step 1.

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

Automatic Refresh of the OAuth Access Token

To have the connector automatically refresh the OAuth access token, set the following properties on the first data connection.

• InitiateOAuth: Set this to REFRESH.
• OAuthClientId: Set this to the client Id in your app settings.
• OAuthClientSecret: Set this to the client secret in your app settings. This is optional if a secret was not generated during app registration.
• OAuthAccessToken: Set this to the access token returned by GetOAuthAccessToken.
• OAuthRefreshToken: Set this to the refresh token returned by GetOAuthAccessToken.
• OAuthSettingsLocation: Set this to the location where you want the connector to save the OAuth values, which persist across connections.

Manual Refresh of the OAuth Access Token

The only value needed to manually refresh the OAuth access token when connecting to data is the OAuth refresh token. Use the RefreshOAuthAccessToken stored procedure to manually refresh the OAuthAccessToken after the ExpiresIn parameter value returned by GetOAuthAccessToken has elapsed, then set the following connection properties:

• OAuthClientId: Set this to the client Id in your app settings.
• OAuthClientSecret: Set this to the client secret in your app settings. This is optional if a secret was not generated during app registration.

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

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

Headless Machines

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

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

Option 1: Obtain and Exchange Verifier Codes

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

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

1. Choose one of these options:
   • If you are using the Embedded OAuth Application, call the GetOAuthAuthorizationURL stored procedure. Open the URL returned by the stored procedure in a browser.
   • If you are using a custom OAuth application, set the following properties:
     • InitiateOAuth: Set to OFF.
     • OAuthClientId: Set to the client Id assigned when you registered your application.
     • OAuthClientSecret: Set to the client secret assigned when you registered your application. This is optional if a secret was not generated during app registration.
     Then call the GetOAuthAuthorizationURL stored procedure with the appropriate CallbackURL. The stored procedure returns the URL to the OAuth endpoint as well as a PKCEVerifier value. Note this value for later use. Open the URL returned by the stored procedure in a browser.
2. Log in and grant permissions to the connector. You are redirected to the redirect URI.

There is a parameter named code appended to the redirect URI. Note the value of this parameter. Later you will set this in the OAuthVerifier connection property.

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

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

• InitiateOAuth: Set this to REFRESH.
• OAuthVerifier: Set this to the noted verifier code (the value of the code parameter in the redirect URI).
• PKCEVerifier: Set this to the value you noted earlier in Step 1.
• OAuthClientId: (custom applications only) Set this to the client Id in your custom OAuth application settings.
• OAuthClientSecret: (custom applications only) Set this to the client secret in the custom OAuth application settings. This is optional if a secret was not generated during app registration.
• OAuthSettingsLocation: Set this to persist the encrypted OAuth authentication values to the specified location.

Test the connection to generate the OAuth settings file, then re-set the following properties to connect:

• InitiateOAuth: Set this to REFRESH.
• OAuthClientId: (custom applications only) Set this to the client Id assigned when you registered your application.
• OAuthClientSecret: (custom applications only) Set this to the client secret assigned when you registered your application. This is optional if a secret was not generated during app registration.
• OAuthSettingsLocation: Set this to the location containing the encrypted OAuth authentication values. Make sure this location gives read and write permissions to the connector to enable the automatic refreshing of the access token.

Option 2: Transfer OAuth Settings

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

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

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

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

• InitiateOAuth: Set this to REFRESH.
• OAuthClientId: (custom applications only) Set this to the client Id assigned when you registered your application.
• OAuthClientSecret: (custom applications only) Set this to the client secret assigned when you registered your application. This is optional if a secret was not generated during app registration.
• OAuthSettingsLocation: Set this to the location of the OAuth settings file you copied from the machine with the browser. Make sure this location gives read and write permissions to the connector to enable the automatic refreshing of the access token.

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 Klaviyo 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:
  

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

• For Mac:
  

  [klaviyo.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.klaviyo 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 an OAuth App

Perform the following steps to generate an OAuth app and its associated credentials:

1. Log in to your user account.
2. Navigate to the App management page.
3. Click Create App.
4. Give the OAuth application a descriptive name. Make note and save Client ID and Client Secret as you will not be able to see the Client Secret again and click on Create and Continue
5. Under Scopes, specify the scopes that your app will request. Ensure that accounts:read is included as it is a required as it is required to successfully establish a connection.
6. Specify a callback URL, which can be any localhost URL (such as http://localhost:33333), in the Redirect URLs field.
7. Under Company details, you can optionally specify your company information.
8. Under App Listing, Provide information about your app. When your app is published, this information will be displayed on your app listing.
9. Click Save.

Note: Even if you have not submitted the app for review (i.e the Oauth Application is still in Draft mode) you can use this app for Authenticating to Klaviyo.

General Changes

Date	Version	Source	Category	Type	Description
2026-05-27	26.0.9643	General	Connection	Removed	Removed the deprecated ReplaceInvalidTypesWithNull connection property. Use the ReplaceInvalidValuesWithNull property instead.
2026-05-22	26.0.9638	Python		Removed	Remove support for Intel x64 architecture on macOS
2026-05-15	26.0.9631	Klaviyo	Data Model	Added	Added the MappedMetrics table to the Klaviyo20260115 schema. This table maps each predefined mapping type to either a standard metric or a custom metric in the account.
2026-05-12	26.0.9628	Klaviyo	Data Model	Added	Marked Klaviyo20250415 as deprecated in the documentation because Klaviyo has deprecated the schema. Added the Klaviyo20260115 schema. Added the following SMS statistics columns to the FlowStats and CampaignStats views: TextMessageRoi, MessageSegmentCountSum, TextMessageCreditUsageAmount, and TextMessageSpend.
2026-05-12	26.0.9628	Klaviyo	Connection	Changed	Updated the default value to Klaviyo20260115 in the Schema connection property.
2026-05-07	26.0.9623	General	Data Model	Added	Added the ColumnCapabilities column to the sys_tablecolumns system table. This column is a bit mask denoting the column's write capabilities.
2026-05-07	26.0.9623	Python		Changed	Updated embedded JRE to jre-17.0.19+10 (Linux x64 / MacOs x64).
2026-04-15	26.0.9601	General	Query Exec	Changed	String comparisons using GREATER, LESS, and CONTAINS operators are now case-insensitive by default.
2026-01-13	25.0.9509	General		Added	Added support for the REGEXP_REPLACE() string function.
2025-12-21	25.0.9486	Python		Added	Added support for custom loggers in Python connectors on Linux and macOS.
2025-12-05	25.0.9470	General		Added	Added support for the INSERT INTO SELECT statement, with driver-side execution for providers that do not support the operation natively.
2025-10-30	25.0.9434	Python		Changed	Updated embedded JRE to jre-17.0.17+10 (Linux x64 / MacOs x64).
2025-10-06	25.0.9410	General		Added	Support for parsing datetime formats using ".S" and ",S" for milliseconds and nanoseconds.
2025-09-12	25.0.9386	General		Added	Added the IsInsertable, IsUpdateable, and IsDeleteable columns to the sys_tables table.

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

For information on how to connect with the klaviyo.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 Klaviyo 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.klaviyo 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.klaviyo as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")

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

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

Executing Queries

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

For example:

cur = conn.execute("SELECT Id, Column1 FROM SampleTable_1")
rs = cur.fetchall()
for row in rs:
	print(row)

Parameterized Queries

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

For example:

cmd = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = ?"
params = ["Bob"]
cur = conn.execute(cmd, params)
rs = cur.fetchall()
for row in rs:
	print(row)

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

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

Insert

The following example adds a new record to the table:

cmd = "INSERT INTO SampleTable_1 (Id, Column1) VALUES (?, ?)"
params = ["Jon Doe", "John"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Update

The following example modifies an existing record in the table:

cmd = "UPDATE SampleTable_1 SET Column1 = ? WHERE Id = ?"
params = ["John", "6"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes an existing record from the table:

cmd = "DELETE FROM SampleTable_1 WHERE Id = ?"
params = ["6"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

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

Calling Stored Procedures Using Execute()

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

cmd = "EXECUTE SampleProcedure Id = ?"
params = ["7"]
conn.execute(cmd, params)

Calling Stored Procedures Using Callproc()

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

cur = conn.cursor()
params = ["7"]
cur.callproc("SampleProcedure", 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 Klaviyo Integration Quickstarts

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

The CData Python Connector for Klaviyo 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 Klaviyo 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("klaviyo:///?InitiateOAuth=GETANDREFRESH;")

For SQLAlchemy 2.0, the dialect name is klaviyo_2. To establish a connection, use the following URL format:

from sqlalchemy import create_engine
engine = create_engine("klaviyo_2:///?InitiateOAuth=GETANDREFRESH;")

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

Note: The following examples employ SQLAlchemy 1.4.

Modeling Data Using a Mapping Class

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

from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class SampleTable_1(Base):
	__tablename__ = "SampleTable_1"
	Id = Column(String, primary_key=True)
	Id = Column(String)
	Column1 = Column(String)

Automatically Reflecting Metadata

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

from sqlalchemy import MetaData
from sqlalchemy.ext.automap import automap_base
meta = MetaData()
abase = automap_base(metadata=meta)
abase.prepare(autoload_with=engine)
SampleTable_1 = abase.classes.SampleTable_1

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)
SampleTable_1_table = Table("SampleTable_1", meta)
insp.reflect_table(SampleTable_1_table, ["Id","Column1"])

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

Querying Data Using the Query Method

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

For example:

engine = create_engine("klaviyo:///?InitiateOAuth=GETANDREFRESH;")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(SampleTable_1).filter_by(Column2="Bob"):
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	print("---------")

Querying Data Using the Execute Method

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

SampleTable_1_table = SampleTable_1.metadata.tables["SampleTable_1"]
for instance in session.execute(SampleTable_1_table.select().where(SampleTable_1_table.c.Column2 == "Bob")):
	print("Id: ", instance.Id)
	print("FullName: ", instance.Name)
	print("City: ", instance.BillingCity)
	print("---------")

Implicit Joining

If mapped classes of related Klaviyo 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(SampleTable_1).order_by(SampleTable_1.AnnualRevenue)
for instance in rs:
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	print("---------")

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

rs = session.execute(SampleTable_1_table.select().order_by(SampleTable_1_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(SampleTable_1.Id).label("CustomCount"), SampleTable_1.Id).group_by(SampleTable_1.Id)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(SampleTable_1_table.select().with_only_columns([func.count(SampleTable_1_table.c.Id).label("CustomCount"), SampleTable_1_table.c.Id]).group_by(SampleTable_1_table.c.Id))
for instance in rs:

LIMIT and OFFSET

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

rs = session.query(SampleTable_1).limit(25).offset(100)
for instance in rs:
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	print("---------")

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

rs = session.execute(SampleTable_1_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(SampleTable_1.Id).label("CustomCount"), SampleTable_1.Id).group_by(SampleTable_1.Id)
for instance in rs:
	print("Count: ", instance.CustomCount)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(SampleTable_1_table.select().with_only_columns([func.count(SampleTable_1_table.c.Id).label("CustomCount"), SampleTable_1_table.c.Id])group_by(SampleTable_1_table.c.Id))
for instance in rs:

SUM

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

rs = session.query(func.sum(SampleTable_1.AnnualRevenue).label("CustomSum"), SampleTable_1.Id).group_by(SampleTable_1.Id)
for instance in rs:
	print("Sum: ", instance.CustomSum)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(SampleTable_1_table.select().with_only_columns([func.sum(SampleTable_1_table.c.AnnualRevenue).label("CustomSum"), SampleTable_1_table.c.Id]).group_by(SampleTable_1_table.c.Id))
for instance in rs:

AVG

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

rs = session.query(func.avg(SampleTable_1.AnnualRevenue).label("CustomAvg"), SampleTable_1.Id).group_by(SampleTable_1.Id)
for instance in rs:
	print("Avg: ", instance.CustomAvg)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(SampleTable_1_table.select().with_only_columns([func.avg(SampleTable_1_table.c.AnnualRevenue).label("CustomAvg"), SampleTable_1_table.c.Id]).group_by(SampleTable_1_table.c.Id))
for instance in rs:

MAX and MIN

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

rs = session.query(func.max(SampleTable_1.AnnualRevenue).label("CustomMax"), func.min(SampleTable_1.AnnualRevenue).label("CustomMin"), SampleTable_1.Id).group_by(SampleTable_1.Id)
for instance in rs:
	print("Max: ", instance.CustomMax)
	print("Min: ", instance.CustomMin)
	print("Id: ", instance.Id)
	print("---------")

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

rs = session.execute(SampleTable_1_table.select().with_only_columns([func.max(SampleTable_1_table.c.AnnualRevenue).label("CustomMax"), func.min(SampleTable_1_table.c.AnnualRevenue).label("CustomMin"), SampleTable_1_table.c.Id]).group_by(SampleTable_1_table.c.Id))
for instance in rs:

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

Obtaining the Table Object

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

SampleTable_1_table = SampleTable_1.metadata.tables["SampleTable_1"]

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(SampleTable_1_table.insert(), {"Id": "Jon Doe", "Column1": "John"})

Update

The following example modifies an existing record in the table:

session.execute(SampleTable_1_table.update().where(SampleTable_1_table.c.Id == "6").values(Id="Jon Doe", Column1="John"))

Delete

The following example removes an existing record from the table:

session.execute(SampleTable_1_table.delete().where(SampleTable_1_table.c.Id == "6"))

When combined with the connector, Pandas can be used to generate data frames that contain your Klaviyo 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("klaviyo:///?InitiateOAuth=GETANDREFRESH;")

Querying Data

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

df = pd.read_sql("""
	SELECT
	   Id,
	   Column1,
     $exNumericCol;
	FROM SampleTable_1;""", engine)
print(df)

Modifying Data

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

df = pd.DataFrame({"Id": ["Jon Doe"], "Column1": ["John"]})
df.to_sql("SampleTable_1", con=engine, if_exists="append", index=False)

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

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

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

Install Required Modules

Install the Petl modules using the pip utility.

pip install petl

Connecting

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

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

Extract, Transform, and Load the Klaviyo Data

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

sql = "SELECT	Id, Column1 FROM SampleTable_1 "
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 Klaviyo tables using Petl's appenddb function.

table1 = [['Id','Column1'],['Jon Doe','John']]
etl.appenddb(table1,cnxn,'SampleTable_1')

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

Views

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

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

import cdata.klaviyo as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tablecolumns WHERE TableName = 'SampleTable_1'"
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.klaviyo as mod
conn = mod.connect("InitiateOAuth=GETANDREFRESH;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedures"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Parameters

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

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

This section details a selection of advanced features of the Klaviyo 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 Klaviyo 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 Klaviyo 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 SampleTable_1 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 SampleTable_1 Table

The following example caches the SampleTable_1 table in the file specified by the CacheLocation property of the connection string.

SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 'Bob'

Common Use Case

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

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

Creating the Cache

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

CACHE SELECT * FROM tableName WHERE ...

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

Updating the Cache

This section describes two ways to update the cache.

Updating with the SELECT Statement

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

CACHE SELECT * FROM SampleTable_1 WHERE Column2 = 'Bob'

Updating with the TRUNCATE Statement

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

  CACHE WITH TRUNCATE SELECT * FROM SampleTable_1 WHERE Column2 = 'Bob'
  

Query the Data in Online or Offline Mode

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

Online: Select Cached Tables

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

SELECT * FROM SampleTable_1#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 SampleTable_1 WHERE Column2='Bob' ORDER BY Column1 ASC

Delete Data from the Cache

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

Common Use Case

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

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

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

Data Type Mapping

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

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

Query Processing

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

For sources that do not support SQL-92, the connector offloads as much of SQL statement processing as possible to Klaviyo 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 Klaviyo Query Evaluation component examines SQL queries and returns information indicating what parts of the query the connector is not capable of executing natively.

The Klaviyo 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 Klaviyo. 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 Klaviyo 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 Klaviyo API.

INSERT Statements

See INSERT Statements for a syntax reference and examples.

UPDATE Statements

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

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
                    

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 SampleTable_1

2. Rename a column:
   

   SELECT [Column1] AS MY_Column1 FROM SampleTable_1

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

   SELECT CAST(AnnualRevenue AS VARCHAR) AS Str_AnnualRevenue FROM SampleTable_1

4. Search data:
   

   SELECT * FROM SampleTable_1 WHERE Column2 = 'Bob'

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

   SELECT COUNT(*) AS MyCount FROM SampleTable_1 

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

   SELECT COUNT(DISTINCT Column1) FROM SampleTable_1 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT Column1 FROM SampleTable_1 

8. Sort a result set in ascending order:
   

   SELECT Id, Column1 FROM SampleTable_1  ORDER BY Column1 ASC

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

   SELECT Id, Column1 FROM SampleTable_1 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 SampleTable_1 WHERE Column2 = @param

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

Pseudo Columns

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

    SELECT * FROM SampleTable_1 WHERE Query = 'Column3 > 100'
    

Window Functions

See Window Functions for SELECT examples containing window functions.

Table-Valued Functions

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

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

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

Window Function Clauses

OVER

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

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

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

PARTITION BY

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

SELECT A, B, <window function> OVER (PARTITION BY A ORDER BY B) From SampleTable_1

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

Window Functions

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

Math

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

COUNT()

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

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

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

COUNT_BIG()

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

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

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

MIN(numeric_column)

Calculates the minimum value of a numerical column per partition.

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

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

MAX(numeric_column)

Calculates the maximum value of a numerical column per partition.

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

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

SUM(numeric_column)

Calculates the sum of a numerical column per partition.

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

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

AVG(numeric_column)

Calculates the average value of a numerical column per partition.

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

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

MEDIAN(numeric_column)

Calculates the median value of a numerical column per partition.

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

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

STDEV(numeric_column)

Calculates the standard deviation of a numerical column per partition.

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

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

STDEVP(numeric_column)

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

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

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

VAR(numeric_column)

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

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

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

VARP(numeric_column)

Calculates the variance population of a numerical column per partition.

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

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

Ranking

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

RANK()

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

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

SELECT Id, Column1, RANK() OVER (ORDER BY Column1) AS Rank FROM SampleTable_1

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

SELECT Id, Column1, RANK() OVER (PARTITION BY Id ORDER BY Column1) AS Rank FROM SampleTable_1

DENSE_RANK()

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

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

SELECT Id, Column1, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Column1) AS Rank FROM SampleTable_1

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

SELECT Id, Column1, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Column1) AS Rank FROM SampleTable_1

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 SampleTable_1 (Column1) VALUES ('John')

To modify existing records, use UPDATE statements.

Update Syntax

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

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

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

The following is an example query:

UPDATE SampleTable_1 SET Column1='John' WHERE Id = @myId

To delete information from a table, use DELETE statements.

DELETE Syntax

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

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

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

The following is an example query:

DELETE FROM SampleTable_1 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 SampleTable_1

Use the following cache statement to cache all rows of a table into the cache table CachedSampleTable_1:

CACHE CachedSampleTable_1 SELECT * FROM SampleTable_1

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 CachedSampleTable_1 SELECT * FROM SampleTable_1 WHERE DateModified > '2013-04-04'

Use the following cache statements to create a table with all available columns that will then cache only a few of them. The sequence of statements cache only Id and Column1 even though the cache table CachedSampleTable_1 has all the columns in SampleTable_1.

CACHE CachedSampleTable_1 SCHEMA ONLY SELECT * FROM SampleTable_1
CACHE CachedSampleTable_1 SELECT Id, Column1 FROM SampleTable_1

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

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

Stored Procedure Syntax

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

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

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

Example Statements

Reference stored procedure inputs by name:

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

Execute a parameterized stored procedure statement:

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

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

PIVOT

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

PIVOT Synax

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

UNPIVOT

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

UNPIVOT Sytax

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

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

The connector models entities in the Klaviyo API as an easy-to-use SQL database, using tables, views, and stored procedures. These are defined in schema files, which are simple, easy-to-read text files that define the structure and organization of data. Live connectivity to these objects means any changes to your Klaviyo account are immediately reflected when using the connector.

The connector exposes the following schemas:

• See the Klaviyo 2026-01-15 Data Model for the available entities in the current default API revision.
• See the Klaviyo 2025-04-15 Data Model (Deprecated) for the available entities in the deprecated 2025-04-15 API revision.

The connector models entities in the Klaviyo API as an easy-to-use SQL database, using tables, views, and stored procedures. These are defined in schema files, which are simple, easy-to-read text files that define the structure and organization of data. Live connectivity to these objects means any changes to your Klaviyo account are immediately reflected when using the connector.

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 Klaviyo account.

Common tables include:

Table	Description
Profiles	Manages all customer profiles in the account.
Events	Lists all event data recorded in the account, such as customer activities and engagement actions.
CampaignStats	Provides performance metrics for campaigns, including data on opens, clicks, and delivery rates.
Tags	Lists all tags in the account.
Lists	Lists all subscriber lists in the account, including list names, Ids, and subscription settings.
Segments	Manages audience segments in the account.
Campaigns	Returns all campaigns in the account. Use filters to narrow results by campaign Id, status, or type.
Templates	Returns all message templates in the account, including email and SMS templates available for reuse.
Metrics	Returns all metrics configured in the account.
Flows	Lists all flows in the account, including automation workflows for messages and actions. Supports retrieving and deleting flow records.
Webhooks	Lists all webhooks configured in the account.
PushTokens	Returns all push notification tokens associated with the account, used to target customer devices for campaigns.
CatalogItems	Returns all catalog items in the account, including product details, pricing, and availability information.
CatalogItemVariants	Lists all item variants for a given catalog item, including variant attributes such as color or size.
CatalogCategories	Lists all product categories configured in the account catalog, including category names, Ids, and metadata.
CampaignMessages	Returns all message records associated with a campaign, including message content, type, and delivery details.
ProfilesSubscriptions	Lists all subscription data for profiles, including consent status and channel-specific preferences.
ProfilesAnalytics	Provides aggregated analytics data for customer profiles, including engagement and purchase activity.
SegmentConditions	Lists all conditions that define each segment, including filters and rules applied to segment membership.
TagGroups	Lists all tag groups in the account.

Stored Procedures

Stored Procedures are actions that are invoked via SQL queries. They perform tasks beyond standard CRUD operations, including managing cloning existing campaigns, generating schema files, and handling authentication flows.

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

CData Python Connector for Klaviyo Tables

Name	Description
CampaignMessages	Returns all message records associated with a campaign, including message content, type, and delivery details.
Campaigns	Returns all campaigns in the account. Use filters to narrow results by campaign Id, status, or type.
CatalogCategories	Lists all product categories configured in the account catalog, including category names, Ids, and metadata.
CatalogCategoryItems	Returns all catalog items that belong to a specific category, identified by category Id.
CatalogItems	Returns all catalog items in the account, including product details, pricing, and availability information.
CatalogItemVariants	Lists all item variants for a given catalog item, including variant attributes such as color or size.
CouponCodes	Manages coupon codes for promotions. Supports querying, creating, and updating coupon code records linked to a coupon.
Coupons	Returns all coupon definitions in the account.
CustomMetrics	Returns all custom metrics created in the account.
Flows	Lists all flows in the account, including automation workflows for messages and actions. Supports retrieving and deleting flow records.
Forms	Returns all sign-up and pop-up forms in the account.
Images	Returns all uploaded images in the account, including metadata such as URLs, file size, and upload timestamps.
Lists	Lists all subscriber lists in the account, including list names, Ids, and subscription settings.
MappedMetrics	Returns all mapped metrics in the account. Mapped metrics define the relationship between mapping types (such as revenue, added_to_cart, ordered_product) and the underlying metric or custom metric used for tracking.
Profiles	Manages all customer profiles in the account.
PushTokens	Returns all push notification tokens associated with the account, used to target customer devices for campaigns.
Segments	Manages audience segments in the account.
TagGroups	Lists all tag groups in the account.
Tags	Lists all tags in the account.
Templates	Returns all message templates in the account, including email and SMS templates available for reuse.
WebFeeds	Manages web feeds for product or content data synchronization.
Webhooks	Lists all webhooks configured in the account.

Returns all message records associated with a campaign, including message content, type, and delivery details.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =
• CampaignId supports the following operator: =

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

SELECT * FROM CampaignMessages Where Id = 'campaignMessageId'
SELECT * FROM CampaignMessages Where CampaignId = 'CampaignId'

Update

Klaviyo allows updating a Campaign Message

Note: When updating a Campaign Message, the channel where the message comes from must always be specified.

UPDATE CampaignMessages SET DefinitionLabel = 'Updated label',  channel='email'  Where ID = '01JP31YSK8N1ET7K47SRGQM7PK'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the campaign message. Use this value to reference or filter message records within a campaign.
CampaignId	String	False	Campaigns.Id		The unique identifier of the campaign associated with this message. Use this value to retrieve all messages belonging to a specific campaign.
CreatedAt	Datetime	True			The date and time when the message was created in Klaviyo.
UpdatedAt	Datetime	True			The date and time when the message was last modified or updated.
Channel	String	False			Specifies the communication channel used to deliver the message, such as email, SMS, or push notification.
ContentBCCEmail	String	False			An optional BCC email address to receive a blind copy of the message when it is sent.
ContentCCEmail	String	False			An optional CC email address to receive a carbon copy of the message when it is sent.
ContentFromEmail	String	False			The sender email address used for this message.
ContentFromLabel	String	False			The display name associated with the sender email address shown to recipients.
ContentPreviewText	String	False			The preview text or snippet displayed in the recipient's inbox before opening the message.
ContentReplyToEmail	String	False			An optional reply-to email address where responses to this message are sent.
ContentSubject	String	False			The subject line of the message displayed to recipients.
DefinitionLabel	String	False			The internal name or label assigned to the message definition.
ContentMediaUrl	String	False			The URL of any media asset, such as an image or video, included in the message content.
DefinitionNotificationType	String	False			Specifies the type of notification or message being sent, such as campaign or transactional.
DefinitionContentTitle	String	False			The title or heading displayed within the message content.
DefinitionContentBody	String	False			The main body content of the message, including text and dynamic placeholders.
DefinitionContentDynamicImage	String	False			Specifies a dynamic image used in the message, such as a personalized image in a push notification.
RenderOptionsShortenLinks	Boolean	False			Indicates whether URLs in the message content should be automatically shortened when rendered.
RenderOptionsAddOrgPrefix	Boolean	False			Indicates whether to automatically add the organization prefix to links included in the message content.
RenderOptionsAddInfoLink	Boolean	False			Indicates whether to include an informational link, such as a help or company link, in the rendered message.
RenderOptionsAddOptOutLanguage	Boolean	False			Indicates whether opt-out or unsubscribe language should be automatically included in the message footer.
SendTimesDatetime	Datetime	False			The scheduled date and time for sending the message to recipients.
SendTimesIsLocal	Boolean	False			Indicates whether the scheduled send time should be interpreted as the recipient's local time.

Returns all campaigns in the account. Use filters to narrow results by campaign Id, status, or type.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =
• Channel supports the following operator: =

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

SELECT * FROM Campaigns Where Id = 'campaignMessageId'
SELECT * FROM Campaigns Where Channel = 'email'

Insert

Klaviyo allows creating new Campaigns. The following query creates a Campaign that targets the lists: NewsletterSubscribers and RecentBuyers.

INSERT INTO Campaigns(Name, IncludedAudience, CampaignMessage) Values ('My Campaign', '["NewsletterSubscribers", "RecentBuyers"]', 'campaignMessageAggregate')    

Update

Klaviyo allows Updating a Campaign.

UPDATE Campaigns SET Name = 'Updated Campaign Name' Where ID = '01K2HQYQ309GDXVH0WM75V12YZ' 

Delete

A campaign can be deleted by providing the Id of the record you want to delete

DELETE from Campaigns Where Id = '01K2HQYQ309GDXVH0WM75V12YZ'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the campaign. Use this value to retrieve or reference a specific campaign record.
Channel	String	True			Specifies the delivery channel used for the campaign, such as email, SMS, or push notification. A channel filter is required when listing campaigns. The allowed values are email, sms, mobile_push.
Name	String	False			The display name of the campaign used for identification in Klaviyo.
Status	String	True			Indicates the current state of the campaign, such as draft, scheduled, or sent.
Archived	Bool	True			Indicates whether the campaign has been archived. Archived campaigns are no longer active but remain available for reference.
ExcludedAudience	String	False			Lists the audiences or segments excluded from receiving this campaign.
IncludedAudience	String	False			Lists the audiences or segments included in the campaign's recipient group.
CreatedAt	Datetime	True			The date and time when the campaign was created in Klaviyo.
ScheduledAt	Datetime	True			The date and time when the campaign was scheduled for delivery.
UpdatedAt	Datetime	True			The date and time when the campaign was last updated, either manually by a user or automatically by the system.
SendTime	Datetime	True			The date and time when the campaign is scheduled to be sent. Returns null if the campaign has not yet been scheduled.
UseSmartSending	Bool	False			Indicates whether Smart Sending is enabled for the campaign. Smart Sending helps prevent subscribers from receiving too many messages in a short period.
SendStrategyDatetime	Datetime	False			Specifies the date and time defined for the send strategy, which determines when the campaign is delivered.
SendStrategyMethod	String	False			Indicates the method used to schedule campaign delivery. Possible values include static, smart_send_time, throttled, immediate, ab_test_campaign, or unsupported.
SendStrategyIsLocal	Bool	False			Indicates whether the campaign should be delivered using the recipient's local time zone rather than a fixed global send time.
SendStrategySendPastRecipientsImmediately	Bool	False			Determines whether recipients in time zones where the scheduled local time has already passed should receive the campaign immediately. Applicable only for local send configurations.
SendStrategyThrottlePercentage	Integer	False			Specifies the percentage of recipients per hour to send the campaign to when throttling is enabled. This controls the pacing of message delivery.
IsTrackingClicks	Bool	False			Indicates whether click tracking is enabled for the campaign. When disabled, the system defaults to the account's tracking settings.
IsTrackingOpens	Bool	False			Indicates whether open tracking is enabled for the campaign. When disabled, the system defaults to the account's tracking settings.
CampaignMessage	String	False			Specifies the message or messages associated with the campaign. This field is required when inserting a new campaign record.

Lists all product categories configured in the account catalog, including category names, Ids, and metadata.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =, IN
• Name supports the following operator: CONTAINS

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

SELECT * FROM CatalogCategories Where Id = 'catalogcategoryId'
SELECT * FROM CatalogCategories Where CONTAINS(Name, 'summer')

Insert

Klaviyo allows creating new CatalogCategories.

INSERT INTO CatalogCategories (ExternalId, Name) Values ('summercollection', 'Summer Collection 2025')    

Update

Klaviyo allows Updating CatalogCategories.

UPDATE CatalogCategories SET Name = 'Summer Collection 2025 (Limited Edition)' Where Id = '$custom:::$default:::summercollection'

Delete

A CatalogCategories can be deleted by providing the Id of the record you want to delete

DELETE from CatalogCategories Where Id = '$custom:::$default:::summercollection'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	False			The unique identifier for the catalog category. This compound Id uses the format {integration}::: {catalog}::: {external_Id}. Currently, only the $custom integration type and the $default catalog are supported.
ExternalId	String	False			The identifier of the catalog category in an external system, used to link Klaviyo categories with external product data sources.
Name	String	False			The display name of the catalog category as it appears in the connected catalog or storefront.
Updated	Datetime	True			The date and time when the catalog category was last modified, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).

Returns all catalog items that belong to a specific category, identified by category Id.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =
• CategoryId supports the following operator: =

By default the connector slices the CategoryId and returns all catalog items from all categories. To return catalog items for a specific category, specify the CategoryId. For example, the following queries are processed server-side:

SELECT * FROM CatalogCategoryItems Where Id = 'catalogcategoryId'
SELECT * FROM CatalogCategoryItems Where CategoryId = 'categoryId'

Insert

Klaviyo allows associating a catalog item with a catalog category. To do this, execute an INSERT statement that defines both the Id and CategoryId.

Note: This INSERT statement does not create a new catalog item. It simply associates the specified Id with the specified CategoryId.

INSERT INTO CatalogCategoryItems (Id, CategoryId) Values ('catalogItemId', 'CatalogCategoryId')    

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	False			The unique identifier for the catalog item. This compound Id uses the format {integration}::: {catalog}::: {external_Id}. Currently, only the $custom integration type and the $default catalog are supported.
CategoryId [KEY]	String	False	CatalogCategories.Id		The unique identifier of the catalog category that the item belongs to. Use this value to retrieve all items within a specific category.
Created	Datetime	False			The date and time when the catalog item was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Description	String	False			A detailed description of the catalog item, typically including product information or key attributes.
ExternalId	String	False			The identifier of the catalog item in an external system, used to synchronize data between Klaviyo and external product sources.
ImageFullUrl	String	False			The URL of the primary full-size image associated with the catalog item.
ImageThumbnailUrl	String	False			The URL of the thumbnail image representing the catalog item.
Images	String	False			A comma-separated list of URLs pointing to all image assets associated with the catalog item.
Price	Double	False			Specifies the price of the catalog item as displayed in Klaviyo emails or campaigns. For most price updates, update the price on any related variants using the Update Catalog Variant endpoint.
Published	Bool	False			Indicates whether the catalog item is currently published and visible in the catalog.
Title	String	False			The title or display name of the catalog item as it appears in the catalog or storefront.
Updated	Datetime	False			The date and time when the catalog item was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Url	String	False			The URL directing to the catalog item's page on the organization's website.

Returns all catalog items in the account, including product details, pricing, and availability information.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =, IN
• Published supports the following operator: =

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

SELECT * FROM CatalogItems Where Id = 'catalogcategoryId'
SELECT * FROM CatalogItems Where Published = true

Insert

You can create new catalog items.

Note: Creating a new catalog item does not automatically associate it with a catalog category. To associate an item with a category, insert a record into the CatalogCategoryItems table.

INSERT INTO CatalogItems (ExternalId, Price, Title, Description, URL) VALUES ('catalogitem-externalid', '44', 'Limited Edition 2025 T-Shirt', 'A blue T-Shirt', 'https://cdata.com')    

Update

You can update catalog items by specifying the Id of the item and setting the fields to update.

UPDATE CatalogItems 
SET Description = 'Limited Edition 2025 T-Shirt, Low on Stock, Order now' 
WHERE Id = 'lowStockItemId'

Delete

You can delete a catalog item by specifying the Id of the record you want to remove.

DELETE from CatalogCategories Where Id = 'lowStockItemId'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the catalog item. This compound Id uses the format {integration}::: {catalog}::: {external_Id}. Currently, only the $custom integration type and the $default catalog are supported.
Created	Datetime	True			The date and time when the catalog item was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Description	String	False			A detailed description of the catalog item, typically including product information or relevant attributes.
ExternalId	String	False			The identifier of the catalog item in an external system, used to link Klaviyo catalog data with external product sources.
ImageFullUrl	String	False			The URL of the primary full-size image representing the catalog item.
ImageThumbnailUrl	String	False			The URL of the thumbnail image associated with the catalog item.
Images	String	False			A comma-separated list of URLs for all images linked to the catalog item.
Price	Double	False			Specifies the price displayed for the catalog item in Klaviyo emails and campaigns. For most price updates, you should also update the prices for related variants using the Update Catalog Variant endpoint.
Published	Bool	False			Indicates whether the catalog item is currently published and available in the account's catalog.
Title	String	False			The title or display name of the catalog item as it appears in the catalog or storefront.
Updated	Datetime	True			The date and time when the catalog item was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Url	String	False			The URL directing to the catalog item's page on the organization's website.

Lists all item variants for a given catalog item, including variant attributes such as color or size.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =
• Sku supports the following operator: =

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

SELECT * FROM CatalogItemVariants WHERE Id = 'catalogItemVariantId'
SELECT * FROM CatalogItemVariants WHERE Sku = 'variant-sku'

Insert

You can create a new catalog item variant by using an INSERT statement.

Note: To create a new record, you must specify the ItemId of the catalog item that the variant belongs to.

INSERT INTO CatalogItemVariants (
  ExternalId, Description, InventoryQuantity, Price, Sku, Title, Url, ItemId
) 
VALUES (
  'variant-external-id', 
  'The description for this variant', 
  5, 
  50, 
  'variant-sku', 
  'The title for this variant', 
  'https://cdata.com', 
  '$custom:::$default:::testingpurpose'
)

Update

You can update a catalog item variant by specifying the Id of the variant and setting the fields you want to change.

UPDATE CatalogItemVariants 
SET Description = 'The description for this variant was changed' 
WHERE Id = 'variant-id'

Delete

You can delete a catalog item variant by specifying the Id of the record you want to remove.

DELETE FROM CatalogItemVariants 
WHERE Id = 'variant-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the catalog item variant. This compound Id uses the format {integration}::: {catalog}::: {external_Id}. Currently, only the $custom integration type and the $default catalog are supported.
ItemId	String	False	CatalogItems.Id		The unique identifier of the catalog item to which this variant belongs. Use this value to link each variant to its parent item.
Created	Datetime	True			The date and time when the catalog item variant was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Description	String	False			A detailed description of the catalog item variant, typically including size, color, or other distinguishing attributes.
ExternalId	String	False			The identifier of the catalog item variant in an external system, used to synchronize variant data between Klaviyo and external product sources.
ImageFullUrl	String	False			The URL of the full-size image representing the catalog item variant.
ImageThumbnailUrl	String	False			The URL of the thumbnail image associated with the catalog item variant.
Images	String	False			A comma-separated list of URLs for all images linked to the catalog item variant.
InventoryPolicy	Int	False			Defines the visibility of the catalog item variant in product feeds or recommendation blocks. Supported values include: 1: the variant will not appear in dynamic product recommendation feeds or blocks if it is out of stock. 0 or 2: the variant can appear in feeds or blocks regardless of inventory quantity.
InventoryQuantity	Int	False			The current quantity of the catalog item variant available in stock.
Price	Double	False			Specifies the price displayed for the catalog item variant in Klaviyo emails or campaigns.
Published	Bool	False			Indicates whether the catalog item variant is published and available in the account's catalog.
Sku	String	False			The stock keeping unit (SKU) identifier assigned to the catalog item variant.
Title	String	False			The title or display name of the catalog item variant as it appears in the catalog or storefront.
Updated	Datetime	True			The date and time when the catalog item variant was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Url	String	False			The URL directing to the catalog item variant's page on the organization's website.

Manages coupon codes for promotions. Supports querying, creating, and updating coupon code records linked to a coupon.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =
• ExpiresAt supports the following operators: =, <=, >=, >
• Status supports the following operator: =

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

SELECT * FROM CouponCodes WHERE Id = 'couponcode-id'
SELECT * FROM CouponCodes WHERE ExpiresAt <= '2025-08-07'
SELECT * FROM CouponCodes WHERE Status = 'ASSIGNED_TO_PROFILE'

Insert

You can create a new coupon code by using an INSERT statement.

Note: You must specify the CouponId of the coupon that this code belongs to.

INSERT INTO CouponCodes (CouponId, UniqueCode) 
VALUES ('coupon-id', 'secret-couponcode-value')

Update

You can update a coupon code by specifying the Id of the record and setting one or more updatable fields.

The following fields can be updated:

• Status
• ExpiresAt

UPDATE CouponCodes 
SET ExpiresAt = '2026-05-07' 
WHERE Id = 'couponcode-id'

Delete

You can delete a coupon code by specifying the Id of the record you want to remove.

DELETE FROM CouponCodes 
WHERE Id = 'couponcode-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the coupon code. This Id is a composite value combining the coupon's unique code with the Id of the parent coupon.
CouponId	String	False	Coupons.Id		The unique identifier of the coupon associated with this coupon code. Use this value to retrieve all codes linked to a specific coupon.
ExpiresAt	Datetime	False			The date and time when the coupon code expires. If not specified or set to null, the expiration date defaults to one year from creation.
Status	String	True			Indicates the current status of the coupon code.
UniqueCode	String	False			The unique code string assigned to an individual customer or profile, representing a single-use or profile-specific coupon instance.

Returns all coupon definitions in the account.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =

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

SELECT * FROM Coupons WHERE Id = 'coupon-id'

Insert

You can create a new coupon by using an INSERT statement.

INSERT INTO Coupons (ExternalId, Description) 
VALUES ('coupon-external-id', 'a new discount coupon')

Update

You can update a coupon by specifying the Id of the record and setting the Description field.

UPDATE Coupons 
SET Description = 'a new discount coupon for 10% Off' 
WHERE Id = 'coupon-id'

Delete

You can delete a coupon by specifying the Id of the record you want to remove.

DELETE FROM Coupons 
WHERE Id = 'coupon-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the coupon within Klaviyo. This internal Id is equivalent to the external Id stored in a connected integration.
Description	String	False			A descriptive summary of the coupon, typically outlining its purpose, discount type, or usage conditions.
ExternalId	String	False			The identifier of the coupon in an external system, such as Shopify or Magento. This value is used to synchronize coupon data between Klaviyo and the external platform.

Returns all custom metrics created in the account.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =

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

SELECT * FROM CustomMetrics WHERE Id = 'custom-metric-id'

Insert

You can create a new custom metric by using an INSERT statement.

INSERT INTO CustomMetrics (
  Name, 
  DefinitionAggregationMethod, 
  DefinitionMetricGroupsMetrics
) 
VALUES (
  'my custom metric name', 
  'CustomMetricDefinitionAggregate', 
  'DefinitionMetricGroupsMetricsAggregate'
)

Update

You can update a custom metric by specifying the Id of the record and setting the Name field.

UPDATE CustomMetrics 
SET Name = 'my custom metric new name' 
WHERE Id = 'custom-metric-id'

Delete

You can delete a custom metric by specifying the Id of the record you want to remove.

DELETE FROM CustomMetrics 
WHERE Id = 'custom-metric-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The Id of the custom metric.
Created	Datetime	True			The datetime when this custom metric was created.
Name	String	False			The name for this custom metric. Names must be unique across the account. Attempting to create a metric with a duplicate name will return a 400 status code.
Updated	Datetime	True			The datetime when this custom metric was updated.
DefinitionAggregationMethod	String	False			Method of aggregation for custom metric measurements. If a metric has a value aggregation method, it will be treated as a revenue metric, such as a Placed Order metric. If a metric has a count aggregation method, it will only be able to report on conversions like an Active on Site metric.
DefinitionMetricGroupsMetrics	String	False			The metric groups associated with this custom metric.

Lists all flows in the account, including automation workflows for messages and actions. Supports retrieving and deleting flow records.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =, IN
• Archived supports the following operator: =
• Updated supports the following operators: >=, >, <=, <, =

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

SELECT * FROM Flows WHERE Id = 'flows-id'
SELECT * FROM Flows WHERE Archived = false
SELECT * FROM Flows WHERE Updated >= '2025-07-08'

Delete

You can delete a flow by specifying the Id of the record you want to remove.

DELETE FROM Flows WHERE Id = 'flows-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	False			The unique identifier for the flow within Klaviyo.
Archived	Bool	False			Indicates whether the flow has been archived. Archived flows are inactive and do not trigger automated actions but remain accessible for record-keeping or reactivation.
Created	Datetime	False			The date and time when the flow was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Name	String	False			The display name of the flow as defined in Klaviyo. Use this value to identify or manage the flow.
Status	String	False			The current operational status of the flow.
TriggerType	String	False			Specifies the type of event or condition that triggers the flow.
Updated	Datetime	False			The date and time when the flow was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).

Returns all sign-up and pop-up forms in the account.

Select

The connector uses the Klaviyo 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.

• Id supports the following operator: =, IN
• Name supports the following operators: =, IN, CONTAINS
• CreatedAt supports the following operators: =, >=, >, <=, <

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

SELECT * FROM Forms 
WHERE Id = 'form-id'

SELECT * FROM Forms 
WHERE Name IN ('form-name1', 'form-name2', 'form-name3')

SELECT * FROM Forms 
WHERE CreatedAt <= '2025-07-08'

Delete

You can delete a form by specifying the Id of the record you want to remove.

DELETE FROM Forms 
WHERE Id = 'form-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	False			The unique identifier for the form, automatically generated by Klaviyo.
Name	String	False			The display name of the form as configured in Klaviyo. Use this value to identify or manage specific forms.
ABTest	Bool	False			Indicates whether the form has an A/B test (split test) configured, regardless of whether the test is currently active or completed.
CreatedAt	Datetime	False			The date and time when the form was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Status	String	False			The current status of the form. A live form with an active draft version is still considered live.
UpdatedAt	Datetime	False			The date and time when the form was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).

Returns all uploaded images in the account, including metadata such as URLs, file size, and upload timestamps.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =, IN
• IsHidden supports the operator: =, IN
• Name supports the operator: =, IN, CONTAINS, LIKE

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

SELECT * FROM Images 
WHERE Id = 'image-id'

SELECT * FROM Images 
WHERE IsHidden = false

SELECT * FROM Images 
WHERE Name LIKE 'cdata%'

Insert

This table does not support INSERT statements. To add a new image, use the UploadImage stored procedure.

Update

You can update images. The fields that can be updated are Name and IsHidden.

UPDATE Images 
SET Name = 'My Image New Name' 
WHERE Id = 'image-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the image stored in Klaviyo.
Format	String	False			Specifies the image file format. Supported formats include jpeg, png, and gif.
IsHidden	Bool	False			Indicates whether the image is hidden from the asset library. The default value is false.
ImageUrl	String	False			Specifies an existing image URL used to import the image into the account.
Name	String	False			The display name assigned to the image. If not provided, the name defaults to the original filename. If the name matches an existing image, a suffix is automatically added.
Size	Int	False			The file size of the image in bytes. The maximum supported file size is 5 MB.
UpdatedAt	Datetime	True			The date and time when the image was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).

Lists all subscriber lists in the account, including list names, Ids, and subscription settings.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =, IN
• Name supports the operator: =, IN

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

SELECT * FROM Lists 
WHERE Id = 'list-id'

SELECT * FROM Lists 
WHERE Name = 'list-name'

Insert

You can create new lists.

INSERT INTO Lists (Name) 
VALUES ('My List')

Update

You can update lists. The field that can be updated is Name.

UPDATE Lists 
SET Name = 'My New List Name' 
WHERE Id = 'list-id'

Delete

You can delete a list by specifying its Id.

DELETE FROM Lists 
WHERE Id = 'list-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the list, automatically generated by Klaviyo.
Created	Datetime	True			The date and time when the list was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Name	String	False			The display name assigned to the list, used to identify it within the account.
OptInProcess	String	True			Specifies the opt-in method used for the list. Supported values include single_opt_in and double_opt_in.
Updated	Datetime	True			The date and time when the list was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).

Returns all mapped metrics in the account. Mapped metrics define the relationship between mapping types (such as revenue, added_to_cart, ordered_product) and the underlying metric or custom metric used for tracking.

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The type of mapping. Allowed values: added_to_cart, cancelled_sales, ordered_product, refunded_sales, revenue, started_checkout, viewed_product.
Updated	Datetime	True			The date and time when this mapping was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
MetricId	String	False			The Id of the metric for this mapping. Set this to update the mapping to a standard metric. Set to null to unset the mapping.
CustomMetricId	String	False			The Id of the custom metric for this mapping. Set this to update the mapping to a custom metric. Set to null to unset the mapping.

Manages all customer profiles in the account.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =, IN
• Email supports the operators: =, IN

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

SELECT * FROM Profiles 
WHERE Id = 'profile-id'

SELECT * FROM Profiles 
WHERE Email IN ('email1@cdata.com', 'email2@cdata.com', 'email3@cdata.com')

Insert

You can create new profiles. For example:

INSERT INTO Profiles 
  (ExternalId, Email, FirstName, LastName, Image, Locale, Address1, City, Country, Ip, Latitude, Longitude, Region, Timezone, Organization, PhoneNumber, Title) 
VALUES 
  ('profiles-external-id', 'email1@example.com', 'John', 'Doe', 'https://image.com', 'en-US', 'Boston - Usa', 'Boston', 'Massachusetts', 'ip address', 'latitude', 'longitude', 'Boston', 'en-US', 'CDATA', '+12345678901', 'title');

Update

You can update profiles. For example:

UPDATE Profiles 
SET City = 'New York' 
WHERE Id = 'profiles-id'

Delete

You can delete a profile by specifying its Id. For example:

DELETE FROM Profiles 
WHERE Id = 'profiles-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the profile, automatically generated by Klaviyo.
ExternalId	String	False			A unique identifier used to associate the Klaviyo profile with a corresponding record in an external system, such as a point-of-sale or CRM platform. The format varies depending on the external system.
Created	Datetime	True			The date and time when the profile was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Email	String	False			The email address associated with the profile.
FirstName	String	False			The first name of the individual associated with the profile.
LastName	String	False			The last name of the individual associated with the profile.
Image	String	False			The URL pointing to the location of the individual's profile image.
LastEventDate	Datetime	True			The date and time of the most recent event that triggered an update to the profile, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Locale	String	False			The locale of the profile, formatted according to the Internet Engineering Task Force – Best Current Practice #47 (IETF BCP 47) language tag standard, such as en-US or fr-CA.
Address1	String	False			The first line of the profile's street address.
Address2	String	False			The second line of the profile's street address, if applicable.
City	String	False			The city associated with the profile's address.
Country	String	False			The full country name associated with the profile's address.
Ip	String	False			The IP address most recently associated with the profile.
Latitude	String	False			The latitude coordinate of the profile's location. It is recommended to provide a precision of four decimal places.
Longitude	String	False			The longitude coordinate of the profile's location. It is recommended to provide a precision of four decimal places.
Region	String	False			The region or administrative area within the country, such as a state or province.
Timezone	String	False			The time zone associated with the profile, ideally following the IANA Time Zone Database format (for example, America/New_York).
Zip	String	False			The postal or ZIP code associated with the profile's address.
Organization	String	False			The organization or company name associated with the profile.
PhoneNumber	String	False			The phone number of the individual in E.164 international format.
Title	String	False			The professional title or job role associated with the profile.
Updated	Datetime	True			The date and time when the profile was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).

Returns all push notification tokens associated with the account, used to target customer devices for campaigns.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =
• Platform supports the operator: =

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

SELECT * FROM PushTokens 
WHERE Id = 'pushtoken-id'

SELECT * FROM PushTokens 
WHERE Platform = 'android'

Delete

You can delete a push token by specifying its Id.

DELETE FROM PushTokens 
WHERE Id = 'pushtoken-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	False			The unique identifier for the push token record in Klaviyo.
Created	Datetime	False			The date and time when the push token was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Token	String	False			The push token value used to identify the device for sending push notifications.
EnablementStatus	String	False			Specifies the enablement status of the push token.
Platform	String	False			Specifies the platform associated with the push token. Supported values include ios and android.
Vendor	String	False			Specifies the vendor that issued the push token.
Background	String	False			Indicates the background state of the device when the push token was recorded.
RecordedDate	String	False			The date when the push token was recorded in Klaviyo.
AppBuild	String	False			The build number of the app that generated the push token.
AppId	String	False			The unique identifier of the app that created the push token.
AppName	String	False			The name of the app that generated the push token.
AppVersion	String	False			The version of the app that generated the push token.
DeviceId	String	False			A relatively stable identifier for the device. This value might change if the app is uninstalled and reinstalled.
DeviceModel	String	False			The model name or identifier of the device that generated the push token.
Environment	String	False			Specifies the environment in which the push token was created, such as debug or release.
KlaviyoSdk	String	False			Specifies the SDK used to generate the push token. Supported values include android, flutter_community, react_native, and swift.
Manufacturer	String	False			The manufacturer of the device associated with the push token.
OsName	String	False			The operating system running on the device. Supported values include android, ios, ipados, macos, and tvos.
OsVersion	String	False			The version of the operating system running on the device.
SdkVersion	String	False			The version of the Klaviyo SDK used to create the push token.

Manages audience segments in the account.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =, IN
• IsActive supports the operator: =, IN

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

SELECT * FROM Segments 
WHERE Id = 'segment-id'

SELECT * FROM Segments 
WHERE IsActive = true

Delete

You can delete a segment by specifying its Id.

DELETE FROM Segments 
WHERE Id = 'segment-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the segment within Klaviyo.
Name	String	False			The display name assigned to the segment, used to identify it within the account.
Updated	Datetime	False			The date and time when the segment was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Created	Datetime	False			The date and time when the segment was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
IsActive	Bool	False			Indicates whether the segment is currently active. Active segments continuously update based on defined conditions.
IsProcessing	Bool	False			Indicates whether the segment is currently processing updates or recalculating its membership.
IsStarred	Bool	False			Indicates whether the segment has been marked as starred for quick access or prioritization.

Lists all tag groups in the account.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =
• Name supports the operator: =, LIKE, CONTAINS

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

SELECT * FROM TagGroups 
WHERE Id = 'tag-group-id'

SELECT * FROM TagGroups 
WHERE CONTAINS(Name, 'cdata')

Insert

You can create new tag groups.

INSERT INTO TagGroups (Name) 
VALUES ('CData')

Update

You can update tag groups. The field that can be updated is Name.

UPDATE TagGroups 
SET Name = 'CData Software' 
WHERE Id = 'tag-group-id'

Delete

You can delete a tag group by specifying its Id.

DELETE FROM TagGroups 
WHERE Id = 'tag-group-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the tag group within Klaviyo.
IsDefault	Bool	True			Indicates whether the tag group is the default group for the account. Each account includes one default tag group that cannot be deleted or duplicated. This value is true for the default tag group and false for all others.
IsExclusive	Bool	False			Specifies whether the tag group is exclusive. In an exclusive tag group, a related resource (such as a campaign or flow) can be linked to only one tag. In a non-exclusive tag group, a resource can be linked to multiple tags within that group.
Name	String	False			The display name of the tag group as defined in Klaviyo.

Lists all tags in the account.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =
• Name supports the operator: =, CONTAINS, LIKE

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

SELECT * FROM Tags 
WHERE Id = 'tag-id'

SELECT * FROM Tags 
WHERE Name = 'CData Tag'

Insert

You can create new tags.

INSERT INTO Tags (Name) 
VALUES ('CData')

Update

You can update tags. The field that can be updated is Name.

UPDATE Tags 
SET Name = 'CData Software' 
WHERE Id = 'tag-id'

Delete

You can delete a tag by specifying its Id.

DELETE FROM Tags 
WHERE Id = 'tag-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the tag within Klaviyo.
Name	String	False			The display name of the tag used to categorize or organize related resources such as campaigns, flows, or segments.

Returns all message templates in the account, including email and SMS templates available for reuse.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =, IN
• Name supports the operators: =, IN

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

SELECT * FROM Templates 
WHERE Id = 'template-id'

SELECT * FROM Templates 
WHERE Name IN ('template-1', 'template-2', 'template-3')

Insert

You can create new templates.

INSERT INTO Templates (Name, EditorType) 
VALUES ('CData Template', 'CODE')

Update

You can update templates. The fields that can be updated are Name and EditorType.

UPDATE Templates 
SET Name = 'CData Template version 2.0' 
WHERE Id = 'template-id'

Delete

You can delete a template by specifying its Id.

DELETE FROM Templates 
WHERE Id = 'template-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the template within Klaviyo.
Name	String	False			The display name of the template as defined in Klaviyo.
EditorType	String	False			Specifies the editor type used to create or edit the template. Supported values include SYSTEM_DRAGGABLE, SIMPLE, CODE, and USER_DRAGGABLE.
Html	String	False			The rendered HTML version of the template used for message content.
Text	String	False			The plain-text version of the template content.
Amp	String	False			The Accelerated Mobile Pages (AMP) version of the template, available when AMP Email functionality is enabled in Klaviyo.
Created	Datetime	True			The date and time when the template was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Updated	Datetime	True			The date and time when the template was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).

Manages web feeds for product or content data synchronization.

Select

The connector uses the Klaviyo API to process WHERE clause conditions with the following columns and operators. Remaining filters are evaluated client-side within the connector.

• Id supports the operator: =
• Name supports the operator: =, CONTAINS, IN

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

SELECT * FROM WebFeeds 
WHERE Id = 'webfeed-id'

SELECT * FROM WebFeeds 
WHERE Name = 'CData WebFeed'

Insert

You can create new web feeds.

INSERT INTO WebFeeds (Name, RequestMethod, Url, ContentType) 
VALUES ('CData WebFeed', 'GET', 'https://cdata.com', 'json')

Update

You can update web feeds. The field that can be updated is Name.

UPDATE WebFeeds 
SET Name = 'CData Software WebFeed' 
WHERE Id = 'webfeed-id'

Delete

You can delete a web feed by specifying its Id.

DELETE FROM WebFeeds 
WHERE Id = 'webfeed-id'

Columns

Name	Type	ReadOnly	References	Description
Id [KEY]	String	True			The unique identifier for the web feed within Klaviyo.
ContentType	String	False			Specifies the format of the web feed content. Supported values include json and xml.
Created	Datetime	True			The date and time when the web feed was created, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Name	String	False			The display name of the web feed as defined in Klaviyo.
RequestMethod	String	False			Specifies the HTTP request method used to retrieve the web feed. Supported values include get and post.
Status	String	True			Indicates the cache or refresh status of the web feed. Possible values include critical_nightly_refresh_timeout, disabled, ok, warning_nightly_refresh_timeout, and warning_periodic_refresh_timeout.
Updated	Datetime	True			The date and time when the web feed was last updated, in ISO 8601 format (YYYY-MM-DDTHH:MM:SS.mmmmmm).
Url	String	False			The URL endpoint for accessing the web feed.