Connecting to Marketo

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

Marketo Version Support

The connector enables SQL92 access to the entities available through version 1 of the REST API.

See Also

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

Linux:

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

macOS:

pip install cdata_marketo_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_marketo_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_marketo" 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_marketo folder is trivial to find:

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

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

1. Import the module as follows:
   

   import cdata.marketo 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("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

Connecting to Marketo

The following connection options are required when connecting to Marketo.

• URL: The URL of the REST API Service. You can locate this in Admin -> Integration -> Web Services -> REST API. Example values: https://123-abc-456.mktorest.com/ or https://123-abc-456.mktorest.com/rest.
• OAuthClientId: The OAuth Client ID associated with your custom service.
• OAuthClientSecret: The OAuth Client Secret associated with your custom service.

For additional connection options, see Connection String Options.

Obtaining OAuth Credentials

Before obtaining OAuth credentials you need to create a custom service if you do not already have one. To do so follow the instructions provided in Creating a Custom Service.

To obtain OAuth credentials for a custom service:

1. Go to the Admin tab.
2. Click on the LaunchPoint option.
3. Select the service and click on View Details.
4. Marketo displays a window that shows the authentication credentials. Use "Client Id" as the value for OAuthClientId and "Client Secret" for OAuthClientSecret.

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 Marketo 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:
  

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

• For Mac:
  

  [marketo.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.marketo as mod
import time

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

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

mod.setCustomLoggerFactory(createLogger)

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

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

Creating a Custom Service

A Custom Service is required to obtain OAuth credentials.

This procedure involves creating a new role with REST API privileges, assigning that role to an existing or new user, and then creating a new service. Note that only users with Admin privileges can create a Custom Service and that the term "service" refers to the entity created to provide OAuth credentials for API access.

Step 1: Create a New Role for API Access

1. Navigate to the Marketo application's Admin area.
2. Navigate to the Security section.
3. Click Users & Roles.
4. Select the Roles tab.
5. Click New Role.
6. Specify a Role Name and select Role permissions. Specify Access API permissions that are specific to the REST API.

Step 2: Assign or Create a New User for API Access

1. Select the Users tab.
2. To assign the role to a user, click Invite New User.
3. Enter the new user information and assign them the role you just created with REST API access.
4. To denote the user as an API Only user, select the API Only option.

Step 3: Create the New Service

1. Navigate to Admin > Integration and click the LaunchPoint option.
2. Click New Service.
3. Specify the Service Type as Custom.
4. Enter a display name and description for the service.
5. Assign the user you just created to the service.

Marketo creates a new REST API service designed for connecting and authenticating to Marketo. This also generates the OAuth-based authentication credentials required for validating user access to the REST API. After creating the Custom Service, refer back to the Obtaining OAuth Credentials section to retrieve the OAuth Client ID and Client Secret needed for authentication.

General Changes

Date	Version	Source	Category	Type	Description
2026-05-27	26.0.9643	General	Connection	Removed	Removed the deprecated ReplaceInvalidTypesWithNull connection property. Use the ReplaceInvalidValuesWithNull property instead.
2026-05-22	26.0.9638	Python		Removed	Remove support for Intel x64 architecture on macOS
2026-05-07	26.0.9623	General	Data Model	Added	Added the ColumnCapabilities column to the sys_tablecolumns system table. This column is a bit mask denoting the column's write capabilities.
2026-05-07	26.0.9623	Python		Changed	Updated embedded JRE to jre-17.0.19+10 (Linux x64 / MacOs x64).
2026-04-15	26.0.9601	General	Query Exec	Changed	String comparisons using GREATER, LESS, and CONTAINS operators are now case-insensitive by default.
2026-03-05	25.0.9560	Marketo	Connection	Added	Added the BulkQueryTimeout connection property, which specifies the maximum time (in minutes) the provider waits for a bulk query response before timing out. Bulk query timeouts were previously managed by the Timeout property. Existing Timeout configurations no longer affect bulk queries.
2026-01-13	25.0.9509	General		Added	Added support for the REGEXP_REPLACE() string function.
2025-12-21	25.0.9486	Python		Added	Added support for custom loggers in Python connectors on Linux and macOS.
2025-12-05	25.0.9470	General		Added	Added support for the INSERT INTO SELECT statement, with driver-side execution for providers that do not support the operation natively.
2025-10-30	25.0.9434	Python		Changed	Updated embedded JRE to jre-17.0.17+10 (Linux x64 / MacOs x64).
2025-10-06	25.0.9410	General		Added	Support for parsing datetime formats using ".S" and ",S" for milliseconds and nanoseconds.
2025-09-12	25.0.9386	General		Added	Added the IsInsertable, IsUpdateable, and IsDeleteable columns to the sys_tables table.
2025-09-10	25.0.9384	General		Changed	All columns in statically defined Views are now reported as read-only.
2025-09-03	25.0.9377	General		Changed	Corrected the behavior when IN criteria with NULL values are used in the projection part. It now returns NULL instead of 0. For example, "NULL IN (1,2)" returns "NULL".
2025-09-01	25.0.9375	General		Added	Added support for using the CAST function with infinity values. This function can cast "inf" and "-inf" to DOUBLE, FLOAT, or REAL.
2025-08-21	25.0.9364	General		Changed	Report behavior change: Fixed inconsistent string value comparisons in non-table queries. For example, "SELECT 'A' = 'a'" previously returned false, but it now returns true.
2025-08-13	25.0.9356	General		Changed	Changed the maximum number of pages held in memory from 15 to 5 for the page providers to decrease heap usage.
2025-07-28	25.0.9340	Marketo		Added	Added the LeadChangeDateTime pseudocolumn to replace filtering on UpdatedAt. LeadChangeDateTime is now marked as an incremental time check column.
2025-07-07	25.0.9319	Python		Removed	Removed the 32-bit version of Windows Python.
2025-07-02	25.0.9314	Python		Removed	Removed support for Python 3.9.
2025-06-25	25.0.9307	General		Removed	Removed the "ADLS Gen 1" value from the ConnectionType property.
2025-06-25	25.0.9307	Python		Added	Added support for Python 3.13 in Windows, Linux, and Mac editions.
2025-06-25	25.0.9307	Python		Removed	Removed support for Python 3.8 as it is no longer supported.
2025-06-20	25.0.9302	General		Added	Created the following functions: TEXT_ENCODE: encodes a string into a different charset (UTF8 → UTF7 and returns a binary array as the result). TEXT_DECODE: takes a binary array and decodes it back into a string when provided the charset. BASE64_ENCODE: takes a binary array and encodes it as a base64 string (varchar). BASE64_DECODE: takes a base 64-encoded string and decodes it into a binary array.
2025-06-18	25.0.9300	General		Changed	The internal code for exception handling has been refactored. Exception messages returned during certain error conditions may now have different wording or formatting.
2025-05-27	25.0.9278	General		Removed	Removed the "Proprietary" enum option from ProxyAuthscheme.
2025-05-12	25.0.9263	Python		Changed	Updated embedded JRE to jre-17.0.15+6 (Linux x64 / MacOS x64) and jre-17.0.15+6 (MacOS aarch64).
2025-02-15	24.0.9177	General		Added	Added support for converting unsigned integer types to the nearest signed data type that has enough precision to hold the unsigned value.This is done for JDBC only because it does not have support for unsigned data types.
2024-12-11	24.0.9111	Marketo		Changed	The 2024 version of the Marketo driver was completely re-designed inorder to continue providing optimal coverage of Marketo entities. A number of tables, views, and columns have been renamed or re-organized to conform to CData standards while providing support for CData's normal feature set. Upgrading to CData's 2024 Marketo driver from prior versions is likely to cause breaking changes, and testing this upgrade with a non-production environment is recommended. Please contact CData's Support team for a full list of changes.

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

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

Executing Stored Procedures

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

Batch Processing

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

Connecting with the cdata.marketo 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.marketo as mod
conn = mod.connect("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

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, Email FROM Leads")
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, Email FROM Leads WHERE Email = ?"
params = ["sample@email.com"]
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 Leads (Id, Email) 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 Leads SET Email = ? WHERE Id = ?"
params = ["John", "c2ef66a5-a545-413b-9312-79a53caadbc4"]
cur = conn.execute(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes an existing record from the table:

cmd = "DELETE FROM Leads WHERE Id = ?"
params = ["c2ef66a5-a545-413b-9312-79a53caadbc4"]
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 GetEmailFullContent Id = ?"
params = ["23685"]
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 = ["23685"]
cur.callproc("GetEmailFullContent", params)

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

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

Insert

The following example adds new records to the table:

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

Update

The following example modifies existing records in the table:

cur = conn.cursor()
cmd = "UPDATE Leads SET Email = ? WHERE Id = ?"
params = [["John", "c2ef66a5-a545-413b-9312-79a53caadbc4"], ["John", "c2ef66a5-a545-413b-9312-79a53caadbc4"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

Delete

The following example removes existing records from the table:

cur = conn.cursor()
cmd = "DELETE FROM Leads WHERE Id = ?"
params = [["c2ef66a5-a545-413b-9312-79a53caadbc4"], ["c2ef66a5-a545-413b-9312-79a53caadbc4"]]
cur.executemany(cmd, params)
print("Records affected: ", cur.rowcount)

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

Python Integration Guides

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

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

Complete List of Marketo Integration Quickstarts

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

The CData Python Connector for Marketo 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 Marketo 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("marketo:///?URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

For SQLAlchemy 2.0, the dialect name is marketo_2. To establish a connection, use the following URL format:

from sqlalchemy import create_engine
engine = create_engine("marketo_2:///?URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

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

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)
Leads_table = Table("Leads", meta)
insp.reflect_table(Leads_table, ["Id","Email"])

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("marketo:///?URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Leads).filter_by(Email="sample@email.com"):
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Email: ", instance.Email)
	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:

Leads_table = Leads.metadata.tables["Leads"]
for instance in session.execute(Leads_table.select().where(Leads_table.c.Email == "sample@email.com")):
	print("Id: ", instance.Id)
	print("FullName: ", instance.Name)
	print("City: ", instance.BillingCity)
	print("---------")

Implicit Joining

If mapped classes of related Marketo 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(Leads).order_by(Leads.AnnualRevenue)
for instance in rs:
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Email: ", instance.Email)
	print("---------")

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

rs = session.execute(Leads_table.select().order_by(Leads_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(Leads.Id).label("CustomCount"), Leads.Id).group_by(Leads.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(Leads_table.select().with_only_columns([func.count(Leads_table.c.Id).label("CustomCount"), Leads_table.c.Id]).group_by(Leads_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(Leads).limit(25).offset(100)
for instance in rs:
	print("Id: ", instance.Id)
	print("Id: ", instance.Id)
	print("Email: ", instance.Email)
	print("---------")

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

rs = session.execute(Leads_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(Leads.Id).label("CustomCount"), Leads.Id).group_by(Leads.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(Leads_table.select().with_only_columns([func.count(Leads_table.c.Id).label("CustomCount"), Leads_table.c.Id])group_by(Leads_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(Leads.AnnualRevenue).label("CustomSum"), Leads.Id).group_by(Leads.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(Leads_table.select().with_only_columns([func.sum(Leads_table.c.AnnualRevenue).label("CustomSum"), Leads_table.c.Id]).group_by(Leads_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(Leads.AnnualRevenue).label("CustomAvg"), Leads.Id).group_by(Leads.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(Leads_table.select().with_only_columns([func.avg(Leads_table.c.AnnualRevenue).label("CustomAvg"), Leads_table.c.Id]).group_by(Leads_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(Leads.AnnualRevenue).label("CustomMax"), func.min(Leads.AnnualRevenue).label("CustomMin"), Leads.Id).group_by(Leads.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(Leads_table.select().with_only_columns([func.max(Leads_table.c.AnnualRevenue).label("CustomMax"), func.min(Leads_table.c.AnnualRevenue).label("CustomMin"), Leads_table.c.Id]).group_by(Leads_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:

Leads_table = Leads.metadata.tables["Leads"]

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(Leads_table.insert(), {"Id": "Jon Doe", "Email": "John"})

Update

The following example modifies an existing record in the table:

session.execute(Leads_table.update().where(Leads_table.c.Id == "c2ef66a5-a545-413b-9312-79a53caadbc4").values(Id="Jon Doe", Email="John"))

Delete

The following example removes an existing record from the table:

session.execute(Leads_table.delete().where(Leads_table.c.Id == "c2ef66a5-a545-413b-9312-79a53caadbc4"))

When combined with the connector, Pandas can be used to generate data frames that contain your Marketo 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("marketo:///?URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

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,
	   Email,
     $exNumericCol;
	FROM Leads;""", 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"], "Email": ["John"]})
df.to_sql("Leads", con=engine, if_exists="append", index=False)

Matplotlib contains a number of tools that can graphically model Marketo 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 Marketo 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 Marketo, you can use the connector's connect function to create a connection using a valid Marketo connection string. If you prefer not to use a direct connection, you can use a SQLAlchemy engine.

import petl as etl
import cdata.marketo as mod
cnxn = mod.connect("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")

Extract, Transform, and Load the Marketo Data

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

sql = "SELECT	Id, Email FROM Leads "
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 Marketo tables using Petl's appenddb function.

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

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.marketo as mod
conn = mod.connect("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tables"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

Views

import cdata.marketo as mod
conn = mod.connect("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")
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.marketo as mod
conn = mod.connect("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_tablecolumns WHERE TableName = 'Leads'"
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.marketo as mod
conn = mod.connect("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")
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.marketo as mod
conn = mod.connect("URL=https://MyMarketoURL.mktorest.com/;OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;")
cur = conn.cursor()
cmd = "SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'GetEmailFullContent'"
cur.execute(cmd)
rs = cur.fetchall()
for row in rs:
	print(row)

This section details a selection of advanced features of the Marketo 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 Marketo 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 Marketo 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 Leads 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.

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 Leads Table

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

SELECT Id, Email FROM Leads WHERE Email = 'sample@email.com'

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 Leads WHERE Email = 'sample@email.com'

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 Leads WHERE Email = 'sample@email.com'
  

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 Leads#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 Leads WHERE Email='sample@email.com' ORDER BY Email 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 Marketo 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 Marketo Query Evaluation component examines SQL queries and returns information indicating what parts of the query the connector is not capable of executing natively.

The Marketo 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 Marketo. 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 Marketo 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 Marketo API.

INSERT Statements

See INSERT Statements for a syntax reference and examples.

UPDATE Statements

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

UPSERT Statements

An UPSERT updates a record if it exists and inserts the record if it does not. See UPSERT Statements for a syntax reference and examples.

DELETE Statements

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

GETDELETED Statements

GETDELETED statements return the Ids of deleted records. See GETDELETED Statements for a syntax reference and examples.

CACHE Statements

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

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

EXECUTE Statements

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

Names and Quoting

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

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

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

• SQL Server:
  

  SELECT DATENAME(yy,GETDATE())

• connector:
  

  SELECT DATENAME('yy',GETDATE())

String Functions

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

Date Functions

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

Math Functions

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

ASCII(character_expression)

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

• character_expression: The character expression.

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

BASE64_ENCODE(input_binary)

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

• input_binary: The binary value to encode.

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

BASE64_DECODE(input_string)

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

• input_string: The Base64-encoded string.

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

CHAR(integer_expression)

Converts the integer ASCII code to the corresponding character.

• integer_expression: The integer from 0 through 255.

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

CHARINDEX(expressionToFind ,expressionToSearch [,start_location ])

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

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

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

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

CHAR_LENGTH(character_expression),

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

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

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

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

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

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

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

CONTAINS(expressionToSearch, expressionToFind)

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

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

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

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

ENDSWITH(character_expression, character_suffix)

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

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

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

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

FILESIZE(uri)

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

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

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

FORMAT(value [, parseFormat], format )

Returns the value formatted with the specified format.

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

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

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

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

HASHBYTES(algorithm, value)

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

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

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

INDEXOF(expressionToSearch, expressionToFind [,start_location ])

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

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

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

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

ISALPHABETIC(character_expression)

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

• character_expression: The string expression to evaluate.

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

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

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

ISALPHANUMERIC(character_expression)

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

• character_expression: The string expression to evaluate.

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

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

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

ISNUMERIC(character_expression)

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

• character_expression: The string expression to evaluate.

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

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

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

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

JSON_EXTRACT(json, jsonpath)

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

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

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

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

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

LEFT ( character_expression , integer_expression )

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

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

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

LEN(string_expression)

Returns the number of characters of the specified string expression.

• string_expression: The string expression.

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

LOCATE(substring,string)

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

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

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

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

LOWER ( character_expression )

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

• character_expression: The character expression.

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

LTRIM(character_expression)

Returns the character expression with leading blanks removed.

• character_expression: The character expression.

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

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

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

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

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

NCHAR(integer_expression)

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

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

OCTET_LENGTH(character_expression),

Returns the number of bytes present in the expression.

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

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

PATINDEX(pattern, expression)

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

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

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

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

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

POSITION(expressionToFind IN expressionToSearch)

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

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

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

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

QUOTENAME(character_string [, quote_character])

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

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

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

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

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

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

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

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

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

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

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

REPLACE(string_expression, string_pattern, string_replacement)

Replaces all occurrences of a string with another string.

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

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

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

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

REPLICATE ( string_expression ,integer_expression )

Repeats the string value the specified number of times.

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

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

REVERSE ( string_expression )

Returns the reverse order of the string expression.

• string_expression: The string.

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

RIGHT ( character_expression , integer_expression )

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

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

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

RTRIM(character_expression)

Returns the character expression after it removes trailing blanks.

• character_expression: The character expression.

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

SOUNDEX(character_expression)

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

• character_expression: The alphanumeric expression of character data.

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

SPACE(repeatcount)

Returns the string that consists of repeated spaces.

• repeatcount: The number of spaces.

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

SPLIT(string, delimiter, offset)

Returns a section of the string between to delimiters.

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

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

STARTSWITH(character_expression, character_prefix)

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

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

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

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

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

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

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

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

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

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

STUFF(character_expression , integer_start , integer_length , replaceWith_expression)

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

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

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

SUBSTRING(string_value FROM start FOR length)

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

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

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

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

You can also drop the FROM and FOR clauses:

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

TEXT_ENCODE(input_string, charset)

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

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

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

TEXT_DECODE(input_binary, charset)

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

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

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

TOSTRING(string_value1)

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

• string_value1: The string to be converted.

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

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

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

TRIM(trimspec trimchar FROM string_value)

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

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

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

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

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

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

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

UNICODE(ncharacter_expression)

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

• ncharacter_expression: The Unicode character expression.

UPPER ( character_expression )

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

• character_expression: The character expression.

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

XML_EXTRACT(xml, xpath [, separator])

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

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

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

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

ABS ( numeric_expression )

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

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

                      SELECT ABS(15);
                      -- Result: 15

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

ACOS ( float_expression )

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

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

                      SELECT ACOS(0.5);
                      -- Result: 1.0471975511966
                    

ASIN ( float_expression )

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

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

                      SELECT ASIN(0.5);
                      -- Result: 0.523598775598299
                    

ATAN ( float_expression )

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

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

                      SELECT ATAN(10);
                      -- Result: 1.47112767430373
                    

ATN2 ( float_expression1 , float_expression2 )

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

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

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

CEILING ( numeric_expression ) or CEIL( numeric_expression )

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

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

                      SELECT CEILING(1.3);
                      -- Result: 2

                      SELECT CEILING(1.5);
                      -- Result: 2

                      SELECT CEILING(1.7);
                      -- Result: 2
                    

COS ( float_expression )

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

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

                      SELECT COS(1);
                      -- Result: 0.54030230586814
                    

COT ( float_expression )

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

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

                      SELECT COT(1);
                      -- Result: 0.642092615934331
                    

DEGREES ( numeric_expression )

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

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

                      SELECT DEGREES(3.1415926);
                      -- Result: 179.999996929531
                    

EXP ( float_expression )

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

• float_expression: The float expression.

                      SELECT EXP(2);
                      -- Result: 7.38905609893065
                    

EXPR ( expression )

Evaluates the expression.

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

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

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

FLOOR ( numeric_expression )

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

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

                      SELECT FLOOR(1.3);
                      -- Result: 1

                      SELECT FLOOR(1.5);
                      -- Result: 1

                      SELECT FLOOR(1.7);
                      -- Result: 1
                    

GREATEST(int1,int2,....)

Returns the greatest of the supplied integers.

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

HEX(value)

Returns a the equivalent hex for the input value.

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

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

JSON_AVG(json, jsonpath)

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

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

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

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

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

JSON_COUNT(json, jsonpath)

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

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

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

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

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

JSON_MAX(json, jsonpath)

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

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

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

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

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

JSON_MIN(json, jsonpath)

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

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

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

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

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

JSON_SUM(json, jsonpath)

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

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

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

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

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

LEAST(int1,int2,....)

Returns the least of the supplied integers.

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

LOG ( float_expression [, base ] )

Returns the natural logarithm of the specified float expression.

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

                      SELECT LOG(7.3890560);
                      -- Result: 1.99999998661119
                    

LOG10 ( float_expression )

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

• float_expression: The expression of type float.

                      SELECT LOG10(10000);
                      -- Result: 4
                    

MOD(dividend,divisor)

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

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

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

NEGATE(real_number)

Returns the opposite to the real number input.

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

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

PI ( )

Returns the constant value of pi.

                  SELECT PI()
                  -- Result: 3.14159265358979 
                

POWER ( float_expression , y )

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

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

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

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

RADIANS ( float_expression )

Returns the angle in radians of the angle in degrees.

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

                      SELECT RADIANS(180);
                      -- Result: 3.14159265358979
                    

RAND ( [ integer_seed ] )

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

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

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

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

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

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

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

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

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

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

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

SIGN ( numeric_expression )

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

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

                      SELECT SIGN(0);
                      -- Result: 0

                      SELECT SIGN(10);
                      -- Result: 1

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

SIN ( float_expression )

Returns the trigonometric sine of the angle in radians.

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

                     SELECT SIN(1);
                     -- Result: 0.841470984807897
                    

SQRT ( float_expression )

Returns the square root of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQRT(100);
                      -- Result: 10
                    

SQUARE ( float_expression )

Returns the square of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQUARE(10);
                      -- Result: 100

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

TAN ( float_expression )

Returns the tangent of the input expression.

• float_expression: The expression of type float.

                      SELECT TAN(1);
                      -- Result: 1.5574077246549
                    

TRUNC(decimal_number,precision)

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

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

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

_ROW_NUMBER_()

Returns a row index as an additional column.

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

CURRENT_DATE()

Returns the current date value.

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

CURRENT_TIMESTAMP()

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

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

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

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

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

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

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

DATEDIFF ( datepart , startdate , enddate )

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

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

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

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

DATE_FORMAT(date,format)

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

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

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

DATEFROMPARTS(integer_year, integer_month, integer_day)

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

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

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

DATENAME(datepart , date)

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

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

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

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

DATEPART(datepart, date [,integer_datefirst])

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

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

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

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

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

Returns the datetime value for the specified date parts.

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

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

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

Returns the datetime value for the specified date parts.

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

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

DATE_TRUNC(date, datepart)

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

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

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

DATE_TRUNC2(datepart, date, [weekday])

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

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

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

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

DAY(date)

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

• date: The datetime string that specifies the date.

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

DAYNAME(date)

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

• date: The datetime string that specifies the date.

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

DAYOFMONTH(date)

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

• date: The datetime string that specifies the date.

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

DAYOFWEEK(date)

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

• date: The datetime string that specifies the date.

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

DAYOFYEAR(date)

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

• date: The datetime string that specifies the date.

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

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

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

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

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

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

EXTRACT(date_part FROM date_column_name)

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

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

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

FDWEEK(date)

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

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

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

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

FDMONTH(date)

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

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

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

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

FDQUARTER(date)

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

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

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

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

FILEMODIFIEDTIME(uri)

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

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

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

FROM_DAYS(datevalue)

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

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

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

FROM_UNIXTIME(time, issecond)

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

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

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

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

GETDATE()

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

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

GETUTCDATE()

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

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

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

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

HOUR(date)

Returns the hour component from the provided datetime.

• date: The datetime string that specifies the date.

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

ISDATE(date, [date_format])

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

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

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

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

LAST_WEEK()

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

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

LAST_MONTH()

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

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

LAST_YEAR()

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

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

LDWEEK(date)

Returns the last day of the provided week.

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

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

LDMONTH(date)

Returns the last day of the provided month.

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

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

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

LDQUARTER(date)

Returns the last day of the provided quarter.

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

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

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

MAKEDATE(year, days)

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

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

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

MINUTE(date)

Returns the minute component from the provided datetime.

• date: The datetime string that specifies the date.

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

MONTH(date)

Returns the month component from the provided datetime.

• date: The datetime string that specifies the date.

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

QUARTER(date)

Returns the quarter associated with the provided datetime.

• date: The datetime string that specifies the date.

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

SECOND(date)

Returns the second component from the provided datetime.

• date: The datetime string that specifies the date.

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

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

Returns the datetime value for the specified date and time.

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

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

STRTODATE(string,format)

Parses the provided string value and returns the corresponding datetime.

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

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

SYSDATETIME()

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

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

SYSUTCDATETIME()

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

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

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

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

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

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

TO_DAYS(date)

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

• date: The datetime string that specifies the date.

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

WEEK(date)

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

• date: The datetime string that specifies the date.

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

YEAR(date)

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

• date: The datetime string.

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

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

L_TODAY()

The current day.

  SELECT * FROM MyTable WHERE MyDateField = L_TODAY()

L_YESTERDAY()

The previous day.

  SELECT * FROM MyTable WHERE MyDateField = L_YESTERDAY()

L_TOMORROW()

The following day.

  SELECT * FROM MyTable WHERE MyDateField = L_TOMORROW()

L_LAST_WEEK()

Every day in the preceding week.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_WEEK()

L_THIS_WEEK()

Every day in the current week.

  SELECT * FROM MyTable WHERE MyDateField = L_THIS_WEEK()

L_NEXT_WEEK()

Every day in the following week.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_WEEK()

Also available:

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

L_LAST_N_DAYS(n)

The previous n days, excluding the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_DAYS(3)

L_NEXT_N_DAYS(n)

The following n days, including the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_DAYS(3)

Also available:

• L_LAST/L_NEXT_90_DAYS

L_LAST_N_WEEKS(n)

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

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_WEEKS(3)

L_NEXT_N_WEEKS(n)

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

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_WEEKS(3)

Also available:

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

A SELECT statement can consist of the following basic clauses.

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

SELECT Syntax

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

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

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

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

Examples

1. Return all columns:
   

   SELECT * FROM Leads

2. Rename a column:
   

   SELECT [Email] AS MY_Email FROM Leads

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

   SELECT CAST(AnnualRevenue AS VARCHAR) AS Str_AnnualRevenue FROM Leads

4. Search data:
   

   SELECT * FROM Leads WHERE Email = 'sample@email.com'

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

   SELECT COUNT(*) AS MyCount FROM Leads 

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

   SELECT COUNT(DISTINCT Email) FROM Leads 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT Email FROM Leads 

8. Sort a result set in ascending order:
   

   SELECT Id, Email FROM Leads  ORDER BY Email ASC

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

   SELECT Id, Email FROM Leads 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 Leads WHERE Email = @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 Marketo.

    SELECT * FROM Leads WHERE CampaignName = 'OldestUpdatedAt'
    

Aggregate Functions

For SELECT examples using aggregate functions, see Aggregate Functions.

JOIN Queries

See JOIN Queries for SELECT query examples using JOINs.

Date Literal Functions

Date Literal Functions contains SELECT examples with date literal functions.

Window Functions

See Window Functions for SELECT examples containing window functions.

Table-Valued Functions

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

COUNT

Returns the number of rows matching the query criteria.

SELECT COUNT(*) FROM Leads WHERE Email = 'sample@email.com'

COUNT(DISTINCT)

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

SELECT COUNT(DISTINCT Id) AS DistinctValues FROM Leads WHERE Email = 'sample@email.com'

AVG

Returns the average of the column values.

SELECT Email, AVG(AnnualRevenue) FROM Leads WHERE Email = 'sample@email.com'  GROUP BY Email

MIN

Returns the minimum column value.

SELECT MIN(AnnualRevenue), Email FROM Leads WHERE Email = 'sample@email.com' GROUP BY Email

MAX

Returns the maximum column value.

SELECT Email, MAX(AnnualRevenue) FROM Leads WHERE Email = 'sample@email.com' GROUP BY Email

SUM

Returns the total sum of the column values.

SELECT SUM(AnnualRevenue) FROM Leads WHERE Email = 'sample@email.com'

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

Inner Join

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

SELECT l.FirstName, l.LastName, a.Link_Value, a.UserAgent FROM Leads l INNER JOIN Activities_ClickLink a ON l.Id = a.LeadId

Left Join

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

SELECT l.FirstName, l.LastName, a.Link_Value, a.UserAgent FROM Leads l LEFT JOIN Activities_ClickLink a ON l.Id = a.LeadId

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 Leads

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, Email, RANK() OVER (ORDER BY Email) AS Rank FROM Leads

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

SELECT Id, Email, RANK() OVER (PARTITION BY Id ORDER BY Email) AS Rank FROM Leads

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, Email, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Email) AS Rank FROM Leads

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

SELECT Id, Email, DENSE_RANK() OVER (PARTITION BY Id ORDER BY Email) AS Rank FROM Leads

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 Leads (Email) 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 Leads SET Email='John' WHERE Id = @myId

An UPSERT statement updates an existing record or creates a new record if an existing record is not identified.

UPSERT Syntax

The UPSERT syntax is the same as for INSERT. Marketo uses the input provided in the VALUES clause to determine whether the record already exists. If the record does not exist, all columns required to insert the record must be specified. See Data Model for any table-specific information.

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

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

The following is an example query:

UPSERT INTO Leads (Email) VALUES ('John')

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 Leads 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 Leads

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

CACHE CachedLeads SELECT * FROM Leads

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 CachedLeads SELECT * FROM Leads 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 Email even though the cache table CachedLeads has all the columns in Leads.

CACHE CachedLeads SCHEMA ONLY SELECT * FROM Leads
CACHE CachedLeads SELECT Id, Email FROM Leads

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

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

Stored Procedure Syntax

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

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

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

Example Statements

Reference stored procedure inputs by name:

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

Execute a parameterized stored procedure statement:

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

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

PIVOT

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

PIVOT Synax

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

UNPIVOT

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

UNPIVOT Sytax

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

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

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

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

Inserting Records from Real Tables

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

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

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

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

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

INSERT INTO DestinationTableWithSameColumns SELECT * FROM SourceTable

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

Inserting Records from Temporary Tables

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

Populate the Temporary Table

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

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

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

Insert Temporary Table Contents into Real Tables

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

INSERT INTO Leads (Email, MyCustomField__c) SELECT Email, MyCustomField__c FROM Leads#TEMP

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

Results

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

Temporary Table Lifespan

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

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

Populate the Temporary Table

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

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

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

Update the Actual Table

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

UPDATE Leads (Id, Email, MyCustomField__c) SELECT Id, Email, MyCustomField__c FROM Leads#TEMP

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

Results

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

Temporary Table Life Span

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

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

Populate the Temporary Table

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

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

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

Delete from the Actual Table

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

DELETE FROM Leads WHERE EXISTS SELECT Id FROM Leads#TEMP

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

Results

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

Temporary Table Life Span

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

The CData Python Connector for Marketo models Marketo entities in relational Tables, Views, and Stored Procedures.

Note: The data model may differ when using the bulk API.

Tables

Tables describes the available tables. The CustomObjects, CustomActivities, Leads, StatiListLeads, Companies, NamedAccounts, Opportunities, Opportunityroles, SalesPersons and ProgramMembers tables are dynamic tables. The data model illustrates a sample of what your Marketo data model might look like. The actual data model is obtained dynamically based on your Marketo instance.

Views

Views describes the available read-only data, such as Activities_NewLead, WeeklyUsageStatistics and more.

Stored Procedures

Stored Procedures are function-like interfaces to Marketo. They can be used to create, update, modify, and retrieve information that cannot be modeled as tables or views. Some procedures are used to download/upload files and interact with the Bulk API.

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

CData Python Connector for Marketo Tables

Name	Description
Activities_Custom	Sample custom activity.
Companies	Query Companies in Marketo.
CustomObjects	Query custom objects for a Marketo organization.
Emails	Query emails for a Marketo organization.
EmailTemplates	Query email templates for a Marketo organization.
Folders	Create, update, delete, and query Folders for a Marketo organization.
Forms	Create, update, delete and query Forms for a Marketo organization.
LandingPageContentSection	Create, update, delete, and query Content Sections of Landing Pages for a Marketo organization.
LandingPages	Create, update, delete and query Landing Pages for a Marketo organization.
LandingPageTemplates	Create, update, delete and query Landing Page Templates for a Marketo organization.
Leads	Query Leads of a Marketo organization.
NamedAccounts	Query Named Accounts for a Marketo organization.
Opportunities	Query Opportunities in Marketo.
OpportunityRoles	Query Opportunity Roles in Marketo.
ProgramMembers	Query Program Members in Marketo.
Programs	Create, update, delete, and query Programs for a Marketo organization.
SalesPersons	Query Sales Persons in Marketo.
SmartCampaigns	Query Smart Campaigns for a Marketo organization.
SmartLists	Query Smart Lists for a Marketo organization.
Snippets	Create, update, delete and query snippets for a Marketo organization.
StaticListMembership	Query relations of static lists and leads in Marketo.
StaticLists	Query Static Lists for a Marketo organization.
Tokens	Create, delete, and query Tokens for a Marketo organization.
Users	Query users for a Marketo organization.
UserWorkspaceRoles	Query user roles for each workspace in Marketo.

Sample custom activity.

Each custom activity in your Marketo organization is returned as a separate table, apart from the main "Activities" table. Each table name is prefixed with 'Activity_' followed by the name of your custom activity.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

The following queries are related to a sample custom activity 'Activities_AttendConference'. This table may not exist in your Marketo instance.

	SELECT * FROM Activities_AttendConference WHERE ListId = '1014'

Insert

	INSERT INTO Activities_AttendConference (ActivityDate, LeadId, PrimaryAttributeValue, ConferenceDate, NumberOfAttendees) VALUES ('2024-01-01T00:01:00Z', 4, 'asd', '2024-01-10', 4)

Bulk

When UseBulkAPI is set to true the ActivityDate filter can be processed server-side. Sample queries:

	SELECT * FROM Activities_AttendConference WHERE ActivityDate > '2024-01-01T00:00:00Z'
	SELECT * FROM Activities_AttendConference WHERE ActivityDate >= '2024-01-01T00:00:00Z' AND ActivityDate <= '2024-03-01T00:00:00Z'
	SELECT * FROM Activities_AttendConference WHERE ListId = '1014' AND ActivityDate > '2024-01-01T00:00:00Z'
	SELECT * FROM Activities_AttendConference WHERE ListId = '1014' AND ActivityDate >' 2024-01-01T00:00:00Z'

Columns

Name	Type	ReadOnly	Operators	Description
ActivityId [KEY]	String	False		Unique id of the activity.
ActivityDate	Datetime	False		Datetime of the activity.
LeadId	Int	False	=,IN	Id of the lead associated to the activity.
CampaignId	Int	False		Id of the associated Smart Campaign, if applicable.
ListId	Int	False	=	Mirror column that can be used only as a filter. Will retrieve activities for all leads that are members of this static list. Not available when UseBulkAPI=true.
ConferenceName	String	False
ConferenceNameValue	String	False
ConferenceDate	Datetime	False
NumberOfAttendees	Int	False

Query Companies in Marketo.

Note: This table is only available for Marketo subscriptions which do not have a native CRM sync enabled.

Select

Note: A server-side filter must be specified to query this table. Queries with the 'OR' operator are not supported.

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

Tip: This table uses server-side projection. Performance can be improved by minimizing the number of columns in projection.

	SELECT * FROM Companies WHERE [Id]=10
	SELECT * FROM Companies WHERE [Id] IN (10, 11, 22)
	SELECT * FROM Companies WHERE [ExternalCompanyId]='CData'
	SELECT * FROM Companies WHERE [ExternalCompanyId] IN ('CData', 'Marketo')
	SELECT * FROM Companies WHERE [Company]='CData'
	SELECT * FROM Companies WHERE [Company] IN ('CData', 'Marketo')
	SELECT * FROM Companies WHERE [ExternalSalesPersonId]='CDataSales'
	SELECT * FROM Companies WHERE [ExternalSalesPersonId] IN ('CDataSales', 'MarketoSales')

Insert

This table supports BATCH INSERT when UseBulkAPI is set to false.

	INSERT INTO Companies (ExternalCompanyId, Company, Industry, Website) VALUES ('cdata', 'CData', 'Tech', 'cdata.com'), ('marketo', 'Marketo', 'Tech', 'marketo.com')

Update

This table supports BATCH UPDATE when UseBulkAPI is set to false.

	UPDATE Companies SET Company='test_UPDATE_Companies', MainPhone='800-555-1234', AnnualRevenue='10000000.00' WHERE ExternalCompanyId='cdata'
	UPDATE Companies SET Company='CData', MainPhone='800-555-1234', AnnualRevenue='10000000.00' WHERE WHERE Id IN (2523781, 2523782, 2523783, 2523784)

Upsert

This table supports BATCH UPSERT when UseBulkAPI is set to false.

	UPSERT INTO Companies (ExternalCompanyId, Company, Industry, Website) VALUES ('testUpsert', 'Google', 'Tech', 'google.com')

Delete

This table supports BATCH DELETE when UseBulkAPI is set to false.

	DELETE FROM Companies WHERE externalCompanyId='cdata'
	DELETE FROM Companies WHERE Id IN (2521059, 2521060, 123, 2521058)
	DELETE FROM Companies WHERE Company='testName'

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=,IN
ExternalCompanyId	String	True	=,IN
AnnualRevenue	Decimal	False
BillingCity	String	False
BillingCountry	String	False
BillingPostalCode	String	False
BillingState	String	False
BillingStreet	String	False
Company	String	False	=,IN
CompanyNotes	String	False
ExternalSalesPersonId	String	False	=,IN
Industry	String	False
MainPhone	String	False
NumberOfEmployees	Int	False
SicCode	String	False
Site	String	False
Website	String	False
CreatedAt	Datetime	True
UpdatedAt	Datetime	True

Query custom objects for a Marketo organization.

Each custom object in your Marketo organization is returned as a separate table. Each table name is prefixed with 'CustomObject_' followed by the name of your custom object.

Select

Note: A server-side filter must be specified to query this table. Queries with the 'OR' operator are not supported.

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

The following queries are related to a sample custom object. This table may not exist in your Marketo instance.

	SELECT * FROM CustomObject_cdata WHERE MarketoGUID IN ('8207b49b-933c-40a0-995d-d12c90572a65', '71cce7c8-e7d1-4a00-8ea7-89806fee56be')
	SELECT * FROM CustomObject_cdata WHERE Mailaddress IN ('support@cdata.com', 'test@cdata.com')

Insert

This table supports BATCH INSERT when UseBulkAPI is set to false.

	INSERT INTO CustomObject_cdata (Names) VALUES ('tes123')

Update

This table supports BATCH UPDATE when UseBulkAPI is set to false.

	UPDATE CustomObject_cdata SET Value1='testupdate' WHERE Mailaddress='test01@cdata.com'
	UPDATE CustomObject_cdata SET Value1='testupdateMultipleValues' WHERE Mailaddress IN ('test01@cdata.com', 'test02@cdata.com')

Upsert

This table supports BATCH UPSERT when UseBulkAPI is set to false.

This table supports BULK UPSERT when UseBulkAPI is set to true.

	UPSERT INTO CustomObject_cdata (Mailaddress, Value1) VALUES ('test01New@cdata.com', 'test01')

Delete

This table supports BATCH DELETE when UseBulkAPI is set to false.

	DELETE FROM [CustomObject_cdata] WHERE MarketoGUID='8207b49b-933c-40a0-995d-d12c90572a65'
	DELETE FROM [CustomObject_cdata] WHERE MarketoGUID IN ('980415e9-3cf0-4168-ae99-8cf3c613c83b', '71cce7c8-e7d1-4a00-8ea7-89806fee56be')
	DELETE FROM [CustomObject_cdata] WHERE Mailaddress='test@cdata.com'
	DELETE FROM [CustomObject_cdata] WHERE Mailaddress IN ('test1@cdata.com', 'test2@cdata.com')

Bulk

When UseBulkAPI is set to true the UpdateAt filter can be processed server-side, in addition to that the following extra filters are available:

Name	Type	ReadOnly	Operators	Description
ListId	Int	True	=	Mirror column that can be used only as a filter. Will retrieve custom objects related to the provided static list Id.
ListName	String	True	=	Mirror column that can be used only as a filter. Will retrieve custom objects related to the provided static list name.
SmartListId	Int	True	=	Mirror column that can be used only as a filter. Will retrieve custom objects related to the provided smart list Id.
SmartListName	String	True	=	Mirror column that can be used only as a filter. Will retrieve custom objects related to the provided smart list name.

Sample queries:

	SELECT * FROM CustomObject_cdata WHERE UpdatedAt>'2024-04-01T00:00:00Z'
	SELECT * FROM CustomObject_cdata WHERE UpdatedAt>'2024-04-01T00:00:00Z' AND UpdatedAt<'2024-09-01T00:00:00Z'
	SELECT * FROM CustomObject_cdata WHERE ListId=2

Columns

Name	Type	ReadOnly	Operators	Description
MarketoGUID [KEY]	String	True	=,IN
UpdatedAt	Datetime	True		The datetime when the custom object was last updated.
CreatedAt	Datetime	True
Mailaddress	String	False	=,IN
Value1	String	False

Query emails for a Marketo organization.

Note: This table uses the draft/approved versioning API. By default both versions are retrieved/modified. To interact with a specific version use the filter Status='draft' or Status='approved'.

GetEmailFullContent can be used to retrieve the full content of an email.

UpdateEmailContent can be used to update the content content of an email.

UpdateEmailFullContent can be used to update the full content of an email.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

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

	SELECT * FROM Emails WHERE Id=15457
	SELECT * FROM Emails WHERE Name='support@cdata.com'
	SELECT * FROM Emails WHERE FolderId=26
	SELECT * FROM Emails WHERE UpdatedAt >= '2023-03-22 00:09:14.0' AND UpdatedAt <= '2023-03-22 01:49:03.0'

Insert

	INSERT INTO Emails (Name, TemplateId, FolderId, FolderType) VALUES ('My Email', 1037, 5111, 'Program')

Update

	UPDATE Emails SET Name='CRUD Test', Description='Testing CRUD' WHERE Id=23852

Delete

	DELETE FROM Emails WHERE Id=23838
	DELETE FROM Emails WHERE Id=23759 AND Status='approved'
	DELETE FROM Emails WHERE Id IN (17008, 23764) AND Status='draft'

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=	The unique, Marketo-assigned identifier of the email.
Status [KEY]	String	True	=	The status of the email, draft or approved version.
Name	String	False	=	The name of the email.
Description	String	False		The description of the email.
Subject	String	False		The email subject.
FromName	String	False		The from name.
FromEmail	String	False		The from email address.
ReplyEmail	String	False		The reply email address.
PreHeader	String	False		The pre-header text for the email.
URL	String	True		The URL of the asset in the Marketo UI.
TemplateId	Int	False		The template associated with the email.
PublishToMSI	Bool	True		Identifies whether the email is published.
Version	Int	True		The Template version type.
Operational	Bool	False		Identifies whether the email is operational.
TextOnly	Bool	False		Identifies whether the email is text only.
WebView	Bool	False		Identifies whether the email is web view.
AutoCopyToText	Bool	False		Identifies whether the email is auto copied to text.
FolderId	Int	False	=	The unique, Marketo-assigned identifier of the parent folder/program.
FolderName	String	True		The name of the folder
FolderType	String	False	=	The type of folder (Folder,Program)
WorkspaceName	String	True		Name of the workspace the email is part of.
CreatedAt	Datetime	True		Datetime the email was created.
UpdatedAt	Datetime	True	=,>,<,>=,<=	Datetime the email was most recently updated.

Query email templates for a Marketo organization.

Note: This table uses the draft/approved versioning API. By default both versions are retrieved/modified. To interact with a specific version use the filter Status='draft' or Status='approved'.

CreateEmailTemplate can be used to create email templates.

GetEmailTemplateContent can be used to retrieve the email template content.

UpdateEmailTemplateContent can be used to update the email template content.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

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

	SELECT * FROM EmailTemplates WHERE ID=1038
	SELECT * FROM EmailTemplates WHERE Name='Test Template 3'
	SELECT * FROM EmailTemplates WHERE Status='approved'
	SELECT * FROM EmailTemplates WHERE ID=1038 AND Status='approved'

Update

	UPDATE EmailTemplates SET Description='test update' WHERE Id=1039

Delete

	DELETE FROM EmailTemplates WHERE Id=1014
	DELETE FROM EmailTemplates WHERE Id=1034 AND Status='draft'
	DELETE FROM EmailTemplates WHERE Name='Serenity (Marketo Starter Template)

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=	The id of the asset.
Status [KEY]	String	True	=	Status of the email template, draft or approved versions.
Name	String	False	=	The name of the asset.
Description	String	False		The description of the asset.
URL	String	True		The URL of the asset in the Marketo UI.
Version	Int	True		The Template version type.
WorkspaceName	String	True		Name of the workspace the email template is part of.
FolderId	Int	True		The unique, Marketo-assigned identifier of the parent folder/program.
FolderName	String	True		The name of the folder
FolderType	String	True		The type of folder (Folder,Program)
CreatedAt	Datetime	True		The date and time the asset was created.
UpdatedAt	Datetime	True		The date and time the asset was last updated.

Create, update, delete, and query Folders for a Marketo organization.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

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

	SELECT * FROM Folders WHERE Name='Marketo8'
	SELECT * FROM Folders WHERE Name='Default'
	SELECT * FROM Folders WHERE Name='Default' AND WorkspaceName='Default'
	SELECT * FROM Folders WHERE MaxDepth=1;
	SELECT * FROM Folders WHERE ParentId=12 AND ParentType='Folder'
	SELECT * FROM Folders WHERE ParentId=12

Insert

	INSERT INTO Folders (Name, ParentId, ParentType, Description) VALUES ('newFolder1234567', 38, 'Folder', 'test insert description')

Update

Note: Folders of Type='Program' can not be updated.

	UPDATE Folders SET Description='test update folders2', IsArchive='true' WHERE Id IN (5910, 5911) AND Type='Folder'

Delete

Note: Folders of Type='Program' and folders with IsSystem=true can not be deleted.

	DELETE FROM Folders WHERE Id=5363 AND Type='Folder'

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=	The unique, Marketo-assigned identifier of the folder.
Type [KEY]	String	False	=	The type of the folder (Folder or Program).
Name	String	False	=	The name of the folder.
Description	String	False		The description of the folder.
FolderType	String	True		The type of the folder (Revenue Cycle Model, Marketing Folder, List, Smart List).
ParentId	Int	False	=	The Id of the parent folder.
ParentType	String	False	=	The type of the parent folder.
Path	String	True		The path of a folder shows its hierarchy in the folder tree, similar to a Unix-style path.
WorkspaceName	String	True	=	The name of the smart campaign workspace.
URL	String	True		The explicit URL of the asset in the designated instance.
IsSystem	Bool	True		Whether or not the folder is a system folder.
IsArchive	Bool	False		Whether or not the folder is archived.
AccessZoneId	Int	True		The access zone id
CreatedAt	Datetime	True		The date and time the folder was created.
UpdatedAt	Datetime	True		The date and time the folder was last updated.
MaxDepth	Int	True	=	Mirror column used to specify the maximum folder depth to traverse. Will be ignored when filtering by Id or Name.

Create, update, delete and query Forms for a Marketo organization.

Note: This table uses the draft/approved versioning API. By default both versions are retrieved/modified. To interact with a specific version use the filter Status='draft' or Status='approved'.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

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

	SELECT * FROM FORMS WHERE status='draft'
	SELECT * FROM FORMS WHERE Id=1001
	SELECT * FROM FORMS WHERE Name='Form1'
	SELECT * FROM FORMS WHERE Id=1001 AND Status='draft'
	SELECT * FROM FORMS WHERE FolderId=1089 AND FolderType='Program'
	SELECT * FROM FORMS WHERE FolderId=1002

Insert

	INSERT INTO Forms (Name, Description, FolderId, FolderType, FontFamily, FontSize, LabelPosition, Language, Locale, ProgressiveProfiling) 
	VALUES ('test insert form', 'test form description', 1089, 'Program', 'Arial', '12px', 'left', 'English', 'en_US', true)

Update

	UPDATE Forms SET Name='test update form', Description='Testing Update', FontSize='13px' WHERE Id=1016

Delete

	DELETE FROM Forms WHERE Id=1004
	DELETE FROM Forms WHERE Id=1004 AND Status='draft'
	DELETE FROM Forms WHERE Name IN ('test', 'My Snippet')

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=	The unique, Marketo-assigned identifier of the form.
Status [KEY]	String	True	=	Status of the form, draft or approved versions.
Name	String	False	=	The name of the form.
Description	String	False		The description of the form.
URL	String	True		The URL of the form in the Marketo UI.
KnownVisitorTemplate	String	False		Template of the known visitor behavior for the form.
KnownVisitorType	String	False		Type of the known visitor behavior for the form.
Theme	String	False		CSS theme for the form to use.
Language	String	False		Language of the form.
Locale	String	False		Locale of the form.
ProgressiveProfiling	Bool	False		Whether progressive profiling is enabled for the form.
LabelPosition	String	False		Default positioning of labels.
ButtonLabel	String	False		Label text of the button.
ButtonWaitingLabel	String	False		Waiting text of the button.
ButtonLocation	Int	False		Location in pixels of the button relative to the left of the form.
FontFamily	String	False		font-family property for the form.
FontSize	String	False		font-size property of the form.
FolderId	Int	False	=	The unique, Marketo-assigned identifier of the parent folder/program.
FolderName	String	True		The name of the folder
FolderType	String	False	=	The type of folder (Folder,Program)
WorkspaceId	Int	True		Workspace Id of the asset.
CreatedAt	Datetime	True		Datetime the form was created.
UpdatedAt	Datetime	True		Datetime the form was most recently updated.
ThankYouList	String	True		JSON array of ThankYouPage JSON objects.

Create, update, delete, and query Content Sections of Landing Pages for a Marketo organization.

Note: This table uses the draft/approved versioning API. By default both versions are retrieved/modified. To interact with a specific version use the filter Status='draft' or Status='approved'.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

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

	SELECT * FROM LandingPageContentSection WHERE LandingPageId='2'
	SELECT * FROM LandingPageContentSection WHERE LandingPageId='2' AND status='draft'

Insert

	INSERT INTO LandingPageContentSection (LandingPageId, Content, Type, Height, Width, FormattingOptionsTop, FormattingOptionsLeft, Opacity, BorderColor, BackgroundColor, HideDesktop, HideMobile, ImageOpenNewWindow, FormattingOptionsZIndex) 
	VALUES (1009, '<html><body>Hello World!</body></html>', 'HTML', '100', '101', '1', '2', '0.8', '#ff00ff', '#00ffff', false, true, true, 1)

Update

	UPDATE LandingPageContentSection BorderColor='#ff0000', BackgroundColor='#0000ff', HideMobile='true', ImageOpenNewWindow='true', Type='HTML', FormattingOptionsTop='1', FormattingOptionsLeft='2', Width='101', HideDesktop='false', Opacity='0.8', Content='<html><body>Hello World!</body></html>', Height='100', FormattingOptionsZIndex='1'
	WHERE LandingPageId=1009 AND Id=1049

Delete

	DELETE FROM LandingPageContentSection WHERE LandingPageId=1009

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	String	True		Id of the content section.
LandingPageId [KEY]	String	False	=	Id of the landing page to which the content section belongs to.
Status [KEY]	String	True	=	Status filter for draft or approved versions.
Index	Int	True		Index of the content section. Index orients the elements from lowest to highest.
Type	String	False		Type of content section.
Content	String	False		Content of the section. Expected values vary based on type. Image: An image URL. RichText: HTML Content.
RawContent	String	True		Raw content of the section. Expected values vary based on type. Image: An image URL. RichText: HTML Content.
ContentType	String	True		Content of the section. Expected values vary based on type. Image: An image URL. RichText: HTML Content.
ContentURL	String	True		Content of the section. Expected values vary based on type. Image: An image URL. RichText: HTML Content.
LinkURL	String	False		RL parameter of a link type section.
FollowupType	String	False		Follow-up behavior of a form. Only available for form-type content sections. Defaults to form defined behavior.
FollowupValue	String	False		Where to follow-up on form submission. When followupType is lp, accepts the integer id of a landing page. For url, it accepts a url string.
Height	String	False		The height of the content.
MinHeight	String	True		The min height of the content.
Width	String	False		The width of the content.
MinWidth	String	True		The min width of the content.
FormattingOptionsTop	String	False		The top margin of the content.
FormattingOptionsLeft	String	False		The left margin of the content.
Opacity	String	False		The opacity of the content.
BorderWidth	String	False		The border width of the content.
BorderStyle	String	False		The border style of the content.
BorderColor	String	False		The border color of the content.
BackgroundColor	String	False		The background color of the content.
HideDesktop	Bool	False		Hide the section when displayed on a desktop browser. Default false.
HideMobile	Bool	False		Hide the section when displayed on a desktop browser. Default false.
ImageOpenNewWindow	String	False		ImageOpenNewWindow
FormattingOptionsZIndex	String	False		The z-index of the content.

Create, update, delete and query Landing Pages for a Marketo organization.

Note: This table uses the draft/approved versioning API. By default both versions are retrieved/modified. To interact with a specific version use the filter Status='draft' or Status='approved'.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

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

	SELECT * FROM LandingPages WHERE status='approved'
	SELECT * FROM LandingPages WHERE folderId=1001
	SELECT * FROM LandingPages WHERE folderId=1001 AND status='draft'
	SELECT * FROM LandingPages WHERE name='string'

Insert

	INSERT INTO LandingPages (Name, CustomHeadHTML, Description, FacebookOgTags, FolderId, FolderType, Keywords, MobileEnabled, Robots, Template, Title)
	VALUES ('test insert landing page', '<!DOCTYPE html>\\n<html>\\n<body>\\n<h1>My landing page</h1>\\n<p>My landing page content.</p>\\n</body></html>', 'Testing  Insert operation', '', 1184, 'Program', '', true, 'index,  nofollow', 1, 'Insert Operation')	

Update

	UPDATE LandingPages SET Description='Testing Update', FacebookOgTags='', Keywords='',
	MobileEnabled=false, Name='Test Update', Robots='index, nofollow',Title='Updating Landing Page',
	CustomHeadHTML='<!DOCTYPE html>\n<html>\n<body>\n<h1>Updating Landing Page</h1>\n<p>Editing Landing Page</p>\n</body></html>'
	WHERE Id=1009

Delete

Note: Approved landing pages cannot be deleted. Use UnApproveAsset to revoke landing page approval.

	DELETE FROM LandingPages WHERE Id=1003
	DELETE FROM LandingPages WHERE Id=1003 WHERE status='draft'

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=	The unique, Marketo-assigned identifier of the landing page.
Status [KEY]	String	True	=	Status filter for draft or approved versions.
Name	String	False	=	The name of the landing page.
Description	String	False		The description of the landing page.
Title	String	False		Title element of the landing page.
URL	String	True		The URL of the landing page in the Marketo UI.
ComputedURL	String	True		Computed URL of landing page.
WorkspaceName	String	False		Workspace name of the asset.
Template	Int	False		Id of the template used.
Robots	String	False		Robots directives to apply to the pages meta tags
MobileEnabled	Bool	False		Whether the page has mobile viewing enabled. Free-form pages only. Default false.
CustomHeadHTML	String	False		Any custom HTML to embed in the tag of the page.
Keywords	String	False		Keywords
FormPrefill	Bool	True		Boolean to toggle whether forms embedded in the page will prefill. Default false.
FacebookOgTags	String	False		Any OpenGraph meta tags to apply to the page.
FolderId	Int	False	=	The unique, Marketo-assigned identifier of the parent folder/program.
FolderName	String	True		The name of the folder
FolderType	String	False	=	The type of folder (Folder,Program)
CreatedAt	Datetime	True		Datetime the landing page was created.
UpdatedAt	Datetime	True		Datetime the landing page was most recently updated.

Create, update, delete and query Landing Page Templates for a Marketo organization.

Note: This table uses the draft/approved versioning API. By default both versions are retrieved/modified. To interact with a specific version use the filter Status='draft' or Status='approved'.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side. The Name and Id filters will be processed server-side only if Status='approved'.

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

	SELECT * FROM LandingPageTemplates WHERE Status='approved'
	SELECT * FROM LandingPageTemplates WHERE FolderId=19
	SELECT * FROM LandingPageTemplates WHERE Name='Test Insert 1' AND Status='approved'
	SELECT * FROM LandingPageTemplates WHERE Id=3 AND Status='approved'

Insert

	INSERT INTO LandingPageTemplates (Name, TemplateType, Description, EnableMunchkin, FolderId, FolderType)
	VALUES ('Test Insert 1', 'guided', 'Testing Insert', true, 19, 'Folder')

Update

	UPDATE LandingPageTemplates SET Description='Testing Update', EnableMunchkin=false, Name='Test Update' WHERE Id=1

Delete

	DELETE FROM LandingPageTemplates WHERE Id=2 AND Status='approved'
	DELETE FROM LandingPageTemplates WHERE Name='Test Insert 1'

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=	The unique, Marketo-assigned identifier of the landing page template.
Status [KEY]	String	True	=	Status of the landing page template, draft or approved versions.
Name	String	False	=	The name of the landing page template.
Description	String	False		The description of the landing page template.
EnableMunchkin	Bool	False		Whether to enable munchkin on the derived pages. Defaults to true.
TemplateType	String	False		Type of template to create 'guided' or 'freeForm'
URL	String	True		The URL of the landing page in the Marketo UI.
WorkspaceName	String	True		Workspace name of the asset.
FolderId	Int	False	=	The unique, Marketo-assigned identifier of the parent folder/program.
FolderName	String	True		The name of the folder
FolderType	String	False	=	The type of folder (Folder,Program)
CreatedAt	Datetime	True		Datetime the landing page template was created.
UpdatedAt	Datetime	True		Datetime the landing page template was most recently updated.

Query Leads of a Marketo organization.

Select

Note: Filters provided with one of the supported operators listed in the "Operators" column are processed server-side, all the other filters are processed client-side.

Tip: This table uses server-side projection. Performance can be improved by minimizing the number of columns in projection.

To optimize response time from the server, identify only the rows and columns you want to retrieve.

SELECT Id, FirstName, LastName FROM Leads WHERE Id IN (1, 2, 5, 10)

To achieve the best performance from this query, confine it to a list of known Leads within Marketo. Create a static list of Leads within Marketo, and then specify the ListId to retrieve them.

SELECT * FROM Leads WHERE ListId=1014

If you do not specify a filter, the driver retrieves all Leads, which might take a long time depending on the number of leads in your Marketo instance. Specify the LeadChangeDateTime pseudo-column to improve query performance by limiting the number of records requested.

Note: LeadChangeDateTime is a pseudo column that can only be used as a server-side filter. The API does not return values for this column. This is a result of the documented API behavior, see this forum post.

	SELECT * FROM Leads WHERE LeadChangeDateTime >= '2024-03-20T15:49:22.000Z'
	SELECT Id, LeadChangeDateTime, UpdatedAt FROM Leads WHERE LeadChangeDateTime >= '2024-01-17T14:32:37.000-05:00'

Insert

This table supports BATCH INSERT when UseBulkAPI is set to false.

	INSERT INTO Leads (FirstName, LastName, Email, DoNotCall, DoNotCallReason, DateOfBirth) VALUES ('test', 'test', 'leadtestinser@cdata.com', true, 'Testing', '01/01/2000')
	INSERT INTO Leads (FirstName, LastName, Email, DoNotCall, DoNotCallReason, DateOfBirth) VALUES ('test1', 'test1', 'leadtest12@cdata.com', true, 'Testing', '01/01/2000'), ('test3', 'test3', 'leadtest32@cdata.com', true, 'Testing', '01/01/2000')
	INSERT INTO Leads (FirstName, LastName, Email, PartitionName) VALUES ('test', 'test', 'testPartitionNew@crud.com', 'testPartition')

To insert multiple leads at once via a #TEMP table, first create the #TEMP table, and then insert that table into your Leads table.

The following example creates a #TEMP table with three new Leads, and then inserts that #TEMP table into the Leads table:

	INSERT INTO Leads#TEMP (FirstName, LastName, Email, Company) VALUES ('John', 'Mangel', 'john@abc.com', 'ABC')
	INSERT INTO Leads#TEMP (FirstName, LastName, Email, Company) VALUES ('Steve', 'Puth', 'steve@abc.com', 'ABC')
	INSERT INTO Leads#TEMP (FirstName, LastName, Email, Company) VALUES ('Andrew', 'Stack', 'andy@abc', 'ABC')

	INSERT INTO Leads (FirstName, LastName, Email, Company) SELECT FirstName, LastName, Email, Company FROM Leads#TEMP

Update

This table supports BATCH UPDATE when UseBulkAPI is set to false.

You can update any field in the Leads table that is not read-only. All critria fields are used as deduplication fields. Use a field for deduplication if it supports server side filtering, otherwise the Id column is used for deduplication.

	UPDATE Leads SET Company='TestCompany', NumberOfEmployees=100 WHERE Id='2521004'
	UPDATE Leads SET Title='My Job', Address='123 Main St' WHERE Email='leadtest12@cdata.com'
	UPDATE LEADS SET Email='testUpdateEmailByIdNew@leads.marketo' WHERE Id=2523020
	UPDATE LEADS SET TestKpQA='test' WHERE TestCustomfieldEmail='indritb1231@cdata.com'

Upsert

This table supports BATCH UPSERT when UseBulkAPI is set to false.

This table supports BULK UPSERT when UseBulkAPI is set to true.

	UPSERT INTO Leads (Email, FirstName, LastName) VALUES ('testLeadUpsert@test.com', 'Lead', 'Upsert_InsertNewRecord')

Delete

This table supports BATCH DELETE when UseBulkAPI is set to false.

	DELETE FROM Leads WHERE Id=508
	DELETE FROM Leads WHERE Id IN (505, 506, 501, 507)
	DELETE FROM Leads WHERE Email='kk@gd.com'

GetDeleted

Note: The API returns only leads which have been deleted in the past 14 days.

	GetDeleted FROM Leads

GetDeleted supports filtering by UpdatedAt.

	GETDELETED FROM Leads WHERE UpdatedAt > '2024-01-01T01:00:00'
	GETDELETED FROM Leads WHERE UpdatedAt > '2024-01-01T01:00:00' AND UpdatedAt <= '2024-02-01T01:00:00' 

Bulk

When UseBulkAPI is set to true, the CreatedAt filter can be processed server-side. In addition, the following extra filters are available:

Name	Type	ReadOnly	Operators	Description
StaticListName	String	True	=	Mirror column that can be used only as a filter. Will retrieve custom objects related to the provided static list name.
SmartListId	Int	True	=	Mirror column that can be used only as a filter. Will retrieve custom objects related to the provided smart list Id.
SmartListName	String	True	=	Mirror column that can be used only as a filter. Will retrieve custom objects related to the provided smart list name.

Sample queries:

	SELECT Id, LeadChangeDateTime, UpdatedAt FROM LEADS WHERE LeadChangeDateTime>'2024-01-17T14:28:57.000-05:00' AND LeadChangeDateTime<'2024-01-29T04:29:55.000-05:00'
	SELECT Id, CreatedAt FROM LEADS WHERE CreatedAt>='2024-01-17T14:27:20.000-05:00' AND CreatedAt<'2024-01-29T04:29:30.000-05:00'
	SELECT * FROM LEADS WHERE ListId=1062
	SELECT * FROM LEADS WHERE StaticListName='test0614'
	SELECT Id FROM LEADS WHERE SmartListId=1095
	SELECT Id FROM LEADS WHERE SmartListName='LeadSmartListTest'

Columns

Name	Type	ReadOnly	Operators	Description
Id [KEY]	Int	True	=,IN	The id of the lead.
CreatedAt	Datetime	True		The datetime when the lead was created.
UpdatedAt	Datetime	True		The datetime when the lead was last updated.
ProgramId	Int	True	=	Mirror column that can be used only as a filter. Will retrieve Leads related to the given email ProgramId. Not available when UseBulkAPI=true.
ListId	Int	True	=	Mirror column that can be used only as a filter. Will retrieve Leads related to the provided static list Id.
PartitionName	String	False		Mirror column that can be used only during inserts to specify the partition of the lead.
ItemURL	String	True		The URL of the lead in the Marketo UI.
LeadChangeDateTime	Datetime	True	=,>,>=,<,<=	A server-side filter for the datetime when the Lead was updated, not necessarily the latest update.
AcmeAccessCode	String	False	=,IN
AcquisitionProgramId	Int	False
Address	String	False
AnnualRevenue	Decimal	False
AnonymousIP	String	False
BillingCity	String	False
BillingCountry	String	False
BillingPostalCode	String	False
BillingState	String	False
BillingStreet	String	False
BlackListed	Bool	False
BlackListedCause	String	False
City	String	False
Company	String	False
ContactCompany	Int	True
Cookies	String	False	=,IN
Country	String	False
Cstmfdtest1	String	False	=,IN
Cstmfdtest2	String	False	=,IN
DateOfBirth	Date	False
Department	String	False	=,IN
DoNotCall	Bool	False
DoNotCallReason	String	False
Ecids	String	True
Email	String	False	=,IN
EmailInvalid	Bool	False
EmailInvalidCause	String	False
EmailSuspended	Bool	False
EmailSuspendedAt	Datetime	False
EmailSuspendedCause	String	False	=,IN
ExternalCompanyId	String	False	=,IN
ExternalSalesPersonId	String	False	=,IN
Fax	String	False
FirstName	String	False
FirstUTMCampaign	String	False
FirstUTMContent	String	False
FirstUTMMedium	String	False
FirstUTMSource	String	False
FirstUTMTerm	String	False
GdprMailableStatus	String	False
Industry	String	False
InferredCity	String	True
InferredCompany	String	True
InferredCountry	String	True
InferredMetropolitanArea	String	True
InferredPhoneAreaCode	String	True
InferredPostalCode	String	True
InferredStateRegion	String	True
IsAnonymous	Bool	False
IsLead	Bool	False
LastName	String	False
LatestUTMCampaign	String	False
LatestUTMContent	String	False
LatestUTMMedium	String	False
LatestUTMSource	String	False
LatestUTMterm	String	False
LeadPartitionId	Int	False	=,IN
LeadPerson	Int	True
LeadRevenueCycleModelId	Int	False
LeadRevenueStageId	Int	False
LeadRole	String	False
LeadScore	Int	False
LeadSource	String	False
LeadStatus	String	False
MainPhone	String	False
MarketingSuspended	Bool	False
MarketingSuspendedCause	String	False
MiddleName	String	False
MktoAcquisitionDate	Datetime	False
MktoCompanyNotes	String	False
MktoDoNotCallCause	String	False
MktoIsCustomer	Bool	False
MktoIsPartner	Bool	False
MktoName	String	True	=,IN
MktoPersonNotes	String	False
MobilePhone	String	False
NumberOfEmployees	Int	False
NumberofEmployeesProspect	String	False
OriginalReferrer	String	True
OriginalSearchEngine	String	True
OriginalSearchPhrase	String	True
OriginalSourceInfo	String	True
OriginalSourceType	String	True
PersonLevelEngagementScore	Int	False	=,IN
PersonPrimaryLeadInterest	Int	True
PersonTimeZone	String	True
PersonType	String	False
Phone	String	False
PostalCode	String	False
Priority	Int	False
Rating	String	False
RegistrationSourceInfo	String	False
RegistrationSourceType	String	False
RelativeScore	Int	False
RelativeUrgency	Int	False
Salutation	String	False
SicCode	String	False
Site	String	False
State	String	False
Test	String	False	=,IN
Test1	Bool	False
Test98	String	False	=,IN
TestBoolean	Bool	False
TestCustomfieldEmail	String	False	=,IN
TestFieldText1	String	False	=,IN
TestInteger	Bool	False
TestInteger_cf	Int	False	=,IN
TestKpQA	String	False	=,IN
Title	String	False
Unsubscribed	Bool	False
UnsubscribeDateFoFS	String	False
UnsubscribeDateUnleashed	String	False
UnsubscribedReason	String	False
UnsubscribeFoFS	String	False
UnsubscribeMarketing	String	False
UnsubscribeSales	String	False
UnsubscribeUnleashed	String	False
Urgency	Double	False
UTMTerm	String	False	=,IN
Website	String	False