SQL Server

SQL Server is a relational database management system by Microsoft. It supports transactional processes, business intelligence and analytics applications for enterprises. Workato's integrations with SQL server help you to accomplish a wide range of tasks such as migrating data, building data pipelines to data warehouses and lakes, backing up data automatically or transforming and exporting data to build complex workflows. Find out how to set up connections to your SQL server from Workato and how Workato can work with SQL server to fulfill your business needs.

Supported versions

All versions of SQL Server are supported. However, some triggers/actions are restricted to newer versions. Refer to individual trigger/action documentation to find out.

How to connect to SQL Server on Workato

The SQL Server connector uses basic authentication to authenticate with SQL Server. Configured SQL Server connection

Field Description
Connection name Give this SQL Server connection a unique name that identifies which SQL Server instance it is connected to.
On-prem secure agent Choose an on-premise agent if your database is running in a network that does not allow direct connection. Before attempting to connect, make sure you have an active on-premise agent. Refer to the On-premise agent guide for more information.
Username Username to connect to SQL Server.
Password Password to connect to SQL Server.
Host URL of your hosted server.
Port Port number that your server is running on, typically 3306.
Database Name of the SQL Server database you wish to connect to.
Azure SQL Choose 'Yes' if connecting to an Azure SQL instance.

Permissions required to connect

At minimum, the database user account must be granted SELECT permission to the database specified in the connection. Check out the example below to find out more about how to set permissions if you are the one setting up the SQL server connection for your business.

Click here to find out how to set up permissions If we are trying to connect to a named database (HR_PROD) in a SQL Server instance, using a new database user workato, the following example queries can be used. First, create a new login and user dedicated to integration use cases with Workato.
CREATE LOGIN workato WITH PASSWORD = 'password1234';
USE HR_PROD;
CREATE USER workato FOR LOGIN workato;
Replace password1234 with a secure password. Remember to set a password that you and others in your organisation can remember. This allows the user to have login access to the SQL Server instance. However, this user will not have access to any tables. The next step is to grant permission to the necessary tables. There are a few ways to do this. One of the simplest ways is to grant access based on a ROLE.
Find out more about database-level roles here
ALTER ROLE db_datareader ADD MEMBER workato;
Alternatively, we can grant access to all tables defined by a SCHEMA, HR.
GRANT SELECT,INSERT ON SCHEMA :: HR TO workato;
To grant permissions only for certain tables, specify their table names separately and run this query.
GRANT SELECT,INSERT ON tablename1 TO workato;
GRANT SELECT,INSERT ON tablename2 TO workato;
Granting selective permission are useful for databases that have sensitive information. Only give Workato access to the tables that contain the information you need for recipes. Finally, check that this user has the necessary permissions. Run a query to see all permissions.
SELECT
  pr.name,
  pr.type_desc,
  perm.permission_name,
  perm.class_desc,
  object_name(perm.major_id) AS "object",
  schema_name(perm.major_id) AS "schema"
FROM sys.database_principals pr
LEFT JOIN sys.database_permissions perm ON perm.grantee_principal_id = pr.principal_id
WHERE pr.name = 'workato';
This should return the following minimum permission to create a SQL Server connection on Workato.
+---------+-----------+-----------------+------------+--------+-------------+
| name    | type_desc | permission_name | class_desc | object | schema      |
+---------+-----------+-----------------+------------+--------+-------------+
| workato | SQL_USER  | CONNECT         | DATABASE   | NULL   | NULL        |
| workato | SQL_USER  | INSERT          | SCHEMA     | NULL   | workatodemo |
| workato | SQL_USER  | SELECT          | SCHEMA     | NULL   | workatodemo |
+---------+-----------+-----------------+------------+--------+-------------+
3 rows in set (0.20 sec)

Working with the SQL Server connector

Using tables, views and stored procedures

After successfully connecting to your SQL Server and selecting an action/trigger in your recipe, you will often be prompted to select either a table, view or stored procedure. This tells Workato which tables you want to pull or send data to.

Tables & Views

The SQL Server connector works with all tables and views. These are available in pick lists in each trigger/action or you can provide the exact name. Views can be called using this as well and be used in the same way as a table.


Table selection from pick list

Select a table/view from pick list


Exact table name provided

Provide exact table/view name in a text field


Case sensitivity of the name of a table/view depends on your database implementation. A default SQL Server is case insensitive. Databases or database objects with CS in the COLLATION indicates that it is case sensitive.

Stored Procedures

Stored procedures are custom written SQL statements that are written and saved within your SQL server. They are able to do a range of functionalities including creating, reading, updating and deleting rows. They can also accept parameters. Find out more about how Workato works with stored procedures.

Using WHERE conditions

This input field is used to filter and identify rows to perform an action on. It is used in multiple triggers and actions in the following ways:

  • filter rows to be picked up in triggers
  • filter rows in Select rows action
  • filter rows to be deleted in Delete rows action

This clause will be used as a WHERE statement in each request. This should follow basic SQL syntax. Refer to this SQL Server documentation for a comprehensive list of rules for constructing WHERE statements. Below, we go through some of the basics needed to form your WHERE statements.

Operators

At the foundation of any WHERE statement, we have operators that help us filter and identify what rows we want returned in Workato. By chaining operators in the same way you would do it in SQL, you'll be able to use them to create robust and complex filters on your data directly from Workato.

Click here for a list of operators
Operator Description Example
= Equal WHERE ID = 445
!=
<>
Not equal WHERE ID <> 445
>
>=
Greater than
Greater than or equal to
WHERE PRICE > 10000
<
<=
Less than
Less than or equal to
WHERE PRICE > 10000
IN(...) List of values WHERE ID IN(445, 600, 783)
LIKE Pattern matching with wildcard characters (% to represent zero or more characters and _ to represent a single character) WHERE EMAIL LIKE '%@workato.com'
BETWEEN Retrieve values with a range WHERE ID BETWEEN 445 AND 783
IS NULL
IS NOT NULL
NULL values check
Non-NULL values check
WHERE NAME IS NOT NULL
AND Requires both preceding and proceeding conditions to be fulfilled to be true WHERE ID = 445 AND NAME IS NOT NULL
OR Requires either the preceding and proceeding conditions to be fulfilled to be true WHERE ID = 445 OR NAME IS NOT NULL

Data types

The other component of a WHERE condition would be to use these operators in conjunction with the proper datatypes. When writing WHERE statements, make sure you compare a variable of data type = integer in your table with a variable of data type = integer instead of data type = string.

Workato also helps to reveal the data types expected for each input field when you select

  • Update rows actions
  • Upsert rows actions

They appear directly below the output field, allowing you to know the expected data type to be sent while building the recipe. Use these hints to send the proper data types over to your SQL server as failing to do so might lead to unexpected behaviour or failed jobs.


input field hints

Hints below each input field inform you about the data type expected


Here are some of the common data types you can expect to see. A more comprehensive list can be found here

Click here for a list of common data types
Data type Description Example
int Allows whole numbers between -2,147,483,648 and 2,147,483,647 -100,1,30,000
decimal Fixed precision and scale numbers that are exact. This is commonly used. Max length can be specified but defaults to 1.11,2.0761,1.61803398875
smallint Allows whole numbers from 0 to 255 1,245,100
bigint Allows whole numbers between -9,223,372,036,854,775,808 and 9,223,372,036,854,775,807 10,000,000,000
bit Integer that can be 0, 1, or NULL 1,0,NULL
varchar(n) Variable width character string of length n Foo_bar
nchar(n) Fixed width character string of length n Foo where n = 3
datetime From January 1, 1753 to December 31, 9999 with an accuracy of 3.33 milliseconds 2011-09-16 13:23:18.767
datetime2 From January 1, 0001 to December 31, 9999 with an accuracy of 100 nanoseconds 2011-09-16 13:23:18.7676720
date Store a date only. From January 1, 0001 to December 31, 9999 2012-10-11
time Store a time only to an accuracy of 100 nanoseconds. Minimum length hh:mm:ss and maximum length hh:mm:ss.nnnnnnnn 08:30:12,09:12:20.12898400

Writing WHERE conditions

Now that we've gone through operators and data types, we are ready to write our WHERE conditions. String values must be enclosed in single quotes ('') and columns used must exist in the table/view.

A simple WHERE condition to filter rows based on values in a single column looks like this.

currency = 'USD'

If used in a Select rows action, this WHERE condition will return all rows that has the value 'USD' in the currency column. Just remember to wrap datapills with single quotes in your inputs.


Using datapills in WHERE condition

Using datapills in `WHERE` condition


Column names with spaces must be enclosed in double quotes ("") or square brackets ([]). For example, currency code must to enclosed in brackets to be used as an identifier.

[currency code] = 'USD'


WHERE condition with enclosed identifier

`WHERE` condition with enclosed identifier


Click the tabs below for more details into the functionality you can explore with your WHERE conditions.

Using AND and OR in your WHERE conditions WHERE conditions can also be used in conjunction with basic SQL logical operators like AND and OR to add more filters on the rows you return.
([currency code] = 'USD' AND totalAmt >1000) OR totalAmt>2000 
When used together, this WHERE condition will return all rows that either have the value 'USD' in the currency code column AND more than 1000 in the totalAmt column OR more than 2000 in the totalAmt column
Using sub-queries in your WHERE conditions Your WHERE condition can also contain subqueries. The following query can be used on the compensation table.
id in (select compensation_id from users where active = 0)
When used in a Delete rows action, this will delete all rows in the compensation table related to users who are no longer active (active = 0).
Using subquery in WHERE condition
Using subquery in WHERE condition

Configuring triggers

SQL Server connector has triggers for both new and updated rows. For the triggers to work, both Unique keys must be configured.Sort columns need to be configured for triggers that find recently updated rows.

A table must satisfy some constraints to be used in a trigger. The following sections contain more information about specific constraints. Read our best practices to find out how you can prepare your table for use with Workato

Unique keys

In all triggers and some actions, this is a required input. Values from this selected column are used to uniquely identify rows in the selected table. As such, the values in the selected column must be unique. Typically, this column is the primary key of the table (e.g. ID).

When used in a trigger, this column must be incremental. This constraint is required because the trigger uses values from this column to look for new rows. In each poll, the trigger queries for rows with a unique key value greater than the previous greatest value.

Click here for a detailed example Let's use a simple example to illustrate this behavior. We have a New row trigger that processed rows from a table. The unique key configured for this trigger is ID. The last row processed has 100 as it's ID value. In the next poll, the trigger will use >= 101 as the condition to look for new rows. Performance of a trigger can be improved if the column selected to be used as the unique key is indexed.

Sort column

This is required for New/updated row triggers. Values in this selected column are used to identify updated rows.

When a row is updated, the Unique key value remains the same. However, it should have it's Sort column updated to reflect the last updated time. Following this logic, Workato keeps track of values in this column together with values in the selected Unique key column. When a change in the Sort column value is observed, an updated row event will be recorded and processed by the trigger.

For SQL Server, only datetime2 and datetime column types can be used.

Click here for a detailed example Let's use a simple example to illustrate this behavior. We have a new/updated row trigger that processed rows from a table. The Unique key and Sort column configured for this trigger is ID and UPDATED_AT respectively. The last row processed by the trigger has ID value of 100 and UPDATED_AT value of 2018-05-09 16:00:00.000000. In the next poll, the trigger will query for new rows that satisfy either of the 2 conditions:
1. UPDATED_AT'2018-05-09 16:00:00.000000'
2. ID > 100 AND UPDATED_AT = '2018-05-09 16:00:00.000000'

Using single row actions/triggers vs using batch of rows actions/triggers

SQL Server connector can read or write to your database either as a single row or in batches. When using batch triggers/actions, you have to provide the batch size you wish to work with. The batch size can be any number between 1 and 100, with 100 being the maximum batch size. Batch triggers and actions are great for jobs when you expect to read, create or update a large number of rows. Choosing to batch your job runs rather than having them split into separate jobs runs not only saves operations but reduces recipe runtimes and decreases load on your servers.

Batch trigger inputs

Batch trigger inputs

Besides the difference in input fields, there is also a difference between the outputs of these 2 types of operations. A trigger that processes rows one at a time will have an output datatree that allows you to map data from that single row.

Single row output

Single row output

However, a trigger that processes rows in batches will output them as an array of rows. The Rows datapill indicates that the output is a list containing data for each row in that batch.

Batch trigger output

Batch trigger output

As a result, the output of batch triggers/actions needs to be handled differently. This recipe uses a batch trigger for new rows in the users table. The output of the trigger is used in a Salesforce bulk upsert action that requires mapping the Rows datapill into the source list.

Using batch trigger output

Using batch trigger output

Outputs from batch triggers/actions can also be used outside of actions that work specifically with lists. By using Workato's repeat step, you'll be able to control batch outputs and use them with any action built for single rows..

Unsure when to use batch actions and single row actions? Check out our best practices section for recipe design tips!

List of Workato triggers and actions

Workato currently supports the following triggers and actions. Find out more details about each by clicking on the links below. You can also navigate to them through the side bar.

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