In this article

1 Overview
- 1.1 Snap Type
- 1.2 Prerequisites
- 1.3 Support for Ultra Pipelines
- 1.4 Known Issues & Limitations
2 Snap Views
3 Snap Settings
4 Examples
- 4.1 Update Data in SQL Server
- 4.2 Example #2
- 4.3 Downloads
5 Snap Pack History

Overview

You can use this Snap to execute an SQL update with the given properties. Document keys will be used as the column update, and their values will be used as the updated column value.

This Snap supports SQL Server 2008 or later versions.

Updates will be batched up until the account's batch size property or until the structure of the update statement changes. An update statement will change if an incoming document contains different keys than a previous document.

Snap Type

SQL Server - Update is a Write-type Snap that executes an SQL Server update.

Prerequisites

None.

Support for Ultra Pipelines

Works in Ultra Pipelines. However, we recommend that you not use this Snap in an Ultra Pipeline. This operation is not supported for Apache Hive databases.

Known Issues & Limitations

None.

Snap Views

Type	Format	Number of views	Examples of Upstream and Downstream Snaps	Description

Type

Format

Number of views

Examples of Upstream and Downstream Snaps

Description

Input

Document

Min: 1
Max: 1

Mapper
Sequence

This Snap has exactly one document input view.

Output

Document

Min: 0
Max: 1

File Writer
Mapper

This Snap has at most one output view. If an output view is available, then the original document that was used to create the statement will be output with the status of the update executed.

Error

Error handling is a generic way to handle errors without losing data or failing the Snap execution. You can handle the errors that the Snap might encounter while running the Pipeline by choosing one of the following options from the When errors occur list under the Views tab. The available options are:

Stop Pipeline Execution: Stops the current pipeline execution when the Snap encounters an error.
Discard Error Data and Continue: Ignores the error, discards that record, and continues with the rest of the records.
Route Error Data to Error View: Routes the error data to an error view without stopping the Snap execution.

Learn more about Error handling in Pipelines.

Snap Settings

Asterisk (*): Indicates a mandatory field.
Suggestion icon (): Indicates a list that is dynamically populated based on the configuration.
Expression icon (): Indicates whether the value is an expression (if enabled) or a static value (if disabled). Learn more about Using Expressions in SnapLogic.
Add icon (): Indicates that you can add fields in the field set.
Remove icon (): Indicates that you can remove fields from the field set.

Field Name	Field Type	Description

Field Name	Field Type	Description
Label* Default Value: SQL Server- Update Example: SQL Server - Update	String	Specify the name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snap in your pipeline.
Schema Name Default Value: N/A Example: "PUBLIC"	String/Expression	Specify the database schema name. In case it is not defined, then the suggestion for the Table Name will retrieve all tables names of all schemas. The property is suggestible and will retrieve available database schemas during suggest values. The values can be passed using the pipeline parameters but not the upstream parameter.
Table Name* Default Value: N/A Example: Employee	String/Expression	Specify the name of the table in the instance. The table name is suggestible and requires an account setting.
Update Condition Default Value: N/A Example: Without using expressions `EmpId = 12` `email = 'you@example.com'` Using expressions `"EMPNO=$EMPNO and ENAME=$EMPNAME"` `email = $email` `emp=$emp` `"emp='" + $emp + "'"` `"EMPNO=" + $EMPNO + " and ENAME='" + $EMPNAME+ "'"`	String/Expression	Specify the SQL WHERE clause of the update statement. You can define specific values or columns to update (Set condition) in the upstream Snap, such as Mapper Snap, and then use the WHERE clause to apply these conditions on the columns sourced from the upstream Snap. For instance, here is a sample of an Update SQL query: `UPDATE table_name` `SET column1 = value1, column2 = value2,` `WHERE condition;` Refer to the example to understand how to use the Update Condition.
Number of retries Default Value: 0 Example: 3	Integer/Expression	Specify the maximum number of attempts to be made to receive a response. The request is terminated if the attempts do not result in a response.
Retry interval (seconds) Default Value: 1 Example: 10	Integer/Expression	Specify the time interval between two successive retry requests. A retry happens only when the previous attempt resulted in an exception.
Snap Execution Default Value: Execute only Example: Validate & Execute	Dropdown list	Select one of the three modes in which the Snap executes. Available options are: Validate & Execute: Performs limited execution of the Snap, and generates a data preview during Pipeline validation. Subsequently, performs full execution of the Snap (unlimited records) during Pipeline runtime. Execute only: Performs full execution of the Snap during Pipeline execution without generating preview data. Disabled: Disables the Snap and all Snaps that are downstream from it.

Examples

Update Data in SQL Server

This example pipeline demonstrates how to extract data, transform it and update the SQL server database.

Download this pipeline.

Step 1: Configure the Mapper Snap to transform the content from the JSON data. On validation, the Snap displays the transformed output.

Step 2: Configure the SQL Server - Update Snap to update the dbo table with the specified Update Condition. This updates the city column from vizag (WHERE condition) to Hyderabad, the country column as India, and the product column as AppLE. On validation, the Snap displays a confirmation message indicating that the data was successfully updated in the database.

Example #2

In this example, we will use the well-known Northwind sample database.

Following is a selection from the "Customers" table:

CustomerID	CustomerName	ContactName	Address	City	PostalCode	Country

CustomerID	CustomerName	ContactName	Address	City	PostalCode	Country
1	Alfreds Futterkiste	Maria Anders	Obere Str. 57	Berlin	12209	Germany
2	Ana Trujillo Emparedados y helados	Ana Trujillo	Avda. de la Constitución 2222	México D.F.	05021	Mexico
3	Antonio Moreno Taquería	Antonio Moreno	Mataderos 2312	México D.F.	05023	Mexico
4	Around the Horn	Thomas Hardy	120 Hanover Sq.	London	WA1 1DP	UK
5	Berglunds snabbköp	Christina Berglund	Berguvsvägen 8	Luleå	S-958 22	Sweden

Assume we wish to update the customer "Alfreds Futterkiste" with a new contact person and city.

Enter Customers in the Table name property and CustomerName='Alfreds Futterkiste' in the Update condition property.

Assume that the Update Snap receives Map data in the input view as follows:

{ "ContactName" : "Alfred Schmidt", "City" : "Hamburg" }

When you save and execute the pipeline, the Snap internally formats a standard SQL UPDATE statement as follows:

UPDATE Customers
SET ContactName='Alfred Schmidt', City='Hamburg'
WHERE CustomerName='Alfreds Futterkiste';

and submit the request to the database server.
The selection from the "Customers" table will now look like this:

CustomerID	CustomerName	ContactName	Address	City	PostalCode	Country

CustomerID	CustomerName	ContactName	Address	City	PostalCode	Country
1	Alfreds Futterkiste	Alfred Schmidt	Obere Str. 57	Hamburg	12209	Germany
2	Ana Trujillo Emparedados y helados	Ana Trujillo	Avda. de la Constitución 2222	México D.F.	05021	Mexico
3	Antonio Moreno Taquería	Antonio Moreno	Mataderos 2312	México D.F.	05023	Mexico
4	Around the Horn	Thomas Hardy	120 Hanover Sq.	London	WA1 1DP	UK
5	Berglunds snabbköp	Christina Berglund	Berguvsvägen 8	Luleå	S-958 22	Sweden

Be careful when updating records. If we had omitted the Update condition property, in the example above, the "Customers" table would have looked like this:

CustomerID	CustomerName	ContactName	Address	City	PostalCode	Country

CustomerID	CustomerName	ContactName	Address	City	PostalCode	Country
1	Alfreds Futterkiste	Alfred Schmidt	Obere Str. 57	Hamburg	12209	Germany
2	Ana Trujillo Emparedados y helados	Alfred Schmidt	Avda. de la Constitución 2222	Hamburg	05021	Mexico
3	Antonio Moreno Taquería	Alfred Schmidt	Mataderos 2312	Hamburg	05023	Mexico
4	Around the Horn	Alfred Schmidt	120 Hanover Sq.	Hamburg	WA1 1DP	UK
5	Berglunds snabbköp	Alfred Schmidt	Berguvsvägen 8	Hamburg	S-958 22	Sweden

SnapLogic Documentation

SQL Server - Update

Analytics