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Known Issues

None.

Snap Views

Type

Format

Number of Views

Examples of Upstream and Downstream Snaps

Description

Input 

Document

  • Min: 1

  • Max: 2

  • Redshift - Select

  • Mapper

  • JSON Generator

A JSON document containing the key-value pairs indicating the columns and values for the new rows to insert in the DLP table.

  • If a second input view is configured for the Snap and it contains a document with schema (metadata) of the source table, the Snap creates the new (target) table using the same schema (metadata). However, if the schema comes from a different database, the Snap might fail with Unable to create table: "<table_name>" error due to data type incompatibility.

  • In the absence of a second input view (the schema/metadata document), the Snap creates a table based on the data types of the columns generated from the first row of the input document (first input view).

Output

Document

  • Min: 0

  • Max: 1

  • Databricks - Select

  • Databricks - Unload

  • Databricks - Delete

A JSON document with the details of the new rows inserted in the DLP table.

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

Info

  • Asterisk ( * ): Indicates a mandatory field.

  • Suggestion icon ((blue star)): Indicates a list that is dynamically populated based on the configuration.

  • Expression icon ((blue star) ): Indicates whether the value is an expression (if enabled) or a static value (if disabled). Learn more about Using Expressions in SnapLogic.

  • Add icon ( (blue star) ): Indicates that you can add fields in the fieldset.

  • Remove icon ( (blue star)): Indicates that you can remove fields from the fieldset.

Field Name

Field Type

Description

Label*

Default Value: Databricks - Insert
Example: Db_Insert_Customer

String

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.

Database name

Default Value: None.
Example: Cust_DB

String/Expression/Suggestion

Enter your corresponding DLP database name for the INSERT statement to add new rows in the table.

Table name*

Default Value: None.
Example: Cust_List

String/Expression/Suggestion

Enter your table name for the INSERT statement to add new rows.

Number of Retries

Example: 3

Minimum value: 0

Default value: 0

Integer

Specifies the maximum number of retry attempts when the Snap fails to write.

Retry Interval (seconds)

Example: 3

Minimum value: 1

Default value: 1

Integer

Specifies the minimum number of seconds the Snap must wait before each retry attempt.

Manage Queued Queries

Default value: Continue to execute queued queries when pipeline is stopped or if it fails.

Example: Cancel queued queries when pipeline is stopped or if it fails

Dropdown list

Select this property to determine whether the Snap should continue or cancel the execution of the queued Databricks SQL queries when you stop the Pipeline.

If you select Cancel queued queries when pipeline is stopped or if it fails, then the read queries under execution are cancelled, whereas the write type of queries under execution are not cancelled. Databricks internally determines which queries are safe to be cancelled and cancels those queries.

Due to an issue with DLP, aborting an ELT Pipeline validation (with preview data enabled) causes only those SQL statements that retrieve data using bind parameters to get aborted while all other static statements (that use values instead of bind parameters) persist.

  • For example, select * from a_table where id = 10 will not be aborted while select * from test where id = ? gets aborted.

To avoid this issue, ensure that you always configure your Snap settings to use bind parameters inside its SQL queries.

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.

Troubleshooting

Error

Reason

Resolution

Missing property value

You have not specified a value for the required field where this message appears.

Ensure that you specify valid values for all required fields.

Examples

Inserting data from a Snowflake table into a DLP table

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In this Pipeline, we configure the Snowflake - Select Snap from the Snowflake Snap Pack with the appropriate account, zero input views, and open two output views—one for capturing the rows data and the other for capturing the table schema (as metadata). See the sample configuration of the Snap and its account below:

Snowflake - Select Snap Settings

Snowflake - Select Snap Views

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Account Settings for Snowflake - Select Snap

Notes

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Databricks_FEP1_DbSelect_SfInsert_Snap2_Account.pngImage Added

We configure the Snap to fetch the table schema (output1) and the table data (rows) from the example_company_employees2 table in the Snowflake instance.

The Snap uses a Snowflake JDBC driver and the corresponding basic auth credentials to connect to the Snowflake instance hosted on the AWS cloud.

Upon validation, the Snap displays the table schema and a sample set of records from the specified table in its two output views. During runtime, the Snap retrieves all data from the example_company_employees2 table that match the Snap’s configuration (WHERE conditions, LIMIT, and so on).

Snowflake - Select Snap Output View for Table data

Snowflake - Select Snap Output View for Table Schema

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Similarly, we use a Databricks - Insert Snap with the appropriate account and two input views to consume the two outputs coming from the Databricks - Select Snap. See the sample configuration of the Snap and its account.

Databricks - Insert Snap Settings

Databricks - Insert Snap Views

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Account Settings for Databricks - Insert Snap

Notes

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Databricks_FEP1_DbSelect_SfInsert_Snap1_Account.pngImage Added

We configure the Snap to consume the table schema (output1) and the table data (rows) from the example_company_employees table in the Snowflake instance.

The Snap uses a Databricks JDBC driver and the corresponding basic auth credentials to connect to the DLP (target) instance hosted on the AWS cloud.

The Snap is configured to perform the Insert operation only during Pipeline execution and hence, it does not display any results during validation. Upon Pipeline Execution, the Snap creates a new table example_company_employees2 in the specified DLP database using the schema from the Snowflake - Select Snap and populates the retrieved rows data in this new table.

Download this Pipeline. 

Downloads

Info

  1. Download and import the Pipeline into SnapLogic.

  2. Configure Snap accounts as applicable.

  3. Provide Pipeline parameters as applicable.

Attachments
patterns*.slp, *.zip
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