Redshift - Insert

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Snap Type:



This Snap executes a SQL INSERT statement using the document's keys as the columns to insert to and the document's values as the values to insert into the columns. 

According to Amazon's Redshift documentation, Amazon strongly recommends using the BULK insert functionality, advising against using the INSERT statement. The Redshift - Insert Snap will use the batch INSERT methodology suggested, but the performance of Redshift in executing might be, as Amazon advises, prohibitively.

SnapLogic has created the Redshift - Bulk Load Snap to address this issue, automating the use of the advised BULK insert using the COPY command. Refer to the Redshift - Bulk Load Snap for more information.

ETL Transformations & Data Flow

The Snap receives data from upstream component and loads the data into the selected table in the Redshift DB.

Input & Output

  • InputEach document in the input view should contain a Map data of key-value entries. Input data may contain values needed to evaluate expressions in the Object type, Output fields, and Conditions properties. Please make sure input data types match column data types in the database table. Otherwise, you may encounter an error message "Cannot find an input data which is related to the output record .....". If the error view is open, all input data in the batch are routed to the error view with the same error information.
  • Output: The output contains the status of insert operation in the database for each input record.
  • Expected upstream Snaps:  Any Snap which produces documents in the output view, such as CSV Parser, JSON Parser, Structure, Data, and so on.
  • Expected downstream Snaps:  Any Snap which receives documents in the input view, such as JSON Formatter, Structure, Data, and so on. CSV Formatter will cause an error since the output data is not a flattened Map data.



Limitations and Known Issues:

Works in Ultra Pipelines if batching is disabled.


Account and Access

This Snap uses account references created on the Accounts page of SnapLogic Manager to handle access to this endpoint. See Redshift Account for information on setting up this type of account.


InputThis Snap has one document input views by default. A second view can be added to for metadata for the table as a document so that the table is created in Redshift with a similar schema as the source table. 

OutputThis Snap has at most one output view containing the status and the original input.

This Snap has at most one error view and produces zero or more documents in the view.

Database Write Snaps output all records of a batch (as configured in your account settings) to the error view if the write fails during batch processing.


Error Message: "Cannot find an input data which is related to the output record ....."

This error occurs when the data types in the input document do not match the column types in the database table. Ensure that the input data types match the column types in the database table.



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

Specify the database schema name. Selecting a schema filters the Table name list to show only those tables within the selected schema. 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.

Default value: None
Example: myschema

Table name*

Specify the name of the table to execute an insert query on.

The values can be passed using the pipeline parameters but not the upstream parameter.

Default value: None
Example: people

Create table if not present

Select this checkbox to automatically create the target table if it does not exist.

  • 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).

Due to implementation details, a newly created table is not visible to subsequent database Snaps during runtime validation. If you wish to immediately use the newly updated data you must use a child Pipeline that is invoked through a Pipeline Execute Snap.

Default value: Not selected 

Preserve case sensitivity
Select this check box to specify whether the letter case used in column labels must be preserved while performing the insert operation. Selecting this option ensures that the precise cases used are retained.

Default value
: Not Selected
Number of retries

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.

Default value: 0
Example: 3

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If you're experiencing issues please see our Troubleshooting Guide.

Retry interval (seconds)

Specifies the time interval between two successive retry requests. A retry happens only when the previous attempt resulted in an exception. 

Default value: 1
Example:  10

Execute during preview

This property enables you to execute the Snap during the Save operation so that the output view can produce the preview data.

Default value: Not selected

Table Creation

If the table does not exist when the Snap tries to do the insert, and the Create table if not present property is selected, the table will be created with the columns and data types required to hold the values in the first input document. If you would like the table to be created with the same schema as a source table, you can connect the second output view of a Select Snap to the second input view of this Snap. The extra view in the Select and Bulk Load Snaps are used to pass metadata about the table, effectively allowing you to replicate a table from one database to another.

The table metadata document that is read in by the second input view contains a dump of the JDBC DatabaseMetaData class. The document can be manipulated to affect the CREATE TABLE statement that is generated by this Snap. For example, to rename the name column to full_name, you can use a Mapper Snap that sets the path $ to full_name.  The document contains the following fields:

  • columns - Contains the result of the getColumns() method with each column as a separate field in the object. Changing the COLUMN_NAME value will change the name of the column in the created table. Note that if you change a column name, you do not need to change the name of the field in the row input documents. The Snap will automatically translate from the original name to the new name. For example, when changing from name to full_name, the name field in the input document will be put into the "full_name" column. You can also drop a column by setting the COLUMN_NAME value to null or the empty string.  The other fields of interest in the column definition are:

    • TYPE_NAME - The type to use for the column.  If this type is not known to the database, the DATA_TYPE field will be used as a fallback.  If you want to explicitly set a type for a column, set the DATA_TYPE field.

    • _SL_PRECISION - Contains the result of the getPrecision() method.  This field is used along with the _SL_SCALE field for setting the precision and scale of a DECIMAL or NUMERIC field.

    • _SL_SCALE - Contains the result of the getScale() method.  This field is used along with the _SL_PRECISION field for setting the precision and scale of a DECIMAL or NUMERIC field.

  • primaryKeyColumns - Contains the result of the getPrimaryKeys() method with each column as a separate field in the object.

  • declaration - Contains the result of the getTables() method for this table. The values in this object are just informational at the moment.  The target table name is taken from the Snap property.

  • importedKeys - Contains the foreign key information from the getImportedKeys() method. The generatedCREATE TABLE statement will include FOREIGN KEY constraints based on the contents of this object. Note that you will need to change the PKTABLE_NAME value if you changed the name of the referenced table when replicating it.

  • indexInfo - Contains the result of the getIndexInfo() method for this table with each index as a separated field in the object.  Any UNIQUE indexes in here will be included in the CREATE TABLE statement generated by this Snap.

  • The Primary key is not enforced in RedShift, therefore it will allow duplicated records inserted with the same primary key. In fact, uniqueness, primary key, and foreign key constraints are informational only in Amazon RedShift. For more information, refer to:
  • The Snap will not automatically fix some errors encountered during table creation since they may require user intervention to resolve correctly. For example, if the source table contains a column with a type that does not have a direct mapping in the target database, the Snap will fail to execute. You will then need to add a Mapper (Data) Snap to change the metadata document to explicitly set the values needed to produce a valid CREATE TABLE statement.


Basic Use Cases

Use Case #1

This Use Case Pipeline demonstrates how to insert input data into a table along with the table's structure

In this use case we will insert a collection of records into a table "SNAPPOD"."EMPLOYEE_DATA" which is defined as below: (Where SNAPPOD is the schema name and EMPLOYEE_DATA is the name of the table).

    "employee_code" integer,
    "employee_name" varchar(50),
    "job" char(20),
    "salary" float,
    "department_code" integer

The input data will be provided from an upstream Snap, in this example JSON Generator is used which contains the data as below: 

Data in the JSON Generator
    { "empno": 101, "ename": "John", "job": "Manager", "sal": 144000, "deptno": 10 },
    { "empno": 102, "ename": "Thomas", "job": "Marketing ", "sal": 84000, "deptno": 20 },
    { "empno": 103, "ename": "Williams", "job": "Technical", "sal": 96000, "deptno": 30 },
    { "empno": 104, "ename": "Scott", "job": "Sales", "sal": 84000, "deptno": 20 },
    { "empno": 105, "ename": "Richard", "job": "Accounts", "sal": 72000, "deptno": 40 },
    { "empno": 106, "ename": "James", "job": "Sales", "sal": 84000, "deptno": 20 }

Use Case #2

The pipeline below shows a typical scenario where data from a Mapper Snap is passed to the Redshift Insert Snap. 

The configuration of the Mapper Snap:

Preview of the successful execution of the Redshift Insert Snap:

Typical Snap Configurations

  File Modified

File RedshiftInsert Adv Use case.slp

Aug 30, 2017 by Aparna Tayi

File sn2180-1-enh_doc-redshift_insert-basic.slp

Aug 30, 2017 by Aparna Tayi

File sn2180-2-enhanced_doc-redshift_insert-advanced.slp

Aug 30, 2017 by Aparna Tayi

Key configuration of the Snap lies in how you pass the SQL statements to insert the records. The statements can be passed:

  • Without Expressions
    The values are passed directly into the Snap.

  • With Expressions
    • Pipeline Parameters
      The Table Name is passed as a pipeline parameter. 


Advanced Use Case

The following describes a pipeline that shows how typically in an enterprise environment, an insert functionality is used. Pipeline download link in the Downloads section below.

In this example we will insert a collection of records into a table "snappod"."EMPLOYEE_DATA" and the Snap also creates the table if it is not existing.

The snap will use two input views, the first input view is to receive data from the upstream Snap and insert the data into the destination table. The second input view receives the structure of the table along with table constraints and creates the table in Redshift DB if the table is not existing. 


Oracle Select Snap: This Snap will have two output streams, 

Output view rows: Selects the data from the given table and writes the records to this output view.

Output view output1: Writes the structure of the table along with the column constraints such as primary key, not null, and so on.

Output View 'output1' (contains table structure)

Output View 'rows' (contains input data)

Redshift Insert Snap:

Contains two input views

Input view 'input1': To receive the table structure and to create the table in Redshift if it is not existing. (The user needs to enable the 'create table if not existing' property)

Input view 'rows': To receive input data from the upstream Snap and to insert them into the selected table on Redshift.


  File Modified

File RedshiftInsert Adv Use case.slp

Aug 30, 2017 by Aparna Tayi

File sn2180-1-enh_doc-redshift_insert-basic.slp

Aug 30, 2017 by Aparna Tayi

File sn2180-2-enhanced_doc-redshift_insert-advanced.slp

Aug 30, 2017 by Aparna Tayi

Redshift IAM Account Setup

  • If the EC2 plex (where your Pipeline is running with IAM role), Redshift cluster, and S3 bucket are in the same AWS account, then you must use Redshift Account (normal IAM account).
  • If the EC2 plex (where your Pipeline is running with IAM role) is in one account and the Redshift cluster and S3 bucket are in a different AWS account, you must use Redshift Cross-account IAM role Account to run your Pipelines successfully.

This is applicable only for Redshift - Bulk Load, Redshift - Unload, and Redshift - S3 Upsert Snaps.