Insert / Update

Description

The Insert/Update transform first looks up a row in a table using one or more lookup keys. If a table row for the specified keys can’t be found, a new row is inserted.

If it can be found and the fields to update are the same, nothing is done.

If they are not all the same, the row in the table is updated.

Supported Engines

Hop Engine

Supported

Spark

Maybe Supported

Flink

Maybe Supported

Dataflow

Maybe Supported

Options

Option	Description
Commit size	The number of rows to change (insert / update) before running a commit.
Connection	The database connection to which data is written
Don’t perform any updates	If enabled, the values in the database are never updated;only inserts are performed.
Key Lookup table	Allows you to specify a list of field values and comparators. You can use the following comparators: =, = ~NULL, <>, <, ⇐, >, >=, LIKE, BETWEEN, IS NULL, IS NOT NULL
SQL button	Click SQL to generate the SQL to create the table and indexes for correct operation.
Transform name	Name of the transform; this name has to be unique in a single pipeline.
Target schema	The name of the Schema for the table to which data is written. This is important for data sources that allow for table names with periods in them.
Target table	Name of the table in which you want to do the insert or update.
Update Fields	Allows you to specify all fields in the table you want to insert/update including the keys. Avoid updates on certain fields by specifying N in the update column.

Option

Description

Commit size

The number of rows to change (insert / update) before running a commit.

Connection

The database connection to which data is written

Don’t perform any updates

If enabled, the values in the database are never updated;only inserts are performed.

Key Lookup table

Allows you to specify a list of field values and comparators. You can use the following comparators: =, = ~NULL, <>, <, ⇐, >, >=, LIKE, BETWEEN, IS NULL, IS NOT NULL

SQL button

Click SQL to generate the SQL to create the table and indexes for correct operation.

Transform name

Name of the transform; this name has to be unique in a single pipeline.

Target schema

The name of the Schema for the table to which data is written. This is important for data sources that allow for table names with periods in them.

Target table

Name of the table in which you want to do the insert or update.

Update Fields

Allows you to specify all fields in the table you want to insert/update including the keys. Avoid updates on certain fields by specifying N in the update column.