1. Component library
  2. Write
  3. Update table

Update table

Update records in a table in a registered datastore. This component allows you to map the values available in the flow with the columns of the target table, in order to update the values of these columns in the datastore.

To understand the configuration of the Update table component, consider a typical SQL update statement:

UPDATE table SET name = 'John Doe' WHERE id = 42

Here we see that there is a condition (WHERE id=42) and a value to update (name should become 'John Doe'). This is what the two inputs are referring to. But obviously you are not dealing with constant values like 'John Doe' or '42'. You have a field in your DC job that you want to map to fields in your database.

Usually the 'condition value' would be a mapping of the key that you have in your job towards the key that is in the database. The 'values to update' property would include the columns that you wish to update based on the values you have in your job.

Analyzer Concurrent Distributed execution possible

Result metrics (WriteDataResult)

  • Errornous rows

    Not parameterized

  • Inserts

    Not parameterized

  • Updates

    Not parameterized

Configured properties

  • Values

    Values to update in the table

    List of InputColumn<Object> Required

  • Column names

    Names of columns in the target table, on which the values will be updated.

    List of String Mapped with Values Required

  • Condition values

    Values that make up the condition of the table update

    List of InputColumn<Object> Required

  • Condition column names

    Names of columns in the target table, which form the conditions of the update.

    List of String Mapped with Condition values Required

  • Datastore

    Datastore to write to

    UpdateableDatastore Required

  • Schema name

    Schema name of target table

    String Optional

  • Table name

    Table to target (update)

    String Optional

  • Buffer size

    How much data to buffer before committing batches of data. Large batches often perform better, but require more memory.

    Choice: Huge (1M values) Large (100k values) Medium (10k values) Small (1000 values) Tiny (100 values) Required

  • How to handle updation errors?

    Choice: Stop the job Save errornuos records to file Required

  • Error log file location

    Directory or file path for saving erroneous records

    File Optional

  • Additional error log values

    Additional values to write to error log

    List of InputColumn<Object> Optional