Using components: Database Destination

Use the Database Destination component to store the output of your package in a relational database table. 

The following database platforms are supported: PostgreSQL, MySQL, Microsoft SQL Server.

Destination components are always the last component in a package.

To define the Database destination:

  1. Add a Database Destination component at the end of your dataflow.
  2. Open the component and name it.

Destination settings

  1. Define the parameters for connecting to your Database as follows (for more information, see Allow Xplenty access to my Database):
    • database connection - either click the drop-down arrow and select an existing connection, or click (create new) to create a new connection.
    • target schema - the target table's schema. If empty, the default schema is used.
    • target table - the name of the target table in your database.
    • create table - automatically create the table defined in the component if it doesn't exist. See below for more information.
    • add columns - automatically add columns that are mapped in the destination and do not exist in the table. See below for more information. 
    • max connectionsmaximum number of concurrent connections to open when writing to the database.
    • split by field - when using more than one connection, this field will be used to split the data to the different connections. Pick a field with low density (a unique key is best) to make sure that data split isn't skewed.
    • batch sizenumber of records that are inserted to the database in each batch (default 100).
    • transaction per batch - if checked, each batch will be committed on its own which may lead to partial data in the target table in case the job fails. Otherwise, each connection will use a single transaction. 
    • operation type - the method of data insertion
      • insert - default behavior. Data will only be appended to the target table.
      • truncate and insert - truncate the target table before the data flow executes.
      • delete and insert - deletes all of the target table before the data flow executes. If a truncate statement can't be executed on the target table due to permissions or other constraints, you can use this instead.
      • merge - data will be inserted and/or updated in the target table according to the defined key(s). See details of the merge process below.
        Note - It's important to select the proper key columns to use in order to merge data correctly.
    • pre action sql - sql code to execute before inserting the data into the target table. If a merge operation is selected, the sql code is executed before the staging table is created.
    • post action sql - sql code to execute after inserting the data into the target table. If a merge operation is selected, the sql code is executed after the staging table is merged into the target table.

    Then click the Test Connection button to help check that the connection is good and that the target table exists.

  2. Map fields from your dataflow to columns in the target table. You can click the green Auto-fill fields icon to automatically populate the list of fields and column names. When mapping fields to table columns, take into consideration the mapping of data types. If you map mismatched data types, the job will fail. The following table displays matching the matching data types:
Xplenty PostgreSQL MySQL Microsoft
SQL Server
String varchar, char, text,  interval varchar, nvarchar, text,  varchar, nvarchar, text, ntext
Integer smallint, int bit, bool, tinyint, smallint, mediumint, int, integer tinyint, smallint, int
Long bigint bigint bigint
Float decimal, real decimal, float decimal, numeric, float
Double double precision double real
DateTime timestamp, date, time date, datetime, timestamp, time datetime, datetime2, smalldatetime, date, time, datetimeoffset
Boolean TINYINT(1) BOOLEAN BIT

    * Note - Use the ToDate function to cast a datetime string expression to datetime data type. Note that a datetime with timezone offset value will be adjusted to UTC when inserted into a database

Automatically creating and altering destination table

Xplenty can automatically create the destination table for you if it doesn't exist and it can also append columns to an existing table. If you define columns as key (regardless of the operation type), Xplenty defines them as the primary key in a new table. The data types in Xplenty are automatically mapped as follows. Note that since Xplenty doesn't have a notion of maximum string length, the string columns are created with the maximum length allowed in the database.

Xplenty PostgreSQL MySQL Microsoft
SQL Server
String VARCHAR(65535) TEXT NVARCHAR(MAX)
Integer INT INT INT
Long BIGINT BIGINT BIGINT
Float REAL FLOAT REAL
Double DOUBLE PRECISION DOUBLE FLOAT
DateTime TIMESTAMP DATETIME DATETIME
Boolean BOOLEAN TINYINT(1) BIT

Merging data into an existing table

The merge operation is done in these steps:

  • First transaction - A staging table is created with a primary key according to your key fields (in the same order) in the database's default schema with the target table's schema.
  • Note: The incoming data must be unique according to the key fields you selected. You may use the aggregate component or the limit component to make sure that the key fields are indeed unique.  A workaround for this kind of failure can be to use a Limit component and add the key field/s as a partition and limit it to 1 thus removing duplicates.
  • Second transaction - The dataflow's output is bulk copied into the staging table.
  • Third transaction - Rows with keys that exist in the bulk copied data are deleted from the target table. All rows in the staging table are inserted into the target table. The staging table is deleted.

Creating packages

  1. Creating a new package
  2. Create a package from a template
  3. Working in the package designer
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  5. Using components: File Storage Source
  6. Using components: Database Source
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  9. Using Components: Google Analytics Source
  10. Using Components: Google BigQuery Source
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  13. Using components: MongoDB Source
  14. Using components: Amazon Redshift Source
  15. Using Components: Rest API Source
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  17. Using components: Select
  18. Using components: Sort
  19. Using components: Rank
  20. Using components: Limit
  21. Using components: Sample
  22. Using components: Join
  23. Using components: Cross Join
  24. Using components: Clone
  25. Using components: Cube and Rollup
  26. Using components: Union
  27. Using components: Filter
  28. Using Components: Window
  29. Using components: Assert
  30. Using components: Aggregate
  31. Using components: Distinct
  32. Using components: File Storage Destination
  33. Using components: Amazon Redshift Destination
  34. Using Components: Salesforce Destination (Beta)
  35. Using components: Google BigQuery Destination
  36. Using components: Google Cloud Spanner Destination
  37. Using components: Database Destination
  38. Using components: MongoDB Destination
  39. Using and setting variables in your packages
  40. Validating a package
  41. Using pattern-matching in source component paths
  42. Using ISO 8601 string functions
  43. Using Expressions in Xplenty
  44. Xplenty Functions

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