JDBC ETL Source

JDBC Channel supports Presto, ES and other databases that have JDBC connection. You can configure and test the connection with JDBC. This connection will allow you to extract the data from Relational DB sources into pipeline in batches after configuring JDBC channel.

Schema Type #

See the topic Provide Schema for ETL Source → to know how schema details can be provided for data sources.

After providing schema type details, the next step is to configure the data source.

Data Source Configuration #

Connection Name: Connections are the service identifiers. A connection name can be selected from the list if you have created and saved connection details for JDBC earlier. Or create one as explained in the topic - JDBC Connection →

Schema Name: Source Schema name for which the list of table will be viewed.

Table Name: Source table name to be selected for which you want to view the metadata.

Query: Hive compatible SQL query to be executed in the component.

In case if a connection with Custom JDBC option is selected, this query will serve as a sub-query for the inspect query specified in the custom JDBC connection configuration.

Design Time Query: Query used to fetch limited records during Application design. Used only during schema detection and inspection.

Enable Query Partitioning: This enables parallel reading of data from the table. It is disabled by default. Tables will be partitioned if this check-box is enabled.

If Enable Query Partitioning is check marked, additional fields will be displayed as given below:

Type-in Partition Column: Select this option if the Partition Column list shown is empty or you do not see the required column in the list.

Partition on Column: This column will be used to partition the data. This has to be a numeric column, on which spark will perform partitioning to read data in parallel.

Data Type: In case if you have typed-in the partitioning column, you need to specify the data type of that column here.

Autodetect Bounds: Check this option to auto-detect the partition boundaries.

If Autodetect Bounds is check marked, additional fields will be displayed as given below:

Row count in Single Query: Enter the number of rows to be read in a single query.

Example: 10,000.

It implies that 10,000 records will be read in one partition.

Column have unique values: Check this column when the records are unique in the table.

If Autodetect Bounds is disabled, then proceed by updating the following fields.

No. of Partitions: Specifies the number of parallel threads to be invoked to partition the table while reading the data.

Lower Bound: Value of the lower bound for partitioning column. This value will be used to decide the partition boundaries. The entire dataset will be distributed into multiple chunks depending on the values.

Upper Bound: Value of the upper bound for partitioning column. This value will be used to decide the partition boundaries. The entire dataset will be distributed into multiple chunks depending on the values.

If Enable Query Partitioning is disabled, then proceed by updating the following field.

Fetch Size: The fetch size determines the number of rows to be fetched per round trip. The default value is 1000.

Detect Schema #

Check the populated schema details. For more details, see Schema Preview →

Incremental Read #

Optionally, you can enable incremental read. For more details, see JDBC Incremental Configuration →

Pre Action #

To understand how to provide SQL queries or Stored Procedures that will be executed during pipeline run, see Pre-Actions →

Notes #

Optionally, enter notes in the Notes → tab and save the configuration.