MSSQL Database Ingestion Source

Data Source Configuration #

Fetch From Source/Upload Data File

Gathr provides two options for reading MSSQL Databases data. You can either choose to fetch data from a MSSQL Database source directly by providing the data source connection details, or, upload a sample data file in one of the supported formats to see the schema details during ingestion application design time.

If Upload Data File is selected to fetch sample data, provide below details.

File Format: Depending on the type of data, select the sample file format (file type).

Gathr supported file formats for MSSQL Database data source are: CSV, JSON, TEXT, Parquet and ORC.

For CSV file format, select its corresponding delimiter.

Header Included: Enable this option to read the first row as a header in case if your MSSQL Databases data is in CSV format.

Upload: Please upload the sample file as per the file format selected above.

If Fetch From Source is selected, then continue to configure the data source.

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 MSSQL Databases earlier. Or create one as explained in the topic - JDBC Connection →

Use the Test Connection option to make sure that the connection with MSSQL Database channel is established successfully.

A success message states that the connection is available. In case of any error in test connection, edit the connection to resolve issue before proceeding further.

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.

Add Configuration: Additional properties can be added using this option as key-value pairs.

More Configurations

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

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.

Schema #

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

Advanced Configuration #

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