Spark JDBC
JDBC is one of the most commonly used data sources in Spark. In this section, we will provide details on how to use the ClickHouse official JDBC connector with Spark.
Read data
- Java
- Scala
- Python
- Spark SQL
Write data
- Java
- Scala
- Python
- Spark SQL
Parallelism
When using Spark JDBC, Spark reads the data using a single partition. To achieve higher concurrency, you must specify
partitionColumn, lowerBound, upperBound, and numPartitions, which describe how to partition the table when
reading in parallel from multiple workers.
Please visit Apache Spark's official documentation for more information
on JDBC configurations.
JDBC Limitations
- As of today, you can insert data using JDBC only into existing tables (currently there is no way to auto create the table on DF insertion, as Spark does with other connectors).