Spark Tips. Optimizing JDBC data source reads - In this blog post, we will discuss how to optimize reading from JDBC data sources in Spark. By default, JDBC data sources load data sequentially using a single executor thread, which can significantly slow down your performance and potentially exhaust the resources of your system. To read data concurrently, the Spark JDBC data source must be configured with appropriate partitioning information so that it can issue multiple concurrent queries to the external database. There are four partitioning options provided by the DataFrameReader: partitionColumn, numPartitions, lowerBound, and upperBound. These partitioning options are used to split the values into multiple partitions, allowing you to read data in parallel. By implementing these optimizations, you can greatly improve the performance of your Spark job when reading from JDBC data sources.
