Pyspark how to run the database table in parallel in AWS Glue job

To run database tables in parallel in an AWS Glue job using PySpark, you can leverage the parallelization capabilities provided by AWS Glue. Here's how you can achieve this:

1. Use DynamicFrames or Spark DataFrames: Both AWS Glue DynamicFrames and Spark DataFrames support parallelized JDBC data loads across multiple tasks.

2. Configure parallelization options: For AWS Glue DynamicFrame, set the 'hashfield' (or 'hashexpression') and 'hashpartitions' options. For Spark DataFrame, set 'numPartitions', 'partitionColumn', 'lowerBound', and 'upperBound'.

3. Example for DynamicFrame:

   connection_options = { "url": "jdbc:mysql://your-database-url:3306/your-database", "dbtable": "your_table", "user": "your_username", "password": "your_password", "hashexpression": "id", "hashpartitions": "10" }

   datasource = glueContext.create_dynamic_frame.from_options( 'mysql', connection_options=connection_options, transformation_ctx="datasource" )

4. Example for Spark DataFrame:

   df = spark.read
   .format("jdbc")
   .option("url", "jdbc:mysql://your-database-url:3306/your-database")
   .option("dbtable", "your_table")
   .option("user", "your_username")
   .option("password", "your_password")
   .option("partitionColumn", "id")
   .option("numPartitions", "10")
   .option("lowerBound", "0")
   .option("upperBound", "1000000")
   .load()

5. Run multiple tables in parallel: To process multiple tables concurrently, you can create separate DynamicFrames or DataFrames for each table and process them in parallel using Spark's parallel processing capabilities.

6. Optimize number of partitions: Adjust the number of partitions based on your data size and available resources. More partitions generally lead to higher parallelism, but too many can cause overhead.

7. Monitor and tune: Use the Spark UI and AWS Glue metrics to monitor job performance and adjust the parallelization settings as needed.

By implementing these strategies, you can significantly reduce the running time of your AWS Glue job by processing database tables in parallel rather than sequentially.
Sources
Parallelize tasks -
Reading from JDBC tables in parallel - AWS Glue

Hi,

I understand that you want to run multiple database tables in parallel using PySpark in AWS Glue jobs.

Spark's fundamental design principle involves sequential processing of stages. Parallel execution on multiple database tables is not supported in Pyspark.

While we can achieve parallelism within a stage (like parallel reading of data from a single table using 'hashfield' and 'hashpartitions'), running multiple database table truly in parallel is not possible as Spark executes stages in sequence.