MEMORY LIMIT EXCEEDED FACEBOOK FLEXIFY DRIVER
In addition, the driver needs to keep track of the progress of each task is making and collect the results at the end. The driver then coordinates tasks running the transformations that will process each file split. In majority of ETL jobs, the driver is typically involved in listing table partitions and the data files in Amazon S3 before it compute file splits and work for individual tasks. Scaling the Apache Spark driverĪpache Spark driver is responsible for analyzing the job, coordinating, and distributing work to tasks to complete the job in the most efficient way possible. In this post of the series, we will go deeper into the inner working of a Glue Spark ETL job, and discuss how we can combine AWS Glue capabilities with Spark best practices to scale our jobs to efficiently handle the variety and volume of our data. However, this is not an exact science and applications may still run into a variety of out of memory (OOM) exceptions because of inefficient transformation logic, unoptimized data partitioning or other quirks in the underlying Spark engine. We also looked at how you can use AWS Glue Workflows to build data pipelines that enable you to easily ingest, transform and load data for analytics.Īpache Spark provides several knobs to control how memory is managed for different workloads. In the third post of the series, we discussed how AWS Glue can automatically generate code to perform common data transformations.
This blog post was last reviewed July, 2022.ĪWS Glue provides a serverless environment to prepare and process datasets for analytics using the power of Apache Spark.
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