#HIGH BILLING RATE IT JOBS BIG DATA DRIVER#
We will monitor the memory profile of Spark driver and executors over time. This dataset is joined with two other datasets (dimension tables – employee and badge data), which are smaller in size, one with 107 records and another with a record count of 12,249 in 10 files. Our setup uses a fact table consisting of employee badge access data stored in S3 with 1.34 million objects and files, and a record count of 1.3 billion. In this blog post, we would show how workload partitioning can help you mitigate these errors by bounding the execution of the Spark application, and also detect abnormalities or skews in your data. Third, any data abnormality or malformed records can cause the Spark application to fail during any of the three stages – read from S3, application of join transform, or write to S3. Second, the Spark executors can run out-of-memory if there is skew in the dataset resulting in imbalanced shuffles or join operations across the different partitions of the fact table. First, the Spark driver can run out-of-memory while listing millions of files in S3 for the fact table. The following diagram illustrates an ETL architecture used commonly by several customers.ĮTL pipelines using Apache Spark applications for this use case or similar backlog ingestion can encounter 3 common errors. One of the common use cases of data warehousing is processing a large number of records from a fact table (employees, sales or items) and joining the same with multiple dimension tables (departments, stores, catalog), and loading the output to the final destination. This Spark runtime optimization also works together with existing Glue features such as push down predicates, AWS Glue S3 lister, grouping, exclusions for S3 paths, and other optimizations.
This is achieved by breaking down the monolithic Spark applications processing a large backlog of tens to hundreds of millions of files into simpler modular Spark applications that can process a bounded number of files or dataset size incrementally. Specifically, this feature makes it easy for customers to make their complex ETL pipelines significantly more resilient to errors. Bounded execution allows customers to partition their workloads by limiting the maximum number of files or dataset size processed incrementally within Glue Spark applications that can be orchestrated sequentially or in parallel. Now, this feature gives them another simple yet powerful construct to bound the execution of their Spark applications. Customers on Glue have been able to automatically track the files and partitions processed in a Spark application using Glue job bookmarks. In this blog post, we introduce a new Spark runtime optimization on Glue – Workload/Input Partitioning for data lakes built on Amazon S3. Inspite of this data-parallel architecture, Spark applications commonly run into out-of-memory (OOM) exceptions on driver and executors due to skew in input data, large number of input files, or large joins and shuffle operations. Spark’s distributed execution uses a Master/Slave architecture with driver and executor processes perform parallel computation over partitions of input dataset. Errors in Spark applications commonly arise from inefficient Spark scripts, distributed in-memory execution of large-scale transformations, and dataset abnormalities. They want to ensure fast and error-free execution of these workloads. This posts discusses a new AWS Glue Spark runtime optimization that helps developers of Apache Spark applications and ETL jobs, big data architects, data engineers, and business analysts scale their data processing and batch jobs running on AWS Glue automatically.Ĭustomers use Spark for a wide variety of ETL and analytics workloads on datasets with diverse characteristics. AWS Glue provides a serverless environment to prepare (extract and transform) and load large amounts of datasets from a variety of sources for analytics and data processing with Apache Spark ETL jobs.
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