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Optimising Spark Job Output for Efficient Data Processing

As an AWS Architect, I have encountered various challenges and devised numerous solutions in the realm of data processing. In this blog, I aim to share my insights with AWS developers and Big Data Engineers on optimising Spark Job outputs within the AWS ecosystem.


Use Case Overview:

In a recent project, our objective was to enhance the efficiency of writing Spark Job outputs and to ensure error-free data availability for downstream jobs. This was particularly challenging given the voluminous nature of the data involved.


Problem Statement: Our data pipeline's Spark job was generating approximately 1 terabyte of transformed data. This resulted in over 1,000 small files, each containing just a few kilobytes of data, being written to an AWS S3 bucket in individual partitions. This fragmentation led to significant read inefficiencies in downstream tasks due to the high number of concurrent S3 requests.

An initial attempt to optimise this process involved implementing coalesce or repartition operations before data writing. However, this led to an unwelcome increase in processing time, primarily due to the overhead of data reshuffling. Moreover, the requirement for primary key-based partitioning of output data added to these time constraints.


Architectural Solution: Drawing upon my experience as an AWS Architect, I proposed a two-tiered approach:

  • Staging Directory Output: The primary Spark job was configured to write its output to a staging directory within AWS. This isolated the initial output from immediate downstream processing.

  • Optimised Coalesce Job: After the main job completion, a specialised, smaller coalesce job was triggered. This job efficiently repartitioned the data based on dataset size and primary key, and subsequently wrote the output to an S3 bucket.


Impact and Benefits: This strategy significantly mitigated the slow read issues associated with AWS S3, a common challenge when dealing with high numbers of small files. Downstream tasks experienced no delays due to S3 read inefficiencies, as they now accessed data from the optimized output location.

Remarkably, the additional coalesce job required only 2 to 3 minutes to complete, vastly reducing the overall processing time. This solution not only streamlined our data processing workflow but also showcased the effective use of AWS services to overcome big data challenges.

Conclusion:

As AWS developers and Big Data Engineers, it's crucial to continuously explore and implement such innovative solutions. This approach underscores the importance of understanding and leveraging AWS capabilities to enhance data processing efficiency and reliability.

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