Optimizing Big Data Processing for Memory-Intensive PySpark Workflows

About project:

Client overview

The client, a data-centric organization, was facing frequent system crashes due to memory limitations when processing large datasets, which impaired their data analysis capabilities. They required a solution to optimize memory management and enhance system stability for efficient handling of big data.

Tech Stack:

PySpark

Tech stack after migration:

PySpark

Time to deliver project:

8-12 weeks

Problem

  • The client came to us with a critical issue: their data processing system was crashing due to insufficient memory errors when handling large datasets exceeding 2-3 million records. This limitation severely impacted their ability to analyze and derive insights from their big data, hindering decision-making processes and operational efficiency.

Inspection

  • Upon investigation, we identified that the root cause of the problem lay in their PySpark implementation. The system was attempting to process extensive dataframes in memory, leading to resource exhaustion. We found instances of inefficient memory usage, potential dataframe duplication, and a lack of proper data partitioning strategies.

Recommendation

  • Optimize PySpark session usage, inspect code for dataframe duplication, and split large datasets into controlled packets for processing. Regularly review and adjust Spark configurations to accommodate growing data volumes.

Resolution

We undertook a comprehensive refactoring and re-engineering of the large PySpark syncs. This involved:

Reworking the processing logic to utilize Spark’s distributed computing capabilities more effectively.
Implementing data streaming techniques to process large datasets in manageable chunks.
Optimizing Spark configurations for better memory management.
Rewriting critical sections of code to reduce memory footprint.

As a result, the system can now handle datasets of 10+ million records without memory issues, a 3-4x improvement over the previous limit. Processing speed for large datasets improved by approximately 40%, and overall system stability increased significantly, with memory-related crashes reduced by 95%.

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