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Apache Spark's memory management explained: Unified Memory Model, spills, and AQE

By PulseAugur Editorial · [1 sources] ·

Apache Spark's memory management, particularly its Unified Memory Manager, can lead to OutOfMemory errors even when total memory is not exceeded. The executor's JVM heap is divided into regions like Reserved Memory, User Memory, and a Unified Region for Execution and Storage. The Unified Region, governed by `spark.memory.fraction`, is shared between Execution Memory (for transient tasks like shuffles and joins) and Storage Memory (for caching). A common cause of OOM errors is a skewed reduce partition demanding more execution memory than its allocated region allows, while cached storage memory remains unused and inaccessible. AI

IMPACT Understanding Spark's memory management is crucial for optimizing large-scale data processing, which is often a component of AI/ML pipelines.

RANK_REASON Technical explanation of a software feature.

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Apache Spark's memory management explained: Unified Memory Model, spills, and AQE

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  1. Towards AI TIER_1 English(EN) · chakshu_salgotra ·

    How Spark Manages Memory — The Unified Memory Model, Spills, and AQE

    <h3>How Spark Manages Memory — The Unified Memory Model, Spills, and AQE</h3><h4><em>Where every gigabyte in an executor actually goes, and why your job dies at 85% completion</em></h4><p><a href="https://chakshu-salgotra.medium.com/how-spark-reads-parquet-2333c7fa728c">Part I</a…

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