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Dynamic Partition Pruning

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Big Data Technology Architecture
Big Data Technology Architecture
Nov 16, 2021 · Big Data

Understanding Adaptive Query Execution and Dynamic Partition Pruning in Apache Spark 3.0

This article explains how Apache Spark 3.0 improves SQL workload performance through Adaptive Query Execution (AQE) and Dynamic Partition Pruning (DPP), detailing their design principles, runtime optimizations, configuration parameters, and practical examples that demonstrate reduced shuffle partitions, smarter join strategies, and handling of data skew.

Adaptive Query ExecutionBig DataDynamic Partition Pruning
0 likes · 9 min read
Understanding Adaptive Query Execution and Dynamic Partition Pruning in Apache Spark 3.0
Big Data Technology Architecture
Big Data Technology Architecture
Jun 20, 2020 · Big Data

Apache Spark 3.0.0 Release: New Features, Improvements, and Timeline

Apache Spark 3.0.0, released after a 21‑month development cycle and several preview and release‑candidate votes, introduces major enhancements such as Dynamic Partition Pruning, Adaptive Query Execution, accelerator‑aware scheduling, DataSource V2, expanded pandas UDFs, new join hints, richer monitoring, SparkR vectorization, Kafka header support, and broader ecosystem integrations, while fixing over 3,400 issues.

Adaptive Query ExecutionApache SparkBig Data
0 likes · 17 min read
Apache Spark 3.0.0 Release: New Features, Improvements, and Timeline