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Mastering Apache Spark: Performance Engineering & Optimization
Paperback
Publisher: Independently Published
Published: Oct 23 2026
Pages: 330
Weight: 1.16
Height: 0.69 Width: 6.69 Depth: 9.61
Language: English
You will learn why a Spark job is slow, how to fix the real cause, and how to keep it fast. The book explains how Spark plans and runs your code, how to read the Spark UI, and how to tune storage, shuffles, joins, memory and clusters, so problems stay fixed.
It is 3 a.m. Your nightly job has been running for six hours. Yesterday it took forty minutes. The bill is climbing, and one task out of two hundred will not finish.
You add memory. Nothing changes. You add machines. The job gets slower.
So what is really going on? The answer is rarely a bigger cluster. It hides in the query plan, in how the data is stored, in one key holding far too many rows. Once you know where to look, you find it in minutes, not nights.
What's insideAcross eight chapters and a practical appendix, you will learn how to:
- Read the engine: follow your code from plan to jobs, stages and tasks, and understand EXPLAIN
- Measure honestly: use the Spark UI, task metrics, event logs and fair benchmarks
- Lay out data well: choose file formats, partitions, file sizes and table formats
- Tame shuffles and skew: set partition counts and cut the data that moves
- Master joins: pick strategies, use broadcasts, trust adaptive execution and fix slow queries
- Control memory: cache wisely, reduce garbage collection and fix spills and crashes
- Write faster code: choose between SQL, DataFrames and Datasets, replace UDFs and tune PySpark
- Run with confidence: size executors, use the cloud, tune streaming and build a lasting practice
The appendix adds a settings reference, a symptom-to-cause guide, a launch checklist and a glossary. Practice scenarios come with answers.
Who it's meant for- Data engineers whose jobs are slow, flaky or costly
- Analysts and scientists using PySpark or Spark SQL who want answers sooner
- Platform engineers and team leads who size clusters and watch the budget
- Developers ready to go beyond it works
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