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Sparklens

Open source

Sparklens is a profiling tool for Apache Spark with a built-in scheduler simulator that shows how well an application scales with more executors.

GitHub stars
593
Last commit
2 yr ago
Repository age
8 years
Licence
Apache-2.0
Self-hosted
Yes

About Sparklens

Sparklens is a profiling and performance-tuning tool for Apache Spark applications from Qubole. Its main goal is to make the scalability limits of a Spark application easy to understand, showing how efficiently it uses the compute resources it was given.

From a single run of an application, a built-in Spark scheduler simulator estimates completion time and cluster utilization for different numbers of executors, which helps judge whether adding executors will pay off. It also narrows problems down to the stages, the driver, skew or lack of tasks that limit scaling, and shows a job and stage timeline of how parallel stages ran.

A reporting service at sparklens.qubole.com lets users upload the Sparklens JSON output and get a shareable HTML report with charts. The tool is written in Scala and licensed under Apache-2.0, and it aims to turn Spark tuning into a defined process instead of trial and error.

Key features

  • Spark application profiling
  • Scheduler simulator for executor counts
  • Estimated runtime and utilization
  • Stage and driver bottleneck analysis
  • Job and stage timeline
  • Shareable web reports from JSON output

Good fit for

  • →Tuning slow Spark jobs
  • →Right-sizing executor counts
Built with
Scala
Tags
apache-spark
performance-tuning
profiling
scala
spark-sql
performance-analysis
big-data

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