Pepperdata Code Analyzer for Apache Spark
What can Pepperdata Code Analyzer for Apache Spark do and where does it sit in Pepperdata's product line?
![georgios-anadiotis-author.jpg](https://www.zdnet.com/a/img/resize/f8a968a7322a33fc2db8533732594881a2e3bd2d/2016/06/27/83b9c200-9470-43e7-8a3e-7a824bfa8e3c/georgios-anadiotis-author.jpg?auto=webp&fit=crop&frame=1&height=192&width=192)
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Breaking down application execution in 5 phases (0-4) can give insights in spotting run time bottlenecks.
In this case, looking at the default Spark UI (bottom) it looks as if phase 3 is the bottleneck, using up lots of CPU.
Looking at Pepperdata's custom UI however (top), it becomes clear that phases 2 and 3 run in parallel, and while phase 3 completes phase 2 continues running and using up CPU.
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Inconsistent runtimes
In 2 runs of the same application, run times are not consistent.
![sparkruntimes2.png](https://www.zdnet.com/a/img/resize/600c3c5fa2f58e50a08f078d083b729c7c2d8507/2017/06/01/e5289c4c-2c0a-4aa9-9f11-00c78ecf88d8/sparkruntimes2.png?auto=webp&width=1280)
3 of 4 Pepperdata
Cluster weather
This has to do with cluster weather: in the 2nd run there is less memory available to the available, as there are other applications running at the same time.
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Pepperdata product line
Pepperdata Code Analyzer for Apache Spark is aimed at engineers and complements Pepperdata existing line of products
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