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Spark performance tuning - Maksud Ibrahimov
- 1. Performance tuning of Apache Spark Melbourne Apache Spark meetup Maksud Ibrahimov February 2016
- 2. Who am I? — Chief Data Scientist at InfoReady - a leading Australian data & analytics business — PhD in Artificial Intelligence from the University of Adelaide — Over last 10+ years, have worked and improved operations of major companies in mining, manufacturing, retail and logistics through machine learning, optimisation and simulation — Particular interest in applying these algorithms in cluster computing environment, hence Apache Spark — User of Spark since 1.0 release 2
- 3. What is Apache Spark? 18/02/16 Performance tuning of Apache Spark 3 — Apache Spark is a fast and general engine for large-scale data processing. — Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. — Write applications quickly in Java, Scala, Python, R — SQL — Streaming — Graph processing — Machine learning
- 4. How easy it is to write programs in Spark? — Fairly easy to start. Within 1-2 day you can start writing simple programs on a single machine — Not too hard to deploy and run on the cluster with preconfigured deployment options, such as Amazon EMR or Hortonworks distribution — Once you start writing programs that run in a cluster with a few nodes you may notice that the execution is not as fast as was expected 18/02/16 Performance tuning of Apache Spark 4
- 5. Generally performance can be improved by tuning the following areas • Partitioning. Do you take full advantage of parallel capabilities of Spark? Do you spill to disk? • Runtime configuration. Is your configuration tuned to your task? • Optimal code. Can you perform your computation more efficiently? Algorithm complexity analysis. • Cluster and hardware. What hardware and how many nodes do I need? How to run jobs quicker while keeping costs down? • Persistence. Do you perform unnecessary recomputes by failing to cache rdds? • Isolating bottlenecks. How do you find which resource is your bottleneck? Block-time analysis? 18/02/16 Performance tuning of Apache Spark 5
- 6. Key concepts to understand for performance tuning — Spark performance metrics — Memory model — Partitioning — DAG and shuffles — Persistence 18/02/16 Performance tuning of Apache Spark 6
- 7. Spark programs consist of jobs, stages and tasks — Each Spark program runs as a job — DAG scheduler splits jobs into stages — Tasks belong to a stage. Task is a unit of work to run on executor, correspond to a single partition — Each task either partitions its output for “shuffle”, or sends the output back to the driver 18/02/16 Performance tuning of Apache Spark 7 Job Stage 1 Task Task Stage 2 Task Task Task Task
- 8. Stage 1 Task 1 Task 2 Task 3 Shuffle anatomy 18/02/16 Performance tuning of Apache Spark 8 Stage 2 Task 1 Task 2 Task 3 Shuffle readShuffle write — Shuffle redistributes data among partitions — Files are written to disk by the end of one stage — Read by next stage — Reducing number of shuffles will generally improve performance
- 9. Spark memory model — Execution memory: shuffles — Storage memory: caching — Pre 1.6.0 had to manually configure memory ratios — 1.6.0: unified memory management 18/02/16 Sample Infoready Presentation 9 Storage Execu-on File system
- 10. How to debug performance? — Web UI is your friend — Failed executors. JVM crashes, memory issues, config issues, network — Identify stragglers • Is a particular node running slow? Turn speculation on • Data skew: max >> median • GC issues • Jstack, jmap, or UI stack dump — Recomputation — Rdd.toDebugString or WebUI — Metrics to watch • GC time. Lots of them gone in Spark 1.6 due to Tungsten • Disk spill 18/02/16 Performance tuning of Apache Spark 10
- 11. Using UI to find the cause of the skew 18/02/16 Performance tuning of Apache Spark 11
- 12. Find the problematic partition. Majority of such problems are related to disk I/O 18/02/16 Performance tuning of Apache Spark 12
- 13. 18/02/16 Performance tuning of Apache Spark 13 Cause: rdd.persist(StorageLevel.MEMORY_AND_DISK)
- 14. The same RDD can be split differntly 18/02/16 Performance tuning of Apache Spark 14 … 100 GB RDD, 4 par--ons, 25 GB each 100 GB RDD, 100 par--ons, 1 GB each 25 GB 25 GB 25 GB 25 GB
- 15. 18/02/16 Sample Infoready Presentation 15 Spilling to disk
- 16. Small tasks vs Large tasks 18/02/16 Sample Infoready Presentation 16 Execu-on memory Disk spill Core 1 Core 2 Core 3 Core 4 Execu-on memory Core 1 Core 2 Core 3 Core 4 Task Task Task Task Task Task Task Task Task Task Task Task Task Task Task Task Task Task Task Task Tasks pool Tasks pool 1 sec each task 60 sec each task
- 17. Partitions — Partitions determine a degree of parallelism — Too big partition • Small amount of tasks and long time to execute each • More memory needed per task => disk spills • More chances for data skew — Too small partition • overhead of launching the task dominates the runtime of a task — Rule of thumb: task runtime just under 1s execution per task and more than 100ms — To control number of partitions use coalesce() and repartition(). Note that both may trigger shuffle. The case when additional shuffle in the beginning may improve performance 18/02/16 Performance tuning of Apache Spark 17
- 18. Partitions 18/02/16 Performance tuning of Apache Spark 18 Number of par--ons Execu-on -me Minimum Op-mal par--on size range
- 19. Partitioning strategies — Fixed number of partitions • numPartitions = 100 — Fixed number records per partition • numPartitions = rdd.count / 100 — Fixed memory size of a partition. Calculate number of partitions based on the memory consumption of rdd or sample of rdd • partitionSpace = 100 Mb • rowsPerPartition = partitionSpace / meanRowSpace(rdd) • numPartitions = rdd.count / rowsPerPartition 18/02/16 Sample Infoready Presentation 19
- 20. CPU vs Memory. What should I add to increase performance of my job? — Parameters I can play with: • Number of cores per node • Amount of memory per node • Both are related to number of nodes • Amount of storage. Normally not a problem • Network. Normally fixed 18/02/16 Performance tuning of Apache Spark 20 vs
- 21. Where is your bottleneck? 18/02/16 Performance tuning of Apache Spark 21 RAM CPU Network Storage I/O
- 22. Block-time analysis 18/02/16 Sample Infoready Presentation 22 Source: Kay Ousterhout, Spark Summit 2015
- 23. How job completion would change if the network is infinitely fast 18/02/16 Sample Infoready Presentation 23 Source: Kay Ousterhout, Spark Summit 2015
- 24. How we do it? — The goal is to make CPU utilisation each of the nodes at 100% — Limit your resources to a single core and just 1G of memory — Run the job on the subset of the data, so the full job runs in about a minute. This way you can iterate through your tuning experiments much faster. — Tweak the memory and partition size until no disk spills. This is the amount of memory needed per core — Scale cores and memory proportionally — Make sure the partition size is the same as for the full job 18/02/16 Performance tuning of Apache Spark 24
- 25. Key lessons — Understand the memory model — Avoid expensive shuffles if possible — Choose number/size of partitions — Use persistence when reusing RDDs — Do not spill to disk — Make the job CPU bound and scale for performance — Experiment on small subsets and limited resources 18/02/16 Performance tuning of Apache Spark 25
- 26. Thank you! 26