Spark / Mesos Cluster Optimization
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Recipes we applied to optimize our Spark / Mesos Cluster.
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Spark / Mesos Cluster Optimization
- 1. HAYSSAM SALEH Spark / Mesos Cluster Optimization Paris Spark Meetup April 28th 2016 @Criteo
- 5. OBJECTIVES — 100 Gb of data / day => 50 million requests — 40To / year — How we turned from a 4 hours job on : ¢ 6 nodes ¢ 8 cores & 32 Gb per node ¢ To a 20 minutes Job on : ¢ 4 nodes, 8 cores & 8 Gb per node
- 6. SUMMARY ¢ Spark concepts ¢ Spark UI offline ¢ Application Optimization — Shuffling — Partitioning — Closures ¢ Parameters Optimization — Spark Shuffling — Mesos application distribution ¢ Elasticsearch Optimization — Google “elasticsearch performance tuning” -> blog.ebiznext.com
- 7. SPARK : LES CONCEPTS ¢ Application — Main application ¢ Job — Roundtrip Driver -> Cluster ¢ Stage — Shuffle Boundary ¢ Task — Thread working on a single RDD partition ¢ Partition — RDD are split into partitions — Partition is unit of work for each task ¢ Executor — System process Application Job Stage Task 1 n n n 1 1 Driver code Spark Action Shuffle Boundary One task / partition RDD Partition
- 9. APPLICATION
- 10. JOBS
- 11. STAGES
- 12. STAGES
- 13. TASKS
- 15. WHY OPTIMIZE SHUFFLING Distance between Data & CPU Duaration (scaled) Cache L1 1 seconde RAM 3 minutes Node to node communication 3 jours
- 16. TRANSFORMATIONS LEADING TO SHUFFLING ¢ repartition ¢ cogroup ¢ ...join ¢ ...ByKey ¢ distinct
- 17. SHUFFLING OPTIMIZATION 1/2 (k1, v1, w1) (k2, v2, w2) (k3, v3, w3) (k1, v4 w4) (k1, v5, w5) (k3,v6,w6) (k2, v7, w7) (k2 v8, w8) (k3,v9,w9) (k1, v1) (k2, v2) (k3,v3) (k1, v4) (k1, v5) (k3,v6) (k2, v7) (k2 v8) (k3,v9) map map map (k1, [v1, v4, v5]) (k3, [v3, v6, v9]) (k2, [v2, v7, v8]) groupByKey groupByKey groupByKey (k1,v1+v4+v5) (k3,v3+v6+v9) (k2, v2+v7+v8]) Shuffling Bonus : OOM
- 18. SHUFFLING OPTIMIZATION 2/2 (k1, v1, w1) (k2, v2, w2) (k3, v3, w3) (k1, v4 w4) (k1, v5, w5) (k3,v6,w6) (k2, v7, w7) (k2 v8, w8) (k3,v9,w9) (k1, v1) (k2, v2) (k3,v3) (k1, v4) (k1, v5) (k3,v6) (k2, v7) (k2 v8) (k3,v9) map map map (k1, v1+v45)) (k3,v3+v6+v9) (k2, v2+v78) reduceByKey (2/2) reduceByKey (2/2) reduceByKey (2/2) (k1, v1) (k2, v2) (k3,v3) (k1, v4+v5) (k3,v6) (k2, v7+v8) (k3,v9) reduceByKey (1/2) reduceByKey (1/2) reduceByKey (1/2) Less shuffling
- 19. OPTIMIZATIONS BROUGHT BY SPARK SQL L Weak typing L Limited Java Support L Schema inference not supported • requires scala.Product
- 20. LES DATASETS – EXPERIMENTAL - Scala Java J API similar to RDD, strongly typed
- 22. CLOSURES
- 25. PARTITIONING ¢ Symptoms — Important number of short lived tasks ¢ Solutions — Review your implementation — Define custom partitionner — Control the number of partitions with ¢ coalesce et repartition
- 26. CHOOSE THE RIGHT SERIALIZATION ALGORITHM
- 27. REDUCE OBJECT SIZE ¢ Kryo Serialization — Reduce space – up to 10X — Perform gain – 2X to 3X 1. Require Spark to use Kryo serialization 2. Optimize class naming => reduce object size 3. Force all serialized classes to register Not applicable to the Dataset API that use Encoders instead
- 28. DATASETS PROMISE
- 29. MEMORY TUNING
- 30. SPARK MEMORY MANAGEMENT– SPARK 1.6.X Shuffle fraction Memory reserved to Spark (300Mb) Memory assigned to user data 1 - spark.memory.fraction Memory assigned to spark data spark.memory.fraction = 0.75 spark.memory.storageFraction = 0.5 16Go 11.78Go 3.92Go
- 31. SPARK MEMORY MANAGEMENT – SPARK 1.6.X ¢ Storage Fraction — Host cached RDDs — Host broadcast variables — Data in this fraction are subject to eviction ¢ Shuffling Fraction — Hold Intermediate Data — Spill to disk when full — Data this fraction cannot be evicted by other threads Memory reserved to Spark (300Mb) Memory assigned to user data 1 - spark.memory.fraction Memory assigned to spark data spark.memory.fraction = 0.75 spark.memory.storageFraction = 0.5 16Go 11.78Go 3.92Go
- 32. SPARK MEMORY MANAGEMENT – SPARK 1.6.X ¢ Shuffle & Storage fractions may borrow memory from each other under certain conditions: — The shuffling fraction cannot extend beyond its defined size if storage fraction uses all its memory — Storage fraction cannot evict data from the shuffling fraction even if it expanded beyond its defined size. Memory reserved to Spark (300Mb) Memory assigned to user data 1 - spark.memory.fraction Memory assigned to spark data spark.memory.fraction = 0.75 spark.memory.storageFraction = 0.5 16Go 11.78Go 3.92Go
- 34. HASH SHUFFLE MANAGER Executor Partition Partition Partition Partition Partition Task Task Task Number of tasks = spark.executor.cores / spark.task.cpus(1) YARN & standalone only Local Filesystem spark.local.dir File 1 File 2 File … …
- 35. SORT SHUFFLE MANAGER Executor Partition Partition Partition Partition Partition Task Task Task Number of tasks = spark.executor.cores / spark.task.cpus(1) YARN & standalone only Filesystem local spark.local.dir Single fileMap AppendOnly Sort & Spill spark.shuffle.spill = true id1 id2 id3 id.. sorted by reducer id
- 36. SHUFFLE MANAGER ¢ spark.shuffle.manager= hash — Perform better when the number of mapper/reducer is small (generation of M * R files) ¢ spark.shuffle.manager= sort — Perform better for an important number of mapper/reducer ¢ Best of both world — spark.shuffle.sort.bypassMergeThreshold ¢ spark.shuffle.manager= tungsten-sort ??? — Similar to sort with the following benefits : ¢ Off-Heap allocation ¢ Work directly on binary object serialization (much smaller than JVM objects) – aka memcopy J ¢ Use 8 bytes / record pour les sort => take advantage on L* cache
- 37. SPARK ON MESOS
- 38. COARSE GRAINED MODE J Static resource allocation Spark Driver CoarseMesosSchedulerBackend Mesos Master Mesos Worker CoarseGrainedExecutorBackend Mesos Worker CoarseGrainedExecutorBackend Mesos Worker CoarseGrainedExecutorBackend Mesos Worker CoarseGrainedExecutorBackend Executor Task Task Task Task Task Task …
- 39. COARSE GRAINED MODE L Only one executor / node L No control over the number of executors 8 cores 16g 8 cores 16g 8 cores 16g 8 cores 16g 16 cores 16g 16 cores 16g 32 cores / 64Go 32 cores / 32Go
- 40. FINE GRAINED MODE L Only one executor / node L No guaranty of resource availability L No control over the number of executors Spark Driver MesosSchedulerBackend Mesos Master Mesos Worker CoarseGrainedExecutorBackend Mesos Worker CoarseGrainedExecutorBackend Mesos Worker CoarseGrainedExecutorBackend Mesos Worker MesosExecutorBackend Executor Task … Executor Task Executor Task Executor Task spark.mesos.mesosExecutor.cores (1)
- 41. SOLUTION 1 : USE MESOS ROLES ¢ Limit the number of cores / node on each mesos worker L Must be configured for each Spark application • Assign a Mesos role to your Spark job 8 cores 16g 8 cores 16g 8 cores 16g 8 cores 16g
- 42. SOLUTION 2 : DYNAMIC ALLOCATION (COARSED GRAINED MODE ONLY) ¢ Principles — Dynamically add/remove Spark executors ¢ Requires a dedicated process to move shuffled data (spawned on each Mesos worker through Marathon)
- 43. QUESTIONS ?