Dongwon Kim – A Comparative Performance Evaluation of Flink
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This document provides a summary and analysis of a performance evaluation comparing the big data processing engine Flink to other engines like Spark, Tez, and MapReduce. The key points are: - Flink completes a 3.2TB TeraSort benchmark faster than Spark, Tez, and MapReduce due to its pipelined execution model which allows more overlap between stages compared to the other engines. - While Tez and Spark attempt to overlap stages, in practice they do not due to the way tasks are scheduled and launched. MapReduce shows some overlap but is still slower. - Flink causes fewer disk accesses during shuffling by transferring data directly from memory to memory instead of writing to disk like
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Dongwon Kim – A Comparative Performance Evaluation of Flink
- 1. A Comparative Performance Evaluation of Flink Dongwon Kim POSTECH
- 2. About Me • Postdoctoral researcher @ POSTECH • Research interest • Design and implementation of distributed systems • Performance optimization of big data processing engines • Doctoral thesis • MR2: Fault Tolerant MapReduce with the Push Model • Personal blog • http://eastcirclek.blogspot.kr • Why I’m here 2
- 3. Outline • TeraSort for various engines • Experimental setup • Results & analysis • What else for better performance? • Conclusion 3
- 4. TeraSort • Hadoop MapReduce program for the annual terabyte sort competition • TeraSort is essentially distributed sort (DS) a4 b3 a1 a2 b1 b2 a2 b1 a3 a4 b3 b4 Disk a2 a4 b3 b1 a1 b4 a3 b2 a1 a3 b4 b2 Disk a2 a4 a1 a3 b3 b1 b4 b2 read shufflinglocal sort write Disk Disk local sort Node 1 Node 2 Typical DS phases : 4Total order
- 5. • Included in Hadoop distributions • with TeraGen & TeraValidate • Identity map & reduce functions • Range partitioner built on sampling • To guarantee a total order & to prevent partition skew • Sampling to compute boundary points within few seconds TeraSort for MapReduce Reduce taskMap task read shuffling sortsort reducemap write read shufflinglocal sort writelocal sortDS phases : reducemap 5 Record range … Partition 1 Partition 2 Partition r boundary points
- 6. • Tez can execute TeraSort for MapReduce w/o any modification • mapreduce.framework.name = yarn-tez • Tez DAG plan of TeraSort for MapReduce TeraSort for Tez finalreduce vertex initialmap vertex Map task read sortmap Reduce task shuffling sort reduce write input data output data 6
- 7. TeraSort for Spark & Flink • My source code in GitHub: • https://github.com/eastcirclek/terasort • Sampling-based range partitioner from TeraSort for MapReduce • Visit my personal blog for a detailed explanation • http://eastcirclek.blogspot.kr 7
- 8. RDD1 RDD2 • Code • Two RDDs TeraSort for Spark Stage 1Stage 0 Shuffle-Map Task (for newAPIHadoopFile) read sort Result Task (for repartitionAndSortWithinPartitions) shuffling sort write read shufflinglocal sort writelocal sort Create a new RDD to read from HDFS # partitions = # blocks Repartition the parent RDD based on the user-specified partitioner Write output to HDFS DS phases : 8
- 9. Pipeline • Code • Pipelines consisting of four operators TeraSort for Flink read shuffling writelocal sort Create a dataset to read tuples from HDFS partition tuples Sort tuples of each partition DataSource Partition SortPartition DataSink local sort No map-side sorting due to pipelined execution Write output to HDFS DS phases : 9
- 10. Importance of TeraSort • Suitable for measuring the pure performance of big data engines • No data transformation (like map, filter) with user-defined logic • Basic facilities of each engine are used • “Winning the sort benchmark” is a great means of PR 10
- 11. Outline • TeraSort for various engines • Experimental setup • Machine specification • Node configuration • Results & analysis • What else for better performance? • Conclusion 11
- 12. Machine specification (42 identical machines) DELL PowerEdge R610 CPU Two X5650 processors (Total 12 cores) Memory Total 24Gb Disk 6 disks * 500GB/disk Network 10 Gigabit Ethernet My machine Spark team Processor Intel Xeon X5650 (Q1, 2010) Intel Xeon E5-2670 (Q1, 2012) Cores 6 * 2 processors 8 * 4 processors Memory 24GB 244GB Disks 6 HDD's 8 SSD's Results can be different in newer machines 12
- 13. 24GB on each node Node configuration Total 2 GB for daemons 13 GB Tez-0.7.0 NodeManager (1 GB) ShuffleService MapTask (1GB) DataNode (1 GB) MapTask (1GB) ReduceTask (1GB) ReduceTask (1GB) MapTask (1GB) MapTask (1GB) MapTask (1GB) … MapReduce-2.7.1 NodeManager (1 GB) ShuffleService DataNode (1 GB) MapTask (1GB) MapTask (1GB) ReduceTask (1GB) ReduceTask (1GB) MapTask (1GB) MapTask (1GB) MapTask (1GB) … 12 GB Flink Spark Spark-1.5.1 NodeManager (1 GB) Executor (12GB) Internal memory layout Various managers DataNode (1 GB) Task slot 1 Task slot 2 Task slot 12 ... Thread pool Flink-0.9.1 NodeManager (1 GB) TaskManager (12GB) DataNode (1 GB) Internal memory layout Various managers Task slot 1 Task slot 2 Task slot 12 ... Task threads Tez MapReduce ReduceTask (1GB)ReduceTask (1GB) 13 12 simultaneous tasks at most Driver (1GB) JobManager (1GB)
- 14. Outline • TeraSort for various engines • Experimental setup • Results & analysis • Flink is faster than other engines due to its pipelined execution • What else for better performance? • Conclusion 14
- 15. How to read a swimlane graph & throughput graphs Tasks Time since job starts (seconds) 2nd stage 1st 2nd 3rd 4th 5th 6th 15 Cluster network throughput Cluster disk throughput In Out Disk read Disk Write - 6 waves of 1st stage tasks - 1 wave of 2nd stage tasks - Two stages are hardly overlapped 1st stage 2nd stage 1st stage 2nd stage No network traffic during 1st stage Each line : duration of each task Different patterns for different stages
- 16. Result of sorting 80GB/node (3.2TB) 1480 sec 1st stage 1st stage 1st stage 2nd stage 2157 sec 2nd stage 2171 sec 1 DataSource 2 Partition 3 SortPartition 4 DataSink • Flink is the fastest due to its pipelined execution • Tez and Spark do not overlap 1st and 2nd stages • MapReduce is slow despite overlapping stages MapReduce in Hadoop-2.7.1 Tez-0.7.0 Spark-1.5.1 Flink-0.9.1 2nd stage 1887 sec 2157 1887 2171 1480 0 500 1000 1500 2000 2500 MapReduce in Hadoop-2.7.1 Tez-0.7.0 Spark-1.5.1 Flink-0.9.1 Time(seconds) 16* Map output compression turned on for Spark and Tez * *
- 17. Tez and Spark do not overlap 1st and 2nd stages Cluster network throughput Cluster disk throughput In Out Disk read Cluster network throughput Cluster disk throughput In Out Disk read Disk write Disk write Disk read Disk write Out In (1) 2nd stage starts (2) Output of 1st stage is sent (1) 2nd stage starts (2) Output of 1st stage is sent (1) Network traffic occurs from start Cluster network throughput (2) Write to HDFS occurs right after shuffling is done 1 DataSource 2 Partition 3 SortPartition 4 DataSink idle idle (3) Disk write to HDFS occurs after shuffling is done (3) Disk write to HDFS occurs after shuffling is done 17
- 18. Tez does not overlap 1st and 2nd stages • Tez has parameters to control the degree of overlap • tez.shuffle-vertex-manager.min-src-fraction : 0.2 • tez.shuffle-vertex-manager.max-src-fraction : 0.4 • However, 2nd stage is scheduled early but launched late scheduled launched 18
- 19. Spark does not overlap 1st and 2nd stages • Spark cannot execute multiple stages simultaneously • also mentioned in the following VLDB paper (2015) Spark doesn’t support the overlap between shuffle write and read stages. … Spark may want to support this overlap in the future to improve performance. Experimental results of this paper - Spark is faster than MapReduce for WordCount, K-means, PageRank. - MapReduce is faster than Spark for Sort. 19
- 20. MapReduce is slow despite overlapping stages • mapreduce.job.reduce.slowstart.completedMaps : [0.0, 1.0] • Wang’s attempt to overlap spark stages 0.05 (overlapping, default) 0.95 (no overlapping) 2157 sec 10% improvement 20 Wang proposes to overlap stages to achieve better utilization 10%??? Why Spark & MapReduce improve just 10%? 2385 sec 2nd stage 1st stage
- 21. Disk Data transfer between tasks of different stages Output file P1 P2 Pn Shuffle server … Consumer Task 1 Consumer Task 2 Consumer Task n P1 … P2 Pn Traditional pull model - Used in MapReduce, Spark, Tez - Extra disk access & simultaneous disk access - Shuffling affects the performance of producers Producer Task (1) Write output to disk (2) Request P1 (3) Send P1 Pipelined data transfer - Used in Flink - Data transfer from memory to memory - Flink causes fewer disk access during shuffling 21 Leads to only 10% improvement
- 22. Flink causes fewer disk access during shuffling Map Reduce Flink diff. Total disk write (TB) 9.9 6.5 3.4 Total disk read (TB) 8.1 6.9 1.2 Difference comes from shuffling Shuffled data are sometimes read from page cache Cluster disk throughput Disk read Disk write Disk read Disk write Cluster disk throughput FlinkMapReduce 22 Total amount of disk read/write equals to the area of blue/green region
- 23. Result of TeraSort with various data sizes node data size (GB) Time (seconds) Flink Spark MapReduce Tez 10 157 387 259 277 20 350 652 555 729 40 741 1135 1085 1709 80 1480 2171 2157 1887 160 3127 4927 4796 3950 23 100 1000 10000 10 20 40 80 160 Time(seconds) node data size (GB) Flink Spark MapReduce Tez What we’ve seen Log scale * Map output compression turned on for Spark and Tez
- 24. Result of HashJoin • 10 slave nodes • org.apache.tez.examples.JoinDataGen • Small dataset : 256MB • Large dataset : 240GB (24GB/node) • Result : 24 Visit my blog Flink is ~2x faster than Tez ~4x faster than Spark 770 1538 378 0 200 400 600 800 1000 1200 1400 1600 1800 Tez-0.7.0 Spark-1.5.1 Flink-0.9.1 Time(seconds) * No map output compression for both Spark and Tez unlike in TeraSort
- 25. Result of HashJoin with swimlane & throughput graphs 25 Idle 1 DataSource 2 DataSource 3 Join 4 DataSink Idle Cluster network throughput Cluster disk throughput In Out Disk read Disk write Disk read Disk write In Out In Out Disk read Disk write Cluster network throughput Cluster disk throughput 0.24 TB 0.41 TB 0.60 TB 0.84 TB 0.68 TB 0.74 TB Overlap 2nd 3rd
- 26. Flink’s shortcoming • No support for map output compression • Small data blocks are pipelined between operators • Job-level fault tolerance • Shuffle data are not materialized • Low disk throughput during the post-shuffling phase 26
- 27. Low disk throughput during the post-shuffling phase • Possible reason : sorting records from small files • Concurrent disk access to small files too many disk seeks low disk throughput • Other engines merge records from larger files than Flink • “Eager pipelining moves some of the sorting work from the mapper to the reducer” • from MapReduce online (NSDI 2010) Flink Tez MapReduce 27
- 28. Outline • TeraSort for various engines • Experimental setup • Results & analysis • What else for better performance? • Conclusion 28
- 29. MR2 – another MapReduce engine • PhD thesis • MR2: Fault Tolerant MapReduce with the Push Model • developed for 3 years • Provide the user interface of Hadoop MapReduce • No DAG support • No in-memory computation • No iterative-computation • Characteristics • Push model + Fault tolerance • Techniques to boost up HDD throughput • Prefetching for mappers • Preloading for reducers 29
- 30. MR2 pipeline • 7 types of components with memory buffers 1. Mappers & reducers : to apply user-defined functions 2. Prefetcher & preloader : to eliminate concurrent disk access 3. Sender & reducer & merger : to implement MR2’s push model • Various buffers : to pass data between components w/o disk IOs • Minimum disk access (2 disk reads & 2 disk writes) • +1 disk write for fault tolerance W1 R2 W2R1 30 1 12 23 3 3 W3
- 31. Prefetcher & Mappers • Prefetcher loads data for multiple mappers • Mappers do not read input from disks <MR2><Hadoop MapReduce> Mapper1 processing Blk1 Mapper2 processing Blk2 Time Disk throughput CPU utilization 2 mappers on a node Blk1 Time Prefetcher Blk2 Blk3 Blk1 2 Blk1 1 Blk2 2 Blk2 1 Blk3 2 Blk3 1 Blk4 Blk4 2 Blk4 1 Disk throughput CPU utilization 2 mappers on a node 31
- 32. Push-model in MR2 • Node-to-node network connection for pushing data • To reduce # network connections • Data transfer from memory buffer • Mappers stores spills in send buffer • Spills are pushed to reducer sides by sender • Fault tolerance (can be turned on/off) • Input ranges of each spill are known to master for reproduce • For fast recovery • store spills on disk for fast recovery (extra disk write) 32 similar to Flink’s pipelined execution MR2 does local sorting before pushing data similar to Spark
- 33. Receiver’s managed memory Receiver & merger & preloader & reducer • Merger produces a file from different partition data • sorts each partition data • and then does interleaving • Preloader preloads each group into reduce buffer • Reducers do not read data directly from disks • MR2 can eliminate concurrent disk reads from reducers thanks to Preloader P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 … … Preloader loads each group (1 disk access for 4 partitions) 33
- 34. Result of sorting 80GB/node (3.2TB) with MR2 MapReduce in Hadoop-2.7.1 Tez-0.7.0 Spark-1.5.1 Flink-0.9.1 MR2 Time (sec) 2157 1887 2171 1480 890 MR2 speedup over other engines 2.42 2.12 2.44 1.66 - 2157 1887 2171 1480 890 0 500 1000 1500 2000 2500 MapReduce in Hadoop-2.7.1 Tez-0.7.0 Spark-1.5.1 Flink-0.9.1 MR2 Time(seconds) 34
- 35. Disk & network throughput 1. DataSource / Mapping • Prefetcher is effective • MR2 shows higher disk throughput 2. Partition / Shuffling • Records to shuffle are generated faster from in MR2 3. DataSink / Reducing • Preloader is effective • Almost 2x throughput Disk read Disk write Out In Cluster network throughput Cluster disk throughput Out In Disk read Disk write Flink MR2 1 1 2 2 3 3 35
- 36. • Experimental results using 10 nodes PUMA (PUrdue MApreduce benchmarks suite) 36
- 37. Outline • TeraSort for various engines • Experimental setup • Experimental results & analysis • What else for better performance? • Conclusion 37
- 38. Conclusion • Pipelined execution for both batch and streaming processing • Even better than other batch processing engines for TeraSort & HashJoin • Shortcomings due to pipelined execution • No fine-grained fault tolerance • No map output compression • Low disk throughput during the post-shuffling phase 38