Hadoop & Big Data benchmarking

1. Benchmarking Hadoop & Big Data benchmarking Dr. ir. ing. Bart Vandewoestyne Sizing Servers Lab, Howest, Kortrijk IWT TETRA User Group Meeting - November 28, 2014 1 / 62

2. Benchmarking Outline 1 Intro: Hadoop essentials 2 Cloudera demo 3 Benchmarks Micro Benchmarks BigBench 4 Conclusions 2 / 62

3. Benchmarking Intro: Hadoop essentials Outline 1 Intro: Hadoop essentials 2 Cloudera demo 3 Benchmarks Micro Benchmarks BigBench 4 Conclusions 3 / 62

4. Benchmarking Intro: Hadoop essentials Hadoop Hadoop is VMware, but the other way around. 4 / 62

5. Benchmarking Intro: Hadoop essentials Hadoop 1.0 Source: Apache Hadoop YARN : moving beyond MapReduce and batch processing with Apache Hadoop 2, Hortonworks, 2014) MapReduce and HDFS are the core components, while other components are built around the core. 5 / 62

6. Benchmarking Intro: Hadoop essentials Hadoop 2.0 Source: Apache Hadoop YARN : moving beyond MapReduce and batch processing with Apache Hadoop 2, Hortonworks, 2014) YARN adds a more general interface to run non-MapReduce jobs within the Hadoop framework. 6 / 62

7. Benchmarking Intro: Hadoop essentials HDFS Hadoop Distributed File System Source: http://www.cac.cornell.edu/vw/MapReduce/dfs.aspx 7 / 62

8. Benchmarking Intro: Hadoop essentials MapReduce MapReduce = Programming Model WordCount example: Source: Optimizing Hadoop for MapReduce, Khaled Tannir 8 / 62

9. Benchmarking Intro: Hadoop essentials Hadoop distributions 9 / 62

10. Benchmarking Cloudera demo Outline 1 Intro: Hadoop essentials 2 Cloudera demo 3 Benchmarks Micro Benchmarks BigBench 4 Conclusions 10 / 62

11. Benchmarking Cloudera demo HDFS 11 / 62

12. Benchmarking Cloudera demo NameNode and DataNodes 12 / 62

13. Benchmarking Cloudera demo Hosts and their roles 13 / 62

14. Benchmarking Cloudera demo NameNode WebUI NameNode WebUI address http://sandy-quad-1.sslab.lan:50070/ 14 / 62

15. Benchmarking Cloudera demo Replication factor 15 / 62

16. Benchmarking Cloudera demo HDFS Blocks 16 / 62

17. Benchmarking Cloudera demo Hue:

18. le upload 17 / 62

19. Benchmarking Cloudera demo Hadoop jobs: counters/metrics 18 / 62

20. Benchmarking Benchmarks Outline 1 Intro: Hadoop essentials 2 Cloudera demo 3 Benchmarks Micro Benchmarks BigBench 4 Conclusions 19 / 62

21. Benchmarking Benchmarks Why benchmark? My three reasons for using benchmarks: 1 Evaluating the eect of a hardware/software upgrade: OS, Java VM,. . . Hadoop, Cloudera CDH, Pig, Hive, Impala,. . . 2 Debugging: Compare with other clusters or published results. 3 Performance tuning: E.g. Cloudera CDH default con

22. g is defensive, not optimal. 20 / 62

23. Benchmarking Benchmarks Micro Benchmarks Outline 1 Intro: Hadoop essentials 2 Cloudera demo 3 Benchmarks Micro Benchmarks BigBench 4 Conclusions 21 / 62

24. Benchmarking Benchmarks Micro Benchmarks Hadoop: Available tests hadoop jar /some/path/to/hadoop-*test*.jar 22 / 62

25. Benchmarking Benchmarks Micro Benchmarks TestDFSIO Read and write test for HDFS. Helpful for getting an idea of how fast your cluster is in terms of I/O, stress testing HDFS, discover network performance bottlenecks, shake out the hardware, OS and Hadoop setup of your cluster machines (particularly the NameNode and the DataNodes). 23 / 62

26. Benchmarking Benchmarks Micro Benchmarks TestDFSIO: write test Generate 10

27. les of size 1 GB for a total of 10 GB: $ hadoop jar hadoop-*test*.jar TestDFSIO -write -nrFiles 10 -fileSize 1000 TestDFSIO is designed to use 1 map task per

28. le (1:1 mapping from

29. les to map tasks) 24 / 62

30. Benchmarking Benchmarks Micro Benchmarks TestDFSIO: write test output Typical output of write test ----- TestDFSIO ----- : write Date time: Mon Oct 06 10:21:28 CEST 2014 Number of files: 10 Total MBytes processed: 10000.0 Throughput mb/sec: 12.874702111579893 Average IO rate mb/sec: 13.013071060180664 IO rate std deviation: 1.4416050051562712 Test exec time sec: 114.346 25 / 62

31. Benchmarking Benchmarks Micro Benchmarks Interpreting TestDFSIO results De

32. nition (Throughput) Throughput(N) = PN i=0

33. lesizei PN i=0 timei De

34. nition (Average IO rate) Average IO rate(N) = PN i=0 ratei N = PN

35. lesizei timei N i=0 Here, N is the number of map tasks. 26 / 62

36. Benchmarking Benchmarks Micro Benchmarks TestDFSIO: read test Read 10 input

37. les, each of size 1 GB: $ hadoop jar hadoop-*test*.jar TestDFSIO -read -nrFiles 10 -fileSize 1000 27 / 62

38. Benchmarking Benchmarks Micro Benchmarks TestDFSIO: read test output Typical output of read test ----- TestDFSIO ----- : read Date time: Mon Oct 06 10:56:15 CEST 2014 Number of files: 10 Total MBytes processed: 10000.0 Throughput mb/sec: 402.4306813151435 Average IO rate mb/sec: 492.8257751464844 IO rate std deviation: 196.51233829270575 Test exec time sec: 33.206 28 / 62

39. Benchmarking Benchmarks Micro Benchmarks In uence of HDFS replication factor When interpreting TestDFSIO results, keep in mind: The HDFS replication factor plays an important role! A higher replication factor leads to slower writes. For three identical TestDFSIO write runs (units are MB/s): HDFS replication factor 1 2 3 Throughput 190 25 13 Average IO-rate 190 10 25 3 13 1 29 / 62

40. Benchmarking Benchmarks Micro Benchmarks TeraSort Goal Sort 1TB of data (or any other amount of data) as fast as possible. Probably most well-known Hadoop benchmark. Combines testing the HDFS and MapReduce layers of an Hadoop cluster. Typical areas where TeraSort is helpful Iron out your Hadoop con

41. guration after your cluster passed a convincing TestDFSIO benchmark

42. rst. Determine whether your MapReduce-related parameters are set to proper values. 30 / 62

43. Benchmarking Benchmarks Micro Benchmarks TeraSort: work ow TeraGen /user/bart/terasort-input TeraSort /user/bart/terasort-output TeraValidate /user/bart/terasort-validate 31 / 62

44. Benchmarking Benchmarks Micro Benchmarks TeraSort: work ow hadoop jar hadoop-mapreduce-examples.jar teragen 10000000000 /user/bart/input 4 hours on our 4-node cluster 32 / 62

45. Benchmarking Benchmarks Micro Benchmarks TeraSort: work ow hadoop jar hadoop-mapreduce-examples.jar teragen 10000000000 /user/bart/input 4 hours on our 4-node cluster hadoop jar hadoop-mapreduce-examples.jar terasort /user/bart/input /user/bart/output 5 hours on our 4-node cluster 33 / 62

46. Benchmarking Benchmarks Micro Benchmarks TeraSort: work ow hadoop jar hadoop-mapreduce-examples.jar teragen 10000000000 /user/bart/input 4 hours on our 4-node cluster hadoop jar hadoop-mapreduce-examples.jar terasort /user/bart/input /user/bart/output 5 hours on our 4-node cluster hadoop jar hadoop-mapreduce-examples.jar teravalidate /user/bart/output /user/bart/validate If something went wrong, TeraValidate's output contains the problem report. 34 / 62

47. Benchmarking Benchmarks Micro Benchmarks TeraSort: duration 35 / 62

48. Benchmarking Benchmarks Micro Benchmarks TeraSort: counters 36 / 62

49. Benchmarking Benchmarks Micro Benchmarks NNBench Goal Load test the NameNode hardware and software. Generates a lot of HDFS-related requests with normally very small payloads. Purpose: put a high HDFS management stress on the NameNode. Can simulate requests for creating, reading, renaming and deleting

50. les on HDFS. 37 / 62

51. Benchmarking Benchmarks Micro Benchmarks NNBench: example Create 1000

52. les using 12 maps and 6 reducers: $ hadoop jar hadoop-*test*.jar nnbench -operation create_write -maps 12 -reduces 6 -blockSize 1 -bytesToWrite 0 -numberOfFiles 1000 -replicationFactorPerFile 3 -readFileAfterOpen true -baseDir /user/bart/NNBench-`hostname -s` 38 / 62

53. Benchmarking Benchmarks Micro Benchmarks MRBench Goal Loop a small job a number of times. checks whether small job runs are responsive and running eciently on the cluster complimentary to TeraSort puts its focus on the MapReduce layer impact on the HDFS layer is very limited 39 / 62

54. Benchmarking Benchmarks Micro Benchmarks MRBench: example Run a loop of 50 small test jobs: $ hadoop jar hadoop-*test*.jar mrbench -baseDir /user/bart/MRBench -numRuns 50 40 / 62

55. Benchmarking Benchmarks Micro Benchmarks MRBench: example Run a loop of 50 small test jobs: $ hadoop jar hadoop-*test*.jar mrbench -baseDir /user/bart/MRBench -numRuns 50 Example output: DataLines Maps Reduces AvgTime (milliseconds) 1 2 1 28822 ! average

56. nish time of executed jobs was 28 seconds. 41 / 62

57. Benchmarking Benchmarks BigBench Outline 1 Intro: Hadoop essentials 2 Cloudera demo 3 Benchmarks Micro Benchmarks BigBench 4 Conclusions 42 / 62

58. Benchmarking Benchmarks BigBench BigBench Source: http://mhpersonaltrainer.mhpersonaltrainer.com/mhpersonaltrainer/56616/index 43 / 62

59. Benchmarking Benchmarks BigBench BigBench Big Data benchmark based on TPC-DS. Focus is mostly on MapReduce engines. Collaboration between industry and academia. https://github.com/intel-hadoop/Big-Bench/ History Launched at First Workshop on Big Data Benchmarking (May 8-9, 2012). Full kit at Fifth Workshop on Big Data Benchmarking (August 5-6, 2014). 44 / 62

60. Benchmarking Benchmarks BigBench BigBench data model Source: BigBench: Towards an Industry Standard Benchmark for Big Data Analytics, Ghazal et al., 2013. 45 / 62

61. Benchmarking Benchmarks BigBench BigBench: Data Model - 3 V's Variety BigBench data is structured, semi-structured, unstructured. Velocity Periodic refreshes for all data. Dierent velocity for dierent areas: Vstructured Vunstructured Vsemistructured Volume TPC-DS: discrete scale factors (100, 300, 1000, 3000, 10000, 3000 and 100000). BigBench: continuous scale factor. 46 / 62

62. Benchmarking Benchmarks BigBench BigBench: Workload Workload queries 30 queries Speci

63. ed in English (sort of) No required syntax (

64. rst implementation in Aster SQL MR) Kit implemented in Hive, Hadoop MR, Mahout, OpenNLP Business functions (McKinsey) Marketing Merchandising Operations Supply chain Reporting (customers and products) 47 / 62

65. Benchmarking Benchmarks BigBench BigBench: Workload - Technical Aspects Data Sources Number of Queries Percentage Structured 18 60 % Semi-structured 7 23 % Unstructured 5 17 % Analytic techniques Number of Queries Percentage Statistics analysis 6 20 % Data mining 17 57 % Reporting 8 27 % 48 / 62

66. Benchmarking Benchmarks BigBench BigBench: Workload - Technical Aspects Query Types Number of Queries Percentage Pure HiveQL 14 46 % Mahout 5 17 % OpenNLP 5 17 % Custom MR 6 20 % 49 / 62

67. Benchmarking Benchmarks BigBench BigBench: Workload - Technical Aspects Query Types Number of Queries Percentage Pure HiveQL 14 46 % Mahout 5 17 % OpenNLP 5 17 % Custom MR 6 20 % Note that your implementation may vary! 50 / 62

68. Benchmarking Benchmarks BigBench BIgBench: Benchmark Process Source: http://www.tele-task.de/archive/video/flash/24896/ 51 / 62

69. Benchmarking Benchmarks BigBench BigBench: Metric Number of queries run: 30 (2 S + 1) Measured times: TL: loading process TP: power test TTT1 :

70. rst throughput test TTDM : data maintenance task TTT2 : second throughput test De

71. nition (BigBench queries per hour) BBQpH = 30 3 S 3600 S TL + S TP + TTT1 + S TTDM + TTT2 Similar to TPC-DS metric. 52 / 62

72. Benchmarking Benchmarks BigBench BigBench: results 53 / 62

73. Benchmarking Benchmarks BigBench BigBench: monitoring 54 / 62

77. Benchmarking Benchmarks BigBench BigBench: in progress 58 / 62 Source: The Hortonworks Blog

78. Benchmarking Conclusions Outline 1 Intro: Hadoop essentials 2 Cloudera demo 3 Benchmarks Micro Benchmarks BigBench 4 Conclusions 59 / 62

79. Benchmarking Conclusions Conclusions Use Hadoop distributions! Hadoop cluster administration ! Cloudera Manager. Micro-benchmarks $ BigBench. 60 / 62

80. Benchmarking Conclusions Conclusions Use Hadoop distributions! Hadoop cluster administration ! Cloudera Manager. Micro-benchmarks $ BigBench. Your best benchmark is your own application! 61 / 62

81. Benchmarking Conclusions Questions? Source: https://gigaom.com/2011/12/19/my-hadoop-is-bigger-than-yours/ 62 / 62

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