Big Data 7. Resource Management

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1 Ghislain Fourny Big Data 7. Resource Management artjazz / 123RF Stock Photo

2 Data Technology Stack User interfaces Querying Data stores Indexing Processing Validation Data models Syntax Encoding Storage 2

3 Where we are User interfaces Querying Data stores Indexing Processing Validation Data models Syntax Encoding Storage 3

4 Last week: MapReduce Input data Map Map Map Map Map Map Map Map Intermediate data (shuffled) Reduce Reduce Reduce Reduce Reduce Reduce Reduce Reduce Output data 4

5 Hadoop infrastructure (version 1) Namenode /dir/file Datanode Datanode Datanode Datanode Datanode Datanode 5

6 Hadoop infrastructure (version 1) Namenode + JobTracker /dir/file Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker Datanode + TaskTracker 6

7 Responsibilities of the MapReduce JobTracker Resource Management 7

8 Responsibilities of the MapReduce JobTracker Resource Management Scheduling 8

9 Responsibilities of the MapReduce JobTracker Resource Management Scheduling Monitoring 9

10 Responsibilities of the MapReduce JobTracker Resource Management Scheduling Monitoring Job lifecycle 10

11 Responsibilities of the MapReduce JobTracker Resource Management Scheduling Monitoring Job lifecycle Fault-tolerance 11

12 Issue 1: scalability M M M M M M M M M M M M < 4,000 nodes < 40,000 tasks 12

13 Issue 2: bottleneck JobTracker Bottleneck TaskTracker TaskTracker TaskTracker TaskTracker TaskTracker TaskTracker 13 13

14 Issue 3: Jack of all trades Scheduling Monitoring 14 14

15 Issue 4: Utilization (task slots) Static (Decide on M/R at configuration time) Fixed-size 15 15

16 Issue 5: Not fungible Map Reduce 16 16

17 Issue 5: Not fungible Working at maximum capacity Idle Map Reduce 17 17

18 kirtchanut / 123RF Stock Photo YARN 18

19 YARN Yet Another Resource Negotiator 19

20 YARN Scheduling Application Monitoring management Resource Manager Application Master Application Master Application Master Application Master Application Master 20

21 Scales more M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M 10,000 nodes 100,000 tasks 21

22 YARN architecture ResourceManager 22

23 YARN architecture ResourceManager NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager 23

24 YARN architecture ResourceManager Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager NodeManager 24

25 Remember... It does ring a bell, doesn't it? 25

26 Master-slave architecture Master Slave Slave Slave Slave Slave Slave 26

27 HDFS server architecture Namenode /dir/file1 /dir/file2 /file3 Datanode Datanode Datanode Datanode Datanode Datanode 27

28 YARN ResourceManager Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 28

29 YARN Client ResourceManager Job Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 29

30 YARN: RM allocates an Application Master Client ResourceManager Job Schedules Container Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 30

31 YARN: RM allocates an Application Master Client ResourceManager Job Schedules Application Master Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 31

32 YARN: RM allocates an Application Master Client ResourceManager Job Application Master Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 32

33 YARN: RM allocates an Application Master Client ResourceManager Job Application Master Container Container NodeManager NodeManager NodeManager NodeManager NodeManager 33

34 Application Master communicates with containers Application Master Container Container Container Execute Monitor Container 34

35 kirtchanut / 123RF Stock Photo YARN's Resource Manager 35

36 Resource Manager Capacity guarantees Cluster Utilization Fairness SLAs 36

37 Communication with clients 37

38 Communication with clients Client Service Application (start, end) Queue information Statistics 38

39 Communication with clients Client Service Application (start, end) Queue information Statistics Admin Service Refresh the node list Queue configuration 39

40 Communication with the node managers 40

41 Communication with the node managers Resource Tracker 41

42 Communication with the node managers Resource Tracker Liveliness 42

43 Communication with the node managers Resource Tracker Liveliness Nodes List Manager valid invalid 43

44 Communication with the application masters 44

45 Communication with the application masters Application Master Service (registration) 45

46 Communication with the application masters Application Master Service (registration) Liveliness 46

47 Communication with the application masters Application Master Service (registration) Liveliness Application Master Service (container requests) 47

48 Communication with the application masters Application Master Service (registration) Liveliness Application Master Service (container requests) Applications Manager 48

49 Communication with the application masters Application Master Service (registration) Liveliness Application Master Service (container requests) Applications Manager + Launcher 49

50 Authentication 50

51 Authentication Application Token 51

52 Authentication Application Token Container Token 52

53 Authentication Application Token Application ACL Container Token 53

54 Pure scheduler Does not monitor tasks. Does not restart upon failure. 54

55 Scheduling strategies: pluggable scheduler 55

56 Scheduling strategies: pluggable scheduler FIFO scheduler 56

57 Scheduling strategies: pluggable scheduler FIFO scheduler 57

58 Scheduling strategies: pluggable scheduler FIFO scheduler 58

59 Scheduling strategies: pluggable scheduler FIFO scheduler 59

60 Scheduling strategies: pluggable scheduler FIFO scheduler 60

61 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 61

62 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 62

63 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 63

64 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 64

65 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 65

66 Scheduling strategies: pluggable scheduler Capacity scheduler Queue 1 Queue 2 66

67 Hierarchical queues Root 67

68 Hierarchical queues Root Math 4 Physics 1 CS 5 68

69 Hierarchical queues Root Math 4 Physics 1 CS 5 40% 10% 50% 69

70 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra TI DB

71 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra TI DB % 10% 10% 32% 40% 71

72 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra Best effort DB % 10% 0% 32% 50% 72

73 Hierarchical queues Root Math 4 Physics 1 CS 5 Analysis Algebra Best effort DB % 10% 50% 32% 0% 73

74 Scheduling strategies: pluggable scheduler Fair scheduler 74

75 Scheduling strategies: pluggable scheduler Fair scheduler 75

76 Scheduling strategies: pluggable scheduler Fair scheduler 76

77 Scheduling strategies: pluggable scheduler Fair scheduler 77

78 Scheduling strategies: pluggable scheduler Fair scheduler 78

79 Scheduling strategies: pluggable scheduler Fair scheduler 79

80 Scheduling strategies: pluggable scheduler Fair scheduler 80

81 Scheduling strategies: pluggable scheduler Fair scheduler 81

82 Fine grained resource requests Memory Application A: 10 GB Application A: 30 GB 82

83 Fine grained resource requests Memory Application A: 10 GB Application A: 30 GB 25% 75% 83

84 Fine grained resource requests Memory CPU 84

85 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) 85

86 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) Application A: 300 GB, 4 cores Application A: 10 GB, 50 cores 86

87 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) Application A: 300 GB, 4 cores Application A: 10 GB, 50 cores 30% Memory, 4% CPU 1% Memory, 50% CPU 87

88 Dominant Resource Fairness Memory (total 1 TB) CPU (total 100 cores) Application A: 300 GB, 4 cores Application A: 10 GB, 50 cores 30% Memory, 4% CPU 1% Memory, 50% CPU 37.5% 62.5% 88

89 Fine grained resource requests Memory CPU Disk Network 89

90 Fine grained resource requests Memory CPU Work in progress Disk Network 90

91 Resource container X GB W cores, U GHz Y TB Z MBps 91

92 kirtchanut / 123RF Stock Photo YARN's Node Manager 92

93 NodeManager: one per node NodeManager NodeManager NodeManager NodeManager 93

94 Monitoring Memory CPU Disk Network 94

95 Reports to ResourceManager Memory CPU ResourceManager Disk Network 95

96 Container 96

97 kirtchanut / 123RF Stock Photo YARN's Application Masters 97

98 Application Master Application Master is per application. 98

99 Application Master Application Master is application-specific. 99

100 Framework-specific application masters MapReduce DAG distributed processing Message Passing Interface Graph processing 100

101 Complexity is moved to the Application Master complexity 101

102 Application Master ResourceManager negotiates resources 102

103 Application Master ResourceManager negotiates resources executes and monitors NodeManager 103

104 Fault tolerance is on the application master 104

105 Fault tolerance is on the application master 105

106 Fault tolerance is on the application master relaunch 106

107 Application-specific monitoring no longer a bottleneck 107

108 Application Master is not trusted 108

109 Application Master is not trusted Evil plan to book containers and not use them 109

110 Summary Separation between scheduling and monitoring 110

111 Summary Separation between scheduling and monitoring Scalability 111

112 Summary Separation between scheduling and monitoring Scalability Availability 112

113 Summary Separation between scheduling and monitoring Scalability Availability Multi-tenancy 113

114 Forward compatibility with DAGs of tasks 114

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