Ghislain Fourny. Big Data 5. Column stores

Transcription

1 Ghislain Fourny Big Data 5. Column stores 1

2 Introduction 2

3 Relational model 3

4 Relational model Schema 4

5 Issues with relational databases (RDBMS) Small scale Single machine 5

6 Can we fix a RDBMS? Scale up (remember?) 6

7 Can we fix a RDBMS? Scale out 7

8 Can we fix a RDBMS? Cluster Scale out 8

9 Can we fix a RDBMS? Cluster Replicate Scale out 9

10 Can we fix a RDBMS? Hard to set up Very high maintenance costs Scale out 10

11 HBase By design running on a scalable cluster of commodity hardware 11

12 HBase By design running on a scalable cluster of commodity hardware HDFS 12

13 Wide column stores: data model 13

14 Founding paper 's BigTable 14

15 The tabular model 15

16 The tabular model: expensive joins 16

17 Design paradigm of BigTable store together what is accessed together 17

18 The tabular model: expensive joins

19 The columnar model: denormalized

20 Rows Row ID A1 1E0 22A 4A2 20

21 Columns Row ID A1 1E0 22A 4A2 21

22 Columns Column family Row ID A1 1E0 22A 4A2 22

23 Column families must be known in advance... Row ID 23

24 Column families must be known in advance... Row ID 000 A B 1 2 I 002 0A1 1E0 22A 4A2 24

25 ... but columns can be added on the fly Row ID 000 A B C 1 2 I II III IV 002 0A1 1E0 22A 4A2 25

26 Primary queries Get Put Scan Delete 26

27 Get Row ID 000 A B C 1 2 I II III IV 002 0A1 1E0 22A 4A2 27

28 Put Row ID 000 A B C 1 2 I II III IV 002 0A1 1E A 4A2 28

29 Scan Row ID 000 A B C 1 2 I II III IV 002 0A1 1E A 4A2 29

30 Delete Row ID 000 A B C 1 2 I II III IV 002 0A1 1E A 4A2 30

31 Some terminology: Key-value model Key Value 31

32 Some terminology: Column-oriented stores Column1 Column2 32

33 Some terminology: Column-oriented key-value stores Also: wide column stores, column family-oriented Row ID A B C 1 2 I II III IV 33

34 Examples of Column-oriented key-value stores 's BigTable 34

35 Warning on terminology NoSQL is very recent! 35

36 Warning on terminology Key-value storage Relational table Words have a "life" File Block NoSQL Object storage 36

37 HBase: physical level 37

38 Physical layer: regions Row ID A B C 1 2 I II III IV 38

39 Physical layer: regions Row ID A B C 1 2 I II III IV 39

40 Physical layer: regions Row ID A B C 1 2 I II III IV Min-incl. Max-excl. 40

41 Physical layer: column families Row ID A B C 1 2 I II III IV Min-incl. Max-excl. Stored together 41

42 Architecture "The same procedure as every year, James." 42

43 HDFS... Namenode /dir/file1 /dir/file2 /file3 Datanode Datanode Datanode Datanode Datanode Datanode 43

44 HBase HMaster Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 44

45 HBase HMaster Replicas! Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 45

46 HMaster HMaster Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 46

47 HMaster DDL operations Create table Delete table 47

48 HMaster assigns regions to RegionServers Row ID 48

49 HMaster assigns regions to RegionServers Row ID 49

50 HMaster assigns regions to RegionServers Row ID 50

51 HMaster splits regions Row ID 51

52 HMaster handles Regionserver failovers 52

53 Architecture HMaster Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 53

54 Regionserver HMaster Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 54

55 Physical storage Row ID Min-incl. A B C 1 2 Stored together I II III IV 55

56 Physical storage Row ID A B C 1 2 I II III IV Store Store Store Store Store Store 56

57 Store = column family Row ID

58 Store = column family Row ID 1 2 Cell 58

59 Store = column family Row ID 1 2 HFile HFile HFile HFile (On HDFS) 59

60 HFile HFile 60

61 HFile HFile That's actually an SSTable (flat sorted list of key-value pairs) 61

62 HFile HFile KeyValue That's actually an SSTable (flat sorted list of key-value pairs) (Stores a cell) 62

63 Versioning Different versions of same cell Latest 63

64 HFile: KeyValue key value 64

65 HFile: KeyValue (prefix code) keylength valuelength key value 65

66 HFile: Key row length row (key) column family length column family column qualifier timestamp key type 66

67 HFile: Key row length row (key) column family length column family column qualifier timestamp key type This one is for the versioning 67

68 Blocks HFile 68

69 Blocks HFile "Quantity" of KeyValues that get read at a time 69

70 Blocks Default HFile 64kb 70

71 Blocks: long keys or values size(keyvalue) > block size No split (longer block) 71

72 Levels of physical storage Table 72

73 Levels of physical storage Table Region 73

74 Levels of physical storage Table Region Store 74

75 Levels of physical storage Table Region Store StoreFile 75

76 Levels of physical storage Table Region Store StoreFile Block 76

77 Levels of physical storage Table Region Store StoreFile Block KeyValue 77

78 HBase: Writing new cells 78

79 On Disk Table Region Store StoreFile Block KeyValue 79

80 Store StoreFile Block Block StoreFile Block Block 80

81 Store MemStore radub85 / 123RF Stock Photo StoreFile Block Block StoreFile Block Block 81

82 In Memory Table Region Store MemStore Cell 82

83 Writing new cells MemStore StoreFile Block Block 83

84 Writing new cells MemStore StoreFile Block Block 84

85 Writing new cells MemStore StoreFile Block Block 85

86 Writing new cells MemStore StoreFile Block Block 86

87 Writing new cells MemStore StoreFile Block Block 87

88 Flush MemStore StoreFile StoreFile Block Block Block Block Sort! 88

89 Flush When: Reaching max Memstore size in a store Reaching overall max Memstore size Reaching full Write-Ahead Log 89

90 Reading from a Store MemStore StoreFile Block Block StoreFile Block Block 90

91 Reading from a Store MemStore StoreFile Block Block StoreFile Block Block 91

92 Compaction StoreFile StoreFile StoreFile Block Block Block Block Block Block 92

93 Compaction StoreFile StoreFile StoreFile Block Block Block Block Block Block 93

94 Compaction StoreFile (Sort again) Block Block Block Block Block Block 94

95 The META table: a table like any other 95

96 The META table: stores region locations table + region start key + region id + replica id info: regioninfo info: server info: serverstartcode T10:15:00 96

97 RegionInfo RegionInfo Table name Start key Region ID Replica ID encodedname End key Split Offline 97

98 Architecture HMaster Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 98

99 Architecture HMaster Create/delete/update table Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 99

100 Architecture HMaster Region? Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver (hosting meta) 100

101 Architecture HMaster Region? Regionserver location(s) Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 101

102 Architecture HMaster Query Regionserver Regionserver Regionserver Regionserver Regionserver Regionserver 102

103 HBase: Underlying APIs grazvydas / 123RF Stock Photo 103

104 HBase implementation (Packaged code) 104

105 HBase APIs REST 105

106 HBase: caching 106

107 HBase Caches: reading faster LRU block cache Level 1 107

108 HBase Caches: reading faster LRU block cache bucket cache Level 1 Level 2 108

109 HBase Caches: reading faster LRU block cache bucket cache HDFS Level 1 Level 2 109

110 LRU Block Cache On the Least Recently Heap Used 110

111 LRU Block Cache: levels of priority Single access priority Multi access priority In-memory access priority111

112 When to NOT use the cache Batch processing 112

113 When to NOT use the cache Random access 113

114 Hash function Source: Jorge Stolfi (Wikipedia) 114

115 Bloom filter Very quickly whether an element belongs to a set (potentially false positives) 115

116 Bloom filter

117 Bloom filter John Smith hash function 1 hash function 2 hash function k

118 Bloom filter Mary Smith hash function 1 hash function 2 hash function k

119 Bloom filter: not in set hash function 1 hash function 2 hash function k Albert Einstein? 119

120 Bloom filter: in set (and correct) hash function 1 hash function 2 hash function k Mary Smith? 120

121 Bloom filter: in set (false positive) hash function 1 hash function 2 hash function k Louis de Broglie? 121

122 Data Locality 122

123 HBase vs. HDFS 123

124 With HDFS load balancer

125 HFile compaction brings back locality 125

126 Best practices 126

127 Number of rows Millions RDBMS Billions HBase 127

128 Number of nodes > 5 128

129 10 Design Principles of Big Data 129

130 1. Learn from the past 130

131 2. Keep the design simple 131

132 3. Modularize the architecture 132

133 4. Homogeneity in the large 133

134 5. Heterogeneity in the small 134

135 6. Separate metadata from data 135

136 7. Abstract logical model from its physical implementation 136

137 8. Shard the data 137

138 9. Replicate the data 138

139 10. Buy lots of cheap hardware 139