HadoopDB

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Miguel Pastor

Brief introduction to a new approach on handling big amount of data

Technology

HadoopDB Miguel Angel Pastor Olivar miguelinlas3 at gmail dot com http://miguelinlas3.blogspot.com http://twitter.com/miguelinlas3

Previous problem -> Shared nothing architectures

Pricing mode (cloud) ,[object Object],[object Object]

Analytics environments: not restart querys

Difficult homogeneous ,[object Object],[object Object]

Background: parallel databases ,[object Object]

Optimizer tailored ,[object Object],[object Object]

No enhacing performance techniques ,[object Object],[object Object]

Connect multiple single-datanode systems ,[object Object]

Queries parallelized along de nodes ,[object Object]

Parallel databases performance ,[object Object]

Architecture background ,[object Object]

Files broken in blocks and ditributed ,[object Object],[object Object]

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HadoopDB

1. HadoopDB Miguel Angel Pastor Olivar miguelinlas3 at gmail dot com http://miguelinlas3.blogspot.com http://twitter.com/miguelinlas3

3. HadoopDB Architecture

4. Results

5. Conclusions

6. Introduction

8. Data amount is exploding

9. Previous problem -> Shared nothing architectures

10.

11. Map/Reduce systems

12.

13.

14. Analytics environments: not restart querys

15. Problem at scaling

16.

17.

18. UDF mechanism

19. Desirable SQL and no SQL interfaces

20.

21.

22.

23. Assumption: failures are rare

24. Assumption: dozens of nodes in clusters

25. Engineering decisions

26. Background: Map/Reduce

27.

28. Works on heterogeneus environment

29.

30.

31. SQL not supported directly ( Hive )

32. HadoopDB

33.

34.

35.

36.

37.

38.

39. Job and Task trackers

40. Architecture

41.

42.

43. Execute the SQL query

44.

45.

46.

47. Plan to deploy as separated service

48.

49. Breaking single data node in ckunks

50.

51.

52.

53. Semantic analyzer connects to catalog

54. DAG of relational operators

55. Optimizer reestructuration

56. Convert plan to M/R jobs

57. DAG in M/R serialized in xml plan

58.

59.

60. Traverse DAG (bottom up). Rule based SQL generator

61. Benckmarking

62.

63.

64. 2 virtual cores

65. 850 GB storage

66. 64 bits Linux Fedora 8

67.

68. 1024 MB heap size

69.

70. PostgreSQL 8.2.5

71. No compress data

72.

73. Used a cloud edition

74.

75. Run on EC2 (not cloud edition available)

76.

77.

78. 18 millions ranking (~1Gigabyte)

79. Stored as plain text in HDFS

80. Loading data

81. Grep Task

82.

83.

84.

85. UDF Aggregation Task

86.

87. DBMS-X 15% overly optimistic

88.

89. Fault tolerance and heterogeneus environments

90. Benchmarks

91.

92. Reduce the number of nodes to achieve the same order of magnitude

93. Fault tolerance is important

94. Conclusions

95.

96. PostgreSQL is not a column store

97. Hadoop and hive relatively new open source projects

98. HadoopDB is flexible and extensible

99. References

100.

101. HadoopDB article

102. HadoopDB project

103. Vertica

104. Apache Hive

105. That´s all!

HadoopDB

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