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Elastic Data Partitioning for Cloud-based SQL Processing Systems

Elastic Data Partitioning for Cloud-based SQL Processing Systems. Lipyeow Lim Information & Computer Science Department University of Hawai`i at M ā noa. Outline. Shared Nothing Parallel DBMS. results. query. Parallel DB layer. DBMS. DBMS. DBMS. DBMS. Network.

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Elastic Data Partitioning for Cloud-based SQL Processing Systems

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  1. Elastic Data Partitioning for Cloud-based SQL Processing Systems Lipyeow Lim Information & Computer Science Department University of Hawai`i at Mānoa Lipyeow Lim -- University of Hawai`i at Manoa

  2. Outline Lipyeow Lim -- University of Hawai`i at Manoa

  3. Shared Nothing Parallel DBMS • results • query Parallel DB layer • DBMS • DBMS • DBMS • DBMS Network Lipyeow Lim -- University of Hawaii at Manoa

  4. Cloud-basedArchitecture Virtual Machines CPU CPU CPU CPU Memory Memory Memory Memory Disk Disk Disk Disk (Virtualized) Network Amazon EC2 Physical Resources Lipyeow Lim -- University of Hawaii at Manoa

  5. “Scaling” Up and Down • results • query Parallel DB layer • DBMS • DBMS • DBMS • DBMS • DBMS • DBMS Network Lipyeow Lim -- University of Hawaii at Manoa

  6. Problem Statement Given • A relation T • A partitioning function F on a fixed partitioning key • An initial number p of partitions/fragments • An initial mapping of p fragments to p database nodes • A target number q of partitions Find • a mapping of {T1, T2, .. Tp} to {T1, T2, ... Tq} and • an assignment of the q fragments to q database nodes Such that we minimize • The number of tuples re-partitioned • The number of tuples moved between database nodes Lipyeow Lim -- University of Hawai`i at Manoa

  7. Partitioning a Relation 2 2 4 6 7 7 7 13 20 : 2 2 4 6 7 7 7 13 20 : • Partitioning attribute/key. • Partitioning type. Eg. Range or Hash • Partitioning constraint. Eg. Equi-width, equi-size • Number of partitions/fragments. hash function Lipyeow Lim -- University of Hawai`i at Manoa

  8. Horizontal Fragmentation: Range Partition Partition 1 Partition 2 • Range Partition on rating column • Partition 1: 0 <= rating < 5 • Partition 2: 5 <= rating <= 10 Lipyeow Lim -- University of Hawaii at Manoa

  9. Range Partition: Query Processing Partition 1 • Which partitions? • Better than non-parallel ? SELECT * FROM Sailors S SELECT * FROM Sailors S WHERE rating = 2 Partition 2 SELECT * FROM Sailors S WHERE age > 30 SELECT * FROM Sailors S WHERE rating < 2 and age < 30 Lipyeow Lim -- University of Hawaii at Manoa

  10. Horizontal Fragmentation: Hash Partition Partition 1 • Hash partitioning using hash function • Partition = rating mod 2 Partition 2 Lipyeow Lim -- University of Hawaii at Manoa

  11. Hash Partition: Query Processing Partition 1 • Which partitions? • Better than non-parallel ? SELECT * FROM Sailors S SELECT * FROM Sailors S WHERE rating = 2 Partition 2 SELECT * FROM Sailors S WHERE age > 30 SELECT * FROM Sailors S WHERE rating < 2 and age < 30 Lipyeow Lim -- University of Hawaii at Manoa

  12. Method N: Naive Resize Lipyeow Lim -- University of Hawai`i at Manoa

  13. Method C : Chunk-based Lipyeow Lim -- University of Hawai`i at Manoa

  14. Method T : Tree-based Lipyeow Lim -- University of Hawai`i at Manoa

  15. Method H : Hash-based Lipyeow Lim -- University of Hawai`i at Manoa

  16. Lipyeow Lim -- University of Hawai`i at Manoa

  17. Lipyeow Lim -- University of Hawai`i at Manoa

  18. Lipyeow Lim -- University of Hawai`i at Manoa

  19. Lipyeow Lim -- University of Hawai`i at Manoa

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