The Gamma Database Machine DeWitt, Ghandeharizadeh, Schneider, Bricker, Hsiao, Rasmussen

1. The Gamma Database MachineDeWitt, Ghandeharizadeh, Schneider,Bricker, Hsiao, Rasmussen Deepak Bastakoty (With slide material sourced from Ghandeharizadeh and DeWitt) EECS 584, Fall 2011

2. Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera.. EECS 584, Fall 2011 2 7/31/2014

3. Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera.. EECS 584, Fall 2011 3 7/31/2014

4. Motivation What ? Parallelizing Databases How to setup system / hardware? How to distribute data ? How to tailor software ? Where does this rabbit hole lead? EECS 584, Fall 2011 4 7/31/2014

5. Motivation • Why ? EECS 584, Fall 2011

6. Motivation Why ? Obtain faster response time Increase query throughput Improve robustness to failure Reduce system cost Enable Massive Scalability (Faster, Safer, Cheaper, and More of it !) EECS 584, Fall 2011 6 7/31/2014

7. Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera.. EECS 584, Fall 2011 7 7/31/2014

8. System Design Schemes • Shared Memory • DeWitt’s DIRECT etc • Shared Disk • Storage Networks • Shared Nothing • Gamma, everything else (PigLatin, MapReduce setups, BigTable, GFS)

9. System Design Schemes • Shared Memory • poor scalability (number of nodes) • custom hardware / systems

10. System Design Schemes • Shared Disk • storage networks • used to be popular • why ? Network

11. Advantages: Shared Disk • Many clients share storage and data. • Redundancy is implemented in one place protecting all clients from disk failure. • Centralized backup: The administrator does not care/know how many clients are on the network sharing storage. Data Sharing High Availability Data Backup

12. Shared Disk • Storage Area Network (SAN): • Block level access, • Write to storage is immediate, • Specialized hardware including switches, host bus adapters, disk chassis, battery backed caches, etc. • Expensive • Supports transaction processing systems. • Network Attached Storage (NAS): • File level access, • Write to storage might be delayed, • Generic hardware, • In-expensive, • Not appropriate for transaction processing systems.

13. Shared Nothing • Each node has its own processor(s), memory, disk(s) Node 1 Node M … … CPU 1 CPU 2 CPU n CPU 1 CPU 2 CPU n Network …. …. DRAM 1 DRAM 1 DRAM 2 DRAM 2 … … DRAM D DRAM D

14. Shared Nothing • Why ? • Low Bandwidth Parallel Data Processing • Commodity hardware (Cheap !!) • Minimal Sharing = Minimal Interference • Hence : Scalability (keep adding nodes!) • DeWitt says : Even writing code is simpler

15. Shared Nothing • Why ? • Low Bandwidth Parallel Data Processing • Commodity hardware (Cheap !!) • Minimal Sharing = Minimal Interference • Hence : Scalability (keep adding nodes!) • DeWitt says : Even writing code is simpler

16. Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera.. EECS 584, Fall 2011 16 7/31/2014

17. No Shared Data Declustering salary name age Logical View Physical View

18. No Shared Data Declustering • Spreading data between disks : • Attribute-less partitioning • Random • Round-Robin • Single Attribute Schemes • Hash De-clustering • Range De-clustering • Multiple Attributes schemes possible • MAGIC, BERD etc

19. No Shared Data Declustering • Spreading data between disks : • Attribute-less partitioning • Random • Round-Robin • Single Attribute Schemes • Hash De-clustering • Range De-clustering • Multiple Attributes schemes possible • MAGIC, BERD etc

20. Hash Declustering salary name age salary is the partitioning attribute. salary % 3 salary salary salary name age name age name age

21. Hash Declustering • Selections with equality predicates referencing the partitioning attribute are directed to a single node: • Retrieve Emp where salary = 60K • Equality predicates referencing a non-partitioning attribute and range predicates are directed to all nodes: • Retrieve Emp where age = 20 • Retrieve Emp where salary < 20K SELECT * FROM Emp WHERE salary=60K SELECT * FROM Emp WHERE salary<20K

22. Range Declustering salary name age salary is the partitioning attribute. 101K-∞ 0-50K 51K-100K salary salary salary name age name age name age

23. Range Declustering • Equality and range predicates referencing the partitioning attribute are directed to a subset of nodes: • Retrieve Emp where salary = 60K • Retrieve Emp where salary < 20K • Predicates referencing a non-partitioning attribute are directed to all nodes. In the example, both queries are directed to one node.

24. Declustering Tradeoffs • Range selection predicate using a clustered B+-tree • 0.01% selectivity (10 records) Throughput (Queries/Second) Range Hash/Random/Round-robin Multiprogramming Level

25. Declustering Tradeoffs • Range selection predicate using a clustered B+-tree • 1% selectivity (1000 records) Throughput (Queries/Second) Hash/Random/Round-robin Range Multiprogramming Level

26. Why the difference ? Declustering Tradeoffs EECS 584, Fall 2011 26 7/31/2014

27. Why the difference ? When selection was small, Range spread out load and was ideal When selection increased, Range caused high workload on one/few nodes while Hash spread out load Declustering Tradeoffs EECS 584, Fall 2011 27 7/31/2014

28. Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera.. EECS 584, Fall 2011 28 7/31/2014

29. Failure Management Key Questions Robustness (How much damage recoverable?) Availability (How likely? Hot Recoverable?) MTTR (Mean Time To Recovery) Consider two declustering schemes Interleaved Declustering (TeraData) Chained Declustering (Gamma DBM) EECS 584, Fall 2011 29 7/31/2014

30. Interleaved Declustering A partitioned table has a primary and a backup copy. The primary copy is constructed using one of the partitioning techniques. The secondary copy is constructed by: Dividing the nodes into clusters (cluster size 4 here), Partition a primary fragment (R0) across the remaining nodes of the cluster: 1, 2, and 3. Realizing r0.0, r0.1, and r0.2. EECS 584, Fall 2011 30 7/31/2014

31. Interleaved Declustering On failure, query load re-directed to backup nodes in cluster MTTR : replace node reconstruct failed primary from backups reconstruct backups stored in failed node Second failure before this can cause unavailability Large cluster size improves failure load balancing but increases risk of data being unavailable

32. Chained Declustering (Gamma) Nodes are divided into disjoint groups called relation clusters. A relation is assigned to one relation cluster and its records are declustered across the nodes of that relation cluster using a partitioning strategy (Range, Hash). Given a primary fragment Ri, its backup copy is assigned to node (i+1) mod M (M is the number of nodes in the relation cluster).

33. Chained Declustering (Gamma) During normal operation: Read requests are directed to the fragments of primary copy, Write requests update both primary and backup copies.

34. Chained Declustering (Gamma) In presence of failure: Both primary and backup fragments are used for read operations, Objective: Balance the load and avoid bottlenecks! Write requests update both primary and backup copies. Note: Load of R1 (on node 1) is pushed to node 2 in its entirety. A fraction of read request from each node is pushed to the others for a 1/8 load increase attributed to node 1’s failure.

35. Chained Declustering (Gamma) MTTR involves: Replace node 1 with a new node, Reconstruct R1 (from r1 on node 2) on node 1, Reconstruct backup copy of R0 (i.e., r0) on node 1. Note: Once Node 1 becomes operational, primary copies are used to process read requests.

36. Chained Declustering (Gamma) Any two node failures in a relation cluster does not result in data un-availability. • Two adjacent nodes must fail in order for data to become unavailable

37. Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera.. EECS 584, Fall 2011 37 7/31/2014

38. Query Processing • General Idea : Divide And Conquer

39. Parallelizing Hash Join • Consider Hash Join • Join of Tables A and B using attribute j (A.j = B.j) consists of two phase: • Build phase: Build a main-memory hash table on Table A using the join attribute j, e.g., build a hash table on the Toy department using dno as the key of the hash table. • Probe phase: Scan table B one record at a time and use its attribute j to probe the hash table constructed on Table A, e.g., probe the hash table using the rows of the Emp department.

40. Parallelism and Hash-Join • R join S where R is the inner table.

41. Example Join of Emp and Dept • Emp join Dept (using dno) Dept EMP

42. Hash-Join: Build • Read rows of Dept table one at a time and place in a main-memory hash table dno % 7

43. Read rows of Emp table and probe the hash table. Hash-Join: Build dno % 7

44. Read rows of Emp table and probe the hash table and produce results when a match is found. Repeat until all rows processed Hash-Join: Build dno % 7

45. Prob: Table used to build hash table larger than memory A divide-and-conquer approach: Use the inner table (Dept) to construct n memory buckets where each bucket is a hash table. Every time memory is exhausted, spill a fixed number of buckets to the disk. The build phase terminates with a set of in-memory buckets and a set of disk-resident buckets. Read the outer relation (Emp) and probe the in-memory buckets for joining records. For those records that map onto the disk-resident buckets, stream and store them to disk. Discard the in memory buckets to free memory space. While disk-resident buckets of inner-relation exist: Read as many (say i) of the disk-resident buckets of the inner-relation into memory as possible. Read the corresponding buckets of the outer relation (Emp) to probe the in-memory buckets for joining records. Discard the in memory buckets to free memory space. Delete the i buckets of the inner and outer relations. Hash-Join (slide for exam etc review only)

46. Hash-Join: Build • Two buckets of Dept table. One in memory and the second is disk-resident. dno % 7

47. Read Emp table and probe the hash table for joining records when dno=1. With dno=2, stream the data to disk. Hash-Join: Probe dno % 7

48. Those rows of Emp table with dno=1 probed the hash table and produce 3 joining records. Hash-Join: Probe dno % 7

49. Read the disk-resident bucket of Dept into memory. Probe it with the disk-resident buckets of Emp table to produce the remaining two joining records. Hash-Join: While loop dno % 7

50. Parallelism and Hash-Join • Each node may perform hash-join independently when: • The join attribute is the declustering attribute of the tables participating in the join operation. • The participating tables are declustered across the same number of nodes using the same declustering strategy. • The system may re-partition the table (see the next bullet) if its aggregate memory exceeds the size of memory the tables are declustered across. • Otherwise, the data must be re-partitioned to perform the join operation correctly.