1. 6/28/2015EECS 584, Fall 20111 The Gamma Database Machine DeWitt, Ghandeharizadeh, Schneider, Bricker, Hsiao, Rasmussen Deepak Bastakoty (With slide material sourced from Ghandeharizadeh and DeWitt)

2. 6/28/2015 EECS 584, Fall 20112 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera..

3. 6/28/2015 EECS 584, Fall 20113 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera..

4. 6/28/2015 EECS 584, Fall 20114 Motivation What ? –Parallelizing Databases How to setup system / hardware? How to distribute data ? How to tailor software ? Where does this rabbit hole lead?

5. 6/28/2015 EECS 584, Fall 20115 Motivation Why ?

6. 6/28/2015 EECS 584, Fall 20116 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 !)

7. 6/28/2015 EECS 584, Fall 20117 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera..

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: Advantages: High Availability Data Backup Data Sharing –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. Shared Disk

12. Storage Area Network (SAN): 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): 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) Network …. CPU 1 CPU 2 CPU n DRAM 1 DRAM 2 DRAM D … … CPU 1 CPU 2 CPU n DRAM 1 DRAM 2 DRAM D … … Node M Node 1

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. 6/28/2015 EECS 584, Fall 201116 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera..

17. No Shared Data Declustering Physical View Bob2010K Shideh1835K Ted5060K Kevin62120K Angela55140K Mike4590K Logical View nameage salary

18. 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 No Shared Data Declustering

19. 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 No Shared Data Declustering

20. Bob2010K Shideh1835K Ted5060K Kevin62120K Angela55140K Mike4590K nameage salary salary % 3 Ted5060KKevin62120K nameage salaryBob2010KMike4590K nameage salaryShideh1835KAngela55140K nameage salary salary is the partitioning attribute. Hash Declustering

21. Selections with equality predicates referencing the partitioning attribute are directed to a single node: 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: 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. Bob2010K Shideh1835K Ted5060K Kevin62120K Angela55140K Mike4590K nameage salary salary is the partitioning attribute. Bob2010KShideh1835K nameage salaryTed5060KMike4590K nameage salaryKevin62120KAngela55140K nameage salary 0-50K51K-100K 101K- ∞ Range Declustering

23. Equality and range predicates referencing the partitioning attribute are directed to a subset of nodes: 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. Predicates referencing a non-partitioning attribute are directed to all nodes. In the example, both queries are directed to one node.

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

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

26. 6/28/2015 EECS 584, Fall 201126 Why the difference ? Declustering Tradeoffs

27. 6/28/2015 EECS 584, Fall 201127 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

28. 6/28/2015 EECS 584, Fall 201128 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera..

29. 6/28/2015 EECS 584, Fall 201129 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)

30. 6/28/2015 EECS 584, Fall 201130 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.

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. 6/28/2015 EECS 584, Fall 201137 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera..

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: 1.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. 2.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. R join S where R is the inner table. Parallelism and Hash-Join

41. Example Join of Emp and Dept Emp join Dept (using dno) SS#NameAgeSalarydno 1Joe24200002 2Mary20250001 3Bob22270001 4Kathy30300002 5Shideh440001 EMP dnodnamefloormgrss# 1Toy15 2Shoe21 Dept SS#NameAgeSalarydnodnamefloormgrss# 1Joe24200002Shoe21 2Mary20250001Toy15 3Bob22270001Toy15 4Kathy30300002Shoe21 5Shideh440001Toy15

42. Hash-Join: Build Read rows of Dept table one at a time and place in a main-memory hash table 1Toy15 2Shoe21 dno % 7

43. Hash-Join: Build Read rows of Emp table and probe the hash table. 1Toy15 2Shoe21 dno % 7 SS#NameAgeSalarydno 1Joe24200002

44. Hash-Join: Build Read rows of Emp table and probe the hash table and produce results when a match is found. Repeat until all rows processed SS#NameAgeSalarydno 1Joe24200002 1Toy15 2Shoe21 dno % 7 SS#NameAgeSalarydnodnamefloormgrss# 1Joe24200002Shoe21

45. Hash-Join (slide for exam etc review only) 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.

46. Hash-Join: Build Two buckets of Dept table. One in memory and the second is disk-resident. 1Toy15 2Shoe21 dno % 7

47. Hash-Join: Probe Read Emp table and probe the hash table for joining records when dno=1. With dno=2, stream the data to disk. 1Toy15 2Shoe21 dno % 7 SS#NameAgeSalarydno 1Joe24200002 2Mary20250001 3Bob22270001 4Kathy30300002 5Shideh440001

48. Hash-Join: Probe Those rows of Emp table with dno=1 probed the hash table and produce 3 joining records. 1Toy15 2Shoe21 dno % 7 SS#NameAgeSalarydno 1Joe24200002 4Kathy30300002

49. Hash-Join: While loop 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. 2Shoe21 dno % 7 SS#NameAgeSalarydno 1Joe24200002 4Kathy30300002

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.

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

52. 6/28/2015 EECS 584, Fall 201152 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Evaluation and Results Bonus : Current Progress, Hadoop, Clustera..

53. System Evaluation Two Key Metrics –Speed-up Given a system with 1 node, does adding n nodes speed it up with a factor of n ? –Scale-up Given a system with 1 node, does the response time remain the same with n nodes ?

54. Ideal Parallelism

55. Speedup (Selection)

56. Scaleup (Selection)

57. Speedup (Join)

58. Scaleup (Join)

59. 6/28/2015 EECS 584, Fall 201159 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Overview of Results Bonus: Current Progress, Hadoop, Clustera..

60. Modern Systems are Gamma too? Ghandeharizadeh’s 2009 summary : –All systems below, like Gamma, are “share nothing” and run on 1000s of nodes Google File System Google’s Bigtable Data Model Google’s Map/Reduce Framework Yahoo’s Pig Latin Hadoop Pig GFS BigTable

61. DeWitt’s Clustera : –DBMS centric cluster management system –Evolved from Gamma –Like MapReduce but Full DBMS support SQL optimization –Outperforms Hadoop! Old is the New New? Data complexity Job complexity Condor Parallel SQL Map/Reduce

62. Old is the New New?

63. 6/28/2015 EECS 584, Fall 201163 Outline Motivation Physical System Designs Data Clustering Failure Management Query Processing Overview of Results Bonus: Current Progress, Hadoop, Clustera.. Questions ?