1. Bigtable: A Distributed Storage System for Structured Data F. Chang, J. Dean, S. Ghemawat, W.C. Hsieh, D.A. Wallach M. Burrows, T. Chandra, A. Fikes, R.E. Gruber Google, Inc. Tianyang HU

2. Outline Introduction Data Model Building Blocks Implementation Refinements Performance Evaluation Future Work 2

3. Motivation Scalability – worldwide applications & users – huge amount of communication & data 3

4. Bigtable Distributed storage system – petabytes of data, thousands of machines – simple data model with dynamic control – applicability, scalability, performance, availability Used by more than 60 applications of Google 4

5. Outline Introduction Data Model Building Blocks Implementation Refinements Performance Evaluation Future Work 5

6. Data Model – Overview Sparse, distributed, persistent multidimensional sorted map. 6

7. Data Model – Example “Webtable” stores copy of web pages & their related information. – row key: URL (reverse hostname) – column key: attribute name – timestamp: time that the page is fetched 7

8. Data Model – Rows Row key: string (usually 10-100KB, max 64KB) Every R/W of data under a single row key is atomic 8

9. Data Model – Rows Sorted by row key in lexicographic order Tablet: a certain range of rows – the unit of distribution & load balancing – good locality for data access 9

10. Data Model – Columns Column families: group of column keys (same type) – the unit of access control Column key: family:qualifier 10

11. Data Model – Timestamps Timestamp: index multiple versions of the same data – not necessarily the “real time” – data clean up, garbage collection 11

12. Outline Introduction Data Model Building Blocks Implementation Refinements Performance Evaluation Future Work 12

13. Building Blocks SSTable file format – persistent, ordered, immutable key-value (string-string) pairs – used internally to store Bigtable data 13

14. Building Blocks GFS – store log & data files – scalability, reliability, performance, fault tolerance Chubby – a highly-available and persistent distributed lock service 14

15. Outline Introduction Data Model Building Blocks Implementation Refinements Performance Evaluation Future Work 15

16. Bigtable Components A library that is linked into every client Many tablet servers – handle R/W to tablets with clients One tablet master – assign tablets to tablet servers – detect addition & expiration of tablet servers – balance tablet-server load 16

17. Architecture 17

18. Tablet Location Three-level hierarchy – root tablet (Only one, stores addresses of METADATA tablets) – METADATA tablets (stores addresses of user tablets) – user tablets 18

19. Tablet Location Client caches (multiple) tablet locations – if the cache is stale, query again 19

20. Tablet Assignment The tablet master uses Chubby to keeps track of – live tablet servers each live tablet server acquires an exclusive lock on a corresponding file – tablet assignment status compare tablets registered in METADATA tablet with tablets in tablet servers 20

21. Tablet Assignment Case 1: some tablets are unassigned – master assigns them to tablet servers with sufficient room Case 2: a tablet server stops its service – master detects it and assigns outstanding tablets to other servers. Case 3: too many small tablets – master initiates merge Case 4: a tablet grows too large – the corresponding tablet server initiates split and notifies master 21

22. Tablet Serving A tablet is stored as a sequence of SSTables in GFS Tablet mutations are logged in commit log – the “commit log” stores redo records – recent tablet versions are stored in memory (memtable) – older tablet versions are stored in GFS 22

23. Tablet Serving Recover a tablet – 1. Tablet server fetches its metadata from METADATA tablet, which contains a list of SSTables that comprises a tablet and redo points. – 2. The server reads the indices of the SSTables into memory. – 3. The server applies all the mutations after the redo point. 23

24. Tablet Serving Write operation on a tablet – 1. The tablet server checks the validity of the operation. – 2. The operation is logged in the commit log. – 3. Commit the operation. – 4. The content of tablet is inserted into memtable. 24

25. Tablet Serving Read operation on a tablet – 1. The tablet server checks the validity of the operation. – 2. Execute the operation on a merged view of memtable & SSTables. 25

26. Compactions Memtable grows as write operations execute Two types of compactions – minor compaction – merging (major) compaction 26

27. Compactions Minor compaction (when memtable size reaches a threshold) – 1. Freeze the memtable – 2. Create a new memtable – 3. Convert the memtable to an SSTable and write to GFS 27

28. Compactions Merging compaction (periodically) – 1. Freeze the memtable – 2. Create a new memtable – 3. Merge a few SSTables & memtable into a new SSTable 28

29. Compactions Major compaction – special case of merging compaction – merges all SSTables & memtable 29

30. Compactions Why freeze & create memtable? – Incoming read and write operations can continue during compactions. Advantages of compaction: – release the memory of the tablet server – reduce the amount of data that has to be read from the commit log during recovery if this tablet server dies 30

31. Outline Introduction Data Model Building Blocks Implementation Refinements Performance Evaluation Future Work 31

32. Refinements Locality groups – group multiple column families together – different locality groups are not typically accessed together – for each tablet, store each locality group in a separate SSTable – more efficient R/W 32

33. Refinements Compression – similar data in same column, neighbouring rows, multiple versions – customized compression on SSTable block level (smallest component) – two-pass compression scheme 1. Bentley and McIlroy’s scheme, compress long strings across a large window 2. fast compression algorithm, look for repetitions in small window experimental compression ratio: 10% (Gzip: 25-33%) 33

34. Refinements Caching – two-level cache on tablet server – Scan cache (high-level): caches key-value pairs Case: read the same data repeatedly – Block cache (low-level): caches SSTable blocks Case: sequential read 34

35. Refinements Commit-log implementation – one commit log per tablet incurs a large # of disk seeks – use single commit log for all tablets on a tablet server – benefits significantly during normal operation – complicates recovery solution: sort the commit log entries first 35

36. Outline Introduction Data Model Building Blocks Implementation Refinements Performance Evaluation Future Work 36

37. Performance Evaluation 37 R/W rate per tablet server aggregate R/W rate

38. Outline Introduction Data Model Building Blocks Implementation Refinements Performance Evaluation Future Work 38

39. Future Work Resource sharing for different applications? Hybrid with relational database? – complex query – security 39