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Hypertable

Hypertable. Doug Judd Zvents, Inc. Background. Web 2.0 = Data Explosion. Web 2.0. Web 1.0. Web 2.0. Web 1.0. Traditional Tools Don’t Scale Well. Designed for a single machine Typical scaling solutions ad-hoc manual/static resource allocation. The Google Stack.

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Hypertable

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  1. Hypertable Doug Judd Zvents, Inc.

  2. Background hypertable.org

  3. Web 2.0 = Data Explosion Web 2.0 Web 1.0 Web 2.0 Web 1.0

  4. Traditional ToolsDon’t Scale Well • Designed for a single machine • Typical scaling solutions • ad-hoc • manual/static resource allocation

  5. The Google Stack • Google File System (GFS) • Map-reduce • Bigtable hypertable.org

  6. Architectural Overview hypertable.org

  7. What is Hypertable? • A open source high performance, scalable database, modelled after Google's Bigtable • Not relational • Does not support transactions hypertable.org

  8. Hypertable Improvements Over Traditional RDBMS • Scalable • High random insert, update, and delete rate

  9. Data Model • Sparse, two-dimensional table with cell versions • Cells are identified by a 4-part key • Row • Column Family • Column Qualifier • Timestamp hypertable.org

  10. Table: Visual Representation hypertable.org

  11. Table: Actual Representation hypertable.org

  12. Anatomy of a Key • Row key is \0 terminated • Column Family is represented with 1 byte • Column qualifier is \0 terminated • Timestamp is stored big-endian ones-compliment hypertable.org

  13. Concurrency • Bigtable uses copy-on-write • Hypertable uses a form of MVCC(multi-version concurrency control) • Deletes are carried out by inserting “delete” records

  14. CellStore • Sequence of 65K blocks of compressed key/value pairs

  15. System Overview

  16. Range Server • Manages ranges of table data • Caches updates in memory (CellCache) • Periodically spills (compacts) cached updates to disk (CellStore) hypertable.org

  17. Client API class Client { void create_table(const String &name, const String &schema); Table *open_table(const String &name); String get_schema(const String &name); void get_tables(vector<String> &tables); void drop_table(const String &name, bool if_exists); };

  18. Client API (cont.) class Table { TableMutator *create_mutator(); TableScanner *create_scanner(ScanSpec &scan_spec); }; class TableMutator { void set(KeySpec &key, const void *value, int value_len); void set_delete(KeySpec &key); void flush(); }; class TableScanner { bool next(CellT &cell); }; hypertable.org

  19. Language Bindings • Currently C++ only • Thrift Broker

  20. Write Ahead Commit Log • Persists all modifications (inserts and deletes) • Written into underlying DFS

  21. Range Meta-Operation Log • Facilitates Range meta operation • Loads • Splits • Moves • Part of Master and RangeServer • Ensures Range state and location consistency

  22. Compression • Cell Stores store compressed blocks of key/value pairs • Commit Log stores compressed blocks of updates • Supported Compression Schemes • zlib (--best and --fast) • lzo • quicklz • bmz • none hypertable.org

  23. Caching • Block Cache • Caches CellStore blocks • Blocks are cached uncompressed • Query Cache • Caches query results • TBD hypertable.org

  24. Bloom Filter • Negative Cache • Probabilistic data structure • Indicates if key is not present

  25. Scaling (part I) hypertable.org

  26. Scaling (part II) hypertable.org

  27. Scaling (part III) hypertable.org

  28. Access Groups • Provides control of physical data layout -- hybrid row/column oriented • Improves performance by minimizing I/OCREATE TABLE crawldb { Title MAX_VERSIONS=3, Content MAX_VERSIONS=3, PageRank MAX_VERSIONS=10, ClickRank MAX_VERSIONS=10, ACCESS GROUP default (Title, Content), ACCESS GROUP ranking (PageRank, ClickRank)}; hypertable.org

  29. Filesystem Broker Architecture • Hypertable can run on top of any distributed filesystem (e.g. Hadoop, KFS, etc.) hypertable.org

  30. Keys To Performance • C++ • Asynchronous communication

  31. C++ vs. Java • Hypertable is CPU intensive • Manages large in-memory key/value map • Alternate compression codecs (e.g. BMZ) • Hypertable is memory intensive • Java uses 2-3 times the amount of memory to manage large in-memory map (e.g. TreeMap) • Poor processor cache performance hypertable.org

  32. Performance Test(AOL Query Logs) • 75,274,825 inserted cells • 8 node cluster • 1 1.8 GHz Dual-core Opteron • 4 GB RAM • 3 x 7200 RPM SATA drives • Average row key: 7 bytes • Average value: 15 bytes • Replication factor: 3 • 4 simultaneous insert clients • 500K random inserts/s • 680K scanned cells/s hypertable.org

  33. Performance Test II • Simulated AOL query log data • 1TB data • 9 node cluster • 1 2.33 GHz quad-core Intel • 16 GB RAM • 3 x 7200 RPM SATA drives • Average row key: 9 bytes • Average value: 18 bytes • Replication factor: 3 • 4 simultaneous insert clients • Over 1M random inserts/s (sustained) hypertable.org

  34. Weaknesses • Range data managed by a single range server • Though no data loss, can cause periods of unavailability • Can be mitigated with client-side cache or memcached hypertable.org

  35. Project Status • Currently in “alpha” • Just released version 0.9.0.7 • Will release “beta” version end of August • Waiting on Hadoop JIRA 1700 hypertable.org

  36. License • GPL 2.0 • Why not Apache?

  37. Questions? • www.hypertable.org hypertable.org

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