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One Size Fits All An Idea Whose Time Has Come and Gone by Michael Stonebraker

One Size Fits All An Idea Whose Time Has Come and Gone by Michael Stonebraker. DBMS Vendors (The Elephants) Sell. One Size Fits All (OSFA). It’s too hard for them to maintain multiple code bases for different specialized purposes * engineering problem * sales problem

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One Size Fits All An Idea Whose Time Has Come and Gone by Michael Stonebraker

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  1. One Size Fits AllAn Idea Whose Time Has Come and GonebyMichael Stonebraker

  2. DBMS Vendors (The Elephants) Sell One Size Fits All (OSFA) It’s too hard for them to maintain multiple code bases for different specialized purposes * engineering problem * sales problem * marketing problem

  3. The OSFA Elephants • Sell code lines that date from the 1970’s • Legacy code • Built for very different hardware configurations • And some cannot adapt to grids…. • That was designed for business data processing (OLTP) • Only market back then • Now warehouses, science, real time, embedded, ..

  4. Current DBMS Gold Standard • Store fields in one record contiguously on disk • Use B-tree indexing • Use small (e.g. 4K) disk blocks • Align fields on byte or word boundaries • Conventional (row-oriented) query optimizer and executor

  5. Terminology -- “Row Store” Record 1 Record 2 Record 3 Record 4 E.g. DB2, Oracle, Sybase, SQLServer, Greenplum, Netezza,Teradata,…

  6. At This Point, RDBMS is “long in the tooth” • There are at least 6 (non trivial) markets where a row store can be clobbered by a specialized architecture • Warehouses (Vertica, SybaseIQ, KX, …) • OLTP (VoltDB) • RDF (Vertica et. al.) • Text (Google, Yahoo, …) • Scientific data (MatLab, SciDB) • Streaming data (StreamBase Coral8, …)

  7. Definition of “Clobbered” • A factor of 50 in performance

  8. Current DBMSs • 30 years of “grow only” bloatware • That is not good at anything • And that deserves to be sent to the “home for tired software”

  9. Pictorially: Other apps DBMS apps OLTP Data Warehouse

  10. The DBMS Landscape – Performance Needs Other apps high low high high OLTP Data Warehouse

  11. One Size Does Not Fit All -- Pictorially Elephants get only “the crevices” SciDB, etc Open source Vertica et. al. VoltDB, etc.

  12. Stonebraker’s Prediction • The DBMS market will move over the next decade or so from OSFA • To specialized (market-specific) architectures • And open source systems • Presumably to the detriment of the elephants

  13. A Couple of Slides of Color on Two of the Markets Data warehouses OLTP

  14. Column Store: IBM 60.25 10,000 1/15/2006 MSFT 60.53 12,500 1/15/2006 Used in: Sybase IQ, Vertica Row Store: IBM 60.25 10,000 1/15/2006 MSFT 60.53 12,500 1/15/2006 Used in: Oracle, SQL Server, DB2, Netezza,… Data Warehouses – Column Stores are the Answer

  15. Data Warehouses – Column Stores Clobber Row Stores • Read only what you need • “Fat” fact tables are typical • Analytics read only a few columns • Better compression • Execute on compressed data • Materialized views help row stores and column stores about equally

  16. Example of “Clobber” • Vertica on an 2 processor system costing ~$2K • Netezza on a 112 processor system costing ~$1M • Customer load time benchmark • Vertica 2.8 times faster – per processor/disk • Customer query benchmark • Vertica 34X on 1/56th the hardware (factor of 1904)

  17. Things to Demand From ANY BI DBMS • Scalable • Runs on a grid (MPP), with partitioning • Replication for HA/DR • “no knobs” operation (more than index selection) • Cannot hire enough DBAs • On-line update – in parallel with query • Ability to run multiple analyses on compatible data • Time travel • On-the-fly reprovisioning

  18. OLTP – The Big Picture • Where the time goes (TPC-C) (Sigmod ’07) • 24% -- the buffer pool • 24% -- locking • 24% -- latching • 24% -- recovery • 4% -- useful work

  19. OLTP – The Big Picture • Have to focus on overhead!!!!! • Better data structures only affects 4% • Have to get rid of all four sources to go really fast • Get rid of one, and you win 25%

  20. H-Store/VoltDB Assumptions • Main memory operation • 1 TB is a VERY big OLTP data base • No disk stalls • No user stalls (disallowed in all apps) • Professional terminal operator replaced by “Aunt Martha” • A BIG OLTP transaction reads or write 200 records • microseconds

  21. H-Store/VoltDB Assumptions • Run transactions to completion • Single threaded • Eliminate “latch crabbing” • And locking

  22. H-Store/VoltDB Assumptions • Built-in high availability and disaster recovery • Failover to a replica • No redo log

  23. H-Store/VoltDB Assumptions • Scalability requires MPP and partitioning • Most transactions are naturally “single-sited” • Place my order • Read my reservation • Update my profile

  24. H-Store/VoltDB Assumptions • Can play tricks to make transactions single-sites • E.g. replicate read-almost-always data • And some companies mandate ‘single-sitedness”

  25. H-Store/VoltDB Summary • No buffer pool overhead • There isn’t one • No crash recovery overhead • Done by failover • (optional) Asynchronous data transmission to reporting system • (optional) Asynchronous local data archive • No latching or locking overhead • Transactions are run to completion – single threaded

  26. OLTP Performance (TPC-C) • Elephant • 850 TPS (1/2 the land speed record per processor) • H-Store • 70,416 TPS (82X) • VoltDB • (33X)

  27. My Vision – Restated OLTP Specialized System Jelly bean grid 33X Elephant HA/DR Limited DBA Warehouse Specialized System Jelly bean grid 50X elephant HA/DR Limited DBA Internal ETL

  28. The Data Center of the Future • Specialized DBMSs • Perhaps a half dozen • Good at a specific task • Running on “jelly bean” MPP • Private or public cloud • Virtualization (sharing of resources) allows you to run 50% headroom rather than 90% • Uniformity lowers DBA costs

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