Casual mass parallel computing

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Slide deck from NoSQL day in Minsk http://www.belarusjug.org/events/nosql-meetup

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Casual mass parallel computing

  1. 1. Casual mass parallel data processing in Java Alexey Ragozin Mar 2014
  2. 2. Building new bicycle …
  3. 3. Build Vs. Buy Build • No dedicated team to support infrastructure • Very specific tasks • Exclusive use of infrastructure • Reasonable scale Buy • Product can bought as service (internal or external) • Large scale • Multi tenancy • You are going to use advanced features (e.g. map/reduce)
  4. 4. “Casual” computing • Small computation farms (< 100 servers) • Team owns both application and grid • Java platform • Reasonably short batches (< 24 hours) • Reasonably small data sets (< 10 TiB)
  5. 5. Simple master slave topology Master process Task queue Slave Slave Slave Scheduler AdvertiseTask Report
  6. 6. Simple master slave topology Control plane  RMI Queue / scheduler  Simple in memory queue  May be more complex than just task queue Data plane …
  7. 7. Data plane Never, ever, try to send data over RMI  File system  Avoid network mounts! In-memory key-value  Client side sharding works best Disk database (RDBMS or NoSQL)  Consider prefetch of data Direct socket streaming …
  8. 8. Distributed objects revised Pit falls of CORBA/RMI • IDL – functional contract • IDL – protocol Separating concerns • Functional contract – wrapper object • Protocol – hidden remote interface
  9. 9. Distributed objects revised Renewed distributed objects paradigm Strong • Polymorphism • Encapsulation  Network protocol, caching aspects etc Weak • Homogenous code base required • Synchronous network communications
  10. 10. Brute force  Build / package  Deploy / SCP  Restart slaves  Start batch  Change code, repeat Deployment problem Computation grid software  Compile and run batch Behind scene  Your classes would be collected  Associated with batch  Deployed on participating slaves
  11. 11. Central scheduler topology Batch controller Slave Slave Slave Pull task Task Report Queue server Task queue Batch controller Add tasks Consume reports
  12. 12. Or more elaborated
  13. 13. Flow organized tasks • Input data available before task starts • e.g. Map/Reduce Collaborative tasks • Tasks communicate intermediate results to each other • e.g. physic simulations Flavors of parallel processing
  14. 14. Get back to data plane Rules of thumb • Insert / delete – never update • Write locally (reducing risks) • Read remotely (retry on error) • Store input as is  File system  Document / column oriented NoSQL • Input and temporary data is different  Choose right store for each
  15. 15. Exploiting file system Avoid network file systems • File system concept is not designed to be distributed • Good network file system cannot not exists • Use simple remote file access protocols • SCP (unencrypteddatatransferoptionsaddedbyCERNguys) • HTTP (ifyoureallydonotwantSCP) Cheap SAN could be build from open source
  16. 16. Algorithmic optimization Parallel computing • N times speed up will increase your OPEX and CAPEX cost by N*lg(N) Algorithmic optimization • Up front costs only • Orders of magnitude optimization opportunities • Exciting coding • Ecological way of computing 
  17. 17. Streaming algorithms Finding N most frequent elements • Min-Count Estimating number of unique values • HyperLogLog Distribution histograms https://github.com/addthis/stream-lib https://github.com/rwl/ParallelColt
  18. 18. NanoCloud – drastically simplified coding for computing clusters
  19. 19. @Test public void hello_remote_world() { Cloud cloud = CloudFactory.createSimpleSshCloud(); cloud.node("myserver.acme.com").exec(new Callable<Void>(){ @Override public Void call() throws Exception { String localhost = InetAddress.getLocalHost().toString(); System.out.println("Hi! I'm running on " + localhost); return null; } }); } As easy as …
  20. 20. All you need is … NanoCloud requirements  SSHd  Java (1.6 and above) present  Works though NAT and firewalls  Works on Amazon EC2  Works everywhere where SSH works
  21. 21. Master – slave communications Master process Slave hostSSH (Single TCP) Slave Slave RMI (TCP) std err std out std in diag Slave controller Slave controller multiplexed slave streams Agent
  22. 22. Links NanoCloud • https://code.google.com/p/gridkit/wiki/NanoCloudTutorial • Maven Central: org.gridkit.lab:telecontrol-ssh:0.7.23 • http://blog.ragozin.info/2013/01/remote-code-execution-in-java-made.html ANT task • https://github.com/gridkit/gridant
  23. 23. Thank you Alexey Ragozin alexey.ragozin@gmail.com http://blog.ragozin.info - my articles http://code.google.com/p/gridkit http://github.com/gridkit - my open source code http://aragozin.timepad.ru - community events in Moscow

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