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NOSQL, CouchDB, and the Cloud
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NOSQL, CouchDB, and the Cloud

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Talk given at Ga. State chapter of the ACM, Feb. 3, 2010.

Talk given at Ga. State chapter of the ACM, Feb. 3, 2010.

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  • 20 yrs old, open source since mid-90’s, iirc.

    like a mobile telephone grid

    compiled (but to bytecode for a VM)

    open source
  • Why Erlang?

    Here are my three big ticket items
    - massively concurrent
    - seamlessly distributed into multi-machine clusters
    - extremely fault tolerant

    Great for my projects
    - data storage & retrieval
    - scalable web apps

    Maybe not so hot for computationally intensive projects
    - unless they lend themselves to parallelism

Transcript

  • 1. NOSQL, COUCHDB AND THE CLOUD Brad Anderson Cloudant 1
  • 2. BRAD ANDERSON • BS Hotel Management • Restaurant Chain Data - econometric modeling, BI/DW • Open Source - trac, dsource.org, couchdb • NOSQLEast 2009 • Cloudant • http://twitter.com/boorad 2
  • 3. AGENDA • NOSQL • COUCHDB • Erlang • Cloud • Dynamo • MapReduce 3
  • 4. YOU’RE SCREWED http://www.bigfatmoneybags.com/blog/wp-content/uploads/2009/12/screwed.jpg 4
  • 5. RELATIONAL DATABASES RDBMS • Rigid Schema / ORM fun 1970-2009 • Scalability • Everything is a Nail http://www.flickr.com/photos/36041246@N00/3419197777/ 5
  • 6. MASTER-SLAVE • Master-Slave Replication • One (and only one) master • One or more slaves • All writes go to the master, replicated to slaves • Reads balanced among master and slaves • Issues • single point of failure • single point of bottleneck • static topology 6
  • 7. MASTER-MASTER • Master-Master Replication • One or more masters • Writes and reads can go to any master • Writes are replicated among masters • Issues • limited performance and scalability (typically due to 2PC) • complexity • static topology 7
  • 8. VERTICAL PARTITION • Vertical Partitioning • Put tables belonging to different functional areas on different database nodes • Scale data & load by function • Move joins to the application level • Issues • no longer truly relational • a functional area grows too much 8
  • 9. HORIZONTAL PARTITION • Horizontal Partitioning • Split tables by key and put partitions (shards) on different nodes • Scale data & load by key • Move joins to the application level • Issues • no longer truly relational • a partition grows too much 9
  • 10. CACHING • Put a cache in front of your database • Distribute • Write-through for scaling reads • Write-behind for scaling reads and writes • Issues • “only” scales read/write load • invalidation 10
  • 11. OKAY, NOT SCREWED http://www.bigfatmoneybags.com/blog/wp-content/uploads/2009/12/screwed.jpg 11
  • 12. NOSQL NOT ONLY SQL A moniker for different data storage systems solving very different problems, all where a relational database is not the right fit. 12
  • 13. RIGHT FIT • Google indexes 400 Pb / day (2007) • CERN, LHC generates 100 Pb / sec • Unique data created each year (IDC, 2007) • 2007 40 Eb • 2010 988 Eb (exponential growth) 13
  • 14. FOUR CATEGORIES • Key/Value Stores • Dynomite, Voldemort, Tokyo • Document Stores • CouchDB, MongoDB • Column Stores / BigTable • HBase, Hypertable • Graph Databases • Neo4j, AllegroGraph, VertexDB 14
  • 15. BIG TAKEAWAY function function data function function data data function function data data data data data data data data function function data data data data data data function function data data function data Bring the function to the data 15
  • 16. 16
  • 17. HUH? ERLANG? • Programming Language created at Ericsson (20 yrs old now) • Designed for scalable, long-lived systems • Compiled, Functional, Dynamically Typed, Open Source 17
  • 18. 3 BIGGIES • Massively Concurrent • green threads, very lightweight != os threads • Seamlessly Distributed • node = os thread = VM, processes can live anywhere • Fault Tolerant • 99.9999999 = 32ms downtime per year - AXD301 18
  • 19. Of fi cia lB et a! CouchDB Apache 19
  • 20. COUCHDB • Schema-free document database server • Robust, highly concurrent, fault-tolerant • RESTful JSON API • Futon web admin console • MapReduce system for generating custom views • Bi-directional incremental replication • couchapp: lightweight HTML+JavaScript apps served directly from CouchDB using views to transform JSON 20
  • 21. FROM INTEREST TO ADOPTION • 100+ production users • Active commercial •3 development books being written • Rapidly maturing • Vibrant, open community 21
  • 22. OF THE WEB Django may be built for the Web, but CouchDB is built of the Web. I've never seen software that so completely embraces the philosophies behind HTTP ... this is what the software of the future looks like. Jacob Kaplan-Moss October 17 2007 http://jacobian.org/writing/of-the-web/ 22
  • 23. DOCUMENTS • Documents are JSON Objects • Underscore-prefixed fields are reserved • Documents can have binary attachments • MVCC _rev deterministically generated from doc content 23
  • 24. ROBUST • Never overwrite previously committed data • In the event of a server crash or power failure, just restart CouchDB -- there is no “repair” • Take snapshots with “cp” • Configurable levels of durability: can choose to fsync after every update, or less often to gain better throughput 24
  • 25. CONCURRENT • Erlang approach: lightweight processes to model the natural concurrency in a problem • For CouchDB that means one process per TCP connection • Lock-free architecture; each process works with an MVCC snapshot of a DB. • Performance degrades gracefully under heavy concurrent load 25
  • 26. REST API • Create PUT /mydb/mydocid • Retrieve GET /mydb/mydocid • Update PUT /mydb/mydocid • Delete DELETE /mydb/mydocid 26
  • 27. 27
  • 28. VIEWS • Custom, persistent representations of document data • “Closeto the metal” -- no dynamic queries in production, so you know exactly what you’re getting • Generated using MapReduce functions written in JavaScript (and other languages) view must have a map function and may also have a • Each reduce function • Leverages view collation, rich view query API 28
  • 29. DOCUMENTS BY AUTHOR 29
  • 30. WORD COUNT 30
  • 31. INCREMENTAL • Computing a view can be expensive, so CouchDB saves the result in a B-tree and keeps it up-to-date • Leafnodes store map results, inner nodes store reductions of children http://horicky.blogspot.com/2008/10/couchdb-implementation.html 31
  • 32. REPLICATION • Peer-based, bi-directional replication using normal HTTP calls • Mediated by a replicator process which can live on the source, target, or somewhere else entirely • Replicate a subset of documents in a DB meeting criteria defined in a custom filter function (coming soon) • Applications (_design documents) replicate along with the data • Ideal for offline applications -- “ground computing” 32
  • 33. CLOUD 33
  • 34. SHOWROOM A cluster of couches 34
  • 35. ARCHITECTURE • Each cluster is a ring of nodes (Dynamo, Dynomite) • Any node can handle request (consistent hashing) • O(1), with a hop • nodes own partitions (ring is divided) • data are distributed evenly across partitions and replicas • mapreduce functions are passed to nodes for execution
  • 36. RESEARCH • Google’s MapReduce, http://bit.ly/bJbyq5 • Amazon’s Dynamo, http://bit.ly/b7FlsN • CAP theorem, http://bit.ly/bERr2H
  • 37. CLUSTER CONTROLS •N - Replication Q •Q - Partitions = 2 •R - Read Quorum •W - Write Quorum • These constants define the cluster
  • 38. N Consistency Throughput Durability N = Number of replicas per item stored in cluster
  • 39. Q Throughput Scalability 2^Q = Number of partitions (shards) in cluster T = Number of nodes in cluster 2^Q / T = Number of partitions per node
  • 40. R Latency Consistency R = Number of successful reads before returning value(s) to client
  • 41. W Latency Durability W = Number of successful writes before returning ‘success’ to client
  • 42. Load Balancer Node 1 24 No de A B C D de No B 2 A Z C Y D X E C N od e D 3 E F D No de E 4 F G
  • 43. request PUT http://boorad.cloudant.com/dbname/blah?w=2 Load Balancer Node 1 24 No de A B C D de No B 2 A Z C Y D X E C N od e D 3 E F D No de E 4 F G
  • 44. request PUT http://boorad.cloudant.com/dbname/blah?w=2 Load Balancer Node 1 24 No de A B C D de No B 2 A Z C Y D X E C N od e D 3 E F D No de E 4 F G
  • 45. request PUT http://boorad.cloudant.com/dbname/blah?w=2 Load Balancer Node 1 24 No de A B C D de No B 2 A Z C Y D X hash(blah) = E E C N od e D 3 E F D No de E 4 F G
  • 46. request PUT http://boorad.cloudant.com/dbname/blah?w=2 N=3 W=2 Load Balancer R=2 Node 1 24 No de A B C D de No B 2 A Z C Y D X hash(blah) = E E C N od e D 3 E F D No de E 4 F G
  • 47. request PUT http://boorad.cloudant.com/dbname/blah?w=2 N=3 W=2 Load Balancer R=2 Node 1 24 No de A B C D de No B 2 A Z C Y D X hash(blah) = E E C N od e D 3 E F D No de E 4 F G
  • 48. request PUT http://boorad.cloudant.com/dbname/blah?w=2 N=3 W=2 Load Balancer R=2 24 Node 1 No node down de A B C D de No B 2 A Z C Y D X hash(blah) = E E C N od e D 3 E F D No de E 4 F G
  • 49. RESULT • For standalone or cluster • one REST API • one URL • For cluster • redundant data • distributed queries • scale out 43
  • 50. QUESTIONS?
  • 51. CREDITS • Emil Eifrem, http://bit.ly/5D40WQ • Sergio Bossa, http://bit.ly/c9UoRZ • Cliff Moon, http://bit.ly/bX887c 45