Why Wordnik went non-relational

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A presentation on the selection criteria, testing + evaluation and successful, zero-downtime migration to MongoDB. Additionally details on Wordnik's speed and stability are covered as well as how …

A presentation on the selection criteria, testing + evaluation and successful, zero-downtime migration to MongoDB. Additionally details on Wordnik's speed and stability are covered as well as how NoSQL technologies have changed the way Wordnik scales.

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  • Moving to a json-based mapper, 10k/second. Moving to direct mapping, 35k/second

Transcript

  • 1. NoSQL Now 2011Why Wordnik went Non-Relational
    Tony Tam
    @fehguy
  • 2. What this Talk is About
    5 Key reasons why Wordnik migrated into a Non-Relational database
    Process for selection, migration
    Optimizations and tips from living survivors of the battle field
  • 3. Why Should You Care?
    MongoDB user for almost 2 years
    Lessons learned, analysis, benefits from process
    We migrated from MySQL to MongoDB with no downtime
    We have interesting/challenging data needs, likely relevant to you
  • 4. More on Wordnik
    World’s fastest updating English dictionary
    Based on input of text up to 8k words/second
    Word Graph as basis to our analysis
    Synchronous & asynchronous processing
    10’s of Billions of documents in NR storage
    20M daily REST API calls, billions served
    Powered by Swagger OSS API framework
    swagger.wordnik.com
    Powered API
  • 5. Architectural History
    2008: Wordnik was born as a LAMP AWS EC2 stack
    2009: Introduced public REST API, powered wordnik.com, partner APIs
    2009: drank NoSQL cool-aid
    2010: Scala
    2011: Micro SOA
  • 6. Non-relational by Necessity
    Moved to NR because of “4S”
    Speed
    Stability
    Scaling
    Simplicity
    But…
    MySQL can go a LONG way
    Takes right team, right reasons (+ patience)
    NR offerings simply too compelling to focus on scaling MySQL
  • 7. Wordnik’s 5 Whys for NoSQL
  • 8. Why #1: Speed bumps with MySQL
    Inserting data fast (50k recs/second) caused MySQL mayhem
    Maintaining indexes largely to blame
    Operations for consistency unnecessary but "cannot be turned off”
    Devised twisted schemes to avoid client blocking
    Aka the “master/slave tango”
  • 9. Why #2: Retrieval Complexity
    Objects typically mapped to tables
    Object Hierarchy always => inner + outer joins
    Lots of static data, so why join?
    “Noun”is not getting renamed in my code’s lifetime!
    Logic like this is probably in application logic
    Since storage is cheap
    I’ll choose speed
  • 10. Why #2: Retrieval Complexity
    One definition = 10+ joins
    50 requests per second!
  • 11. Why #2: Retrieval Complexity
    Embed objects in rows “sort of works”
    Filtering gets really nasty
    Native XML in MySQL?
    If a full table-scan is OK…
    OK, then cache it!
    Layers of caching introduced layers of complexity
    Stale data/corruption
    Object versionitis
    Cache stampedes
  • 12. Why #3: Object Modeling
    Object models being compromised for sake of persistence
    This is backwards!
    Extra abstraction for the wrong reason
    OK, then performance suffers
    In-application joins across objects
    “Who ran the fetch all query against production?!” –any sysadmin
    “My zillionth ORM layer that only I understand” (and can maintain)
  • 13. Why #4: Scaling
    Needed "cloud friendly storage"
    Easy up, easy down!
    Startup: Sync your data, and announce to clients when ready for business
    Shutdown: Announce your departure and leave
    Adding MySQL instances was a dance
    Snapshot + bin files
    mysql> change master to MASTER_HOST='db1', MASTER_USER='xxx', MASTER_PASSWORD='xxx', MASTER_LOG_FILE='master-relay.000431', MASTER_LOG_POS=1035435402;
  • 14. Why #4: Scaling
    What about those VMs?
    So convenient! But… they kind of suck
    Can the database succeed on a VM?
    VM Performance:
    Memory, CPU or I/O—Pick only one
    Can your database really reduce CPU or disk I/O with lots of RAM?
  • 15. Why #5: Big Picture
    BI tools use relational constraints for discovery
    Is this the right reason for them?
    Can we work around this?
    Let’s have a BI tool revolution, too!
    True service architecture makes relational constraints impractical/impossible
    Distributed sharding makes relational constraints impractical/impossible
  • 16. Why #5: Big Picture
    Is your app smarter than your database?
    The logic line is probably blurry!
    What does count(*)really mean when you add 5k records/sec?
    Maybe eventual consistency is not so bad…
    2PC? Do some reading and decide!
    http://eaipatterns.com/docs/IEEE_Software_Design_2PC.pdf
  • 17. Ok, I’m in!
    I thought deciding was easy!?
    Many quickly maturing products
    Divergent features tackle different needs
    Wordnik spent 8 weeks researching and testing NoSQL solutions
    This is a long time! (for a startup)
    Wrote ODM classes and migrated our data
    Surprise! There were surprises
    Be prepared to compromise
  • 18. Choice Made, Now What?
    We went with MongoDB ***
    Fastest to implement
    Most reliable
    Best community
    Why?
    Why #1: Fast loading/retrieval
    Why #2: Fast ODM (50 tps => 1000 tps!)
    Why #3: Document Models === Object models
    Why #4: MMF => Kernel-managed memory + RS
    Why #5: It’s 2011, is there no progress?
  • 19. More on Why MongoDB
    Testing, testing, testing
    Used our migration tools to load test
    Read from MySQL, write to MongoDB
    We loaded 5+ billion documents, many times over
    In the end, one server could…
    Insert 100k records/sec sustained
    Read 250k records/sec sustained
    Support concurrent loading/reading
  • 20. Migration & Testing
    Iterated ODM mapping multiple times
    Some issues
    Type Safety
    cur.next.get("iWasAnIntOnce").asInstanceOf[Long]
    Dates as Strings
    obj.put("a_date", "2011-12-31") !=
    obj.put("a_date", new Date("2011-12-31"))
    Storage Size
    obj.put("very_long_field_name", true) >>
    obj.put("vsfn", true)
  • 21. Migration & Testing
    Expect data model iterations
    Wordnik migrated table to Mongo collection "as-is”
    Easier to migrate, test
    _id field used same MySQL PK
    Auto Increment?
    Used MySQL to “check-out” sequences
    One row per mongo collection
    Run out of sequences => get more
    Need exclusive locks here!
  • 22. Migration & Testing
    Sequence generator in-process
    SequenceGenerator.checkout("doc_metadata,100")
    Sequence generator as web service
    Centralized UID management
  • 23. Migration & Testing
    Expect data access pattern iterations
    So much more flexibility!
    Reach into objects
    > db.dictionary_entry.find({"hdr.sr":"cmu"})
    Access to a whole object tree at query time
    Overwrite a whole object at once… when desired
    Not always! This clobbers the whole record
    > db.foo.save({_id:18727353,foo:"bar"})
    Update a single field:
    > db.foo.update({_id:18727353},{$set:{foo:"bar"}})
  • 24. Flip the Switch
    Migrate production with zero downtime
    We temporarily halted loading data
    Added a switch to flip between MySQL/MongoDB
    Instrument, monitor, flip it, analyze, flip back
    Profiling your code is key
    What is slow?
    Build this in your app from day 1
  • 25. Flip the Switch
  • 26. Flip the Switch
    Storage selected at runtime
    valh = shouldUseMongoDb match {
    case true => new MongoDbSentenceDAO
    case _ => new MySQLDbSentenceDAO
    }
    h.find(...)
    Hot-swappable storage via configuration
    It worked!
  • 27. Then What?
    Watch our deployment, many iterations to mapping layer
    Settled on in-house, type-safe mapper
    https://github.com/fehguy/mongodb-benchmark-tools
    Some gotchas (of course)
    Locking issues on long-running updates (more in a minute)
    We want more of this!
    Migrated shared files to Mongo GridFS
    Easy-IT
  • 28. Performance + Optimization
    Loading data is fast!
    Fixed collection padding, similarly-sized records
    Tail of collection is always in memory
    Append faster than MySQL in every case tested
    But... random access started getting slow
    Indexes in RAM? Yes
    Data in RAM? No, > 2TB per server
    Limited by disk I/O /seek performance
    EC2 + EBS for storage?
  • 29. Performance + Optimization
    Moved to physical data center
    DAS & 72GB RAM => great uncached performance
    Good move? Depends on use case
    If “access anything anytime”, not many options
    You want to support this?
  • 30. Performance + Optimization
    Inserts are fast, how about updates?
    Well… update => find object, update it, save
    Lock acquired at “find”, released after “save”
    If hitting disk, lock time could be large
    Easy answer, pre-fetch on update
    Oh, and NEVER do “update all records” against a large collection
  • 31. Performance + Optimization
    Indexes
    Can't always keep index in ram. MMF "does it's thing"
    Right-balanced b-tree keeps necessary index hot
    Indexes hit disk => mute your pager
    17
    15
    27
  • 32. More Mongo, Please!
    We modeled our word graph in mongo
  • More Mongo, Please!
    Analytics rolled-up from aggregation jobs
    Send to Hadoop, load to mongo for fast access
  • 35. What’s next
    Liberate our models
    stop worrying about how to store them (for the most part)
    New features almost always NR
    Some MySQL left
    Less on each release
  • 36. Questions?
    See more about Wordnik APIs
    http://developer.wordnik.com
    Migrating from MySQL to MongoDB
    http://www.slideshare.net/fehguy/migrating-from-mysql-to-mongodb-at-wordnik
    Maintaining your MongoDB Installation
    http://www.slideshare.net/fehguy/mongo-sv-tony-tam
    Swagger API Framework
    http://swagger.wordnik.com
    Mapping Benchmark
    https://github.com/fehguy/mongodb-benchmark-tools
    Wordnik OSS Tools
    https://github.com/wordnik/wordnik-oss