Lessons Learned from Migrating 2+ Billion Documents at Craigslist Jeremy Zawodny firstname.lastname@example.org Jeremy@Zawodny.com http://blog.zawodny.com/
Outline Recap last year’s MongoSV Talk The Archive, Why MongoDB, etc. http://www.10gen.com/video/mongosv2010/craigslist The Infrastructure The Lessons Wishlist Q&A
Craigslist Numbers 2 data centers ~500 servers ~100 MySQL servers ~700 cities, worldwide ~1 billion hits/day ~1.5 million posts/day
Archive: Where Data Goes To Die Live Numbers ~1.75M posts/day ~14 day avg. lifetime ~60 day retention ~100M posts We keep all postings Users reuse postings Daily archive migration Internal query tools
Archive Pain Coupled Schemas Big Indexes Hardware Failures Replication Lag Poor Search Human Time Costs
MongoDB Wins Scalable Fast Friendly Proven Pragmatic Approachable
MongoDB Details Plan for 5 billion documents Average size: 2KB 3 Replica sets, 3 Servers each Deploy to 2 datacenters Same deployment in each datacenter Posting ID is sharding key
MongoDB Architecture Typical Sharding with Replica Sets (external sphinx full-text indexers not pictured) config client client client client config config mongos mongos mongos shard001 shard003 shard002 replica set replica set replica set
Lesson: Know Your Hardware MongoDB on blades really sucks Single 10k RPM disks can’t take it when data is noticeably larger than RAM Mongo operations can hit the client timeout (30 sec default) Even minutely cron jobs start to spew Lots of time wasted in development environment, trying different kernels, tuning, etc. Most noticeable during heavy writes but can happen if pages fall out of RAM for other reasons
Lesson: Replica Sets Rock Lots of reboots happened during dev environment troubleshooting Each time, one of the remaining nodes took over No “reclone” no config file or DNS changes Stuff “just worked” while nodes bounced up and down
Lesson: Know Your Data MongoDB is UTF-8 Some of our older data is decidedly NOT UTF-8 We have lots of sloppy encoding issues to clean up. But we had to clean them all up. Start data load. Wait 12-36 hours. Witness fail. Fix code. Start over. Sigh. This is a combination of having been sloppy and having old data. Even with a lot less history, this can bite you. Get your encoding house in order!
Lesson: Know Your Data Size MongoDB has a doc size limits 4MB in 1.6.x, 16MB in 1.8.x What to do with outliers? In our case, trim off some useless data. But going from relational to document means this sort of problem is easy to have. One parent, many children. It’d be nice if this was easier to change, but clients have it hard-coded too. Compression would help, of course.
Lesson: Know Your Data Types Field Types and Conversions can be expensive to do after the fact! MongoDB treats strings and numbers differently, but some programming languages (such as Perl) don’t make that distinction obvious This has indexing implications when you later look for 123456789 but had unknowingly stored “123456789” http://search.cpan.org/dist/MongoDB/lib/MongoDB/DataTypes.pod
Data Types, continued “If the type of a field is ambiguous and important to your application, you should document what you expect the application to send to the database and convert your data to those types before sending.” Do you know how to do that in your language of choice? Some drivers may make a “guess” that gets it right most of the time.
Lesson: Know SomeSharding The Balancer can be your frenemy Initial insert rate: 8,000/sec Later drops to 200/sec Too much time spent waiting to page in data that’s going to be sent to another node and never looked at (locally) again Pre-split your data if possible http://blog.zawodny.com/2011/03/06/mongodb-pre-splitting-for-faster-data-loading-and-importing/
Lesson: Know Some Replica Sets Replica Set re-sync requires index rebuilds on the secondary Most painful when a slave is down too long and can’t catch up using the oplog Typically during high write volumes In a large data set, the index rebuilding can take a couple of days w/out many indexes What if you lose another while that is happening?
MongoDBWishlist Replica set node re-sync without out index rebuilding Record (or field) compression (not everyone uses a filesystem that offers compression) Method to tap into the oplog so that changes can be fed to external indexers (Sphinx, Redis, etc.) Hash-based sharding (coming soon?) Cluster snapshot/backup tool
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