Facebook Messages & HBase
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The document discusses Facebook's use of HBase to store messaging data. It provides an overview of HBase, including its data model, performance characteristics, and how it was a good fit for Facebook's needs due to its ability to handle large volumes of data, high write throughput, and efficient random access. It also describes some enhancements Facebook made to HBase to improve availability, stability, and performance. Finally, it briefly mentions Facebook's migration of messaging data from MySQL to their HBase implementation.
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Facebook Messages & HBase
- 1. Facebook Messages & HBase Nicolas Spiegelberg Software Engineer, Facebook April 8, 2011
- 2. Talk Outline ▪ About Facebook Messages ▪ Intro to HBase ▪ Why HBase ▪ HBase Contributions ▪ MySQL -> HBase Migration ▪ Future Plans ▪ Q&A
- 4. The New Facebook Messages Messages Chats Emails SMS
- 6. Monthly data volume prior to launch 15B x 1,024 bytes = 14TB 120B x 100 bytes = 11TB
- 7. Messaging Data ▪ Small/medium sized data HBase ▪ Message metadata & indices ▪ Search index ▪ Small message bodies ▪ Attachments and large messages Haystack ▪ Used for our existing photo/video store
- 8. Open Source Stack ▪ Memcached --> App Server Cache ▪ ZooKeeper --> Small Data Coordination Service ▪ HBase --> Database Storage Engine ▪ HDFS --> Distributed FileSystem ▪ Hadoop --> Asynchronous Map-Reduce Jobs
- 9. Our architecture User Directory Service Clients (Front End, MTA, etc.) What’s the cell for this user? Cell 2 Cell 1 Cell 3 Cell 1 Application Server Application Server Application Server Attachments HBase/HDFS/Z HBase/HDFS/Z KMessage, Metadata, HBase/HDFS/Z K Search Index K Haystack
- 10. About HBase
- 11. HBase in a nutshell • distributed, large-scale data store • efficient at random reads/writes • initially modeled after Google’s BigTable • open source project (Apache)
- 12. When to use HBase? ▪ storing large amounts of data ▪ need high write throughput ▪ need efficient random access within large data sets ▪ need to scale gracefully with data ▪ for structured and semi-structured data ▪ don’t need full RDMS capabilities (cross table transactions, joins, etc.)
- 13. HBase Data Model • An HBase table is: • a sparse , three-dimensional array of cells, indexed by: RowKey, ColumnKey, Timestamp/Version • sharded into regions along an ordered RowKey space • Within each region: • Data is grouped into column families ▪ Sort order within each column family: Row Key (asc), Column Key (asc), Timestamp (desc)
- 14. Example: Inbox Search • Schema • Key: RowKey: userid, Column: word, Version: MessageID • Value: Auxillary info (like offset of word in message) • Data is stored sorted by <userid, word, messageID>: User1:hi:17->offset1 Can efficiently handle queries like: User1:hi:16->offset2 User1:hello:16->offset3 - Get top N messageIDs for a User1:hello:2->offset4 specific user & word ... User2:.... - Typeahead query: for a given user, User2:... get words that match a prefix ...
- 15. HBase System Overview Database Layer HBASE Master Backup Master Region Region Region ... Server Server Server Coordination Service Storage Layer HDFS Zookeeper Quorum Namenode Secondary Namenode ZK ZK ... Peer Peer Datanode Datanode Datanode ...
- 16. HBase Overview HBASE Region Server .... Region #2 Region #1 .... ColumnFamily #2 ColumnFamily #1 Memstore (in memory data structure) HFiles (in HDFS) flush Write Ahead Log ( in HDFS)
- 17. HBase Overview • Very good at random reads/writes • Write path • Sequential write/sync to commit log • update memstore • Read path • Lookup memstore & persistent HFiles • HFile data is sorted and has a block index for efficient retrieval • Background chores • Flushes (memstore -> HFile) • Compactions (group of HFiles merged into one)
- 18. Why HBase? Performance is great, but what else…
- 19. Horizontal scalability ▪ HBase & HDFS are elastic by design ▪ Multiple table shards (regions) per physical server ▪ On node additions ▪ Load balancer automatically reassigns shards from overloaded nodes to new nodes ▪ Because filesystem underneath is itself distributed, data for reassigned regions is instantly servable from the new nodes. ▪ Regions can be dynamically split into smaller regions. ▪ Pre-sharding is not necessary ▪ Splits are near instantaneous!
- 20. Automatic Failover ▪ Node failures automatically detected by HBase Master ▪ Regions on failed node are distributed evenly among surviving nodes. ▪ Multiple regions/server model avoids need for substantial overprovisioning ▪ HBase Master failover ▪ 1 active, rest standby ▪ When active master fails, a standby automatically takes over
- 21. HBase uses HDFS We get the benefits of HDFS as a storage system for free ▪ Fault tolerance (block level replication for redundancy) ▪ Scalability ▪ End-to-end checksums to detect and recover from corruptions ▪ Map Reduce for large scale data processing ▪ HDFS already battle tested inside Facebook ▪ running petabyte scale clusters ▪ lot of in-house development and operational experience
- 22. Simpler Consistency Model ▪ HBase’s strong consistency model ▪ simpler for a wide variety of applications to deal with ▪ client gets same answer no matter which replica data is read from ▪ Eventual consistency: tricky for applications fronted by a cache ▪ replicas may heal eventually during failures ▪ but stale data could remain stuck in cache
- 23. Typical Cluster Layout ▪ Multiple clusters/cells for messaging ▪ 20 servers/rack; 5 or more racks per cluster ▪ Controllers (master/Zookeeper) spread across racks ZooKeeper Peer ZooKeeper Peer ZooKeeper Peer ZooKeeper Peer ZooKeeper Peer HDFS Namenode Backup Namenode Job Tracker Hbase Master Backup Master Region Server Region Server Region Server Region Server Region Server Data Node Data Node Data Node Data Node Data Node Task Tracker Task Tracker Task Tracker Task Tracker Task Tracker 19x... 19x... 19x... 19x... 19x... Region Server Region Server Region Server Region Server Region Server Data Node Data Node Data Node Data Node Data Node Task Tracker Task Tracker Task Tracker Task Tracker Task Tracker Rack #1 Rack #2 Rack #3 Rack #4 Rack #5
- 25. Goal: Zero Data Loss
- 26. Goal of Zero Data Loss/Correctness ▪ sync support added to hadoop-20 branch ▪ for keeping transaction log (WAL) in HDFS ▪ to guarantee durability of transactions ▪ Row-level ACID compliance ▪ Enhanced HDFS’s Block Placement Policy: ▪ Original: rack aware, but minimally constrained ▪ Now: Placement of replicas constrained to configurable node groups ▪ Result: Data loss probability reduced by orders of magnitude
- 27. Availability/Stability improvements ▪ HBase master rewrite- region assignments using ZK ▪ Rolling Restarts – doing software upgrades without a downtime ▪ Interrupt Compactions – prioritize availability over minor perf gains ▪ Timeouts on client-server RPCs ▪ Staggered major compaction to avoid compaction storms
- 28. Performance Improvements ▪ Compactions ▪ critical for read performance ▪ Improved compaction algo ▪ delete/TTL/overwrite processing in minor compactions ▪ Read optimizations: ▪ Seek optimizations for rows with large number of cells ▪ Bloom filters to minimize HFile lookups ▪ Timerange hints on HFiles (great for temporal data) ▪ Improved handling of compressed HFiles
- 29. Operational Experiences ▪ Darklaunch: ▪ shadow traffic on test clusters for continuous, at scale testing ▪ experiment/tweak knobs ▪ simulate failures, test rolling upgrades ▪ Constant (pre-sharding) region count & controlled rolling splits ▪ Administrative tools and monitoring ▪ Alerts (HBCK, memory alerts, perf alerts, health alerts) ▪ auto detecting/decommissioning misbehaving machines ▪ Dashboards ▪ Application level backup/recovery pipeline
- 30. Working within the Apache community ▪ Growing with the community ▪ Started with a stable, healthy project ▪ In house expertise in both HDFS and HBase ▪ Increasing community involvement ▪ Undertook massive feature improvements with community help ▪ HDFS 0.20-append branch ▪ HBase Master rewrite ▪ Continually interacting with the community to identify and fix issues ▪ e.g., large responses (2GB RPC)
- 31. Data migration Another place we used HBase heavily…
- 32. Move messaging data from MySQL to HBase
- 33. Move messaging data from MySQL to HBase ▪ In MySQL, inbox data was kept normalized ▪ user’s messages are stored across many different machines ▪ Migrating a user is basically one big join across tables spread over many different machines ▪ Multiple terabytes of data (for over 500M users) ▪ Cannot pound 1000s of production UDBs to migrate users
- 34. How we migrated ▪ Periodically, get a full export of all the users’ inbox data in MySQL ▪ And, use bulk loader to import the above into a migration HBase cluster ▪ To migrate users: ▪ Since users may continue to receive messages during migration: ▪ double-write (to old and new system) during the migration period ▪ Get a list of all recent messages (since last MySQL export) for the user ▪ Load new messages into the migration HBase cluster ▪ Perform the join operations to generate the new data ▪ Export it and upload into the final cluster
- 36. Facebook Insights Goes Real-Time ▪ Recently launched real-time analytics for social plugins on top of HBase ▪ Publishers get real-time distribution/engagement metrics: ▪ # of impressions, likes ▪ analytics by ▪ Domain, URL, demographics ▪ Over various time periods (the last hour, day, all-time) ▪ Makes use of HBase capabilities like: ▪ Efficient counters (read-modify-write increment operations) ▪ TTL for purging old data
- 37. Future Work It is still early days…! ▪ Namenode HA (AvatarNode) ▪ Fast hot-backups (Export/Import) ▪ Online schema & config changes ▪ Running HBase as a service (multi-tenancy) ▪ Features (like secondary indices, batching hybrid mutations) ▪ Cross-DC replication ▪ Lot more performance/availability improvements
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