Simply Measured's migration from MongoDB to HBase

1. Rebuilding from MongoDB for Scale on HBase
Robert Roland (@robdaemon)
Lead Software Engineer
rob@simplymeasured.com
http://www.simplymeasured.com

2. © 2013 Simply Measured, Inc
Who Are We
Social Media Analytics
Serving 25% of the Interbrand Top 100 Global Brands
Collecting data from Twitter, Facebook, Instagram, YouTube,
Google Analytics and more
Delivered in a marketer’s favorite format – Excel!
GeekWire’s 2013 Startup of the Year
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Twitter Account Report, jetBlue Airlines
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Complete Social Media Snapshot, Red Bull
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Our Setup
Cloudera CDH 4.2.1 on Ubuntu 12.04
3 “control” nodes (HDFS name node, job tracker, HBase master)
11 “data” nodes (HDFS data nodes, task trackers, HBase region servers)
Bare-metal, using managed colo hosting
14 TB of data across 50 HBase tables (One table per data source -Twitter,
Facebook, plus secondary indexes and operations)
Our customers have tracked 1.5 billion Tweets so far, and growing
More than 5 million rows across 3,000 reports generated daily
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Why did we start with MongoDB?
• Ease of development
• Lack of schema is a good and a bad thing
• Easy to use libraries in Ruby (mongomatic)
• Lower initial investment
• You can run MongoDB on one server in production (but you shouldn’t)
• Master/slave was easier to start with than the current sharding model
• Active, engaged community
• Really, really effective marketing masks MongoDB's
shortcomings…
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Why leave MongoDB?
• 10 TB of data was too much for it
• Instability
• No one wants to restart mongos every two days
• Bugs
• Very public, very high profile failures
• Silent failure modes
• What do you mean, the index creation failed and you just attempted
to load an entire collection into RAM?
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Why HBase, or, How is this better than Mongo?
• More linear scaling
• Add new nodes, run a major compaction
• Our data can easily be modeled as a sparse column store
• We value consistency
• Our users will tell us if we’re missing one Tweet out of 1 million
• Monitoring
• Metrics
• Stability
• Excellent vendor support
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How did we migrate?
• Implemented the Strangler pattern
• Dual writes to Mongo and HBase
• Migrate older data a few customers at a time, a few data sources at a
time
• Dual report generation platform – enabled us to compare reports off
our MongoDB platform and our HBase platform
• Migrated existing Ruby 1.8 code to JRuby
• Direct access to the HBase cluster
• I’m a better Java developer than Ruby developer
• Great profiler tools
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Challenges with HBase
• Out of the box configuration is not good enough
• GC Tuning
• Default file size of 256 mb is too small
• Compactions will eat you alive
• Be sure to enable the HDFS trashcan! (fs.trash.interval)
• Configurations can be difficult to manage
• Chef / Puppet if you want to roll your own
• Cloudera Manager
• Schema
• Lack of types means the HBase shell is harder to read
• No native secondary indexes
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What’s been awesome
• Great user community on the mailing lists
• Source code is easy to follow and submit patches
• Stable, stable, stable
• Unless you configure something wrong, like ulimits or xcievers!
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Google Analytics schema
• Row key
• salt|dataSourceId|dimension|…|date
• Columns
• CF: metadata
• dataSourceId – hash, identifies a data source mapping to a customer
• timezone – Google Analytics time zone for this data
• dimensions – delimited list of Google Analytics dimensions
• CF: data
• GA data key – Name of metric as defined by Google Analytics, Protocol
Buffer representing value
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Schema evolution
• Enrichment via Klout, geolocation data, Bit.ly, etc. was stored
in a separate table, and “joined” at query time
• Enrichment is now a column family within each data source
• Started with Protocol Buffers, now writing as qualifiers and
columns
• Ability to use server-side filters, ease of use within HBase shell
• Protocol Buffers still exist in some cases (example coming up)
• Rekeyed several tables over time
• Dual write during migration
• Map/reduce jobs to migrate data
• More column families per table than were necessary
• Lots and lots of memstore pain.
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Protocol Buffers
• Union-like Protocol Buffer for arbitrary key/value pairs
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Tips and Tricks
• Don’t expect to get your row keys correct on the first try
• Look for hot spotting
• Does ordering matter during queries?
• Consider your backup strategy
• S3 seems to work for us
• Replication is also an option
• This is not an RDBMS. Don’t JOIN, denormalize!
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What’s coming up
• Hive
• Easier querying
• Entirely standardized representation of our data
• HCatalog
• Expose our schema to other internal tooling
• A much larger cluster
• Even more data sources
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17. Thank You
Robert Roland
@robdaemon
rob@simplymeasured.com
We’re hiring!
http://simplymeasured.com/about/careers/
Our Tech Blog: http://engineering.simplymeasured.com/
Our Open Source: http://simplymeasured.github.io/
More sample reports: http://simplymeasured.com/tour/sample-reports/