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HBase at Facebook: The Underlying Technology of Messages

There have been lots of discussions and speculations after the announcement that Facebook is using HBase for the new messaging system. In case you missed it, here are the most important bits:

  • Kannan Muthukkaruppan: The underlying Technology of Messages (

    We spent a few weeks setting up a test framework to evaluate clusters of MySQL, Apache Cassandra, Apache HBase, and a couple of other systems. We ultimately chose HBase. MySQL proved to not handle the long tail of data well; as indexes and data sets grew large, performance suffered. We found Cassandra’s eventual consistency model to be a difficult pattern to reconcile for our new Messages infrastructure.

  • How does HBase write performance differ from write performance in Cassandra with consistency level ALL

    While setting the a write consistency level of ALL with a read level of ONE in Cassandra provides a strong consistency model similar to what HBase provides (and in fact using quorum writes and reads would as well), the two operations are actually semantically different and lead to different durability and availability guarantees.

  • Cassandra mailing list: Facebook messaging and choice of HBase over Cassandra

  • Todd Hoff: Facebook’s New Real-Time Messaging System: HBase to Store 135+ Billion Messages a Month (

    HBase is a scaleout table store supporting very high rates of row-level updates over massive amounts of data. Exactly what is needed for a Messaging system. HBase is also a column based key-value store built on the BigTable model. It’s good at fetching rows by key or scanning ranges of rows and filtering. Also what is needed for a Messaging system. Complex queries are not supported however. Queries are generally given over to an analytics tool like Hive, which Facebook created to make sense of their multi-petabyte data warehouse, and Hive is based on Hadoop’s file system, HDFS, which is also used by HBase.

  • Jeremiah Peschka: Facebook messaging - HBase Comes of Age (

    Existing expertise: The technology behind HBase – Hadoop and HDFS – is very well understood and has been used previously at Facebook. […] Since Hive makes use of Hadoop and HDFS, these shared technologies are well understood by Facebook’s operations teams. As a result, the same technology that allows Facebook to scale their data will be the technology that allows Facebook to scale their Social Messaging feature. The operations team already understands many of the problems they will encounter.

  • What version of HBase is Facebook using for its new messaging platform?

    Facebook has an internal branch of HBase which periodically updates from the Apache SVN. As far as I know, the current version in production is very similar to the 0.89.20100924 development release with a couple more patches pulled in from trunk.

    Facebook engineers continue to actively contribute to the open source trunk, though - it’s not an internal “fork”

    Todd Lipcon (HBase committer)

The engineering team behind Facebook’s new messaging system has posted now a video talking more about their choice of HBase. You can watch the a bit over 1 hour long video here.

The engineering team behind Facebook Messages spent the past year building out a robust, scalable infrastructure. We shared some details about the technology on our Engineering Blog ( This tech talk digs deeper into some of the twenty different infrastructure services we created for the project as well as how we’re using Apache HBase.

I’m still watching the video, so my notes will follow.

Why HBase?

Choosing HBase at Facebook

  • Strong consistency model
  • Automatic failover
  • Multiple shards per server for load balancing

  • Prevents cascading failures

  • Compression: save disk space and network bandwidth

  • Read-modify-write operation support, like counter increment
  • Map Reduce supported out of the box

I’m still not sure why one needs a strong consistency model for messages (and that’s the part missing from all these articles).

As a side note, I feel like the decission was based not on some major facts, but rather a sum of small but important features that HBase was offering compared to other solutions (i.e. consistent increments, perfect integration with Hadoop, etc.)

Original title and link: HBase at Facebook: The Underlying Technology of Messages (NoSQL databases © myNoSQL)