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Apachecon Europe 2012: Operating HBase - Things you need to know

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If you’re running HBase in production, you have to be aware of many things. In this talk we will share our experience in running and operating an HBase production cluster for a customer. To avoid common pitfalls, we’ll discuss problems and challenges we’ve faced as well as practical solutions (real-world techniques) for repair.

Even though HBase provides internal tools for diagnosing issues and for repair, running a healthy cluster can still be challenging for an administrator. We'll cover some background on these tools as well as on HBase internals such as compaction, region splits and their distribution.

We'll also introduce our tool to visualize region sizing and distribution in the cluster, that we recently open sourced.

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Apachecon Europe 2012: Operating HBase - Things you need to know

  1. 1. Operating HBase –Things You Need to Know Christian Gügi
  2. 2. Outline● HBase internals● Overview of HBase utilities● HBase split visualisation with Hannibal● Challenges & lessons learned● Resources to get started 2
  3. 3. About me● Software Architect @ Sentric● Founder and organizer of the Swiss Big Data User Group http://www.bigdata-usergroup.ch● Contact: christian.guegi@sentric.ch http://www.sentric.ch @chrisgugi 3
  4. 4. HBase Internals 4
  5. 5. Data Model● A sparse, multi-dimensional, sorted map● Table consist of rows, each has a row key● Each row may have any number of columns● Rows are sorted lexicographically based on row key● Column = Column Family : Column Qualifier – Cell → {rowkey, column, timestamp} [Bigtable: A Distributed Storage System for Structured Data]● Region: contiguous set of sorted rows● Region: unit of distribution and availability 5
  6. 6. Physical Data Organization Region content Column Family anchor Column Family Store Store(WAL on HFDS) Memstore Memstore HLog HFile HFile HFile (on HDFS) (on HDFS) (on HDFS)● Column families are stored separately on disk – Unit of access control with different patterns● Writes are held (sorted) in memory until flush● Sorted on disk in predictable order – By row key, column key, descending timestamp 6
  7. 7. Flushes and Compaction● Flushing/compaction per Region – One thread (CompactSplitThread) per region server● Minor compaction – Merges two or more HFiles into one● Major compaction – Picks up all HFiles in the region, merges them and removes deleted k/v● Regions are split when grown too large 7
  8. 8. System Architecture HBase API RegionServer Master HFile Memstore Write-Ahead Log HDFS ZooKeeper [HBase: The Definitive Guide] 8
  9. 9. Key Design & Distribution● Bad idea: continuous number or timestamp (sequential row keys) – RegionServer hot-spotting● Better: use hash function and/or composite key – Distribute keys over random regions – Uniform reads/writes across key space● Proper key design is very essential – E.g. reversed URL (Bigtable paper) 9
  10. 10. OverviewHBase Utilities 10
  11. 11. Useful Tools● hbck – checks and fixes table integrity and region consistency● HFile – examine contents of HFile● HLog – examine contents of HLog file● OfflineMetaRepair – rebuild meta table from file system● HBase web interfaces – Master – RegionsServer 11
  12. 12. Monitoring Tools● Ganglia● Nagios● OpenTSDB● … All tools use metrics provided through JMX 12
  13. 13. Manual Splitting● Via master web interface – Split● HBase shell split command● RegionSplitter – Create table with pre-split regions – Rolling split of all regions on existing table – . /bin/hbase org.apache.hadoop.hbase.util.RegionSplitter 13
  14. 14. Disable Automatic Splitting● Determined by hbase.hregion.max.filesize● Set to max. 100GB● OK, but: – How do I monitor my region growth? – Where do I split when I have irregular data growth? 14
  15. 15. HBase Split Visualisation with Hannibal 15
  16. 16. Hannibal● Open source, project on github – https://github.com/sentric/hannibal● Web based● Implemented in Scala● Compatible with HBase 0.90● Support > 0.92 added soon● Check it out! 16
  17. 17. How well are regions balancedover the cluster? 17
  18. 18. How well are the regions split forthe table? 18
  19. 19. How did the region evolve overtime? 19
  20. 20. Future Plans● HBase 0.92 client API changes allow to query Compaction-State on Regions through HBaseAdmin → differentiate major from minor compactions● Add tool to find best region-key for irregular data growth● Expose metrics through JMX 20
  21. 21. Challenges& Lessons Learned 21
  22. 22. Challenges● Everyone is still learning● Some issues only appear at scale – At scale, nothing works as advertised● Production cluster configuration – Hardware issues – Tuning cluster configuration to our work loads● HBase stability● Monitoring health of HBase 22
  23. 23. Lessons Learned● Schema & key design – What’s queried together should be stored together● Monitoring/Operational tooling is most important● Forget “emergency actions”, it takes some time● You need DevOps in production● Huge know-how curve, you need to know the whole ecosystem – Hadoop, HDFS, Map/Red, ZooKeeper 23
  24. 24. Resources to get started● https://github.com/sentric/hannibal● http://hbase.apache.org/book.html● https://github.com/jmhsieh/hbase-repair- scripts● http://www.sentric.ch/blog/best-practice- why-monitoring-hbase-is-important● HBase: The Definitive Guide 24
  25. 25. Thank you! Questions? @chrisgugi 25