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HBase Read High Availability Using Timeline Consistent Region Replicas

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HBaseCon 2014 presentation.

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HBase Read High Availability Using Timeline Consistent Region Replicas

  1. 1. Page1 © Hortonworks Inc. 2011 – 2014. All Rights Reserved HBase Read High Availability Using Timeline-Consistent Region Replicas Enis Soztutar (enis@hortonworks.com) Devaraj Das (ddas@hortonworks.com)
  2. 2. Page2 © Hortonworks Inc. 2011 – 2014. All Rights Reserved About Us Enis Soztutar Committer and PMC member in Apache HBase and Hadoop since 2007 HBase team @Hortonworks Twitter @enissoz Devaraj Das Committer and PMC member in Hadoop since 2006 Committer at HBase Co-founder @Hortonworks
  3. 3. Page3 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Outline of the talk PART I: Use case and semantics  CAP recap  Use case and motivation  Region replicas  Timeline consistency  Semantics PART II : Implementation and next steps  Server side  Client side  Data replication  Next steps & Summary
  4. 4. Page4 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Part I Use case and semantics
  5. 5. Page5 © Hortonworks Inc. 2011 – 2014. All Rights Reserved CAP reCAP Partition tolerance Consistency Availability Pick Two HBase is CP
  6. 6. Page6 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Availability CAP reCAP • In a distributed system you cannot NOT have P • C vs A is about what happens if there is a network partition! • A an C are NEVER binary values, always a range • Different operations in the system can have different A / C choices • HBase cannot be simplified as CP Partition tolerance Consistency Pick Two HBase is CP
  7. 7. Page7 © Hortonworks Inc. 2011 – 2014. All Rights Reserved HBase consistency model For a single row, HBase is strongly consistent within a data center Across rows HBase is not strongly consistent (but available!). When a RS goes down, only the regions on that server become unavailable. Other regions are unaffected. HBase multi-DC replication is “eventual consistent” HBase applications should carefully design the schema for correct semantics / performance tradeoff
  8. 8. Page8 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Use cases and motivation More and more applications are looking for a “0 down time” platform  30 seconds downtime (aggressive MTTR time) is too much Certain classes of apps are willing to tolerate decreased consistency guarantees in favor of availability  Especially for READs Some build wrappers around the native API to be able to handle failures of destination servers  Multi-DC: when one server is down in one DC, the client switches to a different one Can we do something in HBase natively?  Within the same cluster?
  9. 9. Page9 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Use cases and motivation Designing the application requires careful tradeoff consideration  In schema design since single-row is strong consistent, but no multi-row trx  Multi-datacenter replication (active-passive, active-active, backups etc) It is good to be able to give the application flexibility to pick-and-choose  Higher availability vs stronger consistency Read vs Write  Different consistency models for read vs write  Read-repair, latest ts-wins vs linearizable updates
  10. 10. Page10 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Initial goals Support applications talking to a single cluster really well  No perceived downtime  Only for READs If apps wants to tolerate cluster failures  Use HBase replication  Combine that with wrappers in the application
  11. 11. Page11 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Introducing…. Region Replicas in HBase Timeline Consistency in HBase
  12. 12. Page12 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Region replicas For every region of the table, there can be more than one replica  Every region replica has an associated “replica_id”, starting from 0  Each region replica is hosted by a different region server Tables can be configured with a REGION_REPLICATION parameter  Default is 1  No change in the current behavior One replica per region is the “default” or “primary”  Only this can accepts WRITEs  All reads from this region replica return the most recent data Other replicas, also called “secondaries” follow the primary  They see only committed updates
  13. 13. Page13 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Region replicas Secondary region replicas are read-only  No writes are routed to secondary replicas  Data is replicated to secondary regions (more on this later)  Serve data from the same data files are primary  May not have received the recent data  Reads and Scans can be performed, returning possibly stale data Region replica placement is done to maximize availability of any particular region  Region replicas are not co-located on same region servers  And same racks (if possible)
  14. 14. Page14 © Hortonworks Inc. 2011 – 2014. All Rights Reserved rowkey column:value column:value … RegionServer Region memstore DataNode b2 b9 b1 DataNode b2 b1 DataNode b1 Client Read and write RegionServer
  15. 15. Page15 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Page 15 rowkey column:value column:value … RegionServer Region DataNode b2 b9 b1 DataNode b2 b1 DataNode b1 Client Read and write memstore RegionServer rowkey column:value column:value … memstore Region replica Read only
  16. 16. Page16 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Introduced a Consistency enum  STRONG  TIMELINE Consistency.STRONG is default Consistency can be set per read operation (per-get or per-scan) Timeline-consistent read RPCs sent to more than one replica Semantics is a bit different than Eventual Consistency model
  17. 17. Page17 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency public enum Consistency { STRONG, TIMELINE } Get get = new Get(row); get.setConsistency(Consistency.TIMELINE); ... Result result = table.get(get); … if (result.isStale()) { ... }
  18. 18. Page18 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Semantics Can be though of as in-cluster active-passive replication Single homed and ordered updates  All writes are handled and ordered by the primary region  All writes are STRONG consistency Secondaries apply the mutations in order Only get/scan requests to secondaries Get/Scan Result can be inspected to see whether the result was from possibly stale data
  19. 19. Page19 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Example Client1 X=1 Client2 WAL Data: Replica_id=0 (primary) Replica_id=1 Replica_id=2 replication replication X=3 WAL Data: WAL Data: X=1X=1Write
  20. 20. Page20 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Example Client1 X=1 Client2 WAL Data: Replica_id=0 (primary) Replica_id=1 Replica_id=2 replication replication X=3 WAL Data: WAL Data: X=1 X=1 X=1 X=1 X=1 X=1Read X=1Read X=1Read
  21. 21. Page21 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Example Client1 X=1 Client2 WAL Data: Replica_id=0 (primary) Replica_id=1 Replica_id=2 replication replication WAL Data: WAL Data: Write X=1 X=1 X=2 X=2 X=2
  22. 22. Page22 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Example Client1 X=1 Client2 WAL Data: Replica_id=0 (primary) Replica_id=1 Replica_id=2 replication replication WAL Data: WAL Data: X=2 X=1 X=2 X=2 X=2 X=2Read X=2Read X=1Read
  23. 23. Page23 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Example Client1 X=1 Client2 WAL Data: Replica_id=0 (primary) Replica_id=1 Replica_id=2 replication replication WAL Data: WAL Data: X=2 X=1 X=3 X=2 Write X=3 X=3
  24. 24. Page24 © Hortonworks Inc. 2011 – 2014. All Rights Reserved TIMELINE Consistency Example Client1 X=1 Client2 WAL Data: Replica_id=0 (primary) Replica_id=1 Replica_id=2 replication replication WAL Data: WAL Data: X=2 X=1 X=3 X=2 X=3 X=3Read X=2Read X=1Read
  25. 25. Page25 © Hortonworks Inc. 2011 – 2014. All Rights Reserved PART II Implementation and next steps
  26. 26. Page26 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Region replicas – recap Every region replica has an associated “replica_id”, starting from 0 Each region replica is hosted by a different region server  All replicas can serve READs One replica per region is the “default” or “primary”  Only this can accepts WRITEs  All reads from this region replica return the most recent data
  27. 27. Page27 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Updates in the Master Replica creation  Created during table creation No distinction between primary & secondary replicas Meta table contain all information in one row Load balancer improvements  LB made aware of replicas  Does best effort to place replicas in machines/racks to maximize availability Alter table support  For adjusting number of replicas
  28. 28. Page28 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Updates in the RegionServer Treats non-default replicas as read-only Storefile management  Keeps itself up-to-date with the changes to do with store file creation/deletions
  29. 29. Page29 © Hortonworks Inc. 2011 – 2014. All Rights Reserved IPC layer high level flow Client YES Response within timeout (10 millis)? NO Send READ to all secondaries Send READ to primary Poll for response Wait for response Take the first successful response; cancel others Similar flow for GET/Batch- GET/Scan, except that Scan is sticky to the server it sees success from.
  30. 30. Page30 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Performance and Testing No significant performance issues discovered  Added interrupt handling in the RPCs to cancel unneeded replica RPCs Deeper level of performance testing work is still in progress Tested via IT tests  fails if response is not received within a certain time
  31. 31. Page31 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Next steps What has been described so far is in “Phase-1” of the project Phase-2  WAL replication  Handling of Merges and Splits  Latency guarantees – Cancellation of RPCs server side – Promotion of one Secondary to Primary, and recruiting a new Secondary Use the infrastructure to implement consensus protocols for read/write within a single datacenter
  32. 32. Page32 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Data Replication Data should be replicated from primary regions to secondary regions A regions data = Data files on hdfs + in-memory data in Memstores Data files MUST be shared. We do not want to store multiple copies Do not cause more writes than necessary Two solutions:  Region snapshots : Share only data files  Async WAL Replication : Share data files, every region replica has its own in-memory data
  33. 33. Page33 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Data Replication – Region Snapshots Primary region works as usual  Buffer up mutations in memstore  Flush to disk when full  Compact files when needed  Deleted files are kept in archive directory for some time Secondary regions periodically look for new files in primary region  When a new flushed file is seen, just open it and start serving data from there  When a compaction is seen, open new file, close the files that are gone  Good for read-only, bulk load data or less frequently updated data Implemented in phase 1
  34. 34. Page34 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Data Replication - Async WAL Replication Being implemented in Phase 2 Uses replication source to tail the WAL files from RS  Plugs in a custom replication sink to replay the edits on the secondaries  Flush and Compaction events are written to WAL. Secondaries pick new files when they see the entry A secondary region open will:  Open region files of the primary region  Setup a replication queue based on last seen seqId  Accumulate edits in memstore (memory management issues in the next slide)  Mimic flushes and compactions from primary region
  35. 35. Page35 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Memory management & flushes Memory Snapshots-based approach  The secondaries looks for WAL-edit entries Start-Flush, Commit-Flush  They mimic what the primary does in terms of taking snapshots – When a flush is successful, the snapshot is let go  If the RegionServer hosting secondary is under memory pressure – Make some other primary region flush Flush-based approach  Treat the secondary regions as regular regions  Allow them to flush as usual  Flush to the local disk, and clean them up periodically or on certain events – Treat them as a normal store file for serving reads
  36. 36. Page36 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Summary Pros  High-availability for read-only tables  High-availability for stale reads  Very low-latency for the above Cons  Increased memory from memstores of the secondaries  Increased blockcache usage  Extra network traffic for the replica calls  Increased number of regions to manage in the cluster
  37. 37. Page37 © Hortonworks Inc. 2011 – 2014. All Rights Reserved References Apache branch hbase-10070 (https://github.com/apache/hbase/tree/hbase- 10070) HDP-2.1 comes with experimental support for Phase-1 More on the use cases for this work is in Sudarshan’s (Bloomberg) talk  “Case Studies” track titled “HBase at Bloomberg: High Availability Needs for the Financial Industry”
  38. 38. Page38 © Hortonworks Inc. 2011 – 2014. All Rights Reserved Thanks Q & A

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