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HBase MTTR, Stripe Compaction and Hoya Ted Yu

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Presentation on theme: "HBase MTTR, Stripe Compaction and Hoya Ted Yu"— Presentation transcript:

1 HBase MTTR, Stripe Compaction and Hoya Ted Yu (

2 About myself Been working on Hbase for 3 years Became Committer & PMC member June 2011

3 Outline Overview to HBase Recovery HDFS issues Stripe compaction Hbase-on-Yarn Q & A

4 We’re in a distributed system Hard to distinguish a slow server from a dead server Everything, or, nearly everything, is based on timeout Smaller timeouts means more false positive HBase works well with false positive, but they always have a cost. The less the timeouts the better

5 HBase components for recovery

6 Recovery in action

7 Recovery process Failure detection: ZooKeeper heartbeats the servers. Expire the session when it does not reply Region assignment: the master reallocates the regions to the other servers Failure recovery: read the WAL and rewrite the data again The client stops the connection to the dead server and goes to the new one. ZK Heartbeat Client Region Servers, DataNode Data recovery Master, RS, ZK Region Assignment

8 Failure detection – Set a ZooKeeper timeout to 30s instead of the old 180s default. – Beware of the GC, but lower values are possible. – ZooKeeper detects the errors sooner than the configured timeout 0.96 – HBase scripts clean the ZK node when the server is kill - 9ed => Detection time becomes 0 – Can be used by any monitoring tool

9 With faster region assignment Detection: from 180s to 30s Data recovery: around 10s Reassignment : from 10s of seconds to seconds

10 DataNode crash is expensive! One replica of WAL edits is on the crashed DN – 33% of the reads during the regionserver recovery will go to it Many writes will go to it as well (the smaller the cluster, the higher that probability) NameNode re-replicates the data (maybe TBs) that was on this node to restore replica count – NameNode does this work only after a good timeout (10 minutes by default)

11 HDFS – Stale mode Live Stale Dead As today: used for reads & writes, using locality Not used for writes, used as last resort for reads As today: not used. And actually, it’s better to do the HBase recovery before HDFS replicates the TBs of data of this node 30 seconds, can be less. 10 minutes, don’t change this

12 Results Do more read/writes to HDFS during the recovery Multiple failures are still possible – Stale mode will still play its role – And set dfs.timeout to 30s – This limits the effect of two failures in a row. The cost of the second failure is 30s if you were unlucky

13 Here is the client

14 The client You want the client to be patient Retries when the system is already loaded is not good. You want the client to learn about region servers dying, and to be able to react immediately. You want the solution to be scalable.

15 Scalable solution The master notifies the client – A cheap multicast message with the “dead servers” list. Sent 5 times for safety. – Off by default. – On reception, the client stops immediately waiting on the TCP connection. You can now enjoy large hbase.rpc.timeout

16 Faster recovery (HBASE-7006) Previous algorithm – Read the WAL files – Write new Hfiles – Tell the region server it got new Hfiles Put pressure on namenode – Remember: avoid putting pressure on the namenode New algo: – Read the WAL – Write to the regionserver – We’re done (have seen great improvements in our tests) – TBD: Assign the WAL to a RegionServer local to a replica

17 RegionServer0 RegionServer_x RegionServer_y WAL-file3 …… …….. WAL-file2 …… …….. WAL-file1 …… …….. HDFS Splitlog-file-for-region3 …… …….. Splitlog-file-for-region2 …… …….. Splitlog-file-for-region1 …… …….. HDFS RegionServer3 RegionServer2 RegionServer1 writes reads Distributed log Splitting

18 RegionServer0 RegionServer_x RegionServer_y WAL-file3 …… …….. WAL-file2 …… …….. WAL-file1 …… …….. HDFS Recovered-file-for-region3 …… …….. Recovered-file-for-region2 …… …….. Recovered-file-for-region1 …… …….. HDFS RegionServer3 RegionServer2 RegionServer1 writes reads Distributed log Replay replays

19 Write during recovery Concurrent writes allowed during the WAL replay – same memstore serves both Events stream: your new recovery time is the failure detection time: max 30s, likely less! Caveat: HBASE-8701 WAL Edits need to be applied in receiving order


21 MemStore flush Real life: some tables are updated at a given moment then left alone – With a non empty memstore – More data to recover It’s now possible to guarantee that we don’t have MemStore with old data Improves real life MTTR Helps online snapshots

22 .META. – There is no –ROOT- table in 0.95/0.96 – But.META. failures are critical A lot of small improvements – Server now says to the client when a region has moved (client can avoid going to meta) And a big one –.META. WAL is managed separately to allow an immediate recovery of META – With the new MemStore flush, ensure a quick recovery

23 Data locality post recovery HBase performance depends on data-locality After a recovery, you’ve lost it – Bad for performance Here comes region groups Assign 3 favored RegionServers for every region On failures assign the region to one of the secondaries The data-locality issue is minimized on failures

24 Discoveries from cluster testing HDFS-5016 Heartbeating thread blocks under some failure conditions leading to loss of datanodes HBASE-9039 Parallel assignment and distributed log replay during recovery Region splitting during distributed log replay may hinder recovery

25 Compactions example Architecting the Future of Big Data Memstore fills up, files are flushed When enough files accumulate, they are compacted MemStore HDFS writes HFile …

26 But, compaction cause slowdowns Looks like lots of I/O for no apparent benefit Example effect on reads (note better average)

27 © Hortonworks Inc. 2011 Key ways to improve compactions Read from fewer files –Separate files by row key, version, time, etc. –Allows large number of files to be present, uncompacted Don't compact the data you don't need to compact –For example, old data in OpenTSDB-like systems –Obviously, results in less I/O Make compactions smaller –Without too much I/O amplification or too many files –Results in less compaction-related outages HBase works better with few large regions; however, large compactions cause unavailability

28 © Hortonworks Inc. 2011 Stripe compactions (HBASE-7667) Architecting the Future of Big Data Somewhat like LevelDB, partition the keys inside each region/store But, only 1 level (plus optional L 0 ) Compared to regions, partitioning is more flexible –The default is a number of ~equal-sized stripes To read, just read relevant stripes + L 0, if present HFile Region start key: ccceee Row-key axis iii: region end keyggg H H HFile L0 get 'hbase'

29 © Hortonworks Inc. 2011 Stripe compactions – writes Architecting the Future of Big Data Data flushed from MemStore into several files Each stripe compacts separately most of the time MemStore HDFS HFile H H H H H H H H

30 © Hortonworks Inc. 2011 Stripe compactions – other Architecting the Future of Big Data Why Level 0 ? – Bulk loaded files go to L 0 – Flushes can also go into single L 0 files (to avoid tiny files) – Several L 0 files are then compacted into striped files Can drop deletes if compacting one entire stripe +L 0 –No need for major compactions, ever Compact 2 stripes together – rebalance if unbalanced –Very rare, however - unbalanced stripes are not a huge deal Boundaries could be used to improve region splits in future

31 © Hortonworks Inc. 2011 Stripe compactions - performance Architecting the Future of Big Data EC2, c1.xlarge, preload; then measure random read perf –LoadTestTool + deletes + overwrites; measure random reads

32 Hbase on Yarn Hoya is a YARN application All components are YARN services Input is cluster specification, persisted as JSON document on HDFS HDFS and ZooKeeper are shared by multiple cluster instances The cluster can also be stopped and later resumed

33 Hoya Architecture Hoya Client: parses commandline, executes local operations, talks to HoyaMasterService HoyaMasterService: AM service, deploys the HBase master locally HoyaRegionService: installs and executes the region server

34 HBase Master Service Deployment HoyaMasterService requested to create cluster Local Hbase dir chosen for expanded image User supplied config dir overwrites conf files in conf directory Hbase conf patched with hostname of master HoyaMasterService monitors reporting from RM

35 Failure Handling Region Service failures trigger new RS instances MasterService failures not trigger restart RegionService monitors ZK node for master MasterService monitors state of Hbase master

36 Runtime classpath dependencies

37 Q & A Thanks!

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