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HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
HBase: Where Online Meets Low Latency
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HBase: Where Online Meets Low Latency

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Speakers: Nick Dimiduk (Hortonworks) and Nicolas Liochon (Scaled Risk) …

Speakers: Nick Dimiduk (Hortonworks) and Nicolas Liochon (Scaled Risk)

HBase is an online database so response latency is critical. This talk will examine sources of latency in HBase, detailing steps along the read and write paths. We'll examine the entire request lifecycle, from client to server and back again. We'll also look at the different factors that impact latency, including GC, cache misses, and system failures. Finally, the talk will highlight some of the work done in 0.96+ to improve the reliability of HBase.

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  • Recap – refresher on the read path
    Request received
    Scanners created over memstore and store files
    Data blocks read from cache or disk as appropriate
    Results merged by the region
    Response sent back to client
  • Goal: avoid disk at all cost!
  • Goal: don’t go to disk unless absolutely necessary.
    Tactic: Candidate HFile elimination.
    Regular compactions => only 3 files to seek
    Alternative: StoreFileManager cleverness
  • Necessary for fewer hfiles and fewer seeks
    IO resource contention
    Buffer cache to the rescue
  • Transcript

    • 1. HBase Low Latency Nick Dimiduk, Hortonworks (@xefyr) Nicolas Liochon, Scaled Risk (@nkeywal) HBaseCon May 5, 2014
    • 2. Agenda • Latency, what is it, how to measure it • Write path • Read path • Next steps
    • 3. What’s low latency Latency is about percentiles • Long tail issue • There are often order of magnitudes between « average » and « 95 percentile » • Post 99% = « magical 1% ». Work in progress here. • Meaning from micro seconds (High Frequency Trading) to seconds (interactive queries) • In this talk milliseconds
    • 4. Measure latency – during test bin/hbase org.apache.hadoop.hbase.PerformanceEvaluation • More options related to HBase: autoflush, replicas, … • Latency measured in micro second • Easier for internal analysis • YCSB • Useful for comparison between tools • Set of workload already defined
    • 5. Measure latency : Exposed by HBase "QueueCallTime_num_ops" : 33044, "QueueCallTime_min" : 0, "QueueCallTime_max" : 86, "QueueCallTime_mean" : 0.2525420651252875, "QueueCallTime_median" : 0.0, "QueueCallTime_75th_percentile" : 0.0, "QueueCallTime_95th_percentile" : 1.0, "QueueCallTime_99th_percentile" : 1.0, a "SyncTime_num_ops" : 379081, "SyncTime_min" : 0, "SyncTime_max" : 865, "SyncTime_mean" : 3.0293341000999785, "SyncTime_median" : 2.0, "SyncTime_75th_percentile" : 3.0, "SyncTime_95th_percentile" : 4.0, "SyncTime_99th_percentile" : 253.5899999999999,
    • 6. HBase write path – high level RegionServer (HBase) DataNode (Hadoop DFS) HLog (WAL) HRegion HStore StoreFile HFile StoreFile HFile MemStore ... ... HStore BlockCache HRegion ... HStoreHStore ... 1 2 3 4 5
    • 7. Deeper in the write path • Two parts • Single put (WAL) • The client just sends the put • Multiple puts from the client (new behavior since 0.96) • The client is much smarter • Four stages to look at for latency • Start (establish tcp connections, etc.) • Steady: when expected conditions are met • Machine failure: expected as well • Overloaded system: you may need to add machines or tune your workload
    • 8. Single put: communication • Create a « Call » object, with an id, as queries are multiplexed • protobuf it • tcp write (in trunk it can be queued for a separate thread as well) • Wait for the answer • Separate thread, separate queue • unprotobuf the answer • Implies locks and multiple threads communicating with queues
    • 9. Single put: server side scheduling • Threads to receives « Call » • Threads to handle the call execution • Threads to write the answer on the wire • Multiple threads, communicating with queues
    • 10. Single put: real work • The server must • Take a row lock (HBase strong consistency) • Write into the WAL queue • Write into the memstore • Sync the queue (HDFS flush) • Free the lock • WALs queue is shared between all the regions/handlers • Sync is avoided if another handlers did the work • You may flush more than expected
    • 11. Latency sources • Candidate one: network • 0.5ms within a datacenter. • Candidate two: HDFS Flush • Millisecond world: everything can go wrong • Network • OS Scheduler • All this goes into the post 99% percentile Metric Time in ms Mean 0.33 50% 0.26 95% 0.59 99% 1.24
    • 12. Latency sources • Split (and presplits) • Autosharding is great! • Puts have to wait • Impacts: seconds • Balance • Regions move • Triggers a retry for the client • hbase.client.pause = 100ms since HBase 0.96 • Garbage Collection • Impacts: 10’s of ms, even with a good config • Covered with the read path of this talk
    • 13. From steady to loaded and oveloaded • Number of concurrent tasks is a factor of • Number of cores • Number of disks • Number of remote machines used • Difficult to estimate • Queues are doomed to happen • So for low latency • Specific Scheduler since Hbase 0.98 (HBASE-8884). Requires specific code. • Priorities: work in progress.
    • 14. Loaded & overloaded • Step 1: Loaded system • Tasks are queued: creates latency • Specific metric in HBase • Step 2: Limit reached • MemStore takes too much room: blocks until it’s flushed • hbase.regionserver.global.memstore.size.lower.limit • hbase.regionserver.global.memstore.size • hbase.hregion.memstore.block.multiplier • Too many Hfiles: blocks until compations keeps up • hbase.hstore.blockingStoreFiles • Too many WALs files • Don’t change this
    • 15. Machine failure • Failure • Dectect • Reallocate • Replay WAL • Replaying WAL is NOT required for puts • Failure = Dectect + Reallocate + Retry • That’s in the range of ~1s for simple failures • Silent failures leads puts you in the 10s range if the hardware does not help
    • 16. Single puts • Millisecond range • Spikes do happen in steady mode • 100ms • Causes: GC, load, splits
    • 17. Streaming puts Htable#setAutoFlushTo(false) Htable#put Htable#flushCommit
    • 18. Streaming puts • Write into a buffer • When the buffer is full, in the background • Select the puts that matches load conditions • Send them • Manage retries and delay • The buffer is freed for other client operations • Blocks only if there is an a not retryable error or if the buffer is full
    • 19. Multiple puts • hbase.client.max.total.tasks (default 100) • hbase.client.max.perserver.tasks (default 5) • hbase.client.max.perregion.tasks (default 1) • Decouple the client from a latency peak of a region server • Increase the throughput by 50% • Does not solve the problem of an unbalanced cluster • But makes split and GC more transparent
    • 20. Conclusion on write path • Single puts can be very fast • It’s not a « hard real time » system: there are peaks • Latency peaks can be hidden when streaming puts • Including autosplits
    • 21. And now for the read path
    • 22. HBase read path – high level RegionServer (HBase) DataNode (Hadoop DFS) HLog (WAL) HRegion HStore StoreFile HFile StoreFile HFile MemStore ... ... HStore BlockCache HRegion ... HStoreHStore ... 1 5 2 3 3 2 4
    • 23. Deeper in the read path • Get/short scan are assumed for low-latency operations • Again, two APIs • Single get: HTable#get(Get) • Multi-get: HTable#get(List<Get>) • Four stages, same as write path • Start (tcp connection, …) • Steady: when expected conditions are met • Machine failure: expected as well • Overloaded system: you may need to add machines or tune your workload
    • 24. Multi get / Client
    • 25. Multi get / Client Group Gets by RegionServer
    • 26. Multi get / Client Execute them one by one
    • 27. Multi get / Server
    • 28. Multi get / Server
    • 29. Access latency magnidesStorage hierarchy: a different view Dean/2009 Memory is 100000x faster than disk! Disk seek = 10ms
    • 30. Known unknowns • For each candidate HFile • Exclude by file metadata • Timestamp • Rowkey range • Exclude by bloom filter • StoreFileManager (0.96, HBASE-7678) StoreFileScanner# shouldUseScanner()
    • 31. Unknown knowns • Merge sort results polled from Stores • Seek each scanner to a reference KeyValue • Retrieve candidate data from disk • Multiple HFiles => mulitple seeks • hbase.storescanner.parallel.seek.enable=true • Short Circuit Reads • dfs.client.read.shortcircuit=true • Block locality • Happy clusters compact! HFileBlock# readBlockData()
    • 32. Remembered knowns: BlockCache • Reuse previously read data • Smaller BLOCKSIZE => better utilization • TODO: compression (HBASE-8894) BlockCache#getBlock()
    • 33. BlockCache Showdown • LruBlockCache • Quite good most of the time • < 30 GB • BucketCache • Offheap alternative • > 30 GB http://www.n10k.com/blog/block cache-showdown/
    • 34. Latency enemies: Compactions • Fewer HFiles => fewer seeks • Evict data blocks! • Evict Index blocks!! • hfile.block.index.cacheonwrite • Evict bloom blocks!!! • hfile.block.bloom.cacheonwrite • OS buffer cache to the rescue • Compactected data is still fresh • Better than going all the way back to disk
    • 35. Latency enemies: Garbage Collection • Use Heap. Not too much. With CMS. • Max heap: 30GB, probably less • Healthy cluster load • regular, reliable collections • 25-100ms pause on regular interval • Overloaded RegionServer suffers GC overmuch
    • 36. Off-heap to the rescue? • BucketCache (0.96, HBASE-7404) • Network interfaces (HBASE-9535) • MemStore et al (HBASE-10191)
    • 37. Failure • Machine failure • Detect + Reallocate + Replay • Strong consistency requires replay • Cache starts from scratch
    • 38. Read latency in summary • Steady mode • Cache hit: < 1 ms • Cache miss: + 10 ms per seek • Writing while reading: cache churn • GC: 25-100ms pause on regular interval Network request + (1 - P(cache hit)) * 10 ms • Same long tail issues as write • Overloaded: same scheduling issues as write • Partial failures hurt a lot
    • 39. Hedging our bets • HDFS Hedged reads (since HDFS 2.4) • Strongly consistent • Works at the HDFS level • Timeline consistency (HBASE-10070) • Reads on secondary regions • If a region does not answer quickly enough, go to another one • Not strongly consistent • Helps a lot latency for read path.
    • 40. HBase ranges for 99% latency Put Streamed Multiput Get Timeline get Steady milliseconds milliseconds milliseconds milliseconds Failure seconds seconds seconds milliseconds GC 10’s of milliseconds milliseconds 10’s of milliseconds milliseconds
    • 41. What’s next • Less GC • Use less objects • Offheap • Prefered location (HBASE-4755) • The « magical 1% » • Most tools stops at the 99% latency • YCSB for example • What happens after is much more complex • But key to improve average
    • 42. Thanks! Nick Dimiduk, Hortonworks (@xefyr) Nicolas Liochon, Scaled Risk (@nkeywal) HBaseCon May 5, 2014

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