HBase Low Latency

Nick Dimiduk, Hortonworks (@xefyr)Nicolas Liochon, Scaled Risk (@nkeywal)

HBaseCon May 5, 2014

Agenda• Latency, what is it, how to measure it

• Write path

• Read path

• Next steps

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

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

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,

HBase write path – high level

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

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

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

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

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 msMean 0.33

50% 0.26

95% 0.59

99% 1.24

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

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.

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

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

Single puts

• Millisecond range• Spikes do happen in steady mode• 100ms• Causes: GC, load, splits

Streaming puts

Htable#setAutoFlushTo(false)Htable#putHtable#flushCommit

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

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

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

And now for the read path

HBase read path – high level

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

Multi get / Client

Multi get / ClientGroup Gets byRegionServer

Multi get / Client

Execute themone by one

Multi get / Server

Multi get / Server

http://hadoop-hbase.blogspot.com/2012/05/hbasecon.html

Access latency magnides

Dean/2009

Memory is 100000xfaster than disk!

Disk seek = 10ms

Known unknowns• For each candidate HFile• Exclude by file metadata

• Timestamp• Rowkey range

• Exclude by bloom filter

• StoreFileManager (0.96, HBASE-7678)

StoreFileScanner#shouldUseScanner()

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()

Remembered knowns: BlockCache• Reuse previously read data• Smaller BLOCKSIZE => better utilization• TODO: compression (HBASE-8894)

BlockCache#getBlock()

BlockCache Showdown• LruBlockCache• Quite good most of the time• < 30 GB

• BucketCache• Offheap alternative• > 30 GB

http://www.n10k.com/blog/blockcache-showdown/

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

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

Off-heap to the rescue?

• BucketCache (0.96, HBASE-7404)• Network interfaces (HBASE-9535)• MemStore et al (HBASE-10191)

Failure• Machine failure

• Detect + Reallocate + Replay

• Strong consistency requires replay

• Cache starts from scratch

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

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.

HBase ranges for 99% latency

Put Streamed Multiput Get Timeline get

Steady milliseconds milliseconds milliseconds milliseconds

Failure seconds seconds seconds milliseconds

GC10’s of milliseconds milliseconds

10’s of milliseconds milliseconds

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

Thanks!Nick Dimiduk, Hortonworks (@xefyr)

Nicolas Liochon, Scaled Risk (@nkeywal)

HBaseCon May 5, 2014