accumulo summit 2015: performance models for apache accumulo: the heavy tail of a shared-nothing...

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PERFORMANCE MODELS FOR APACHE ACCUMULO: THE HEAVY TAIL OF A SHARED-

NOTHING ARCHITECTURE

Chris McCubbin Director of Data Science Sqrrl Data, Inc.

TODAY’S TALK

1.  Quick intro to performance optimization 2.  Techniques for targeted distributed application

modeling performance improvement 3.  A deep dive in to improving bulk load application

performance 4.  A shallow dive in to partial schemas

2 ©2014 Sqrrl Data, Inc

SO, YOUR DISTRIBUTED APPLICATION IS SLOW

•  Today’s distributed applications run on tens or hundreds of library components

•  Many versions so internet advice could be ineffective, or worse, flat out wrong

•  Hundreds of settings •  Some, shall we say, could be better documented

•  Shared-nothing architectures are usually “shared-little” architectures with tricky interactions

•  Profiling is hard and time-consuming

©2014 Sqrrl Data, Inc 3

ROUND UP THE ‘USUAL SUSPECTS’?

•  “Common knowledge” that some things can cause performance issues

•  Too much network usage •  Disk Bound •  Stragglers •  Framework settings •  Unbalanced distribution •  SerDe

•  This might be a good start, but we really want to focus on the biggest problem if we can

•  Technology, installations and use cases have high variability: what works for one job on one cluster may be useless on another


PERFORMANCE ANALYSIS CYCLE


Simulate & Experiment

Modify Code

Analyze

Start: Create Model

Refine Model

Outputs: Better Code

+ Models

MAKING A MODEL


•  Determine points of low-impact metrics •  Add some if needed

•  Create parallel state machine models with components driven by these metrics

•  Estimate running times and bottlenecks from a-priori information and/or apply measured statistics

•  Focus testing on validation of the initial model and the (estimated) pain points

•  Apply Amdahl’s Law

•  Rinse, repeat

The Apache Accumulo™ sorted, distributed key/value store is a secure, robust, scalable, high performance data storage and retrieval system. •  Many applications in real-time storage and analysis of “big data”:

•  Spatio-temporal indexing in non-relational distributed databases - Fox et al 2013 IEEE International Congress on Big Data

•  Big Data Dimensional Analysis - Gadepally et al IEEE HPEC 2014 •  Leading its peers in performance and scalability:

•  Achieving 100,000,000 database inserts per second using Accumulo and D4M - Kepner et al IEEE HPEC 2014

•  An NSA Big Graph experiment (Technical Report NSA-RD-2013-056002v1) •  Benchmarking Apache Accumulo BigData Distributed Table Store Using Its

Continuous Test Suite - Sen et al 2013 IEEE International Congress on Big Data

For more papers and presentations, see http://accumulo.apache.org/papers.html


•  Collections of KV pairs form Tables •  Tables are partitioned into Tablets

•  Metadata tablets hold info about other tablets, forming a 3-level hierarchy

•  A Tablet is a unit of work for a Tablet Server

Data Tablet -‐∞ : thing

Data Tablet thing : ∞

Data Tablet -‐∞ : Ocelot

Data Tablet Ocelot : Yak

Data Tablet Yak : ∞

Data Tablet -‐∞ to ∞

Table: Adam’s Table Table: Encyclopedia Table: Foo

SCALING UP: DIVIDE & CONQUER

Well-‐Known Loca9on

(zookeeper)

Root Tablet -‐∞ to ∞

Metadata Tablet 2 “Encyclopedia:Ocelot” to ∞

Metadata Tablet 1 -‐∞ to “Encyclopedia:Ocelot”


BULK INGEST OVERVIEW •  Accumulo supports two mechanisms to bring

data in: streaming ingest and bulk ingest. •  Bulk Ingest

•  Goal: maximize throughput without constraining latency.

•  Create a set of Accumulo Rfiles by some means, then register those files with Accumulo.

•  RFiles are groups of sorted key-value pairs with some indexing information

•  MapReduce has a built-in key sorting phase: a good fit to produce RFiles


BULK INGEST MODEL

10

Map Reduce Register

Time

©2014 Sqrrl Data, Inc

BULK INGEST MODEL

11

Time

•  100% CPU •  20% Disk •  0% Network •  46 seconds



Hypothetical Resource Usage


Map Reduce Register

INSIGHT

12

Time




•  Spare disk here, spare CPU there – can we even out resource consumption? •  Why did reduce take 168 seconds? It should be more like 40 seconds. •  No clear bottleneck during registration – is there a synchronization or

serialization problem?


Map Reduce Register

Reduce Thread

Map Thread

LOOKING DEEPER: REFINED BULK INGEST MODEL

13

Map Setup Map Sort

Sort Reduce Output

Spill Merge

Shuffle

Serve

Time


Parallel Latch

BULK INGEST MODEL PREDICTIONS •  We can constrain parts of the model by physical

throughput limitations •  Disk -> memory (100Mbps avg 7200rpm seq. read rate)

•  Input reader •  Memory -> Disk (100Mbps)

•  Spill, OutputWriter •  Disk -> Disk (50Mbps)

•  Merge •  Network (Gigabit = 125Mbps)

•  Shuffle •  And/or algorithmic limitations

•  Sort, (Our) Map, (Our) Reduce, SerDe


PERFORMANCE GOAL MODEL


Performance goals obtained through: •  Simulation of individual components •  Prediction of available resources at runtime

INSTRUMENTATION

application version 1.3.3 SYSTEM DATA application sha 8d17baf8 node num 1 input type arcsight

yarn.nodemanager.resource.memory-mb 43008 map num containers 20 input block size 32

yarn.scheduler.minimum-allocation-mb 2048 red num containers 20 input block count 20

yarn.scheduler.maximum-allocation-mb 43008 cores physical 12 input total 672054649

yarn.app.mapreduce.am.resource.mb 2048 cores logical 24 output map 9313303723

yarn.app.mapreduce.am.command-opts -Xmx1536m disk num 8 output map:combine input records 243419324

mapreduce.map.memory.mb 2048 disk bandwidth 100 output map:combine records out 209318830

mapreduce.map.java.opts -Xmx1638m replication 1 output map:spill 7325671992

mapreduce.reduce.memory.mb 2048 monitoring TRUE output final 573802787

mapreduce.reduce.java.opts -Xmx1638m output map:combine 7301374577

mapreduce.task.io.sort.mb 100 TIME mapreduce.map.sort.spill.percent 0.8 map:setup avg 8 RATIOS mapreduce.task.io.sort.factor 10 map:map avg 12 input explosion factor 13.877904 mapreduce.reduce.shuffle.parallelcopies 5 map:sort avg 12 compression intermediate 1.003327786 mapreduce.job.reduce.slowstart.completedmaps 1 map:spill avg 12 load combiner output 0.783972562 mapreduce.map.output.compress FALSE map:spill count 7 total ratio 0.786581455 mapred.map.output.compression.codec n/a map:merge avg 46

description baseline map total 290 CONSTANTS red:shuffle avg 6 avg schema entry size (bytes) 59

red:merge avg 38

red:reduce avg 68 effective MB/sec 1.618488025 red:total avg 112 red:reducer count 20

job:total 396


PERFORMANCE MEASUREMENT Baseline (naive implementation)


Reduce Thread

Map Thread

Map Setup Map Sort

Sort Reduce Output

Spill Merge

Shuffle

Serve

PATH TO IMPROVEMENT

1.  Profiling revealed much time spent serializing/deserializing Accumulo’s Key class

1.  Supported by recent investigations on e.g. spark jobs 1.  “as much as half of the CPU time is spent deserializing and

decompressing data.” https://www.eecs.berkeley.edu/~keo/publications/nsdi15-final147.pdf

2.  With proper configuration, MapReduce supports comparison of MR keys in serialized form

3.  Rewriting Key’s serialization lead to an order-preserving encoding, easy to compare in serialized form

4.  Configure MapReduce to use native code to compare Keys 5.  Tweak map input size and spill memory for as few spills as

possible


PERFORMANCE MEASUREMENT Optimized sorting

•  Improvements: •  Time for map-side merge went down •  Sort performance drastically improved in both

map and reduce phases •  300% faster


PERFORMANCE MEASUREMENT Optimized sorting

Insights: •  Map is slower than expected •  Intermediate data inflation ratio (output from map) is very high, and the

mapper is now disk-bound •  Amdahl’s law strikes again

•  Reducer Output is also already disk bound. •  Can we trade disk time in Map for ‘free’ CPU time in Reduce?


Reduce Thread

Map Thread

Map Setup Map Sort

Sort Reduce Output

Spill Merge

Shuffle

Serve

PATH TO IMPROVEMENT

•  Evaluation of data passed from map to reduce revealed inefficiencies:

•  Constant timestamp cost 8 bytes per key •  Repeated column names could be encoded/

compressed •  Some Key/Value pairs didn’t need to be created until

reduce •  Blocks of data output from the mapper guaranteed to

transfer ‘en masse’ to the same reducer •  Hypothesis

•  Create ‘dehydrated’ key-value pairs of consecutive values when possible

•  Spend CPU time in reduce to ‘rehydrate’ the key-values prior to output

•  Fewer keys in shuffle also means the sort phase is more efficient


PERFORMANCE MEASUREMENT Optimized map code

•  Improvement: •  Big speedup in map function

•  Twice as fast •  Reduced intermediate inflation sped up all

steps between map and reduce


DO TRY THIS AT HOME

With these steps, we achieved 6X speedup: •  Perform comparisons on serialized objects •  With Map/Reduce, calculate how many merge

steps are needed •  Avoid premature data inflation •  Leverage compression to shift bottlenecks •  Always consider how fast your code should run

Hints for Accumulo Application Optimization


POSTSCRIPT: CARRYING IMPROVEMENTS IN TO THE APPLICATION


•  Recall that we “dehydrated” consecutive KVs into one KV out of map, and “rehydrated” them in reduce

•  Specifically, document storage •  We can do this if we know the schema of the

document in advance

•  What if we just store dehydrated documents on disk?

POSTSCRIPT: PARTIAL SCHEMAS


•  Advantages •  Bulk ingest just got even faster (no rehydrate step) •  Disk footprint smaller •  Potentially faster query response

•  Potential issues •  Need to keep schemas around (but still want to

have flexible schemas) •  How do you handle (lazy) updates? •  Documents need to be rehydrated at some point…

when? And what’s the perf trade-off? •  Perhaps we should model this?

•  To be continued…

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QUESTIONS?

Chris McCubbin Director of Data Science Sqrrl Data, Inc.

accumulo summit 2015: performance models for apache accumulo: the heavy tail of a shared-nothing...

Technology

tablet server data tablet

data performance models

ocelot data tablet ocelot

analysis of big data

yak data tablet yak

thing data tablet thing

high performance data

performance issues