Tweaking performance on high-load projects

Dmitriy DumanskiyCogniance, mGage project

Java Team Lead

Project evolution

Mgage Mobclix

XXXX

Mgage delivery load

3 billions req/mon. ~8 c3.xLarge Amazon instances.

Average load : 2400 req/secPeak : x10

Mobclix delivery load

14 billions req/mon. ~16 c3.xLarge Amazon

instances.Average load : 6000 req/sec

Peak : x6

XXXX delivery Load

20 billions req/mon. ~14 c3.xLarge Amazon instances.

Average load : 11000 req/secPeak : x6

Is it a lot?

Average load : 11000 req/sec

Twitter : new tweets

15 billions a month Average load : 5700 req/sec

Peak : x30

Delivery load

Requests per month

Max load per

instance, req/sec

RequirementsServers, AWS c3.xLarge

Mgage 3 billions 300 HTTPTime 95% < 60ms 8

Mobclix 14 billions 400 HTTPTime 95% < 100ms 16

XXXX 20 billions 800 HTTPSTime 99% < 100ms 14

Delivery load

c3.XLarge - 4 vCPU, 2.8 GHz Intel Xeon E5-2680LA - ~2-3

1-2 cores reserved for sudden peaks

BE tech stacksMobclix : Spring, iBatis, MySql, Solr, Vertica, Cascading, Tomcat

Mgage :Spring, Hibernate, Postgres, Distributed ehCache, Hadoop, Voldemort, Jboss

XXXX:Spring, Hibernate, MySQL, Solr, Cascading, Redis, Tomcat

Initial problem

● ~1000 req/sec● Peaks 6x● 99% HTTPS with response time < 100ms

Real problem

● ~85 mln active users, ~115 mln registered users● 11.5 messages per user per day● ~11000 req/sec● Peaks 6x● 99% HTTPS with response time < 100ms● Reliable and scalable for future grow up to 80k

Architecture

AdServer Console (UI)

Reporting

Architecture

Console (UI)

MySql

SOLR Master

SOLR Slave SOLR SlaveSOLR Slave

SOLR? Why?● Pros:

○ Quick search on complex queries○ Has a lot of build-in features (master-

slave replication, RDBMS integration)● Cons:

○ Only HTTP, embedded performs worth○ Not easy for beginners○ Max load is ~100 req/sec per instance

“Simple” query

"-(-connectionTypes:"+"\""+getConnectionType()+"\""+" AND connectionTypes:[* TO *]) AND "+"-connectionTypeExcludes:"+"\""+getConnectionType()+"\""+" AND " + "-(-

OSes:"+"(\""+osQuery+"\" OR \""+getOS()+"\")"+" AND OSes:[* TO *]) AND " + "-osExcludes:"+"(\""+osQuery+"\" OR \""+getOS()+"\")" "AND (runOfNetwork:T OR

appIncludes:"+getAppId()+" OR pubIncludes:"+getPubId()+" OR categories:("+categoryList+"))" +" AND -appExcludes:"+getAppId()+" AND -pubExcludes:"

+getPubId()+" AND -categoryExcludes:("+categoryList+") AND " + keywordQuery+" AND " + "-(-devices:"+"\""+getHandsetNormalized()+"\""+" AND devices:[* TO *]) AND " + "-deviceExcludes:"+"\""+getHandsetNormalized()+"\""+" AND " + "-(-carriers:"+"\""

+getCarrier()+"\""+" AND carriers:[* TO *]) AND " + "-carrierExcludes:"+"\""+getCarrier()+"\""+" AND " + "-(-locales:"+"(\""+locale+"\" OR \""+langOnly+"\")"

+" AND locales:[* TO *]) AND " + "-localeExcludes:"+"(\""+locale+"\" OR \""+langOnly+"\") AND " + "-(-segments:("+segmentQuery+") AND segments:[* TO *]) AND " + "-segmentExcludes:("+segmentQuery+")" + " AND -(-geos:"+geoQuery+" AND geos:[*

TO *]) AND " + "-geosExcludes:"+geoQuery

Architecture

MySql

Solr Master

SOLR Slave

AdServer

SOLR Slave

AdServer

SOLR Slave

AdServer

No-SQL

AdServer - Solr Slave

Delivery:volitile DeliveryData cache;

Cron Job:DeliveryData tempCache = loadData();

cache = tempCache;

Why no-sql?

● Realtime data● Quick response time● Simple queries by key● 1-2 queries to no-sql on every request. Average load

10-20k req/sec and >120k req/sec in peaks. ● Cheap solution

Why Redis? Pros

● Easy and light-weight● Low latency and response time.

99% is < 1ms. Average latency is ~0.2ms● Up to 100k 'get' commands per second on

c1.X-Large● Cool features (atomic increments, sets,

hashes)● Ready AWS service — ElastiCache

Why Redis? Cons

● Single-threaded from the box● Utilize all cores - sharding/clustering● Scaling/failover not easy● Limited up to max instance memory (240GB largest

AWS)● Persistence/swapping may delay response● Cluster solution not production ready

DynamoDB vs RedisPrice per month Put, 95% Get, 95% Rec/sec

DynamoDB 58$ 300ms 150ms 50

DynamoDB 580$ 60ms 8ms 780

DynamoDB 5800$ 16ms 8ms 1250

Redis 200$ (c1.medium) 3ms <1ms 4000

ElastiCache 600$ (c1.xlarge) <1ms <1ms 10000

What about others?

● Cassandra● Voldemort● Memcached

Redis RAM problem

● 1 user entry ~ from 80 bytes to 3kb● ~85 mln users● Required RAM ~ from 1 GB to 300 GB

Data compression speed

Data compression size

Data compression

Json → Kryo binary → 4x times less data → Gzipping → 2x times less data == 8x less data

Now we need < 40 GB

+ Less load on network stack

AdServer BE

Average response time — ~1.2 msLoad — 800 req/sec with LA ~4

c3.XLarge == 4 vCPU

AdServer BE

● Logging — 12% of time (5% on SSD);● Response generation — 15% of time;● Redis request — 50% of time;● All business logic — 23% of time;

Reporting

AdServer Hadoop ETL

MySQLConsole

S3 S3

Delivery logs Aggregated logs

Log structure{ "uid":"test", "platform":"android", "app":"xxx", "ts":1375952275223, "pid":1, "education":"Some-Highschool-or-less", "type":"new", "sh":1280, "appver":"6.4.34", "country":"AU", "time":"Sat, 03 August 2013 10:30:39 +0200", "deviceGroup":7, "rid":"fc389d966438478e9554ed15d27713f51", "responseCode":200, "event":"ad", "device":"N95", "sw":768, "ageGroup":"18-24", "preferences":["beer","girls"] }

Log structure

● 1 mln. records == 0.6 GB.● ~900 mln records a day == ~0.55 TB.● 1 month up to 20 TB of data.● Zipped data is 10 times less.

Reporting

Customer : “And we need fancy reporting”

But 20 TB of data per month is huge. So what we can do?

ReportingDimensions:device, os, osVer, sreenWidth, screenHeight, country, region, city, carrier, advertisingId, preferences, gender, age, income, sector, company, language, etc...

Use case:I want to know how many users saw my ad in San-Francisco.

ReportingGeo table:Country, City, Region, CampaignId, Date, counters;

Device table:Device, Carrier, Platform, CampaignId, Date, counters;

Uniques table:CampaignId, UID

Predefined report types → aggregation by predefined dimensions → 500-1000 times less

data 20 TB per month → 40 GB per month

Of course - hadoop● Pros:

○ Unlimited (depends) horizontal scaling

● Cons:○ Not real-time○ Processing time directly depends on quality code

and on infrastructure cost.○ Not all input can be scaled○ Cluster startup is so... long

Alternatives?

● Storm● Redshift● Vertica● Math models?

Elastic MapReduce

● Easy setup● Easy extend● Easy to monitor

Timing● Hadoop (cascading) :

○ 25 GB in peak hour takes ~40min (-10 min). CSV output 300MB. With cluster of 4 c3.xLarge.

● MySQL:○ Put 300MB in DB with insert statements ~40 min.

Timing● Hadoop (cascading) :

○ 25 GB in peak hour takes ~40min (-10 min). CSV output 300MB. With cluster of 4 c3.xLarge.

● MySQL:○ Put 300MB in DB with insert statements ~40 min.

● MySQL:○ Put 300MB in DB with optimizations ~5 min.

Optimized are

● No “insert into”. Only “load data” - ~10 times faster● “ENGINE=MyISAM“ vs “INNODB” when possible - ~5

times faster● For “upsert” - temp table with “ENGINE=MEMORY” - IO

savings

CascadingHadoop:

void map(K key, V val,

OutputCollector collector) {

...

}

void reduce(K key, Iterator<V> vals, OutputCollector collector) {

...

}

Cascading:Scheme sinkScheme = new TextLine(new Fields( "word", "count"));

Pipe assembly = new Pipe("wordcount");

assembly = new Each(assembly, new Fields( "line" ), new RegexGenerator(new Fields("word"), ",") );

assembly = new GroupBy(assembly, new Fields( "word"));

Aggregator count = new Count(new Fields( "count"));

assembly = new Every(assembly, count);

Why cascading?

Hadoop Job 1

Hadoop Job 2

Hadoop Job 3

Result of one job should be processed by another job

Lessons Learned

Redis sharding

Redis shard 0 Redis shard 1 Redis shard 2

AdServer

shardNumber = UID.hashCode() / 3

Resharding problem

All data already in shards, how to add new shards?

Resharding problem. Solution

Old Shard NewShard

1. Get NEW UID. If not present - a).

AdServer

a) Get OLD UID 2. Save UID to new Shard

Removal script

Postgres partitioning

● Queries on small partitions● Distributed index● Less index size● Small partitions may fit RAM memory● Easy to remove/move

Cost of IO

L1 cache 3 cyclesL2 cache 14 cyclesRAM 250 cyclesDisk 41 000 000 cyclesNetwork 240 000 000 cycles

Cost of IO

@Cacheable is everywhere

Hadoop

Map input : 300 MBMap output : 80 GB

Hadoop

● mapreduce.map.output.compress = true● codecs: GZip, BZ2 - CPU intensive● codecs: LZO, Snappy● codecs: JNI

~x10

Hadoop

Consider Combiner

Hadoop

Text, IntWritable, BytesWritable, NullWritable, etc

Simpler - better

Hadoop

Missing data:map(T value, ...) {

Log log = parse(value);

Data data = dbWrapper.getSomeMissingData(log.getCampId());

}

Hadoop

Missing data:map(T value, ...) {

Log log = parse(value);

Data data = dbWrapper.getSomeMissingData(log.getCampId());

}

Wrong

Hadoop

Unnecessary data:map(T value, ...) {

Log log = parse(value);

Key resultKey = makeKey(log.getCampName(), ...);

output.collect(resultKey, resultValue);

}

Hadoop

Unnecessary data:map(T value, ...) {

Log log = parse(value);

Key resultKey = makeKey(log.getCampName(), ...);

output.collect(resultKey, resultValue);

}

Wrong

Hadoop

Unnecessary data: RecordWriter.write(K key, V value) {

Entity entity = makeEntity(key, value);

dbWrapper.save(entity);

}

Hadoop

Unnecessary data: RecordWriter.write(K key, V value) {

Entity entity = makeEntity(key, value);

dbWrapper.save(entity);

}

Wrong

Hadoop public boolean equals(Object obj) {

EqualsBuilder equalsBuilder = new EqualsBuilder();

equalsBuilder.append(id, otherKey.getId());

...

}

public int hashCode() {

HashCodeBuilder hashCodeBuilder = new HashCodeBuilder();

hashCodeBuilder.append(id);

...

}

Hadoop public boolean equals(Object obj) {

EqualsBuilder equalsBuilder = new EqualsBuilder();

equalsBuilder.append(id, otherKey.getId());

...

}

public int hashCode() {

HashCodeBuilder hashCodeBuilder = new HashCodeBuilder();

hashCodeBuilder.append(id);

...

}

Wrong

Hadooppublic void map(...) {

…for (String word : words) {

output.collect(new Text(word), new IntVal(1));

}

}

Hadooppublic void map(...) {

…for (String word : words) {

output.collect(new Text(word), new IntVal(1));

}

}

Wrong

Hadoopclass MyMapper extends Mapper {

Text word = new Text();

IntVal one = new IntVal(1);

public void map(...) {

for (String word : words) {

word.set(word);

output.collect(word, one);

}

}

}

Network

Per 1 AdServer instance :Income traffic : ~100Mb/secOutcome traffic : ~50Mb/sec

LB all traffic :Almost 10 Gb/sec

Amazon

AWS ElastiCacheSLOWLOG GET 1) 1) (integer) 35 2) (integer) 1391709950 3) (integer) 34155 4) 1) "GET" 2) "2ads10percent_rmywqesssitmfksetzvj" 2) 1) (integer) 34 2) (integer) 1391709830 3) (integer) 34863 4) 1) "GET" 2) "2ads10percent_tteeoomiimcgdzcocuqs"

AWS ElastiCache

35ms for GET? WTF?Even java faster

AWS ElastiCache

● Strange timeouts (with SO_TIMEOUT 50ms)● No replication for another cluster● «Cluster» is not a cluster● Cluster uses usual instances, so pay for 4

cores while using 1

AWS Limits. You never know where

● Network limit● PPS rate limit● LB limit● Cluster start time up to 20 mins● Scalability limits● S3 is slow for many files

Facts

● HTTP x2 faster HTTPS● HTTPS keep-alive +80% performance● Java 7 40% faster Java 6 (our case)● All IO operations minimized