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Handling 20 billion requests a month? Easy
Dmitriy DumanskiyCogniance, mGage project
Java Team Lead
Java blog : habrahabr.ru/users/doom369/topics
Project evolution
Ad Project 1 Ad Project 2
XXXX
Ad Project 1 delivery load
3 billions req/mon. ~8 c3.xLarge Amazon instances.
Average load : 2400 req/secPeak : x10
Ad Project 2 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
Ad Project 1 3 billions 300 HTTPTime 95% < 60ms 8
Ad Project 2 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 stacksAd Project 2: Spring, iBatis, MySql, Solr, Vertica, Cascading, Tomcat
Ad Project 1 :Spring, Hibernate, Postgres, Distributed ehCache, Hadoop, Voldemort, Jboss
XXXX:Spring, Hibernate, MySQL, Solr, Cascading, Redis, Tomcat
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 (push-
notifications, 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
Solr
Index size < 1 Gb - response time 20-30 msIndex size < 100 Gb - response time 1-2 secIndex size < 400 Gb - response time from 10
secs
Architecture
MySql
Solr Master
SOLR Slave
AdServer
SOLR Slave
AdServer
SOLR Slave
AdServer
No-SQL
AdServer - Solr Slave
Delivery:volatile 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● Data loss possible
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
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
1 hour batch
Delivery 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
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
Facts
● HTTP x2 faster HTTPS● HTTPS keep-alive +80% performance● Java 7 40% faster Java 6 (our case)● All IO operations minimized● Less OOP - better performance
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
Java 7. Random
return items.get(new Random().nextInt(items.size()))
Java 7. Random
return items.get(ThreadLocalRandom().current().nextInt(items.size()))
~3x
Java 7. Less garbage
new ArrayList():this.elementData = {};
insteadOf
this.elementData = new Object[10];
new HashMap():Entry<K,V>[] table = {};
insteadOf
this.table = new Entry[16];
Java 7. Less garbage
Before:class String {
int offset;
int count;
char value[];
int hash;
}
After:class String {
char value[];
int hash;
}
Java 7. GC
200mb per second - 0.5% CPU time
Smaller heap - better performance
Java 7. String
● Substring● Split
Use latest versions
Jedis 2.2.3 uses commons-pool 1.6Jedis 2.3 uses commons-pool 2.0
commons-pool 2.0 - ~2x times faster
Small tweaks. Date
new Date() vs
System.currentTimeMillis()
Small tweaks. SimpleDateFormat
return new SimpleDateFormat(“MMM yyyy HH:mm:ss Z”).parse(dateString)
Small tweaks. SimpleDateFormat
● ThreadLocal● Joda - threadsafe DateTimeFormat
Small tweaks. Pattern
public Item isValid(String ip) {
Pattern pattern = Pattern.compile("xxx");
Matcher matcher = pattern.matcher(ip);
return matcher.matches();
}
Small tweaks. Pattern
final Pattern pattern = Pattern.compile("xxx");
final Matcher matcher = pattern.matcher(“”);
public Item isValid(String ip) {
matcher.reset(ip);
return matcher.matches();
}
Small tweaks. String.split
item.getPreferences().split(“[_,;,-]”);
Small tweaks. String.split
item.getPreferences().split(“[_,;,-]”);
vsstatic final Pattern PATTERN = Pattern.compile("[_,;,-]");
PATTERN.split(item.getPreferences()) - ~2x fastervs
custom code - up to 5x faster
Small tweaks. FOR loop
for (A a : arrayListA) {
// do something
for (B b : arrayListB) {
// do something
for (C c : arrayListC) {
// do something
}
}
}
Small tweaks. FOR loopfor (Iterator<A> i = arrayListA.iterator(); i.hasNext();) {
a = i.next();
}
public Iterator<E> iterator() {
return new Itr();
}
private class Itr implements Iterator<E> {
int cursor = 0;
int lastRet = -1;
int expectedModCount = modCount;
}
Small tweaks. FOR loop
Small tweaks. LinkedList
Just don’t use it
Small tweaks. Primitives
double coord = Double.valueOf(textLine);
Avoid concurrency
volatile the simplest way
Avoid concurrency
JedisPool.getResource() - syncJedisPool.returnResource() - syncOutputStreamWriter.write() - sync
UUID.randomUUID() - sync
Avoid concurrency
JedisPool.getResource()
JedisPool.returnResource()
replace with
ThreadLocal<JedisConnection>
Avoid concurrency
ThreadLocal<JedisConnection> - requires ~1000 open connections for Redis.
More connections — slower redis response.Dead end.
Avoid concurrency
OutputStreamWriter.write()
● No flush() on every request and big buffered writer ● Async writer
No guarantee for no data loss. Dead end.
Avoid concurrency
OutputStreamWriter.write()
Or buy SSD =)+30-60% on disk IO
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
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
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
RecordWriter.write(K key, V value) {
Entity entity = makeEntity(key, value);
dbWrapper.save(entity);
}
Hadoop
Minimize IO: 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);
}
}
}
Amazon
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
