The document presents a talk by Joe Mészáros on revolutionizing online advertisement metrics using Enbrite.ly's antifraud, brand safety, and viewability products. It discusses the data collection and processing methods using Apache Spark on Amazon EMR, as well as real-world applications for detecting bot traffic in advertisements. The speaker emphasizes the importance of optimizing configurations, minimizing technical debt, and ensuring robust monitoring in distributed applications.

1. Apache Spark @enbrite.ly
Budapest Spark Meetup
March 30, 2016

2. Joe MÉSZÁROS
software engineer
@joemesz
joemeszaros

3. Who we are?
Our vision is to revolutionize the KPIs and metrics the online
advertisement industry currently using. With our products,
Antifraud, Brandsafety and Viewability we provide actionable
data to our customers.

4. Agenda
● What we do?
● How we do? - enbrite.ly data platform
● Real world antifraud example
● LL + Spark in scale +/-

5. DATA
COLLECTION
ANALYZE
DATA PROCESSION
ANTI FRAUD
VIEWABILITY
BRAND SAFETY
REPORT + API
What we do?

6. How we do? DATA COLLECTION

7. How we do? DATA PROCESSION

8. Amazon EMR
● Most popular cloud service provider
● Amazon Big Data ecosystem
● Applications: Hadoop, Spark, Hive, ….
● Scaling is easy
● Do not trust the BIG guys (API problem)
● Spark application in EMR runs on YARN (cluster
manager)
For more information: https://aws.amazon.com/elasticmapreduce/

9. Tools we use
https://github.com/spotify/luigi | 4500 ★ | more than 200 contributors
Workflow engine, that helps you build
complex data pipelines of batch jobs.
Created by Spotify’s engineering team.

10. Your friendly plumber, that sticks your Hadoop, Spark, … jobs
with simple dependency definition and failure management.

11. class SparkMeetupTask(luigi.Task):
param = luigi.Parameter(default=42)
def requires(self):
return SomeOtherTask(self.param)
def run(self):
with self.output().open('w') as f:
f.write('Hello Spark meetup!')
def output(self):
return luigi.LocalTarget('/meetup/message')
if __name__ == '__main__':
luigi.run()

12. Web interface

13. Web interface

14. Let me tell you a short story...

15. Tools we created GABO LUIGI
Luigi + enbrite.ly extensions = Gabo Luigi
● Dynamic task configuration + dependencies
● Reshaped web interface
● Define reusable data pipeline template
● Monitoring for each task

16. Tools we created GABO LUIGI

17. Tools we created GABO LUIGI
We plan to release it to the wild and make it open
source as part of Spotify’s Luigi! If you are
interested, you are front of open doors :-)

18. Tools we created GABO MARATHON
Motivation: Testing with large data sets and slow batch jobs is
boring and wasteful!

19. Tools we created GABO MARATHON
Graphite

20. Real world example
You are fighting against robots and want to humanize
ad tech era. You have a simple idea to detect bot traffic,
which saves the world. Let’s implement it!

21. Real world example
THE IDEA: Analyse events which are too hasty and deviate
from regular, humanlike profiles: too many clicks in a defined
timeframe.
INPUT: Load balancer access logs files on S3
OUTPUT: Print invalid sessions

22. Step 1: convert access log files to events
Step 2: sessionize events
Step 3: detect too many clicks
How to solve it?

23. The way to access log
{
"session_id": "spark_meetup_jsmmmoq",
"timestamp": 1456080915621,
"type": "click"
}
eyJzZXNzaW9uX2lkIjoic3Bhcmtfb
WVldHVwX2pzbW1tb3EiLCJ0aW1l
c3RhbXAiOjE0NTYwODA5MTU2M
jEsInR5cGUiOiAiY2xpY2sifQo=
Click event attributes
(created by JS tracker)
Access log format
TS CLIENT_IP STATUS "GET https://api.endpoint?event=eyJzZXNzaW9uX2lkIj..."
1. 2.
3.

24. Step 1: log to event
Simplify: log files are on the local storage, only click events.
SparkConf conf = new SparkConf().setAppName("LogToEvent");
JavaSparkContext sparkContext = new JavaSparkContext(conf);
JavaRDD<String> rawEvents = sparkContext.textFile(LOG_FOLDER);
// 2016-02-29T23:50:36.269432Z 178.165.132.37 200 "GET
https://api.endpoint?event=eyJzZXNzaW9uX2lkIj..."

25. Step 1: log to event
JavaRDD<String> rawUrls = rawEvents.map(l -> l.split("s+")[3]);
// GET https://api.endpoint?event=eyJzZXNzaW9uX2lkIj...
JavaRDD<String> rawUrls = rawEvents.map(l -> l.split("s+")[3]);
// GET https://api.endpoint?event=eyJzZXNzaW9uX2lkIj...
JavaRDD<String> eventParameter = rawUrls
.map(u -> parseUrl(u).get("event"));
// eyJzZXNzaW9uX2lkIj…
JavaRDD<String> rawUrls = rawEvents.map(l -> l.split("s+")[3]);
// GET https://api.endpoint?event=eyJzZXNzaW9uX2lkIj...
JavaRDD<String> eventParameter = rawUrls
.map(u -> parseUrl(u).get("event"));
// eyJzZXNzaW9uX2lk
JavaRDD<String> base64Decoded = eventParameter
.map(e -> new String(Base64.getDecoder().decode(e)));
// {"session_id": "spark_meetup_jsmmmoq",
// "timestamp": 1456080915621, "type": "click"}
IoUtil.saveAsJsonGzipped(base64Decoded);

26. Step 2: event to session
SparkConf conf = new SparkConf().setAppName("EventToSession");
JavaSparkContext sparkContext = new JavaSparkContext(conf);
JavaRDD<String> jsonEvents = IoUtil.readFrom(LOCAL_STORAGE);
JavaRDD<ClickEvent> clickEvents = jsonEvents
.map(e -> readJsonObject(e));
SparkConf conf = new SparkConf().setAppName("EventToSession");
JavaSparkContext sparkContext = new JavaSparkContext(conf);
JavaRDD<String> jsonEvents = IoUtil.readFrom(LOCAL_STORAGE);
JavaRDD<ClickEvent> clickEvents = jsonEvents
.map(e -> readJsonObject(e));
JavaPairRDD<String, Iterable<ClickEvent>> groupedEvents =
clickEvents.groupBy(e -> e.getSessionId());
JavaPairRDD<String, Session> sessions = grouped
.flatMapValues(sessionizer);

27. Step 2: event to session
//Sessionizer
public Session call(Iterable<ClickEvent> clickEvents) {
List<ClickEvent> ordered = sortByTimestamp(clickEvents);
Session session = new Session();
for (ClickEvent event: ordered) {
session.addClick(event)
}
return session;
}

28. Step 2: event to session
class Session {
public Boolean isBad = False;
public List<Long> clickTimestamps;
public void addClick(ClickEvent e) {
clickTimestamps.add(e.getTimestamp());
}
public void badify() { this.isBad = True; }
}

29. Step 3: detect bad sessions
JavaRDD<Session> sessions = IoUtil.readFrom(LOCAL_STORAGE);
JavaRDD<Session> markedSessions = sessions
.map(s -> s.clickTimestamps.size() > THRESHOLD);
JavaRDD<Session> badSessions = markedSessions
.filter(s -> s.isBad());
badSessions.collect().foreach(println);

30. Congratulation!
MISSION COMPLETED
YOU just saved the world with a
simple idea within ~10 minutes.

31. Using Spark pros
● Sparking is funny, community, tools
● Easy to start with it
● Language support: Python, Scala, Java, R
● Unified stack: batch, streaming, SQL,
ML

32. Using Spark cons
● You need memory and memory
● Distributed application, hard to debug
● Hard to optimize

33. Lessons learned
● Do not use default config, always optimize!
● Eliminate technical debt + automate
● Failures happen, use monitoring from the very
first breath + fault tolerant implementation
● Sparking is funny, but not a hammer for
everything

34. Data platform future
● Would like to play with Redshift
● Change data format (avro, parquet, …)
● Would like to play with streaming
● Would like to play with Spark 2.0

35. WE ARE HIRING!
working @exPrezi office, K9
check out the company in Forbes :-)
amazing company culture
BUT the real reason ….

36. WE ARE HIRING!
… is our mood manager, Bigyó :)

37. Joe MÉSZÁROS
software engineer
joe@enbrite.ly
@joemesz
@enbritely
joemeszaros
enbritely
THANK YOU!
QUESTIONS?