Budapest Spark Meetup - Apache Spark @enbrite.ly presentation held on
March 30, 2016.
The vision we all share at enbrite.ly is to create the next generation decision supporting system in online advertising that combines the market needs; anti-fraud, viewability, brand safety and traffic quality assurances in one platform. We do this by analyzing vast amount of data to create value for our customers. In the last 6 months we created our ETL pipeline, the core component of our data platform based on Apache Spark. In this presentation I share the journey from the whiteboard designs to the maintenance of a TB-scale data pipeline. I share the lessons we learned and the ups and downs using Spark in scale.
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 +/-
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()
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
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!
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..."
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 ….