1.
APACHE SPARK REX

2.
ABOUT ME
Didier Marin
PhD in Computer Science (UPMC)
Machine Learning, Reinforcement Learning & Robotics
Co-founder of Heuritech
Likes functional programming and distributed computing

3.
We develop tools to make sense from raw text data
Customer insight using the text of visited web pages

4.
Data Analytics Platform
Qualify users using their web logs
50M lines/day
Match CRM and web data

5.
WHY SPARK ?
Performance, in particular when
batch size < total RAM in cluster
More general than MR, high-level API
Extensions (ML, streaming) and
connectors (Cassandra)
Growing community

6.
PARSING LOGS
defparseLine(line:String):
Either[ParsingError,LogData]=???
vallogs=sc.textFile("logfile").map(parseLine(_))
valvalidLogs=logs.flatMap(_.right.toOption)

7.
LAMBDA ARCHITECTURE

8.
IMPLEMENTATION

9.
CLUSTER CONFIGURATION
LXC + salt
N containers : 1 master/executor + (N-1) executors
Cassandra node for each Spark executor
Using an "uber"-JAR to submit jobs
Sharing data through NFS

10.
MANAGING SPARK'S MEMORY
Default: 40 % working memory, 60 % cache
20 % of cache used to unroll blocks
Explicit caching for huge RDDs we reuse:
validLogs.persist(StorageLevel.MEMORY_AND_DISK)
Partition tuning may be necessary (spark.default.parallelism)

11.
AGGREGATION
valwords=sc.parallelize(List("a","b","a","c"))
words.groupBy(x=>x).mapValues(_.size).collect
//Array((a,2),(b,1),(c,1))
words.map(x=>(x,1)).reduceByKey(_+_).collect
//Array((a,2),(b,1),(c,1))

12.
AGGREGATION
groupBy

13.
see also &
AGGREGATION
reduceByKey
combineByKey foldByKey

14.
Databricks knowledge base
Spark users mailing list
Parsing Apache logs with Spark (Scala)
USEFUL LINKS
github.com/databricks/spark-knowledgebase
apache-spark-user-list.1001560.n3.nabble.com
alvinalexander.com/scala/analyzing-apache-access-logs-files-
spark-scala

15.
THANK YOU !
contact@heuritech.com