The document details a Spark meetup held on February 4, 2015, covering various aspects of Apache Spark and its applications in big data processing, including comparisons to Hadoop and discussions on Spark streaming and machine learning. Key speakers presented on topics such as real-time data processing, use cases like 'next product to buy,' and the integration of Spark with Cassandra for both batch and real-time analytics. The event highlighted Spark's advantages over traditional MapReduce, showcasing its speed and flexibility for data processing tasks.

1. Spark Meetup chez Viadeo
Mercredi 4 février 2015
• 19h-19h45 : Présentation de la technologie Spark et exemple de nouveaux cas métiers
pouvant être traités par du BigData temps réel.
Cédric Carbone - Cofondateur d'Influans – cedric@influans.com -Spark vs Hadoop
MapReduce
-Spark Streaming vs Storm
-Le Machine Learning avec Spark -Use case métier : NextProductToBuy
• 19h45-20h : Extension de Spark (Tachyon / Spark JobServer).
Jonathan Lamiel - Talend Labs – jlamiel@talend.com
-La mémoire partagée de Spark avec Tachyon -Rendre Spark Interactif avec Spark
JobServer
• 20h-21h : Big Data analytics avec Spark & Cassandra.
DuyHai DOAN - Technical Advocate at DataStax – duy_hai.doan@datastax.com
Apache Spark is a general data processing framework which allows you perform data
processing tasks in memory. Apache Cassandra is a highly available and massively scalable
NoSQL data-store.
By combining Spark flexible API and Cassandra performance, we get an interesting combo
for both real-time and batch processing.

2. Map Reduce
➜ Map() : parse inputs and generate 0 to n <key, value>
➜ Reduce() : sums all values of the same key and
generate a <key, value>
WordCount Example
➜ Each map take a line as an input and break into words
• It emits a key/value pair of the word and 1
➜ Each Reducer sums the counts for each word
• It emits a key/value pair of the word and sum

3. Map Reduce

4. Hadoop MapReduce v1

5. Hadoop MapReduce v1

6. Hadoop MapReduce v1

7. MapReduce v1
Not good for low-latency jobs on smallest dataset

8. MapReduce v1
Good for off-line batch jobs on massive data

9. Hadoop 1
➜ Batch ONLY
• High latency jobs
HDFS (Redundant, Reliable Storage)
MapReduce1
Cluster Resource Management + Data Processing
BATCH
HIVE
Query
Pig
Scripting
Cascading
Accelerate Dev.

10. Hadoop2 : Big Data
Operating System
➜ Customers want to store ALL DATA in one place and
interact with it in MULTIPLE WAYS
• Simultaneously & with predictable levels of service
• Data analysts and real-time applications
HDFS (Redundant, Reliable Storage)
MapReduce1
Data Processing
BATCH
YARN (Cluster Resource Management)
Other
Data Processing
…

11. Hadoop2 : Big Data
Operating System
➜ Customers want to store ALL DATA in one place and
interact with it in MULTIPLE WAYS
• Simultaneously & with predictable levels of service
• Data analysts and real-time applications
HDFS (Redundant, Reliable Storage)
BATCH INTERACTIVE STREAMING GRAPH ML IN-MEMORYONLINE SEARCH
YARN (Cluster Resource Management)

12. Hadoop2 : Big Data
Operating System
➜ Customers want to store ALL DATA in one place and
interact with it in MULTIPLE WAYS
• Simultaneously & with predictable levels of service
• Data analysts and real-time applications
HDFS (Redundant, Reliable Storage)
YARN (Cluster Resource Management)
BATCH
(MapReduce)
INTERACTIVE
(Tez)
STREAMING
(Storm, Samza
SparkStreaming)
GRAPH
(Giraph,
GraphX)
Machine
Learning
(MLLIb)
In-Memory
(Spark)
ONLINE
(Hbase HOYA)
OTHER
(ElasticSearch)

13. https://spark.apache.org
Apache Spark™ is a
fast and general
engine for large-scale
data processing.

14. The most active project
0
50
100
150
200
250
Patches
MapReduce Storm
Yarn Spark
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Lines Added
MapReduce Storm
Yarn Spark

15. Spark won the
Daytona GraySort contest!
Run programs up to 100x
faster than Hadoop
MapReduce in memory, or 10x
faster on disk.
Sort on disk 100TB of data 3x faster than Hadoop
MapReduce using 10x fewer machines.

16. RDD & Operation
Resilient Distributed Datasets (RDDs)
Operations
➜ Transformations (e.g. map, filter, groupBy)
➜ Actions (e.g. count, collect, save)

17. Spark
scala> val textFile = sc.textFile("README.md")
➜ textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3
scala> textFile.count()
➜ res0: Long = 126
scala> textFile.first()
➜ res1: String = # Apache Spark
scala> val linesWithSpark = textFile.filter(line =>
line.contains("Spark"))
➜ linesWithSpark: spark.RDD[String]=spark.FilteredRDD@7dd4
scala>
textFile.filter(line=>line.contains("Spark")).count()
➜ res3: Long = 15

18. Streaming
Streaming

19. Storm

20. Storm

21. Storm vs Spark
Spark Streaming Storm Storm Trident
Processing model Micro batches Record-at-a-time Micro batches
Thoughput ++++ ++ ++++
Latency Second Sub-second Second
Reliability Models Exactly once At least once Exactly once
Embedded Hadoop Distro HDP, CDH, MapR HDP HDP
Support Databricks N/A N/A
Community ++++ ++ ++
Spark Storm
Scope Batch, Streaming, Graph, ML, SQL Streaming only

22. Machine Learning Library (Mllib)

23. Collaborative Filtering

24. Collaborative Filtering
(learning)

25. Collaborative Filtering
(learning)

26. Collaborative Filtering
(learning)

27. Collaborative Filtering :
Let’s use the model

28. Collaborative Filtering :
similar behaviors

29. Collaborative Filtering
Prediction

30. Netflix Prize (2009)
Netflix is a provider of on-demand Internet streaming media

31. Input Data
UserID::MovieID::Rating::Timestamp
1::1193::5::978300760
1::661::3::978302109
1::914::3::978301968
Etc…
2::1357::5::978298709
2::3068::4::978299000
2::1537::4::978299620

32. The result
1 ; Lyndon Wilson ; 4.608531808535918 ; 858 ; Godfather, The (1972)
1 ; Lyndon Wilson ; 4.596556961095434 ; 318 ; Shawshank Redemption, The (1994)
1 ; Lyndon Wilson ; 4.575789377957803 ; 527 ; Schindler's List (1993)
1 ; Lyndon Wilson ; 4.549694932928024 ; 593 ; Silence of the Lambs, The (1991)
1 ; Lyndon Wilson ; 4.46311974037361 ; 919 ; Wizard of Oz, The (1939)
2 ; Benjamin Harrison ; 4.99545499047152 ; 318 ; Shawshank Redemption, The (1994)
2 ; Benjamin Harrison ; 4.94255532354725 ; 356 ; Forrest Gump (1994)
2 ; Benjamin Harrison ; 4.80168679606128 ; 527 ; Schindler's List (1993)
2 ; Benjamin Harrison ; 4.7874247577586795 ; 1097 ; E.T. the Extra-Terrestrial (1982)
2 ; Benjamin Harrison ; 4.7635998147872325 ; 110 ; Braveheart (1995)
3 ; Richard Hoover ; 4.962687467351026 ; 110 ; Braveheart (1995)
3 ; Richard Hoover ; 4.8316542374095315 ; 318 ; Shawshank Redemption, The (1994)
3 ; Richard Hoover ; 4.7307103243995385 ; 356 ; Forrest Gump (19

33. Real Time Big Data
Use Case
Next Product To Buy
➜ Right Person
➜ Right Product
➜ Right Price
➜ Right Time
➜ Right Channel

34. Questions?
Cédric Carbone
cedric@influans.com
@carbone
www.hugfrance.fr
hug-france-orga@googlegroups.com
@hugfrance