The document is a presentation by Stefano Baghino on Apache Spark, discussing the transition from big data to fast data, highlighting the functionalities and advantages of Spark, including its in-memory processing and ease of use for developers. It covers concepts such as resilient distributed datasets (RDDs), Spark's architecture, and real-time data processing through Spark Streaming. The presentation aims to provide insights into Spark's ecosystem and its applications in various programming environments.

1. From Big Data
to Fast Data
An introduction to Apache Spark
Stefano Baghino
Codemotion Milan 2015

2. From Big Data to Fast
Data with Functional
Reactive Containerized
Microservices and AI-
driven Monads in a
galaxy far far away…

3. Hello!
I am Stefano Baghino
Software Engineer @ DATABIZ
stefano.baghino@databiz.it
@stefanobaghino
Favorite PL: Scala
My hero: XKCD’s Beret Guy
What I fear: [object Object]

4. Agenda
u Big Data?
u Fast Data?
u What do we have now?
u How can we do better?
u What is Spark?
u What does it do?
u How does it work?
And also code, somewhere here and there.

5. 1.
What is Big Data?
More than a buzzword, I guess

6. “

7. Really, what is it?
u Data that cannot be stored on a single box
u Requires horizontal scalability
u Requires a shift from traditional solutions

8. 2.
What is Fast Data?
More than yet another buzzword

9. Basically:
Streaming
The need to process huge
quantities of incoming
data in real-time

10. Disk I/O all the time
Each step reads input
from and writes output to
disk
Let’s look at MapReduce
Limited model
It’s difficult to fit all algos
in the MapReduce model

11. Ok, so what is so good about Spark?
May sit on top of an existing
Hadoop deployment.
Builds heavily on simple
functional programming ideas.
Computes and caches data in-
memory to deliver blazing
performances.

12. Fast? Really? Yes!
Hadoop 102.5 TB Spark 100 TB Spark 1 PB
Elapsed Time 72’
23’
234’
# Cores 50400
6592
6080
Rate/Node 0.67 GB/min
20.7 GB/min
22.5 GB/min
Source: https://databricks.com/blog/2014/10/10/spark-petabyte-sort.html

13. So, where can I use it?
Java
Scala
Python

14. Momentum
+700 contributors
+50 companies

15. 3.
What is Spark?
Let’s get to the point

16. The architecture

17. Deploy on the cluster manager of your choice
Local
127.0.0.1
Standalone
Hadoop
Mesos

18. Working with Spark
◎ Resilient Distributed Dataset
◎ Closely resembles a Scala collection
◎ Very natural to use for Scala devs
By the user’s point of view, the RDD is effectively
a collection, hiding all the details of its
distribution throughout the cluster.

19. Example
Word Count
Let’s get our hands a little bit dirty

20. The anatomy of a Resilient Distributed Dataset

21. What about
resilience?
Let’s learn what RDDs
really are and how Spark
works in order to get it

22. What is an RDD, really?
create
filter
filter
join
collect
create

23. Transformations
Produce a new RDD,
extending the execution
graph at each step
e.g.:
u map
u flatMap
u filter
What can I do with an RDD?
Actions
They are “terminal”
operations, actually calling
for the execution to
extract a value
e.g.:
u collect
u reduce

24. The execution model
1. Create DAG of RDDs to represent comp.
2. Create logical execution plan for the DAG
3. Schedule and execute individual tasks

25. The execution model in action
Let’s count distinct names grouped by their initial
sc.textFile("hdfs://...")
.map(n => (n.charAt(0), n))
.groupByKey()
.mapValues(n => n.toSet.size)
.collect()

26. Step 1: Create the logical DAG
HadoopRDD
MappedRDD
ShuffledRDD
MappedValuesRDD
Array[(Char, Int)]
sc.textFile...
map(n => (n.charAt(0),...
groupByKey()
mapValues(n => n.toSet...
collect()

27. Step 2: Create the execution plan
u Pipeline as much as possible
u Split into “stages” based on the need to “shuffle” data
HadoopRDD
MappedRDD
ShuffledRDD
MappedValuesRDD
Array[(Char, Int)]
Alice
Bob
Andy
(A, Alice)
(B, Bob)
(A, Andy)
(A, (Alice, Andy))
(B, Bob)
(A, 2)
Res0 = [(A, 2),….]
(B, 1)
Stage
1
Res0 = [(A, 2), (B, 1)]
Stage
2

28. So, how is it a Resilient Distributed Dataset?
Being a lazy, immutable representation of
computation, rather than an actual collection
of data, RDDs achieve resiliency by simply
being re-executed when their results are
lost*.
* because distributed systems and Murphy’s Law are best buddies.

29. The ecosystem
Spark SQL
Structured data
Spark Streaming
Real-time
MLLib
Machine learning
GraphX
Graph processing
Spark Core
Standalone Scheduler
YARN
Mesos
Spark R
Stat. analysis

30. What we’ll see today: Spark Streaming
Spark SQL
Structured data
Spark Streaming
Real-time
MLLib
Machine learning
GraphX
Graph processing
Spark Core
Standalone Scheduler
YARN
Mesos
Spark R
Stat. analysis

31. Let’s get to
Spark Streaming
It’s Fast Data time!

32. Surprise!
You already know
everything you
need

33. Spark Streaming
Spark
Streaming
Spark
Live data stream
“Mini-batches”
Processed result

34. “Mini-batches” are DStreams
These “mini-batches” are DStreams or
discretized streams and they are basically a
collection of RDDs.
DStreams can be created from streaming
sources or by applying transformations to an
existing DStream.

35. Example
Twitter streaming
“Sentiment analysis” for dummies
Sure, it’s on Github!
https://github.com/stefanobaghino/spark-twitter-stream-example

36. A lot more to be said!
u Caching
u Shared variables
u Partioning optimization
u DataFrames
u A huge API
u A huge ecosystem

37. Tomorrow at Codemotion!
Spark SQL
Structured data
Spark Streaming
Real-time
MLLib
Machine learning
GraphX
Graph processing
Spark Core
Standalone Scheduler
YARN
Mesos
Spark R
Stat. analysis

38. Thanks!
Any questions?
You can find me at:
@stefanobaghino
stefano.baghino@databiz.it