In this talk we introduce the notion of distributed computing then we tackle the Spark advantages. The Spark core content is very tiny because the whole explanation has been done live using a Spark Notebook (https://github.com/andypetrella/spark-notebook/blob/geek/conf/notebooks/Geek.snb). This talk has been given together by @xtordoir and myself at the University of Liège, Belgium.

1. BigData, newborn technologies
evolving fast. Why Apache Spark
outruns Apache Hadoop
Andy Petrella, Nextlab
Xavier Tordoir, SilicoCloud

2. Andy
@Noootsab, I am
@NextLab_be owner
@SparkNotebook creator
@Wajug co-driver
@Devoxx4Kids organizer
Maths & CS
Data lover: geo, open, massive
Fool
Who are we?
Xavier
@xtordoir
SilicoCloud
-> Physics
-> Data analysis
-> genomics
-> scalable systems
-> ...

3. So what...
Part I
● What
○ distributed resources
○ data
○ managers
● Why:
○ fastest
○ smartest
○ biggest
● How:
○ Map Reduce
○ Limitations
○ Extensions
PART II
● Spark
○ Model
○ Caching and lineage
○ Master and Workers
○ Core example
● Beyond Processing
○ Streaming
○ SQL
○ GraphX
○ MLlib
○ Example
● Use cases
○ Parallel batch processing of
timeseries
○ ADAM

4. Part I: The Distributed Age

5. What is a distributed environment
Computations needs three kind of resources:
● CPU
● MEM
● Data storage
However, it’s hard to extent each of them at will on a single
machine

6. What is a distributed environment
Lacking of one of these will result in higher response time
or reduced accuracy.
Unfortunately, it doesn’t matter how parallelized is the
algorithm or optimized are the computations
If the solution can’t be inside, it must be outside.

7. What is a distributed environment

8. Distributed File System
You have 100 nodes in your cluster, but only 1 dataset.
Will you replicate it on all nodes?
Extended case: your dataset is 1 Zettabyte (10⁹Tb)?
Lonesome solution:
● split the file on nodes
● axing the algorithm to access local data subsets

9. HDFS towards Tachyon
Hadoop Distributed File System
Implements GoogleFS
Store and read files splitted and replicated on nodes
1Zb file = 8E12 x 128Mb files
IOPs are expensive and require more CPU clocks than
DRAM access
Hence... Tachyon: memory-centric distributed file system

10. Nodes will fail, jobs cannot
We need resilience
Management
Resources are generally fewer than required by algorithm.
We need scheduling
The requirements are fluctuating
We need elasticity

11. Mesos and Marathon
Mesos: High available cluster manager
Nodes: attach or remove them on the fly
Nodes are offering resources -- Applications accept them
Node crash: the application restarts the assigned tasks
Marathon: Meta application on Mesos
Application crash: automatically restarted on different node

12. Why: for everybody and now ?
Fastest:
1. Time to result
2. Near real time processing

13. Runtime is smaller, Dev lifecyle is shorter
→ no synchronization-hell
It can even be really interactive
→ consoles or notebooks tools.
Why for everybody and now

14. Why for everybody and now
No bottlenecks → new-coming data are readily available for
processing
Opens the doors for online models!

15. Why for everybody and now
Smartest: train more and more models, ensembling lots of
them is no more a problem
More complex modelling can be tackled if required

16. Why for everybody and now
Accessing an higher level of accuracy is tricky and might
require lots and lots of models.
Running a model takes quite some time, specially if the
data has to be read every single time.
Example: Netflix contest winner (AT&T labs) ensembled 500 models to gain 10% accuracy.
Although in 2009 it wasn’t possible to use it in production, today this could change.

17. Why for everybody and now
Biggest: no need for sampling big datasets
…
…
That’s it!

18. How!?
Google papers stimulated the open software community,
hence competitive tools now exist.
In the area of computation in distributed environment, there
are two disruptive papers:
● Google’s Mapreduce
● Berkeley’s Spark

19. How!?
MapReduce (Google white paper 2004):
Programming model for distributed data intensive
computations
Helps dealing with parallelization, fault-tolerance, data
distribution, load balancing

20. Functions:
Map ≅ transform data to key value pairs
Reduce ≅ aggregate key value pairs per key (e.g. sum,
max, count)
Mappers and Reducers are sent to data location (nodes)
How!?

21. Map
Reduce: apply a binary associative operator on all
elements
Image from RxJava: https://github.com/ReactiveX/RxJava/wiki/Transforming-Observables
How!?

22. Hadoop implementation has some limitations
Mappers and Reducers ship functions to data while java is not a functional
language
⇒ Composability is difficult and more IO/network operations are required
Iterative algorithms (e.g. stochastic gradient) have to read data at each step
(while data has not changed, only parameters)
How!?

23. How!?
MapReduce on steroids
I) Functional paradigm:
- process built lazily based on simple concepts
- Map and Reduce are two of them
II) Cache data in memory. No more IO.

24. So what...
Part I
● What
○ distributed resources
○ data
○ managers
● Why:
○ fastest
○ smartest
○ biggest
● How:
○ Map Reduce
○ Limitations
○ Extensions
PART II
● Spark
○ Model
○ Caching and lineage
○ Master and Workers
○ Core example
● Beyond Processing
○ Streaming
○ SQL
○ GraphX
○ MLlib
○ Example (notebook)
● Use cases
○ Parallel batch processing of
timeseries
○ ADAM

25. Part II: Spark to the Rescue

26. RDDs
Think of an RDD[T] as an immutable, distributed collection
of objects of type T
• Resilient => Can be reconstructed in case of failure
• Distributed => Transformations are parallelizable
operations
• Dataset => Data loaded and partitioned across cluster
nodes (executors)

27. RDD[T]
Data distribution hierarchy:
- RDD[T]
- Elements
[ x1, x2 ]
[ x10 ]
[ x8,x5,x6 ]
[ x11 ]
[ x14,x13 ]
[ x9,x16 ]
[ x3 ]
[ x7,x12 ]
[ x15 ]
[ x17,x4 ]
Executor 1
- Executors
- Partitions
Executor 2 Executor 3 Executor 4

28. Execution
Execution is split in fundamental units: Tasks
Tasks running in parallel are grouped in Stages

29. Execution
Core1
Task0
(read/process/write)
Task0
(read/process/write)
Task0
(read/process/write)
Core2
Task1
(read/process/write)
Task1
(read/process/write)
Task1
(read/process/write)
Core3
Task2
(read/process/write)
Task2
(read/process/write)
Task2
(read/process/write)
Stage2 Stage1 Stage0

30. Master and Workers

31. Spark Streaming
When you have big fat streams behaving as one single
collection
t
DStream[T]
RDD[T] RDD[T] RDD[T] RDD[T] RDD[T]
DStreams: Discretized Streams (= Sequence of RDDs)

32. Spark SQL
Mapping: RDD -> “table”, Element Field -> “column”

33. MLLib: Distributed ML
Classification
● linear SVM, logistic regression, classification trees, naive Bayes Models
Regression
● SVM, regression trees, linear regression (regularized)
Clustering & dimensionality reduction
● singular value decomposition, PCA, k-means clustering
“The library to teach them all”

34. GraphX
Connecting the dots
Graph processing at scale.
> Take edges
> Link nodes
> Combine/Send messages

35. Use cases examples
- Parallel batch processing of time series
- Bayesian Network in financial market
- IoT platform (Lambda architecture)
- OpenStreetMap cities topologies classification
- Markov Chain in Land Use/Land Cover prediction
- Genomics: ADAM

36. Genomics
Biological systems are very complex
One human sequence is 60Gb

37. ADAM
Credits: AmpLab (UC Berkeley)

38. Stratification using 1000Genomes
http://www.1000genomes.org/
ref: http://upload.wikimedia.org/wikipedia/en/e/eb/Genetic_Variation.jpg

39. Machine Learning model
Clustering: KMeans
ref: http://en.wikipedia.org/wiki/K-means_clustering

40. Machine Learning model
MLLib, KMeans
MLLib:
● Machine Learning Algorithms
● Data structures (e.g. Vector)

41. Mashup
prediction
Sample [NA20332] is in cluster #0 for population Some( ASW)
Sample [NA20334] is in cluster # 2 for population Some( ASW)
Sample [HG00120] is in cluster # 2 for population Some( GBR)
Sample [NA18560] is in cluster # 1 for population Some( CHB)

42. Mashup
#0 #1 #2
GBR 0 0 89
ASW 54 0 7
CHB 0 97 0

43. Cluster
40 m3.xlarge
160 cores + 600G

44. Eggo project (public genomics data in ADAM format on s3)
We…
1000genomes in ADAM format on S3.
Open Source GA4GH Interop services implementation
Machine learning on 1000genomes
Genomic data and distributed computing

45. The end (of the slides)
Thanks for your attention!
Xavier Tordoir
xavier@silicocloud.eu
Andy Petrella
andy.petrella@nextlab.be