Apache Spark - Fast and Expressive Big Data …Fast and Expressive Big Data Analytics with Python UC BERKELEY UC Berkeley / MIT spark-project.org What is Spark? Fast and expressive

Matei Zaharia

Fast and Expressive Big Data Analytics with Python

UC BERKELEY

spark-project.org UC Berkeley / MIT

What is Spark? Fast and expressive cluster computing system interoperable with Apache Hadoop

Improves efficiency through: » In-memory computing primitives » General computation graphs

Improves usability through: » Rich APIs in Scala, Java, Python » Interactive shell

Up to 100× faster (2-10× on disk)

Often 5× less code

Project History Started in 2009, open sourced 2010

17 companies now contributing code » Yahoo!, Intel, Adobe, Quantifind, Conviva, Bizo, …

Entered Apache incubator in June

Python API added in February

An Expanding Stack Spark is the basis for a wide set of projects in the Berkeley Data Analytics Stack (BDAS)

Spark

Spark Streaming

(real-time)

GraphX (graph)

…

Shark (SQL)

MLbase (machine learning)

More details: amplab.berkeley.edu

This Talk Spark programming model

Examples

Demo

Implementation

Trying it out

Why a New Programming Model?

MapReduce simplified big data processing, but users quickly found two problems:

Programmability: tangle of map/red functions

Speed: MapReduce inefficient for apps that share data across multiple steps » Iterative algorithms, interactive queries

Data Sharing in MapReduce

iter. 1 iter. 2 . . .

Input

HDFS read

HDFS write

HDFS read

HDFS write

Input

query 1

query 2

query 3

result 1

result 2

result 3

. . .

HDFS read

Slow due to data replication and disk I/O

iter. 1 iter. 2 . . .

Input

Distributed memory

Input

query 1

query 2

query 3

. . .

one-time processing

10-100× faster than network and disk

What We’d Like

Spark Model Write programs in terms of transformations on distributed datasets

Resilient Distributed Datasets (RDDs) » Collections of objects that can be stored in

memory or disk across a cluster » Built via parallel transformations (map, filter, …) » Automatically rebuilt on failure

Example: Log Mining Load error messages from a log into memory, then interactively search for various patterns

lines = spark.textFile(“hdfs://...”) !errors = lines.filter(lambda s: s.startswith(“ERROR”)) !messages = errors.map(lambda s: s.split(“\t”)[2]) !messages.cache() !

Block 1

Block 2

Block 3

Worker

Worker

Worker

Driver

messages.filter(lambda s: “foo” in s).count() !messages.filter(lambda s: “bar” in s).count()!

. . . !

tasks

results

Cache 1

Cache 2

Cache 3

Base RDD Transformed RDD

Action

Result: full-text search of Wikipedia in 2 sec (vs 30 s for on-disk data)

Result: scaled to 1 TB data in 7 sec (vs 180 sec for on-disk data)

Fault Tolerance RDDs track the transformations used to build them (their lineage) to recompute lost data

messages = textFile(...).filter(lambda s: “ERROR” in s) ! .map(lambda s: s.split(“\t”)[2]) ! !

HadoopRDD  path  =  hdfs://…  

FilteredRDD  func  =  lambda  s:  …  

MappedRDD  func  =  lambda  s:  …  

Example: Logistic Regression

Goal: find line separating two sets of points

+

–

+ + +

+

+

+ + +

– – –

–

–

– – –

+

target

–

random initial line

Example: Logistic Regression

data = spark.textFile(...).map(readPoint).cache() !!w = numpy.random.rand(D) !!for i in range(iterations): ! gradient = data.map(lambda p: ! (1 / (1 + exp(-p.y * w.dot(p.x)))) * p.y * p.x! ).reduce(lambda x, y: x + y) ! w -= gradient !!print “Final w: %s” % w !

Logistic Regression Performance

0 500

1000 1500 2000 2500 3000 3500 4000

1 5 10 20 30

Runn

ing

Tim

e (s

)

Number of Iterations

Hadoop PySpark

110 s / iteration

first iteration 80 s further iterations 5 s

Demo

Supported Operators map !

filter !

groupBy!

union !

join !

leftOuterJoin!

rightOuterJoin!

reduce !

count !

fold !

reduceByKey!

groupByKey!

cogroup!

flatMap!

take !

first !

partitionBy!

pipe !

distinct !

save !

... !

Other Engine Features General operator graphs (not just map-reduce)

Hash-based reduces (faster than Hadoop’s sort)

Controlled data partitioning to save communication

171

72

23

0

50

100

150

200

Itera

tion

time

(s)

PageRank Performance

Hadoop

Basic Spark

Spark + Controlled Partitioning

1000+ meetup members

60+ contributors

17 companies contributing

Spark Community

This Talk Spark programming model

Examples

Demo

Implementation

Trying it out

Overview Spark core is written in Scala

PySpark calls existing scheduler, cache and networking layer (2K-line wrapper)

No changes to Python

Your app Spark

client

Spark worker

Python child

Python child

PySp

ark

Spark worker

Python child

Python child

Overview Spark core is written in Scala

PySpark calls existing scheduler, cache and networking layer (2K-line wrapper)

No changes to Python

Your app Spark

client

Spark worker

Python child

Python child Py

Spar

k

Spark worker

Python child

Python child

Main PySpark author:

Josh Rosen

cs.berkeley.edu/~joshrosen

Object Marshaling Uses pickle library for both communication and cached data » Much cheaper than Python objects in RAM

Lambda marshaling library by PiCloud

Job Scheduler Supports general operator graphs

Automatically pipelines functions

Aware of data locality and partitioning join

union

groupBy

map

Stage 3

Stage 1

Stage 2

A: B:

C: D:

E:

F:

G:

= cached data partition

Interoperability Runs in standard CPython, on Linux / Mac » Works fine with extensions, e.g. NumPy

Input from local file system, NFS, HDFS, S3 » Only text files for now

Works in IPython, including notebook

Works in doctests – see our tests!

Getting Started Visit spark-project.org for video tutorials, online exercises, docs

Easy to run in local mode (multicore), standalone clusters, or EC2

Training camp at Berkeley in August (free video): ampcamp.berkeley.edu

Getting Started Easiest way to learn is the shell:

$ ./pyspark!

>>> nums = sc.parallelize([1,2,3]) # make RDD from array !

>>> nums.count() !3 !

>>> nums.map(lambda x: 2 * x).collect() ![2, 4, 6] !

Writing Standalone Jobs

from pyspark import SparkContext!!if __name__ == "__main__": ! sc = SparkContext(“local”, “WordCount”) ! lines = sc.textFile(“in.txt”) ! ! counts = lines.flatMap(lambda s: s.split()) \ ! .map(lambda word: (word, 1)) \ ! .reduceByKey(lambda x, y: x + y) !! counts.saveAsTextFile(“out.txt”) !!!

Conclusion PySpark provides a fast and simple way to analyze big datasets from Python

Learn more or contribute at spark-project.org

Look for our training camp on August 29-30!

My email: matei@berkeley.edu

Behavior with Not Enough RAM

68.8

58.1

40.7

29.7

11.5

0

20

40

60

80

100

Cache disabled

25% 50% 75% Fully cached

Iter

atio

n tim

e (s

)

% of working set in memory

The Rest of the Stack Spark is the foundation for wide set of projects in the Berkeley Data Analytics Stack (BDAS)

Spark

Spark Streaming

(real-time)

GraphX (graph)

…

Shark (SQL)

MLbase (machine learning)

More details: amplab.berkeley.edu

Performance Comparison Im

pal

a (d

isk)

Imp

ala

(mem

)

Red

shift

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isk)

Shar

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e T

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(s)

SQL

Sto

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Spar

k

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35

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ughp

ut (M

B/s

/no

de)

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phL

ab

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(min

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