JavaOne 2016: Getting Started with Apache Spark: Use Scala, Java, Python, or All of Them?

David Taieb
STSM - IBM Cloud Data Services
Developer advocate
david_taieb@us.ibm.com
My boss wants me to work on Apache
Spark: should I use Scala, Java or Python or
a combination of the three?
Java One 2016, San Francisco

©2016 IBM Corporation
Objectives
By the end of this session, you should be able to:
– Have Basic knowledge of Apache Spark (at least enough to get
you started)
– Understand key differences between Java, Scala and Python
– Better informed about deciding which language to use
– Understand the role of the Notebook in quickly building data
solution.

Let’s start with a story…
Disclaimer
All characters and events depicted in this Story are entirely fictitious. Any similarity to
actual use cases, events or persons is actually intentional

Meet Ben “the developer”
• Hold a master degree in computer science
• 10 year experience, 6 years with the company
• Full stack Web developer
• Languages of choice: Java, Node.js, HTML5/CSS3
• Data: No SQL (Cloudant, Mongo), relational
• Protocols: REST, JSON, MQTT
• No major experience with Big data
Favorite Quote:
“The Best Line of Code is the
One I Didn't Have to Write!”

Meet Natasha “the data scientist”
• Hold a PHD in data science
• 5 year experience, 2 years with the company
• Experienced in Python and R
• Expert in Machine Learning and Data visualization
• Software engineering is not her thing
Favorite Quote:
“In God we trust.
All others bring data”
W. Edwards Deming

Surprise meeting with VP of development
We have an urgent need for our marketing department
to build an application that can provide real-time sentiment analysis on Twitter data
courtesy of http://linkq.com.vn/

Key Constraints
• You only have 6 weeks to build the application
• Target consumer is the LOB user: must be easy to use even for
non technical people
• Web interface: should be accessible from a standard browser
(desktop or mobile)
• It must scale out of the box: I want you to use Apache Spark

Some learning to do
What exactly is
Apache Spark?

What is Apache spark ?
Spark is an open source
in-memory
computing framework for
distributed data processing
and
iterative analysis
on massive data volumes

Spark Core Libraries
general compute engine, handles distributed task
dispatching, scheduling and basic I/O functions
Spark SQL
executes SQL
statements
performs streaming
analytics using
micro-batches
common machine
learning and statistical
algorithms
distributed graph
processing framework
Spark
Streaming
Mllib
Machine
Learning
GraphX
Graph
Spark Core

Spark is evolved quickly and has strong traction
Spark is one of the most active
open source projects
Interest over time (Google Trends)
Source: https://www.google.com/trends/explore#q=apache%20spark&cmpt=q&tz=http://www.indeed.com/jobanalytics/jobtrends?q=apache+spark&l=
Job Trends (Indeed.com)

Resilient Distributed Datasets (RDDs)
• A collection of elements that Spark works on in parallel
• May be kept in memory or on disk
• Applications can also explicitly tell Spark to
cache an RDD, which is great for iterative algorithms
• An RDD contains the “raw data”, plus the function
to compute it
• Fault-tolerance: if any partition of an RDD is lost, it will automatically be
recomputed using the transformations that originally created it
RDD built from a Java collection
RDD built from an external dataset
(local FS, HDFS, Hbase,…)

RDD’s fault tolerance scenario
RD
D1
Map
Transformation
Data Node A
File Input Split
1
File Input Split
2
File Input Split
3
RD
D1
RD
D1
RD
D2
RD
D2
Reduce
Transformation
RD
D3
RD
D3
filter
Transformation
Data Node B

Spark SQL and Data Frames
• Spark’s interface for working with structured and semi-structured data
• Ability to load from a verity of data sources (parquet, JSON, Hive)
• Supports SQL syntax for both JDBC/ODBC connectors
• Special RDD tooling (Join between RDD and an SQL table, Expose UDF’s)
• DataFrames
• A Data Frame is a distributed collection of data organized into named columns.
• It is conceptually equivalent to a table in a relational database
• DataFrames can be constructed from a wide array of sources such as: structured data
files, tables in Hive, external databases, or existing RDDs
• Provide better performance than RDD thanks to Spark SQL’s catalyst query optimizer

Consuming Spark
Spark Application
(driver)
Master
(cluster Manager)
Worker
Node
Worker
Node
Worker
Node
Worker
Node
…
Spark Cluster
Kernel
Master
(cluster Manager)
Worker
Node
Worker
Node
…
Spark Cluster
Notebook Server
Browser
Http/WebSockets
Kernel Protocol
Batch Job
(Spark-Submit)
Interactive
Notebook
• RDD Partitioning
• Task packaging and
dispatching
• Worker node
scheduling

IBM’s commitment to Spark
• Contribute to the Core
• Spark Technology Cluster (STC)
• Open source SystemML
• Foster Community
• Educate 1M+ data scientists and engineers
• Sponsor AMPLab
IBM Analytics for Apache
Spark
• IBM Analytics for Apache Spark Managed Service
• www.ibm.com/analytics/us/en/technology/cloud-data-services/spark-as-a-service
IBM Data Science
Experience
• DataScience Experience:
• datascience.ibm.com
IBM Packages for
Apache Spark
•IBM Packages for Apache Spark:
• ibm.biz/spark-kit

Ben and Natasha start brainstorming
• I’ll work on data acquisition from
Twitter and enrichment with
sentiment analysis scores using Spark
Streaming
• I know Java very well, but I don’t have
time to learn Python.
• However, I am willing to learn Scala if
that helps improve my productivity
• I’ll perform the data exploration and
analysis
• I know Python and R, but am not familiar
enough with Java or Scala
• I like pandas and numpy. I’m ok to learn
Spark but expect the same level of apis
• I need to work iteratively with the data

More learning
What exactly is a
Notebook?

https://www.pinterest.com/pin/309763280589997658/&psig=AFQjCNGrBtz45g-g-yU_j9Wj3eOuqhIz8w&ust=1472442566813212

http://ia.media-imdb.com/images/M/MV5BMTk3OTM5Njg5M15BMl5BanBnXkFtZTYwMzA0ODI3._V1_SX640_SY720_.jpg

What is a Notebook
Text, Annotations
Code, Data
Visualizations,
Widgets, Output
• Web based UI for running
apache spark console
commands
• Easy, no install spark
accelerator
• Best way to start working
with spark

What is Jupyter?
with a “y”, clever ah?
Browser
Kernel
Code Output
https://www.bluetrack.com/uploads/items_images/kernel-of-corn-stress-balls1_thumb.jpg?r=1
Look! Apache Toree for Spark
"Open source, interactive data science and
scientific computing"
– Formerly IPython
– Large, open, growing community and ecosystem
Very popular
– “~2 million users for IPython” [1]
– $6m in funding in 2015 [3]
– 200 contributors to notebook subproject alone [4]
– 275,000 public notebooks on GitHub [2]

Language war?
Likes Scala
Likes Python
Easy to learn Scala has many powerful features that may be hard
to learn, but I only want to use the features that are
similar to Java
Fairly Easy once you get familiar with Python
Idiosyncrasies
Performance Scala is super fast, because it runs in the JVM
Yeah, Python is interpreted and slower, but
there are ways to optimize when needed
Ecosystem Scala ecosystem is small but can reuse Java
ecosystem as it is fully interoperable with Java
Statistics: Pandas, Numpy, matplotlib
Machine Learning: scikit-learn, PyML
Tooling IntelliJ, Scala IDE for Eclipse PyCharm, PyDev (Eclipse)
Interactive
Scala repl (Console),
Scala Notebook for Spark (Limited viz libraries)
Python interpreter, Python Notebook
(Jupyter, Zeppelin) provide great viz libs

Scala vs Java
• Less Verbose
public class Circle{
private double radius;
private double xCenter;
private double yCenter;
public Circle(double radius,double xCenter,
double yCenter){
this.radius = radius;
this.xCenter = xCenter;
this.yCenter = yCenter;
}
public void setRadius(double radius){
this.radius=radius;}
public double getRadius(){return radius;}
public void setXCenter(double xCenter){
this.xCenter=xCenter;}
public double getXCenter(){return xCenter;}
public void setYCenter(double yCenter){
this.yCenter=yCenter;}
public double getYCenter(){return yCenter;}
}
Java
class Circle(
var radius:Double,
var xCenter:Double,
var yCenter:Double
)
Scala
Define a Class with a few fields

Scala vs Java
• Less Verbose
• Mixes OOP and FP
Sort a list of circles by radius size and by center abscissa
Collections.sort(circles, new Comparator<Circle>() {
public int compare(Circle a, Circle b) {
return a.getRadius()-b.getRadius()
}
});
Collections.sort(circles, new Comparator<Circle>() {
public int compare(Circle a, Circle b) {
return a.getXCenter()-b.getXCenter();
}
});
circles.sortBy(
c=>(c.radius,c.xCenter)
)
Scala
Java 7 and below
circles
.sort(Comparator.comparing(Circle::getRadius())
.thenComparing(Circle::getXCenter())
Java 8

Scala vs Java
• Less Verbose
• Concurrency: Actors and Futures
class JavaOneActor extends Actor {
def receive = {
case ”version" => println(”JavaOne 2016")
case “location” => println(“San Francisco”)
case _ => println(”Sorry, I didn’t get that")
}
}
object JavaOneActorMain extends App {
val system = ActorSystem(”JavaOne”)
val actor= system.actorOf(Props[JavaOneActor],name=”JavaOne")
actor ! ”version"
actor ! ”location”
actor ! “Is it raining?”
}

Scala vs Java
• Less Verbose
• Actors
• Better type system
• Parameterized Types
• Abstract Types
• Compound Types
• Singleton Types
• Tuples Types
• Function Types
• Lambdas Types
• Self-Recursive Types
• Functors
• Monads

Scala vs Java
• Less Verbose
• Actors
• Runs as fast
• Scala compiles into Java Bytecode
• Full interoperability with Java
• Runs in the JVM
• Of course, algorithm and optimizations matters

Scala vs Java
• Less Verbose
• Actors
• Runs as fast
• Some features can be confusing
• SBT looks like black magic
• Everything is a function: easy to write cryptic code
• Implicits
• Currying
• Partial functions
• Partially applied functions
courtesy of http://www.codeodor.com/

Let’s look at an example Spark Application
The world famous Word Count!

Java
JavaRDD<String> textFile = sc.textFile("hdfs://...");
JavaRDD<String> words = textFile.flatMap(new FlatMapFunction<String, String>() {
public Iterable<String> call(String s) { return Arrays.asList(s.split(" ")); }
});
JavaPairRDD<String, Integer> pairs = words.mapToPair(new PairFunction<String, String, Integer>() {
public Tuple2<String, Integer> call(String s) { return new Tuple2<String, Integer>(s, 1); }
});
JavaPairRDD<String, Integer> counts = pairs.reduceByKey(new Function2<Integer, Integer, Integer>() {
public Integer call(Integer a, Integer b) { return a + b; }
});
counts.saveAsTextFile("hdfs://...");
Create RDD from a file on the hadoop cluster
New RDD made of all words in the files
For each word create a tuple (word,1)
Aggregate all the results, using the
word as the key
Save the results back to the hadoop cluster

Scala
val textFile = sc.textFile("hdfs://...")
val counts = textFile.flatMap(line => line.split(" "))
.map(word => (word, 1))
.reduceByKey(_ + _)
counts.saveAsTextFile("hdfs://...")
word as the key

Python
text_file = sc.textFile("hdfs://...")
counts = text_file.flatMap(lambda line: line.split(" "))
.map(lambda word: (word, 1))
.reduceByKey(lambda a, b: a + b)
counts.saveAsTextFile("hdfs://...")
word as the key

OK, let’s agree on the architecture
Watson Tone
Analyzer
Input Stream
Enrich data with
Emotion Tone Scores
Processed data
Notebook
Agree to disagree on the Language

Dividing the tasks
• Implement a Spark Streaming
connector to Twitter
• Call Watson Tone Analyzer for each
tweets
• Return a Spark DataFrame with the
tweets enriched with Tone scores
• Code written in Scala, delivered as a Jar
• Will test in Scala Notebook
• Works in a Python Notebook
• Load the twitter data with Tone score from
a persisted store
• Perform the data exploration and analysis:
trending hashtags and sentiments
• Produce visualizations to LOB Users

A word on Watson Tone Analyzer
• Uses linguistic analysis to detect 3 types of tones: Emotion, Social Tendencies and Language styles
• Available as a cloud service on IBM Bluemix
Input
http://www.ibm.com/watson/developercloud/tone-analyzer.html
Results

How is Ben doing?
Spark Streaming
to Twitter
Get Watson Tone
sentiment scores
Enrich Tweets
with new Scores
Make spark Dataframe
available
ssc = new StreamingContext( sc, Seconds(5) )
ssc.addStreamingListener( new StreamingListener )
val keys = config.getConfig("tweets.key").split(",");
val stream = org.apache.spark.streaming.twitter.TwitterUtils.createStream( ssc, None );
val tweets = stream.filter { status =>
Option(status.getUser).flatMap[String] {
u => Option(u.getLang)
}.getOrElse("").startsWith("en") &&
CharMatcher.ASCII.matchesAllOf(status.getText) &&
( keys.isEmpty || keys.exists{status.getText.contains(_)
})
...
Create a Spark StremingContext
with a 5 sec batch window
Create a Twitter Stream
Filter any tweet that are not in English
or that do not match the word filters

How is Ben doing?
Spark Streaming
to Twitter
Get Watson Tone
sentiment scores
Enrich Tweets
with new Scores
available
case class DocumentTone( document_tone: Sentiment )
case class Sentiment(tone_categories: Seq[ToneCategory]);
…
val sentimentResults: String =
EntityEncoder[String].toEntity("{"text": " + JSONObject.quote( status.text ) + "}" ).flatMap {
entity =>
val s = broadcastVar.value.get("watson.tone.url").get + "/v3/tone?version=" + broadcastVar.value.get("watson.api.version").get
val toneuri: Uri = Uri.fromString( s ).getOrElse( null )
client( Request( method = Method.POST, uri = toneuri, headers = …, body = entity.body))
.flatMap { response =>
if (response.status.code == 200 ) {
response.as[String]
} else {
println("Error received from Watson Tone Analyzer.”)
null
}
}
}.run
//Return Sentiment object pickled from response
upickle.read[DocumentTone](sentimentResults).document_tone
Call the Watson Tone Analyzer
Service using a POST Request
Pickle the JSON Response and
return a DocumentTone Object
Define the Sentiment Data Model

How is Ben doing?
Spark Streaming
to Twitter
Get Watson Tone
sentiment scores
Enrich Tweets
with new Scores
available
lazy val client = PooledHttp1Client()
val rowTweets = tweets.map(status=> {
val sentiment = ToneAnalyzer.computeSentiment( client, status, broadcastVar )
var colValues = Array[Any](
status.getUser.getName, //author
status.getCreatedAt.toString, //date
status.getUser.getLang, //Lang
status.getText, //text
Option(status.getGeoLocation).map{ _.getLatitude}.getOrElse(0.0), //lat
Option(status.getGeoLocation).map{_.getLongitude}.getOrElse(0.0) //long
)
var scoreMap = getScoreMap(sentiment)
colValues = colValues ++ ToneAnalyzer.sentimentFactors.map { f => round(f) * 100.0}
//Return [Row, (sentiment, status)]
(Row(colValues.toArray:_*),(sentiment, status))
})
Enrich the Tweets with Sentiment
Scores, returns a Row Object

How is Ben doing?
Spark Streaming
to Twitter
Get Watson Tone
sentiment scores
Enrich Tweets
with new Scores
available
def createTwitterDataFrames(sc: SparkContext) : (SQLContext, DataFrame) = {
if ( workingRDD.count <= 0 ){
println("No data receive. Please start the Twitter stream again to collect data")
return null
}
try{
val df = sqlContext.createDataFrame( workingRDD, schemaTweets )
df.registerTempTable("tweets")
println("A new table named tweets with " + df.count() + " records has been correctly created and can be accessed through the
SQLContext variable")
println("Here's the schema for tweets")
df.printSchema()
(sqlContext, df)
}catch{
case e: Exception => {logError(e.getMessage, e ); return null}
}
}
Transform the RDD of Enriched
Tweets to a DataFrame
Display the DataFrame Schema

How’s Natasha doing?
Load the Tweets
from cluster
Compute the distribution
of Sentiments
Compute top 5
hashtags
Compute aggregate Tone
scores for each 5 top hashtags
Load the Tweets data from a
parquet file in Object Storage
Register a Temp SQL Table so it
can be queried later on

Load the Tweets
from cluster
of Sentiments
Compute top 5
hashtags
Build an array that contains the number of
tweets where score is greater than 60%

Load the Tweets
from cluster
of Sentiments
Compute top 5
hashtags
Creates a flat Map of all the
words in the tweets and filter
to keep only the hashtags
Map then Reduce to count the
occurrences of each hashtag

Load the Tweets
from cluster
of Sentiments
Compute top 5
hashtags
Create RDD from tweets dataframe
Keep only the entries in the top 10
Index by Tag-Tone

Load the Tweets
from cluster
of Sentiments
Compute top 5
hashtags
Count the occurrences for each score
Count the tone average for each tag and count
Final Reduce to get the data the way
we want it for display

Load the Tweets
from cluster
of Sentiments
Compute top 5
hashtags

How good is Python at handling data?
Given the following quarterly sales series
Year Quarter Revenue
Return a list that contains the revenue for a specific quarter, 0 if not defined. e.g.: 1st Quarter: [8977551.03
4th Quarter: [9179464.4, 6717172.01, 2694937.3, 0]
Let’s look at a simple task

Let’s take a look at the application

Meeting back with the VP
This is great, but why do I need to run 2
notebooks – one in Scala and one in
Python? Please Fix it!

Can’t we both get along?
• It would be great if we could run Scala code from a Python Notebook
• And be able to transfer variables between the 2 languages
Open Source Pixiedust Python library let’s us do just that
https://github.com/ibm-cds-labs/pixiedust

New Version with Scala mix-in

What about the LOB User?
courtesy: http://www.flickr.com
C-Suite executive need to be able to run
the application from Notebook, select
filters and see real-time charts without
writing code!

Embedded application with Pixiedust plugin

Conclusion
• Programming languages are just tools, you need to
choose the right one for you (the programmer) and the
task:
• Scala is better suited for engineering work that
involves large, reusable components
• Python is the language of choice for data scientists
• If you are starting with Spark and just want to play:
• Start local: no need for a big cluster yet, get installed
and started in minutes
• Use the language you are most familiar with, or is
easiest to learn
• Use Notebooks to learn the APIs

Resources
• http://programming-scala.org
• http://python.org
• http://spark.apache.org
• www.ibm.com/analytics/us/en/technology/cloud-data-services/spark-as-a-service
• http://datascience.ibm.com
• http://ibm.biz/spark-kit
• www.ibm.com/watson/developercloud/tone-analyzer.html
• developer.ibm.com/clouddataservices/2016/01/15/real-time-sentiment-analysis-of-
twitter-hashtags-with-spark/
• developer.ibm.com/clouddataservices/start-developing-with-spark-and-notebooks/
• www.ibm.com/analytics/us/en/technology/spark/
• github.com/ibm-cds-labs/spark.samples
• github.com/ibm-cds-labs/pixiedust

Questions!
David Taieb
david_taieb@us.ibm.com
@DTAIEB55
courtesy: http://omogemura.com/

JavaOne 2016: Getting Started with Apache Spark: Use Scala, Java, Python, or All of Them?

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (19)

Similaire à JavaOne 2016: Getting Started with Apache Spark: Use Scala, Java, Python, or All of Them?

Similaire à JavaOne 2016: Getting Started with Apache Spark: Use Scala, Java, Python, or All of Them? (20)

Dernier

Dernier (20)

JavaOne 2016: Getting Started with Apache Spark: Use Scala, Java, Python, or All of Them?

Notes de l'éditeur