The document discusses the advantages of using Scala and Spark for big data applications, highlighting Scala's strong typing and concise syntax, as well as its compatibility with functional programming. It emphasizes Spark's performance benefits, such as real-time processing, fast in-memory data access, and its scalability for large datasets. The document also includes resources for learning Scala and Spark through meetups and training programs in Seattle.

1. #datapopupseattle
Scala and Spark are Ideal
for Big Data
John Nestor
Sr Architect, 47 Degrees
47deg

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3. Scala and Spark are
Ideal for Big Data
John Nestor
47 Degrees
Seattle Unstructured Data Science Pop-Up
October 7, 2015
www.47deg.com
347deg.com

4. 47deg.com
Why Scala?
• Strong typing
• Concise elegant syntax
• Runs on JVM (Java Virtual Machine)
• Supports both object-oriented and functional
• Small simple programs through large parallel
distributed systems
• Easy to cleanly extend with new libraries and DSL’s
• Ideal for parallel and distributed systems
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Scala: Strong Typing and Concise Syntax
• Strong typing like Java.
• Compile time checks
• Better modularity via strongly typed interfaces
• Easier maintenance: types make code easier to
understand
• Concise syntax like Python.
• Type inference. Compiler infers most types that had to be
explicit in Java.
• Powerful syntax that avoid much of the boilerplate of Java
code (see next slide).
• Best of both worlds: safety of strong typing with conciseness
(like Python).
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Scala Case Class
• Java version
class User {
private String name;
private Int age;
public User(String name, Int age) {
this.name = name; this.age = age;
}
public getAge() { return age; }
public setAge(Int age) { this.age = age;}
}
User joe = new User(“Joe”, 30);
• Scala version
case class User(name:String, var age:Int)
val joe = User(“Joe”, 30)
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Functional Scala
• Anonymous functions.
(a:Int,b:Int) => a+b
• Functions that take and return other functions.
• Rarely need variables or loops
• Immutable collections: Seq[T], Map[K,V], …
• Works well with concurrent or distributed systems
• Natural for functional programming
• Functional collection operations (a small sample)
• map, flatMap, reduce, …
• filter, groupBy, sortBy, take, drop, …
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Scala Availability and Support
• Open Source
• Typesafe provides support. Founded my Martin
Odersky who designed Scala.
• IDEs: Intellij IDEA and Eclipse
• Libraries: lots now and more every day
• ScalaNLP - Epic (natural language processing)
• Major Scala users: LinkedIn, Twitter, Goldman Sachs,
Coursera, Angies List, Whitepages
• Major systems written in Scala: Spark, Kafka
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Typesafe Scala Components
• Scala Compiler (includes REPL)
• Scala Standard Libraries
• SBT - Scala Build Tool
• Play - scaleable web applications
• Scala JS - compiles Scala to JavaScript
• Akka - for parallel and distributed computation
• Spray - high performance asynchronous TCP/ HTTP library
• Spark - Typesafe also supports Spark
• Slick - for SQL database access
• ConductR - Scala deployment/devops tool
• Reactive Monitoring (Beta)
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Why Spark?
• Support for not only batch but also (near) real-time
• Fast - keeps data in memory as much as possible
• Often 10X to 100X Hadoop speed
• A clean easy-to-use API
• A richer set of functional operations than just map and
reduce
• A foundation for a wide set of integrated data
applications
• Can recover from failures - recompute or (optional)
replication
• Scalable for very large data sets and reduced time
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Spark RDDs
• RDD[T] - resilient distributed data set
• typed (must be serializable)
• immutable
• ordered
• can be processed in parallel
• lazy evaluation - permits more global optimizations
• Rich set of functional operations ( a small sample)
• map, flatMap, reduce, …
• filter, groupBy, sortBy, take, drop, …
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Spark Components
• Spark Core
• Scalable multi-node cluster
• Failure detection and recovery
• RDDs and functional operations
• MLLib - for machine learning
• linear regression, SVMs, clustering, collaborative
filtering, dimension reduction
• more on the way!
• GraphX - for graph computation
• Streaming - for near real-time
• Dataframes - for SQL and Json
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Spark Availability and Support
• Open Source - top level Apache project
• Over 750 contributors from over 200 organizations
• Can process multiple petabytes on clusters of over
8000 nodes
• Databricks. Matei Zaharia who wrote the original Spark
is a founder and CTO
• Packages (more every day)
• Zeppelin - Scala notebooks
• Cassandra, Kafka connectors
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Clusters and Scalability
• Scala Akka clusters (process distribution, micro services)
• message passing
• remote Actors
• Spark clusters (data distribution)
• local
• Stand alone (optionally with ZooKeeper)
• Apache Mesos
• Hadoop Yarn
• can run above on Amazon and Google clouds
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Why Scala for Spark?
• Why not Python, R, or Java for Spark?
• Spark is written in Scala
• Scala source code is important Spark documentation
• Spark is best extended in Scala
• The primary API for Spark is Scala
• The functional features of Scala and Spark are a
natural fit and easiest to use in Scala
• If you want to build scalable high performance
production code based on Spark, R by itself is too
specialized, Python is too slow and Java is tedious to
write and maintain
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Demo
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Seattle Resources
• Seattle Meetups
• Scala at the Sea Meetup
http://www.meetup.com/Seattle-Scala-User-Group/
• Seattle Spark Meetup
http://www.meetup.com/Seattle-Spark-Meetup/
• Seattle Training: Spark and Typesafe Scala Classes
http://www.47deg.com/events#training
• UW Scala Professional Certificate Program
http://www.pce.uw.edu/certificates/scala-functional-reactive-programming.html
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