getting started in apache spark and flink (with scala) - part ii

11.07.2016 | Spark tutorial | A. Panchenko, G. Hintz, S. Remus

Getting started in Apache Sparkand Flink (with Scala)Alexander Panchenko, Gerold Hintz, Steffen Remus

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Outline

Scala basics of Scala programming language

Spark motivation / what do you get on top of MapReduce basics of Spark: RDDs, transformations, actions, shuffling “tricks” useful in Spark context Spark Hands-on session run Spark notebook and solve easy tasks setup Spark project & submit job to cluster

Flink theory difference from Spark

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Three main benefits to use Spark

1. Spark is easy to use—you can develop applications on your laptop, using a high-level API

2. Spark is fast, enabling interactive use and complex algorithms3. Spark is a general engine, letting you combine multiple types of

computations (e.g., SQL queries, text processing, and machine learning) that might previously have required different engines.

This tutorial is based on the book by creators of Spark:Karau H., Konwinski A., Windell P., Zaharia M. “LearningSpark. Lighting-fast Data Analysis.” O’Really. 2015

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Data Science Tasks

Experimentation: development of the model Python, MATLAB, R iPython notebooks Interactive computing Easy-to-useProduction: using the model Java, Scala, C++/C Unit tests Fault tolerance No interactive computing ScalabilityScala + Spark can be used for both!

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A Brief History of Spark

Spark is an open source project Spark started in 2009 as a research project in the UC Berkeley RAD Lab Research papers were published about Spark at academic conferences

and soon after its creation in 2009 In 2011, the AMPLab started to develop higher-level components on

Spark, such as Shark (Hive on Spark) and Spark Streaming Currently one of the most active project in Scala language:

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What Is Apache Spark?

Spark Core: resilient distributed dataset (RDD) Spark SQL: Hive tables, Parquet, JSON, Datasets

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What Is Apache Spark?

Components for distributed execution in Spark

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Spark Runtime Architecture

The components of a distributed Spark application

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Spark Runtime Architecture

The master/slave architecture with one central coordinator and many distributed workers

The central coordinator is called the driver The driver communicates with distributed workers called executors The driver is the process where the main() method of your program runs The driver:

Converting a user program into tasks Scheduling tasks on executors

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Downloading Spark and Getting Started

Download a version “Pre-built for Hadoop 2.X and later”:http://spark.apache.org/downloads.html

Directories you see here that come with Spark: README.md

Contains short instructions for getting started with Spark. bin

Contains executable files that can be used to interact with Spark in various ways (e.g., the Spark shell, which we will cover later in this chapter).

core, streaming, python, … Contains the source code of major components of the Spark project.

examples Contains some helpful Spark standalone jobs that you can look at and run to

learn about the Spark API.

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Introduction to Spark’s Scala Shell

Run: bin/spark-shell Type in the shell the Scala line count:

We can run parallel operations on the RDD, such as counting the lines of text in the file or printing the first one

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Filtering: lambda functions

Filtering example (Scala):

Filtering example (Java 7):

Filtering example (Java 8):

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Standalone Spark Applications

Link to Spark (Maven or SBT), e.g.: Write a sample class, e.g. word count:

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Standalone Spark Applications

SBT build file

Build JAR and run it:

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Programming with RDDs

RDD -- Resilient Distributed Dataset Immutable distributed collection of objects Each RDD is split into multiple partitions Partitions may be computed on different nodes

Creating an RDD Loading an external dataset

Distributing a collection of objects

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Programming with RDDs

Once created, RDDs offer two types of operations: Transformations Actions

Transformations construct a new RDD from a previous one Actions, compute a result based on an RDD

either return it to the driver program or save it to an external storage system, e.g. HDFS

RDDs are recomputed each time you run an action To reuse an RDD you need to persist it in memory:

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Spark Execution Steps (Shell & Standalone)

1. Create some input RDDs from external data.

2. Transform them to define new RDDs using transformations like filter().

3. Persist any intermediate RDDs that will need to be reused.

4. Launch actions such as count() and first() to kick off a parallel computation, which is then optimized and executed by Spark.

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RDD Operations: Transformations

filter() operation does not mutate the existing inputRDD It returns a pointer to an entirely new RDD inputRDD can still be reused later in the program, e.g.:

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RDD Operations: Actions

Return some result and launch actual computation:

take() to retrieve a small number of elements

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Common Transformations and Actions

Element-wise transformations Mapped and filtered RDD from an input RDD:

Squaring the values in an RDD:

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Common Transformations and Actions

Element-wise transformations Splitting lines into multiple words:

Difference between flatMap() and map() on an RDD:

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Common Transformations and Actions

Some simple set operations:

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Common Transformations and Actions

Basic RDD transformations on an RDD containing {1, 2, 3, 3}:

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Common Transformations and Actions

Two-RDD transformations on RDDs containing {1, 2, 3} and {3, 4, 5}:

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Common Transformations and Actions

Basic actions on an RDD containing {1, 2, 3, 3}:

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Common Transformations and Actions

Basic actions on an RDD containing {1, 2, 3, 3}:

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Persistence (Caching)

Double execution: Reusing result:

Persistence levels:

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Working with Key/Value Pairs

Pair RDDs are a useful building block in many programs Allow you to act on each key in parallel or regroup data For instance:

reduceByKey() method that can aggregate data for each key join() method that can merge two RDDs by grouping elements with the same

key

Creating Pair RDDs = creating Scala tuples: Creating a pair RDD using the first word as the key

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Transformations on Pair RDDs

Transformations on one pair RDD (example: {(1, 2), (3, 4), (3, 6)})

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Transformations on Pair RDDs

Transformations on one pair RDD (example: {(1, 2), (3, 4), (3, 6)})

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Transformations on Pair RDDs

Transformations on two pair RDDs (rdd = {(1, 2), (3, 4), (3, 6)} other = {(3, 9)})

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Transformations on Pair RDDs

Using partial functions syntax for Pair RDDs in Scala

Simple filter on second element:

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Transformations on Pair RDDs

Word and document counts: Per-key average with reduceByKey() and mapValues():

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Transformations on Pair RDDs

Word count example revisited:

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Transformations on Pair RDDs

Example of a join (inner join is the default):

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Actions Available on Pair RDDs

Actions on pair RDDs (example ({(1, 2), (3, 4), (3, 6)}))

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Example: PageRank

links – (pageID, link List) – a list of neighbors of each page ranks – (pageID,rank) – current rank for each page

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Important topics not covered in this intro

MLlib Machine Learning in the distributed way Basic Linear Algebra in the distributed way: sparse and dense vectors

and matrices

Partitioning No free lunch, neither automagic scaling of any algorithm Making efficient algorithm = trying to minimize shuffling of the data

Spark SQL, Spark 2.0, Datasets, DataFrames Something like Python’s pandas or R’s DataFrame Great for interactive data mining and for working with CSV files