Ghislain Fourny Big Data for Engineers Spring 2020 · Ghislain Fourny Big Data for Engineers Spring...

Ghislain Fourny

Big Data for Engineers Spring 202010. Distributed Computations II: Spark

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YARN

kirtchanut / 123RF Stock Photo

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YARN

Scheduling

Applicationmanagement

Monitoring

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Resource Manager Application MasterApplication MasterApplication MasterApplication MasterApplication Master

YARN

ResourceManager

NodeManager NodeManager NodeManager NodeManager NodeManager

Container

ContainerContainer

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Forward compatibility with DAGs of transformations

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Introduction to Spark

Nikki Zalewski / 123RF Stock Photo

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MapReduce high-level data

MapShuffleMap

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MapReduce high-level data

This is a very

specific topology!

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... and it's under-using YARN

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... because YARN supports any DAG

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YARN

we can build somethingmoregeneral

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MapReduce (even more) high-level: two-step...

Map

Reduce

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... to any DAGs

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Full-DAG query processing

entersSpark

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ResilientDistributedDataset

Spark's first-class citizen

RDD

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it's just a

Bigcollection


RDD

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RDD

... and it is par ti tion ed17

Creation

RDD lifecycle

RDD

LocalFilesystem HDFS S3 On the

fly

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Transformation

RDD lifecycle RDD

RDD19

ActionRDD lifecycle RDD

LocalFilesystem HDFS S3 On

screen

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Lineage graphRDD

RDD

RDD

RDDRDD

RDD

RDD

RDD

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Lazy EvaluationRDD

RDD

RDD

RDDRDD

RDD

RDD

RDD

Each actiontriggers anevaluation

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Lazy EvaluationRDD

RDD

RDD

RDDRDD

RDD

RDD

RDD

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Spark: Execution

Applicationor

Shell

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Spark: Hello, World!

val rdd1 = sc.parallelize(List("Hello, World!", "Hello, there!")

)

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)

ValueHello, World!Hello, there!

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)

val rdd2 = rdd1.flatMap(value => value.split(" ")

)

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ValueHello, World!Hello, there!



)


)ValueHello,World!Hello,there!

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)


)

rdd2.countByValue()

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ValueHello,World!Hello,there!



)


)

rdd2.countByValue() Key ValueHello, 2there! 1World! 1

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Overview of transformations

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Transformations

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Transformations: filter

function:

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Transformations: map

function:

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Transformations: flatMap

function:

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Transformations: distinct

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Transformations: sample

fraction+ seed

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Transformations on two RDDs

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Transformations: union

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Transformations: intersection

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Transformations: subtract

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Transformations: cartesian product

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Overview of actions

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Actions: collect

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Actions: count

22 45

Actions: count by value

12 4 646

Actions: take

4

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Actions: top

4

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Actions: takeSample

4

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Actions: reduce

+

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Pair RDDs

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Transformations: keys

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Transformations: values

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Transformations: reduce by key

+

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Transformations: group by key

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Transformations: sort by key

<=

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Transformations: map values

function:

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Transformations: join

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Transformations: subtract by key

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Actions: count by key

6 1 460

Actions: lookup

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Physical layer

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Parallel execution

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Parallel execution

Task 1 Task 2 Task 3 Task 464

Parallel execution

Task 1 Task 2 Task 3 Task 465

Default: one task per HDFS block

Transformation

Parallel executionLogicallayer

Physicallayer

RDD 1

RDD 2

66Task 1 Task 2 Task 3 Task 4

Spreading tasks over executors

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Executor 1

Executor 2

Executor 3

Executor 4

Task 1

Task 2

Task 3

Task 4

Task 5

Task 6

Task 7

Task 8

Task 9

Task 10 Task 11

Spreading tasks over cores

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Executor 1

Executor 2

Task 1

Task 2

Task 3

Task 4

Task 5

Task 6

Task 7

Task 8

Task 9

Task 10 Task 11

Core 1

Core 2

Core 1

Core 2

Memory

Memory

Transformation

Sequence of (parallelizable) transformations

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Transformation

Transformation

Physical layer (3 transformations)

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Optimization

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Stage

Optimization

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Sequence of (parallelizable) transformationsLogicallayer

Stage

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Transformation

Transformation

Transformation

Narrow Dependency

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Narrow Dependency

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Stays on same machine Same stage

Spreading a stage over cores

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Executor 1

Executor 2

Task 1

Task 2

Task 3

Task 4

Task 5

Task 6

Task 7

Task 8

Task 9

Task 10 Task 11

Core 1

Core 2

Core 1

Core 2

Memory

Memory

Setting up executors

spark-submit --num-executors 42my-application.jar

Setting up cores

spark-submit --executor-cores 2my-application.jar

Setting up memory

spark-submit --executor-memory 5Gmy-application.jar

Most important parameters

spark-submit –-num-executors 42--executor-cores 2--executor-memory 3Gmy-application.jar

Wide Dependency

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Wide Dependency

82

Needs to be sent overthe network

New stage

Job as sequence of stages

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Stage 1

Stage 2

Stage 3

wait for completion

wait for completion

Shuffle

Shuffle

General DAG with stages

Join

Simple shuffle

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Stage 1 Stage 2

Stage 3

Stage 4

Terminology

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Stage

Transformation

Task

Verticalgrouping

Horizontal splitting

Job

Sequence

Performance tuning

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Inefficiency

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Inefficiency

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Inefficiency

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Inefficiency

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Inefficiency

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Inefficiency

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Inefficiency

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Persisting RDDs

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Persisting RDDs

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Persisting RDDs

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Persisting RDDs

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Persisting RDDs

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Join

Simple shuffle

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Avoiding a wide dependency

100


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Pre-partitioning


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Key-values with samekey already on same machine


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No need to leave the machine(No shuffling needed!)


Join

Simple shuffle

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Pre-partitioning

Pre-partitioned

Optimized Join

Simple shuffle

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Data Frames

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RDDs...

function:

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Spark and Python (PySpark)

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rdd = spark.sparkContext.textFile('hdfs:///dataset.txt')

rdd2 = rdd.filter(lambda l: "Spark" in l)

rdd3 = rdd2.map(lambda l: (count(l), l))

rdd4 = rdd3.reduceByKey(lambda l1, l2: l1+l2)

result = rdd4.take(10)

Spark and Python (PySpark): with JSON

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rdd = spark.sparkContext.textFile('hdfs:///dataset.json')

rdd2 = rdd.filter(lambda l: parseJSON(l))

rdd3 = rdd2.filter(lambda l: l['key'] = 0)

rdd4 = rdd3.map(lambda l: (l['key'], l['otherfield']))

result = rdd4.countByKey()

DataFrames

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DataSet<Row>

DataFrames

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Columnar storage

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Memory footprint

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"Raw" Spark DataFrames

DataFrames

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df = spark.read.json('hdfs:///dataset.json')

df.createOrReplaceTempView("dataset")

df2 = df.sql("SELECT * FROM dataset ""WHERE guess = target ""ORDER BY target ASC, country DESC, date DESC")

result = df2.take(10)

DataFrames

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Spark SQL

Schema inference

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foo,bar1,true2,true3,false4,true5,true6,false7,true

dataset.csv

Schema inference

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foo,bar1,true2,true3,false4,true5,true6,false7,true

foointeger1234567

barbooleantruetruefalsetruetruefalsetrue

dataset.csv DataFrame

Schema inference

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{ "foo" : 1, "bar" : true}{ "foo" : 2, "bar" : true}{ "foo" : 3, "bar" : false}{ "foo" : 4, "bar" : true}{ "foo" : 5, "bar" : true}{ "foo" : 6, "bar" : false}{ "foo" : 7, "bar" : true}

dataset.json

Schema inference

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{ "foo" : 1, "bar" : true}{ "foo" : 2, "bar" : true}{ "foo" : 3, "bar" : false}{ "foo" : 4, "bar" : true}{ "foo" : 5, "bar" : true}{ "foo" : 6, "bar" : false}{ "foo" : 7, "bar" : true}

foointeger1234567


dataset.json DataFrame

DataFrames (with logical transformations)

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df2 = df.filter(df['name'] = 'Einstein')

df3 = df.sortBy(asc("theory"), desc("date"))

df4 = df.select('year')


Available types

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Byte ShortIntegerLong

FloatDouble

Decimal

String

Boolean

Binary

Timestamp

Date

Array

Struct

Map

Numbers Other atomics Structured

Type mapping

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DataFrame JavaByteType byteShortType shortIntegerType intLongType longFloatType floatDoubleType doubleBooleanType booleanStringType StringDecimalType java.math.BigDecimalTimestampType java.sql.TimestampDateType java.sql.DateBinaryType byte[]

DataFrame JavaArrayType java.util.ListMapType java.util.MapStructType Row

Other data formats

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df = spark.read.json("hdfs:///dataset.json")

df = spark.read.parquet("hdfs:///dataset.parquet")

df = spark.read.csv("hdfs:///dir/*.csv")

df = spark.read.text("hdfs:///dataset[0-7].txt")

df = spark.read.jdbc("jdbc:postgresql://localhost/test?user=fred&password=secret",

...)

df = spark.read.format("avro").load("hdfs:///dataset.avro")

...

Your own schema

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First Last Picture Birthday FlagString String byte[] Date boolean

DataSet<Person>

Statically known

DataFrames

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DataFrames

126





Spark SQL

SQL Brush-Up!

DataFrames (with logical transformations)

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df2 = df.filter(df['name'] = 'Einstein')

df3 = df.sortBy(asc("theory"), desc("date"))

df4 = df.select('year')


DataFrames

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Query (DataFrames/SparkSQL)

Physical query (RDDs)

Logical plan

Physical plan

Optimizations with Catalyst

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Source: DataBricks blog entry on Catalyst

Dealing with nestedness: arrays

SELECT Last, EXPLODE(Countries)FROM input

First (String) Last (String) Countries (Array of Strings)

Albert Einstein [ "D", "I", "CH", "A", "BE", "US" ]

Srinivasa Ramanujan [ "IN", "UK" ]

Kurt Gödel [ "CZ", "A", "US" ]

Leonhard Euler [ "CH", "RU" ] Last (String) Countries (String)

Einstein D

Einstein I

Einstein CH

Einstein A

Einstein BE

Einstein US

Ramanujan IN

Ramanujan UK

Gödel CZ

Gödel A

Gödel US

Euler CH

Euler RU

Dealing with nestedness: objects

SELECT Name.First, Name.LastFROM input

Name (Object) Countries (Int)

{ "First" : "Albert", "Last" : "Einstein" } 6

{ "First" : "Srinivasa", "Last" : "Ramanujan" } 2

{ "First" : "Kurt", "Last" : "Gödel" } 3

{ "First" : "John", "Last" : "Nash" } 1

{ "First" : "Alan", "Last" : "Turing" }, 1

{ "First" : "Leonhard", "Last" : "Euler" } 2

First (String) Last (String)Albert Einstein

Srinivasa Ramanujan

Kurt Gödel

John Nash

Alan Turing

Leonhard Euler

Limits of DataFrames: Heterogeneity

{ "foo" : 1, "bar" : true}{ "foo" : 2, "bar" : true}{ "foo" : [3, 4], "bar" : false}{ "foo" : 4, "bar" : true}{ "foo" : 5, "bar" : true}{ "foo" : 6, "bar" : false}{ "foo" : 7, "bar" : true}

foostring"1""2""[3, 4]""4""5""6""7"


dataset.json DataFrame

DataFrames and RDDs

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rdd = df.rdddf = sc.createDataFrame(rdd, schema)

df = rdd.toDF()or

10 Design Principles of Big Data

1. Learn from the past

2. Keep the design simple

3. Modularize the architecture

4. Homogeneity in the large

5. Heterogeneity in the small

6. Separate metadata from data

7. Abstract logical model from its physical implementation

8. Shard the data

9. Replicate the data

10. Buy lots of cheap hardware

Ghislain Fourny Big Data for Engineers Spring 2020 · Ghislain Fourny Big Data for Engineers Spring...

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