Intro to Big Data - Spark

Data Ingestion with SparkUII

Yogyakarta

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About Me

Sofian Hadiwijaya@[email protected]

Co Founder at Pinjam.co.idTech Advisor at Nodeflux.ioSoftware Innovator (IoT and AI) at Intel

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Wikipedia big data

In information technology, big data is a loosely-defined term used to describe data sets so large and complex that they become awkward to work with using on-hand database management tools.

6Source: http://en.wikipedia.org/wiki/Big_data

How big is big?

• 2008: Google processes 20 PB a day• 2009: Facebook has 2.5 PB user data + 15 TB/day • 2009: eBay has 6.5 PB user data + 50 TB/day• 2011: Yahoo! has 180-200 PB of data• 2012: Facebook ingests 500 TB/day

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That’s a lot of data

8Credit: http://www.flickr.com/photos/19779889@N00/1367404058/

So what?

s/data/knowledge/g

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No really, what do you do with it?

• User behavior analysis• AB test analysis• Ad targeting• Trending topics• User and topic modeling• Recommendations• And more...

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How to scale data?

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Divide and Conquer

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Parallel processing is complicated

• How do we assign tasks to workers?• What if we have more tasks than slots?• What happens when tasks fail?• How do you handle distributed synchronization?

13Credit: http://www.flickr.com/photos/sybrenstuvel/2468506922/

Data storage is not trivial• Data volumes are massive• Reliably storing PBs of data is challenging• Disk/hardware/network failures• Probability of failure event increases with number of machines

For example:1000 hosts, each with 10 disksa disk lasts 3 yearhow many failures per day?

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Hadoop cluster

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Cluster of machine running Hadoop at Yahoo! (credit: Yahoo!)

Hadoop

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Hadoop provides

• Redundant, fault-tolerant data storage• Parallel computation framework• Job coordination

17http://hapdoop.apache.org

Joy

18Credit: http://www.flickr.com/photos/spyndle/3480602438/

Hadoop origins

• Hadoop is an open-source implementation based on GFS and MapReduce from Google• Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. (2003) The Google File System• Jeffrey Dean and Sanjay Ghemawat. (2004) MapReduce: Simplified Data Processing on Large Clusters. OSDI 2004

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Hadoop Stack

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MapReduce(Distributed Programming Framework)

Pig(Data Flow)

Hive(SQL)

HDFS(Hadoop Distributed File System)

Cascading(Java)

HBa

se(C

olum

nar D

atab

ase)

HDFS

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HDFS is...

• A distributed file system• Redundant storage• Designed to reliably store data using commodity hardware• Designed to expect hardware failures• Intended for large files• Designed for batch inserts• The Hadoop Distributed File System

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HDFS - files and blocks

• Files are stored as a collection of blocks• Blocks are 64 MB chunks of a file (configurable)• Blocks are replicated on 3 nodes (configurable)• The NameNode (NN) manages metadata about files and blocks• The SecondaryNameNode (SNN) holds a backup of the NN data• DataNodes (DN) store and serve blocks

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Replication

• Multiple copies of a block are stored• Replication strategy:

• Copy #1 on another node on same rack• Copy #2 on another node on different rack

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HDFS - writes

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DataNode

Block

Slave node

NameNodee

Master

DataNode

Block

Slave node

DataNode

Block

Slave node

File

Client

Rack #1 Rack #2

Note: Write path for a single block shown. Client writes multiple blocks in parallel.

block

HDFS - reads

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DataNode

Block

Slave node

NameNodee

Master

DataNode

Block

Slave node

DataNode

Block

Slave node

File

ClientClient reads multiple blocks in parallel and re-assembles into a file.

block 1 block 2block N

What about DataNode failures?

• DNs check in with the NN to report health• Upon failure NN orders DNs to replicate under-replicated blocks

27Credit: http://www.flickr.com/photos/18536761@N00/367661087/

MapReduce

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MapReduce is...

• A programming model for expressing distributed computations at a massive scale• An execution framework for organizing and performing such computations• An open-source implementation called Hadoop

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Typical large-data problem

• Iterate over a large number of records• Extract something of interest from each• Shuffle and sort intermediate results• Aggregate intermediate results• Generate final output

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Map

Reduce

(Dean and Ghemawat, OSDI 2004)

MapReduce Flow

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MapReduce architecture

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TaskTracker

Task

Slave node

JobTracker

Master

TaskTracker

Task

Slave node

TaskTracker

Task

Slave node

Job

Client

What about failed tasks?

• Tasks will fail• JT will retry failed tasks up to N attempts• After N failed attempts for a task, job fails• Some tasks are slower than other• Speculative execution is JT starting up multiple of the same task• First one to complete wins, other is killed

33Credit: http://www.flickr.com/photos/phobia/2308371224/

MapReduce - Java API• Mapper:void map(WritableComparable key,

Writable value,OutputCollector output,Reporter reporter)

• Reducer:void reduce(WritableComparable key,

Iterator values,OutputCollector output,Reporter reporter)

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MapReduce - Java API• Writable

• Hadoop wrapper interface• Text, IntWritable, LongWritable, etc

• WritableComparable

• Writable classes implement WritableComparable• OutputCollector

• Class that collects keys and values• Reporter

• Reports progress, updates counters• InputFormat

• Reads data and provide InputSplits• Examples: TextInputFormat, KeyValueTextInputFormat

• OutputFormat

• Writes data• Examples: TextOutputFormat, SequenceFileOutputFormat

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MapReduce - Counters are...

• A distributed count of events during a job• A way to indicate job metrics without logging• Your friend

• Bad:System.out.println(“Couldn’t parse value”);

• Good:reporter.incrCounter(BadParseEnum, 1L);

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MapReduce - word count mapperpublic static class Map extends MapReduceBase

implements Mapper<LongWritable, Text, Text, IntWritable> {

private final static IntWritable one = new IntWritable(1);private Text word = new Text();

public void map(LongWritable key, Text value,OutputCollector<Text, IntWritable> output,Reporter reporter) throws IOException {

String line = value.toString();StringTokenizer tokenizer = new StringTokenizer(line);while (tokenizer.hasMoreTokens()) {word.set(tokenizer.nextToken());output.collect(word, one);

}}

}

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MapReduce - word count reducerpublic static class Reduce extends MapReduceBase implements

Reducer<Text, IntWritable, Text, IntWritable> {

public void reduce(Text key,Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output,Reporter reporter) throws IOException {

int sum = 0;while (values.hasNext()) {sum += values.next().get();

}output.collect(key, new IntWritable(sum));

}}

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MapReduce - word count mainpublic static void main(String[] args) throws Exception {

JobConf conf = new JobConf(WordCount.class);conf.setJobName("wordcount");

conf.setOutputKeyClass(Text.class);conf.setOutputValueClass(IntWritable.class);

conf.setMapperClass(Map.class);conf.setCombinerClass(Reduce.class);conf.setReducerClass(Reduce.class);

conf.setInputFormat(TextInputFormat.class);conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(args[0]));FileOutputFormat.setOutputPath(conf, new Path(args[1]));

JobClient.runJob(conf);}

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MapReduce - running a job

• To run word count, add files to HDFS and do:

$ bin/hadoop jar wordcount.jar org.myorg.WordCount input_dir output_dir

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MapReduce is good for...

• Embarrassingly parallel algorithms• Summing, grouping, filtering, joining• Off-line batch jobs on massive data sets• Analyzing an entire large dataset

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MapReduce is ok for...

• Iterative jobs (i.e., graph algorithms)• Each iteration must read/write data to disk• IO and latency cost of an iteration is high

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MapReduce is not good for...

• Jobs that need shared state/coordination• Tasks are shared-nothing• Shared-state requires scalable state store

• Low-latency jobs• Jobs on small datasets• Finding individual records

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Hadoop combined architecture

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TaskTracker

DataNode

Slave node

JobTracker

Master

TaskTracker

DataNode

Slave node

TaskTracker

DataNode

Slave node

SecondaryNameNode

Backup

NameNode

Hadoop to Complicated

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Welcome Spark

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What is Spark?

Distributed data analytics engine, generalizing Map ReduceCore engine, with streaming, SQL, machine learning, and graph processing modules

Most Active Big Data Project

Activity in last 30 days*

*as of June 1, 2014

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Big Data Systems Today

MapReduce

Pregel

Dremel

GraphLabStorm

Giraph

DrillImpala

S4 …

Specialized systems(iterative, interactive and

streaming apps)

General batchprocessing

Unified platform

Spark Core: RDDsDistributed collection of objects What’s cool about them?

• In-memory• Built via parallel transformations

(map, filter, …)• Automatically rebuilt on failure

Data Sharing in MapReduce

iter. 1 iter. 2 . . .

Input

HDFSread

HDFSwrite

HDFSread

HDFSwrite

Input

query 1

query 2

query 3

result 1

result 2

result 3

. . .

HDFSread

Slow due to replication, serialization, and disk IO

iter. 1 iter. 2 . . .

Input

What We’d Like

Distributedmemory

Input

query 1

query 2

query 3

. . .

one-timeprocessing

10-100× faster than network and disk

A Unified Platform

MLlibmachine learning

Spark Streamingreal-time

Spark Core

GraphXgraph

SparkSQL

Spark SQL

Unify tables with RDDsTables = Schema + Data

Spark SQL

Unify tables with RDDsTables = Schema + Data = SchemaRDD

coolPants = sql("""SELECT pid, colorFROM pants JOIN opinions WHERE opinions.coolness > 90""")

chosenPair =coolPants.filter(lambda row: row(1) == "green").take(1)

GraphX

Unifies graphs with RDDs of edges and vertices

MLlib

Vectors, Matrices

MLlib

Vectors, Matrices = RDD[Vector]Iterative computation

Spark StreamingTime

Input

Spark Streaming

RDD

RDD

RDD

RDD

RDD

RDD

Time

Express streams as a series of RDDs over timeval pantsers = spark.sequenceFile(“hdfs:/pantsWearingUsers”)

spark.twitterStream(...).filter(t => t.text.contains(“Hadoop”)).transform(tweets => tweets.map(t => (t.user,

t)).join(pantsers).print()

What it Means for Users

Separate frameworks:

…HDFS read

HDFS writeET

L HDFS read

HDFS writetra

in HDFS read

HDFS writequ

ery

HDFS

HDFS read ET

Ltra

inqu

ery

Spark: Interactiveanalysis

Spark Cluster

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Benefits of Unification

• No copying or ETLing data between systems• Combine processing types in one program• Code reuse• One system to learn• One system to maintain

Data Ingestion

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Collection vs Ingestion

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Data collection

• Happens where data originates • “logging code” • Batch v. Streaming • Pull v. Push

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Data Ingestion

• Receives data • Sometimes coupled with storage • Routing data

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ThanksSofianhw

[email protected]

Pinjam.co.id

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Intro to Big Data - Spark

Data & Analytics

Transcript of Intro to Big Data - Spark