IOD 2013 - Crunch Big Data in the Cloud with IBM BigInsights and Hadoop

Crunch Big Data in the Cloud with IBM BigInsights and Hadoop IBD-3475

Leons Petrazickis, IBM Canada

@leonsp

© 2013 IBM Corporation

Please note

IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion.

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The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.

Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

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BigDataUniversity.com

Hadoop Architecture

Agenda

• Terminology review

• Hadoop architecture

– HDFS

– Blocks

– MapReduce

– Type of nodes

– Topology awareness

– Writing a file to HDFS

6

7

Hadoop cluster

Rack 1

Node 2

Node n

…

Terminology review

Node 1

Node 2

Node n

…

Rack 2

Node 1

Node 2

Node n

…

Rack n

Node 1

…

Hadoop architecture

• Two main components:

– Hadoop Distributed File System (HDFS)

8

– MapReduce Engine

Hadoop distributed file system (HDFS)

9

• Hadoop file system that runs on top of existing file system

• Designed to handle very large files with streaming data access patterns

• Uses blocks to store a file or parts of a file

HDFS - Blocks

10

• File Blocks

– 64MB (default), 128MB (recommended) – compare to 4KB in UNIX

– Behind the scenes, 1 HDFS block is supported by multiple operating system (OS) blocks

• Advantages of blocks:

– Fixed size – easy to calculate how many fit on a disk

– A file can be larger than any single disk in the network

– If a file or a chunk of the file is smaller than the block size, only needed space is used. Eg: 420MB file is split as:

• Fits well with replication to provide fault tolerance and availability

128MB 128MB 36MB 128MB

128 MB

OS Blocks

HDFS Block

HDFS - Replication

• Blocks with data are replicated to multiple nodes

• Allows for node failure without data loss

11

Node 1

Node 2

Node 3

MapReduce engine

12

• Technology from Google

• A MapReduce program consists of map and reduce

functions

• A MapReduce job is broken into tasks that run in

parallel

Types of nodes - Overview

13

• HDFS nodes

– NameNode

– DataNode

• MapReduce nodes

– JobTracker

– TaskTracker

• There are other nodes not discussed in this course

Types of nodes - Overview

14

Types of nodes - NameNode

15

• NameNode

– Only one per Hadoop cluster

– Manages the filesystem namespace and metadata

– Single point of failure, but mitigated by writing state to

multiple filesystems

– Single point of failure: Don’t use inexpensive

commodity hardware for this node, large memory

requirements

Types of nodes - DataNode

16

• DataNode

– Many per Hadoop cluster

– Manages blocks with data and

serves them to clients

– Periodically reports to name

node the list of blocks it stores

– Use inexpensive commodity

hardware for this node

Types of nodes - JobTracker

17

• JobTracker node

– One per Hadoop cluster

– Receives job requests submitted by client

– Schedules and monitors MapReduce jobs on task

trackers

Types of nodes - TaskTracker

18

• TaskTracker node

– Many per Hadoop cluster

– Executes MapReduce operations

– Reads blocks from DataNodes

19

…lesson continued in the next video>

Topology awareness

20

Bandwidth becomes progressively smaller in the following scenarios:

Topology awareness

21


1. Process on the same node.


1. Process on the same node

2. Different nodes on the same rack

Topology awareness

22




3. Nodes on different racks in the same data center

Topology awareness

23




3. Nodes on different racks in the same data center

4. Nodes in different data centers

Topology awareness

24

Writing a file to HDFS

25


26


27


28


29


30


31


32


33


34


35

Thank You

What is Hadoop?

Agenda

38

• What is Hadoop?

• What is Big Data?

• Hadoop-related open source projects

• Examples of Hadoop in action

• Big Data solutions and the Cloud

What is Hadoop?

39

Relational Database

1GB

What is Hadoop?

40

Relational Database

1GB

10GB

What is Hadoop?

41

Relational Database

1GB

10GB

100GB

What is Hadoop?

42

Relational Database

1GB

10GB

100GB

What is Hadoop?

43

Relational Database

1TB

What is Hadoop?

44

Relational Database

1TB

10TB 100TB

What is Hadoop?

45

Relational Database

1TB

10TB 100TB

What is Hadoop?

46

Relational Database

1TB

10TB 100TB

RFIDs

Sensors

Facebook

Twitter

What is Hadoop?

47

• Written in Java

• Using inexpensive commodity hardware

• A variety of data (structured, unstructured, semi-structured)

• Massive amounts of data through parallelism

• Optimized to handle

• Not for OLTP, not for OLAP/DSS, good for Big Data

• Open source project

• Reliability provided through replication

• Current version: 0.20.2

• Great performance

What is Big Data?

48

RFID Readers

What is Big Data?

49

2 Billion internet users

What is Big Data?

50

4.6 Billion mobile phones

What is Big Data?

51

7TB of data processed by Twitter every day

7TB

a day

What is Big Data?

52

10TB of data processed by Facebook every day

10TB

a day

What is Big Data?

53

About 80% of this data is unstructured

Hadoop-related open source projects

54

jaql PIG

ZooKeeper

http://avro.apache.org/

http://lucene.apache.org/java/

http://uima.apache.org/index.html

http://www.eclipse.org/

http://pig.apache.org/

http://zookeeper.apache.org/

Examples of Hadoop in action – IBM Watson

55

Examples of Hadoop in action

56

• In the telecommunication industry

• In the media

• In the technology industry

Hadoop is not for all types of work

57

• Not to process transactions (random access)

• Not good when work cannot be parallelized

• Not good for low latency data access

• Not good for processing lots of small files

• Not good for intensive calculations with little data

Big Data solutions and the Cloud

58

• Big Data solutions are more than just Hadoop

– Add business intelligence/analytics functionality

– Derive information of data in motion

• Big Data solutions and the Cloud are a perfect fit.

– The Cloud allows you to set up a cluster of systems in minutes and it’s relatively inexpensive.

Thank You

HDFS – Command Line

Agenda

• HDFS Command Line Interface

• Examples

61

HDFS Command line interface

62

• File System Shell (fs)

• Invoked as follows:

hadoop fs <args>

• Example:

Listing the current directory in hdfs

hadoop fs –ls .


63

• FS shell commands take paths URIs as argument

• URI format:

scheme://authority/path

• Scheme:

• For the local filesystem, the scheme is file

• For HDFS, the scheme is hdfs

hadoop fs –copyFromLocal file://myfile.txt hdfs://localhost/user/keith/myfile.txt

• Scheme and authority are optional

• Defaults are taken from configuration file core-site.xml


64

• Many POSIX-like commands

• cat, chgrp, chmod, chown, cp, du, ls, mkdir, mv, rm, stat, tail

• Some HDFS-specific commands

• copyFromLocal, copyToLocal, get, getmerge, put, setrep

HDFS – Specific commands

65

• copyFromLocal / put

• Copy files from the local file system into fs

hadoop fs -copyFromLocal <localsrc> .. <dst>

hadoop fs -put <localsrc> .. <dst>

Or


66

• copyToLocal / get

• Copy files from fs into the local file system

hadoop fs -copyToLocal [-ignorecrc] [-crc] <src> <localdst>

hadoop fs -get [-ignorecrc] [-crc] <src> <localdst>

Or


67

• getMerge

• Get all the files in the directories that match the source file pattern

• Merge and sort them to only one file on local fs

• <src> is kept

hadoop fs -getmerge <src> <localdst>


68

• setRep

• Set the replication level of a file.

• The -R flag requests a recursive change of replication level for an entire tree.

• If -w is specified, waits until new replication level is achieved.

hadoop fs -setrep [-R] [-w] <rep> <path/file>

Thank You

Hadoop MapReduce

Agenda

71

• Map operations

• Reduce operations

• Submitting a MapReduce job

• Distributed Mergesort Engine

• Two fundamental data types

• Fault tolerance

• Scheduling

• Task execution

What is a Map operation?

72

• Doing something to every element in an array is a common operation:

var a = [1,2,3];

for (i = 0; i < a.length; i++)

a[i] = a[i] * 2;


73


var a = [1,2,3];


• New value for variable a would be:

var a = [2,4,6];

a[i] = a[i] * 2;


74


var a = [1,2,3];



var a = [2,4,6];

This can

be written as

a function

a[i] = a[i] * 2;


75


var a = [1,2,3];



var a = [2,4,6];

a[i] = a[i] * 2; a[i] = fn(a[i]);

Like this,

where fn

is

a function

defined

as:

function

fn(x)

{return

x*2;}


76


var a = [1,2,3];


a[i] = fn(a[i]);

Now, all of this can also be

converted into a “map” function


77

• …like this, where fn is a function passed as an argument:

function map(fn, a) {


a[i] = fn(a[i]);

}


78




a[i] = fn(a[i]);

}

• You can invoke this map function like this:

map(function(x){return x*2;}, a);


79




a[i] = fn(a[i]);

}

• You can invoke this map function like this:


This is function fn whose definition is included in the call


80


a[i] = a[i] * 2;

}

• In summary, now you can rewrite:

as a map operation:


What is a Reduce operation?

81

• Another common operation on arrays is to combine all their values:

function sum(a) {

var s = 0;


s += a[i];

return s;

}


82


function sum(a) {

var s = 0;


s += a[i];

return s;

}

This can

be written

as a

function


83


function sum(a) {

var s = 0;


s = fn(s,a[i]);

return s;

}

Like this, where function fn is defined so it adds its arguments: function fn(a,b){ return a+b; }


84


function sum(a) {

var s = 0;


s = fn(s, a[i]);

return s;

}

The whole function sum can also be rewritten so that fn is passed as an

argument


85


function reduce(fn, a, init) {

var s = init;


s = fn(s, a[i]);

return s;

}

Like this… The function name was changed to reduce, and now it takes

three arguments, a function, an array, and an initial value


86


function sum(a) {

var s = 0;


s += a[i];

return s;

}

as a reduce operation:

reduce(function(a,b){return a+b;},a,0);

87


Submitting a MapReduce job

88


89


90


91


92


93


94


95


96


97

98


MapReduce – Distributed Mergesort Engine

99


100


101


102


103


104


105


106


107


108


109

110


Two Fundamental data types

111

Input Output

map

reduce

• Key/value pairs

• Lists


112

Input Output

map <k1, v1>

reduce

• Key/value pairs

• Lists


113

Input Output

map <k1, v1> list(<k2, v2>)

reduce

• Key/value pairs

• Lists


114

Input Output


reduce <k2, list(v2)>

• Key/value pairs

• Lists


115

Input Output


reduce <k2, list(v2)> list(<k3, v3>)

• Key/value pairs

• Lists

Simple data flow example

116


117


118


119


120

121


Fault tolerance

122

Fault tolerance

123

• Task Failure

Fault tolerance

124

• Task Failure

• If a child task fails, the child JVM reports to the TaskTracker before it exits. Attempt is marked failed, freeing up slot for another task.

Fault tolerance

125

• Task Failure


• If the child task hangs, it is killed. JobTracker reschedules the task on another machine.

Fault tolerance

126

• Task Failure


• If the child task hangs, it is killed. JobTracker reschedules the task on another machine.

• If task continues to fail, job is failed.

Fault tolerance

127

• TaskTracker Failure

Fault tolerance

128


• JobTracker receives no heartbeat

Fault tolerance

129



• Removes TaskTracker from pool of TaskTrackers to schedule tasks on.

Fault tolerance

130




• JobTracker Failure

Fault tolerance

131




• JobTracker Failure

• Singe point of failure. Job fails

132


Scheduling

133

Scheduling

134

• FIFO scheduler (with priorities)

Scheduling

135


• Each job uses the whole cluster, so jobs wait their turn.

Scheduling

136



• Fair scheduler

Scheduling

137



• Fair scheduler

• Jobs placed in pools. If a user submits more jobs than another user, he will not get any more cluster resources than the other user, on average. Can define custom pools with guaranteed minimum capacity.

Scheduling

138



• Fair scheduler


• Capacity scheduler

Scheduling

139



• Fair scheduler


• Capacity scheduler

• Allows Hadoop to simulate, for each user, a separate MapReduce cluster with FIFO scheduling.

Task execution

140

Task execution

141

• Speculative Execution

Task execution

142


• Job execution is time sensitive to slow-running tasks. Hadoop detects slow-running tasks and launches another, equivalent task as a backup. The output from the first of these tasks to finish is used.

Task execution

143



• Task JVM Reuse

Task execution

144



• Task JVM Reuse

• Tasks run in their own JVMs for isolation. Jobs that have a large number of short-lived tasks or tasks with lengthy initialization can benefit from sequential JVM reuse through configuration.

Thank You

Pig, Hive, and JAQL

Agenda

147

• Overview

• Pig

• Hive

• Jaql

Agenda

148

• Overview

• Pig

• Hive

• Jaql

Similarities of Pig, Hive and Jaql

149

All translate their respective high-level languages to MapReduce jobs

All offer significant reductions in program size over Java

All provide points of extension to cover gaps in functionality

All provide interoperability with other languages

None support random reads/writes or low-latency queries

Comparing Pig, Hive, and Jaql

150

Pig Hive Jaql

Developed by Yahoo! Facebook IBM

Language name Pig Latin HiveQL Jaql

Type of language Data flow

Declarative

(SQL dialect) Data flow

Data structures it

operates on Complex

Geared

towards

structured data

Loosely structured

data, JSON

Schema optional? Yes

No, but data

can have many

schemas Yes

Turing complete?

Yes when

extended with

Java UDFs

Yes when

extended with

Java UDFs Yes

Agenda

151

• Overview

• Pig

• Hive

• Jaql

Pig components

• Two Components

Language (called Pig Latin)

Compiler

• Two execution environments

Local (Single JVM)

pig -x local

Distributed (Hadoop cluster)

pig -x mapreduce, or simply pig

152

Pig Latin

Compiler

Local

Distributed

Pig

Execution Environment

152

Running Pig

Script

pig scriptfile.pig

Grunt (command line)

pig (to launch command line tool)

Embedded

Call in to Pig from Java

153 153

Pig Latin sample code

154

#pig

grunt> records = LOAD ‘econ_assist.csv’

using PigStorage (‘,’)

AS (country:chararray, sum:long);

grunt> grouped = GROUP records BY country;

grunt> thesum = FOREACH grouped

GENERATE group,

SUM(records, sum);

grunt> DUMP thesum;

154

Pig Latin – Statements, operations & commands

155

Pig Latin program

… LOAD ‘input.txt’;

… ls *.txt

…

… DUMP…

An operation

as a statement A

command

as a

statement

Logical Plan

Compile Physical

Plan

Execute

155

Pig Latin statements

UDF Statements

REGISTER, DEFINE

Commands

Hadoop Filesystem (cat, ls, etc.)

Hadoop MapReduce (kill)

Utility (exec, help, quit, run, set)

Operators

Diagnostic: DESCRIBE, EXPLAIN, ILLUSTRATE

Relational: LOAD, STORE, DUMP, FILTER, etc.

156 156

Pig Latin – Relational operators

Loading and storing

Eg: LOAD (into a program), STORE (to disk), DUMP (to the screen)

Filtering Eg: FILTER, DISTINCT, FOREACH...GENERATE, STREAM, SAMPLE

Grouping and joining Eg: JOIN, COGROUP, GROUP, CROSS

Sorting Eg: ORDER, LIMIT

Combining and splitting Eg: UNION, SPLIT

157 157

Pig Latin – Relations and schema

Result of a relational operator is a relation

A relation is a set of tuples

Relations can be named using an alias (Eg: “x”)

158

x = LOAD ‘sample.txt’ AS (id: int, year:int);

DUMP x

Output is a tuple. Eg: (1,1987)

158

Pig Latin – Relations and schema

Structure of a relation is a schema

Use the DESCRIBE operator to see the schema. Eg:

The output is the schema:

159

DESCRIBE x

x: {id: int, year: int}

159

Pig Latin expressions

Statements that contain relational operators may also contain expressions.

Kinds of expressions:

Constant Field Projection

Map lookup Cast Arithmetic

Conditional Boolean Comparison

Functional Flatten

160 160

Pig Latin – Data types

• Simple types:

int float bytearray

long double chararray

Complex types:

Tuple – Sequence of fields of any type

Bag – Unordered collection of tuples

Map – Set of key-value pairs. Keys must be chararray.

161 161

Pig Latin – Function types

Eval

Input: One or more expressions

Output: An expression

Example: MAX

Filter

Input: Bag or map

Output: boolean

Example: IsEmpty

162 162

Load

Input: Data from external storage

Output: A relation

Example: PigStorage

Store

Input: A relation

Output: Data to external storage

Example: PigStorage

163

Pig Latin – Function types

163

Pig Latin – User-Defined Functions

• Written in Java

Packaged in a JAR file

Register JAR file using the REGISTER statement

Optionally, alias it with DEFINE statement

164 164

Agenda

165

• Overview

• Pig

• Hive

• Jaql

Hive architecture

166

Metastore

(Relational

database

for metadata)

Hadoop

JDBC/ODBC

CLI

Web

Interface

Parser,

Planner

Optimizer

DDL Queries

166

Running Hive

Hive Shell

Interactive

hive

Script

hive -f myscript

Inline

hive -e 'SELECT * FROM mytable'

167 167

Hive services

hive --service servicename

where servicename can be:

hiveserver

server for Thrift, JDBC, ODBC clients

hwi

web interface

jar

hadoop jar with Hive jars in classpath

metastore

out of process metastore

168 168

Hive - Metastore

Stores Hive metadata

Configurations

Embedded

in-process metastore, in-process database

Local

in-process metastore, out-of-process database

Remote

out-of-process metastore, out-of-process database

169 169

Hive – Schema-On-Read

Faster loads into the database (simply copy or move)

Slower queries

Flexibility – multiple schemas for the same data

170 170

Hive - Configuration

• Three ways to configure hive:

• hive-site.xml

- fs.default.name

- mapred.job.tracker

- Metastore configuration settings

hive –hiveconf

“Set” command in the Hive Shell

171 171

Hive Query Language (HiveQL)

SQL dialect

Does not support full SQL92 specification

No support for:

HAVING clause in SELECT

Correlated subqueries

Subqueries outside FROM clauses

Updateable or materialized views

Stored procedures

172 172

Sample code

173

#hive

hive> CREATE TABLE foreign_aid

(country STRING, sum BIGINT)

ROW FORMAT DELIMITED

FIELDS TERMINATED BY ‘,’

STORED AS TEXTFILE;

hive> SHOW TABLES;

hive> DESCRIBE foreign_aid;

hive> LOAD DATA INPATH ‘econ_assist.csv’

OVERWRITE INTO TABLE foreign_aid;

hive> SELECT * FROM foreign_aid LIMIT 10;

hive> SELECT country, SUM(sum) FROM foreign_aid

GROUP BY country;

173


Extensions

MySQL-like extensions

MapReduce extensions

Multi-table insert, MAP, REDUCE, TRANSFORM clauses

Data Types

Simple

TINYINT, SMALLINT, INT, BIGINT, FLOAT, DOUBLE, BOOLEAN, STRING

Complex

ARRAY, MAP, STRUCT

174 174


Built-in Functions SHOW FUNCTIONS

DESCRIBE FUNCTION

175 175

Hive – User-Defined Functions

Written in Java

Three UDF types:

UDF

Input: single row, output: single row

UDAF

Input: multiple rows, output: single row

UDTF

Input: single row, output: multiple rows

Register UDF using ADD JAR

Create alias using CREATE TEMPORARY FUNCTION

176 176

Agenda

177

• Overview

• Pig

• Hive

• Jaql

Jaql architecture

178

Interactive shell / Applications

Script

Compiler / Parser / Rewriter

File Systems

(HDFS, GPFS, Local)

Databases

(DBMS, HBase)

Streams

(Web, Pipes)

Storage layer

I/O layer

178

Jaql data model: JSON

JSON = JavaScript object Notation

Flexible (Schema is optional)

Powerful modeling for semi-structured data

Popular exchange format

179 179

JSON example

180

[

{ACCT_NUM:18,AUTH_DATE:”2011-01-29”,

AUTH_AMT:”111.11”,ZIP:98765,MERCH_NAME:”Acme”},


AUTH_AMT:”222.22”,ZIP:98765,MERCH_NAME:”Exxme”,

NICKNAME:”Xyz”},


AUTH_AMT:”3.33”,ZIP:12345,MERCH_NAME:”Acme”,

ROUTE:[”68.86.85.188”,”64.215.26.111”]},

… ]

180

Running Jaql

Jaql Shell

Interactive. Eg: jaqlshell

Batch Eg: jaqlshell -b myscript.jaql

Inline Eg: jaqlshell -e jaqlstatement

Modes

Cluster Eg: jaqlshell -c

Minicluster Eg: jaqlshell

181 181

Jaql query language

• Sources and sinks

Eg: Copy data from a local file to a new file on HDFS

source sink

read(file(“input.json”)) -> write(hdfs(“output”))

Core Operators

Filter Group Tee

Transform Join Sort

Expand Union Top

182

source sink operator operator …

182

Jaql query language

• Variables

Equal operator (=) binds source output to a variable

e.g. $tweets = read(hdfs(“twitterfeed”))

Pipes, streams, and consumers

Pipe operator (->) streams data to a consumer

Pipe expects array as input

e.g. $tweets → filter $.from_src == 'tweetdeck';

$ – implicit variable referencing current array value

183 183

Jaql query language

• Categories of Built-in Functions

system schema agg

core xml number

hadoop regex string

io binary function

array date random

index nil record

184 184

Jaql – Data Storage

Data store examples Amazon S3 DB2 HBase HDFS

HTTP JDBC Local FS

Data format examples JSON AVRO CSV XML

185 185

Jaql sample code

186

#jaqlshell -c

jaql> $foreignaid =

read(del(“econ_assist.csv”,

{schema: schema

{country: string, sum: long}

} )

)

jaql> $foreignaid

-> group by $country = ($.country)

into {$country.country, sum($[*].sum)};

186

Hadoop core lab – Part 3

BigDataUniversity.com

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oAny Agenda Builder kiosk onsite

http://iod13surveys.com/surveys.html

IOD 2013 - Crunch Big Data in the Cloud with IBM BigInsights and Hadoop

Technology

Transcript of IOD 2013 - Crunch Big Data in the Cloud with IBM BigInsights and Hadoop