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.
Information regarding potential future products is intended to outline our general
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The information mentioned regarding potential future products is not a
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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.

First step



Request a lab environment
 http://bit.ly/requestLab

Agenda
• Terminology review
• Hadoop architecture
– HDFS
– Blocks
– MapReduce
– Type of nodes
– Topology awareness
– Writing a file to HDFS

6

Terminology review
Hadoop cluster
Rack 1

Rack n

Rack 2

Node 1

Node 1

Node 1

Node 2

Node 2

Node 2

…

…
Node n

7

Node n

…

…
Node n

Hadoop architecture
• Two main components:
– Hadoop Distributed File System (HDFS)
– MapReduce Engine

8

Hadoop distributed file system (HDFS)
• 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

9

HDFS - Blocks
•

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

HDFS Block

128 MB

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:

128MB
•
10

128MB

128MB

36MB

Fits well with replication to provide fault tolerance and availability

HDFS - Replication
• Blocks with data are replicated to multiple nodes
• Allows for node failure without data loss
Node 3

Node 1

Node 2

11

MapReduce engine
• Technology from Google
• A MapReduce program consists of map and reduce
functions
• A MapReduce job is broken into tasks that run in
parallel

12

Types of nodes - Overview
• HDFS nodes
– NameNode
– DataNode
• MapReduce nodes
– JobTracker
– TaskTracker
• There are other nodes not discussed in this course

13

Types of nodes - NameNode
• 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

15

Types of nodes - DataNode
• 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

16

Types of nodes - JobTracker
• JobTracker node
– One per Hadoop cluster
– Receives job requests submitted by client
– Schedules and monitors MapReduce jobs on task
trackers

17

Types of nodes - TaskTracker
• TaskTracker node
– Many per Hadoop cluster
– Executes MapReduce operations
– Reads blocks from DataNodes

18

…lesson continued in the next video>

19

Topology awareness
Bandwidth becomes progressively smaller in the following scenarios:

20

Topology awareness
1. Process on the same node.

21

Topology awareness
1. Process on the same node
2. Different nodes on the same rack

22

Topology awareness
1. Process on the same node
2. Different nodes on the same rack
3. Nodes on different racks in the same data center

23

Topology awareness
1.
2.
3.
4.

24

Process on the same node
Different nodes on the same rack
Nodes on different racks in the same data center
Nodes in different data centers

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?

1G
B
Relational
Database

39

What is Hadoop?

10GB
1G
B
Relational
Database

40

What is Hadoop?

100GB

10GB
1G
B
Relational
Database

41

What is Hadoop?

100GB

10GB
1G
B
Relational
Database

42

What is Hadoop?

1TB

Relational
Database

43

What is Hadoop?

10TB 100TB

1TB

Relational
Database

44

What is Hadoop?

10TB 100TB

1TB

Relational
Database

45

What is Hadoop?

Facebook

10TB 100TB
RFIDs

1TB

Relational
Database
Sensors
Twitter

46

What is Hadoop?
• Open source project
• Written in Java

• Optimized to handle
• Massive amounts of data through parallelism

• A variety of data (structured, unstructured, semi-structured)
• Using inexpensive commodity hardware
• Great performance
• Reliability provided through replication
• Not for OLTP, not for OLAP/DSS, good for Big Data
• Current version: 0.20.2
47

What is Big Data?
RFID Readers

48

What is Big Data?
2 Billion internet users

49

What is Big Data?
4.6 Billion mobile phones

50

What is Big Data?
7TB of data processed by Twitter every day

7TB
a day

51

What is Big Data?
10TB of data processed by Facebook every day

10TB
a day

52

What is Big Data?
About 80% of this data is unstructured

53

Hadoop-related open source projects

jaql
PIG

ZooKeeper

54

Examples of Hadoop in action – IBM Watson

55

Examples of Hadoop in action
• In the telecommunication industry
• In the media
• In the technology industry

56

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
• 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.
58

Agenda
• HDFS Command Line Interface
• Examples

61

HDFS Command line interface
• File System Shell (fs)

• Invoked as follows:

hadoop fs <args>
• Example:
Listing the current directory in hdfs

hadoop fs –ls .
62

• 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
63

• 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

64

HDFS – Specific commands
• copyFromLocal / put
• Copy files from the local file system into fs

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

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

65

• copyToLocal / get
• Copy files from fs into the local file system

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

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

66

• 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>

67

• 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>

68

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?
• 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;

72


var a = [1,2,3];
a[i] = a[i] * 2;
• New value for variable a would be:

var a = [2,4,6];

73


var a = [1,2,3];
a[i] = a[i] * 2;

var a = [2,4,6];

74

This can
be written as
a function


var a = [1,2,3];
a[i] * 2;
a[i] = fn(a[i]);

var a = [2,4,6];

75

Like this,
where fn
is
a function
defined
as:
function
fn(x)
{return
x*2;}


var a = [1,2,3];
a[i] = fn(a[i]);

Now, all of this can also be
converted into a “map” function

76

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

function map(fn, a) {
a[i] = fn(a[i]);
}

77


a[i] = fn(a[i]);
}
• You can invoke this map function like this:

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

78


a[i] = fn(a[i]);
}
• You can invoke this map function like this:

This is function fn whose definition is included in the call

79

• In summary, now you can rewrite:

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

as a map operation:


80

What is a Reduce operation?
• Another common operation on arrays is to combine all their values:

function sum(a) {

var s = 0;

s += a[i];
return s;
}

81


function sum(a) {

var s = 0;

s += a[i];
return s;
}

82

This can
be written
as a
function


function sum(a) {

var s = 0;

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

83

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


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
84


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
85


function sum(a) {

var s = 0;

s += a[i];
return s;
}
as a reduce operation:

reduce(function(a,b){return a+b;},a,0);
86


87


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
• Key/value pairs
• Lists
Input
map
reduce

111

Output

• Key/value pairs
• Lists
Input
map
reduce

112

<k1, v1>

Output

• Key/value pairs
• Lists
Input
map
reduce

113

Output

<k1, v1>

list(<k2, v2>)

• Key/value pairs
• Lists
Input
map

<k1, v1>

list(<k2, v2>)

reduce

114

Output

<k2, list(v2)>

• Key/value pairs
• Lists
Input
map

<k1, v1>

list(<k2, v2>)

reduce

115

Output

<k2, list(v2)>

list(<k3, v3>)


121

Fault tolerance
• Task Failure

123

Fault tolerance
• 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.

124

Fault tolerance
• Task Failure

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

125

Fault tolerance
• 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.

126

Fault tolerance
• TaskTracker Failure

127

Fault tolerance
• JobTracker receives no heartbeat

128

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

129

Fault tolerance

• JobTracker Failure

130

Fault tolerance

• JobTracker Failure
• Singe point of failure. Job fails

131


132

Scheduling
• FIFO scheduler (with priorities)

134

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

135

Scheduling

• Fair scheduler

136

Scheduling

• 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.

137

Scheduling

• Fair scheduler

• Capacity scheduler

138

Scheduling

• Fair scheduler

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

139

Task execution
• Speculative Execution

141

Task execution
• 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.

142

Task execution

• Task JVM Reuse

143

Task execution

• 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.

144

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
Pig

Hive

Jaql

Developed by

Yahoo!

Facebook

IBM

Language name

Pig Latin

HiveQL

Jaql

Data flow

Declarative
(SQL dialect)

Data flow

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

Type of language

Data structures it
operates on

150

Yes

Agenda
•
•
•
•

151

Overview
Pig
Hive
Jaql

Pig components
• Two Components



Language (called Pig Latin)
Compiler

Pig
Pig Latin
Compiler

• Two execution environments


Local (Single JVM)




Distributed (Hadoop cluster)


152

152

pig -x local
pig -x mapreduce, or simply pig

Execution Environment
Local
Distributed

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
#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

154

Pig Latin – Statements, operations & commands
Pig Latin program

An operation
as a statement
A
command
as a
statement

… LOAD ‘input.txt’;
… ls *.txt

Logical Plan

…
… DUMP…

Compile

Physical
Plan

Execute

155

155

Pig Latin statements


UDF Statements




Commands








156

Hadoop Filesystem (cat, ls, etc.)
Hadoop MapReduce (kill)
Utility (exec, help, quit, run, set)

Operators


156

REGISTER, DEFINE

Diagnostic: DESCRIBE, EXPLAIN, ILLUSTRATE
Relational: LOAD, STORE, DUMP, FILTER, etc.

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



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

158

158

Pig Latin – Relations and schema



Structure of a relation is a schema
Use the DESCRIBE operator to see the schema. Eg:
DESCRIBE x



The output is the schema:
x: {id: int, year: int}

159

159

Pig Latin expressions




Statements that contain relational operators may
also contain expressions.
Kinds of expressions:
Constant
Map lookup
Conditional
Functional

160

160

Field
Cast
Boolean
Flatten

Projection
Arithmetic
Comparison

Pig Latin – Data types
• Simple types:
int
long



bytearray
chararray

Complex types:
Tuple
Bag
Map

161

float
double

161

– Sequence of fields of any type
– Unordered collection of tuples
– Set of key-value pairs. Keys must be chararray.

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

Pig Latin – Function types


Load
Input: Data from external storage
Output: A relation
Example: PigStorage



Store
Input: A relation
Output: Data to external storage
Example: PigStorage

163

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
DDL

JDBC/ODBC

Queries

CLI
Metastore
(Relational
database
for metadata)

Web
Interface

Hadoop

166

166

Parser,
Planner
Optimizer

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





170

170

Faster loads into the database (simply copy
or move)
Slower queries
Flexibility – multiple schemas for the same
data

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
#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

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



175

175

SHOW FUNCTIONS
DESCRIBE FUNCTION

Hive – User-Defined Functions



Written in Java
Three UDF types:









176

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

Agenda
•
•
•
•

177

Overview
Pig
Hive
Jaql

Jaql architecture

Interactive shell / Applications

Script
Compiler / Parser / Rewriter
I/O layer

Storage layer
File Systems
(HDFS, GPFS, Local)

178

178

Databases
(DBMS, HBase)

Streams
(Web, Pipes)

Jaql data model: JSON






179

179

JSON = JavaScript object Notation
Flexible (Schema is optional)
Powerful modeling for semi-structured data
Popular exchange format

JSON example
[
{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

180

Running Jaql


Jaql Shell
Interactive.
 Batch
 Inline




Cluster
 Minicluster

181

-b myscript.jaql

-e jaqlstatement

Modes


181

Eg: jaqlshell
Eg: jaqlshell
Eg: jaqlshell
Eg: jaqlshell
Eg: jaqlshell

-c

Jaql query language
source

…

operator

operator

sink

• 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
Transform
Expand

182

182

Group
Join
Union

Tee
Sort
Top

Jaql query language
• Variables




Pipes, streams, and consumers





183

183

Equal operator (=) binds source output to a variable
e.g. $tweets = read(hdfs(“twitterfeed”))

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

Jaql query language
• Categories of Built-in Functions
system
core
hadoop
io
array
index

184

184

schema
xml
regex
binary
date
nil

agg
number
string
function
random
record

Jaql – Data Storage


Data store examples





Amazon S3
HTTP

185

HBase
Local FS

Data format examples
JSON

185

DB2
JDBC

AVRO

CSV

XML

HDFS

Jaql sample code
#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

186

Acknowledgements and Disclaimers
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