Post on 26-Jan-2015
description
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 product direction and it should not be relied on in making a purchasing decision.
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.
First step
Request a lab environment
http://bit.ly/requestLab
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
Bandwidth becomes progressively smaller in the following scenarios:
1. Process on the same node.
Bandwidth becomes progressively smaller in the following scenarios:
1. Process on the same node
2. Different nodes on the same rack
Topology awareness
22
Bandwidth becomes progressively smaller in the following scenarios:
1. Process on the same node
2. Different nodes on the same rack
3. Nodes on different racks in the same data center
Topology awareness
23
Bandwidth becomes progressively smaller in the following scenarios:
1. Process on the same node
2. Different nodes on the same rack
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
Writing a file to HDFS
26
Writing a file to HDFS
27
Writing a file to HDFS
28
Writing a file to HDFS
29
Writing a file to HDFS
30
Writing a file to HDFS
31
Writing a file to HDFS
32
Writing a file to HDFS
33
Writing a file to HDFS
34
Writing a file to HDFS
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
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
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 .
HDFS Command line interface
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
HDFS Command line interface
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
HDFS – Specific commands
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
HDFS – Specific commands
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>
HDFS – Specific commands
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;
What is a Map operation?
73
• Doing something to every element in an array is a common operation:
var a = [1,2,3];
for (i = 0; i < a.length; i++)
• New value for variable a would be:
var a = [2,4,6];
a[i] = a[i] * 2;
What is a Map operation?
74
• Doing something to every element in an array is a common operation:
var a = [1,2,3];
for (i = 0; i < a.length; i++)
• New value for variable a would be:
var a = [2,4,6];
This can
be written as
a function
a[i] = a[i] * 2;
What is a Map operation?
75
• Doing something to every element in an array is a common operation:
var a = [1,2,3];
for (i = 0; i < a.length; i++)
• New value for variable a would be:
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;}
What is a Map operation?
76
• 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] = fn(a[i]);
Now, all of this can also be
converted into a “map” function
What is a Map operation?
77
• …like this, where fn is a function passed as an argument:
function map(fn, a) {
for (i = 0; i < a.length; i++)
a[i] = fn(a[i]);
}
What is a Map operation?
78
• …like this, where fn is a function passed as an argument:
function map(fn, a) {
for (i = 0; i < a.length; i++)
a[i] = fn(a[i]);
}
• You can invoke this map function like this:
map(function(x){return x*2;}, a);
What is a Map operation?
79
• …like this, where fn is a function passed as an argument:
function map(fn, a) {
for (i = 0; i < a.length; i++)
a[i] = fn(a[i]);
}
• You can invoke this map function like this:
map(function(x){return x*2;}, a);
This is function fn whose definition is included in the call
What is a Map operation?
80
for (i = 0; i < a.length; i++)
a[i] = a[i] * 2;
}
• In summary, now you can rewrite:
as a map operation:
map(function(x){return x*2;}, a);
What is a Reduce operation?
81
• Another common operation on arrays is to combine all their values:
function sum(a) {
var s = 0;
for (i = 0; i < a.length; i++)
s += a[i];
return s;
}
What is a Reduce operation?
82
• Another common operation on arrays is to combine all their values:
function sum(a) {
var s = 0;
for (i = 0; i < a.length; i++)
s += a[i];
return s;
}
This can
be written
as a
function
What is a Reduce operation?
83
• Another common operation on arrays is to combine all their values:
function sum(a) {
var s = 0;
for (i = 0; i < a.length; i++)
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; }
What is a Reduce operation?
84
• Another common operation on arrays is to combine all their values:
function sum(a) {
var s = 0;
for (i = 0; i < a.length; i++)
s = fn(s, a[i]);
return s;
}
The whole function sum can also be rewritten so that fn is passed as an
argument
What is a Reduce operation?
85
• Another common operation on arrays is to combine all their values:
function reduce(fn, a, init) {
var s = init;
for (i = 0; i < a.length; i++)
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
What is a Reduce operation?
86
• Another common operation on arrays is to combine all their values:
function sum(a) {
var s = 0;
for (i = 0; i < a.length; i++)
s += a[i];
return s;
}
as a reduce operation:
reduce(function(a,b){return a+b;},a,0);
87
…lesson continued in the next video>
Submitting a MapReduce job
88
Submitting a MapReduce job
89
Submitting a MapReduce job
90
Submitting a MapReduce job
91
Submitting a MapReduce job
92
Submitting a MapReduce job
93
Submitting a MapReduce job
94
Submitting a MapReduce job
95
Submitting a MapReduce job
96
Submitting a MapReduce job
97
98
…lesson continued in the next video>
MapReduce – Distributed Mergesort Engine
99
MapReduce – Distributed Mergesort Engine
100
MapReduce – Distributed Mergesort Engine
101
MapReduce – Distributed Mergesort Engine
102
MapReduce – Distributed Mergesort Engine
103
MapReduce – Distributed Mergesort Engine
104
MapReduce – Distributed Mergesort Engine
105
MapReduce – Distributed Mergesort Engine
106
MapReduce – Distributed Mergesort Engine
107
MapReduce – Distributed Mergesort Engine
108
MapReduce – Distributed Mergesort Engine
109
110
…lesson continued in the next video>
Two Fundamental data types
111
Input Output
map
reduce
• Key/value pairs
• Lists
Two Fundamental data types
112
Input Output
map <k1, v1>
reduce
• Key/value pairs
• Lists
Two Fundamental data types
113
Input Output
map <k1, v1> list(<k2, v2>)
reduce
• Key/value pairs
• Lists
Two Fundamental data types
114
Input Output
map <k1, v1> list(<k2, v2>)
reduce <k2, list(v2)>
• Key/value pairs
• Lists
Two Fundamental data types
115
Input Output
map <k1, v1> list(<k2, v2>)
reduce <k2, list(v2)> list(<k3, v3>)
• Key/value pairs
• Lists
Simple data flow example
116
Simple data flow example
117
Simple data flow example
118
Simple data flow example
119
Simple data flow example
120
121
…lesson continued in the next video>
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 a child task fails, the child JVM reports to the TaskTracker before it exits. Attempt is marked failed, freeing up slot for another task.
• If the child task hangs, it is killed. JobTracker reschedules the task on another machine.
Fault tolerance
126
• 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.
• 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
• TaskTracker Failure
• JobTracker receives no heartbeat
Fault tolerance
129
• TaskTracker Failure
• JobTracker receives no heartbeat
• Removes TaskTracker from pool of TaskTrackers to schedule tasks on.
Fault tolerance
130
• TaskTracker Failure
• JobTracker receives no heartbeat
• Removes TaskTracker from pool of TaskTrackers to schedule tasks on.
• JobTracker Failure
Fault tolerance
131
• TaskTracker Failure
• JobTracker receives no heartbeat
• Removes TaskTracker from pool of TaskTrackers to schedule tasks on.
• JobTracker Failure
• Singe point of failure. Job fails
132
…lesson continued in the next video>
Scheduling
133
Scheduling
134
• FIFO scheduler (with priorities)
Scheduling
135
• FIFO scheduler (with priorities)
• Each job uses the whole cluster, so jobs wait their turn.
Scheduling
136
• FIFO scheduler (with priorities)
• Each job uses the whole cluster, so jobs wait their turn.
• Fair scheduler
Scheduling
137
• FIFO scheduler (with priorities)
• Each job uses the whole cluster, so jobs wait their turn.
• 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
• FIFO scheduler (with priorities)
• Each job uses the whole cluster, so jobs wait their turn.
• 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.
• Capacity scheduler
Scheduling
139
• FIFO scheduler (with priorities)
• Each job uses the whole cluster, so jobs wait their turn.
• 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.
• 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
• Speculative 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.
Task execution
143
• Speculative 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.
• Task JVM Reuse
Task execution
144
• Speculative 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.
• 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
Hive Query Language (HiveQL)
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
Hive Query Language (HiveQL)
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”},
{ACCT_NUM:19,AUTH_DATE:”2011-01-29”,
AUTH_AMT:”222.22”,ZIP:98765,MERCH_NAME:”Exxme”,
NICKNAME:”Xyz”},
{ACCT_NUM:20,AUTH_DATE:”2011-01-30”,
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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