Hadoop and its Ecosystem Components in Action

SQL Server Live! Orlando 2012

SQTH12 ‐ Hadoop and its Ecosystem Components in Action ‐ Andrew Brust 1

Hadoop and its Ecosystem Components in Action

Andrew BrustCEO and Founder

Blue Badge Insights

Level: Intermediate

• CEO and Founder, Blue Badge Insights• Big Data blogger for ZDNet• Microsoft Regional Director, MVP• Co-chair VSLive! and 17 years as a speaker• Founder, Microsoft BI User Group of NYC

– http://www.msbinyc.com

• Co-moderator, NYC .NET Developers Group– http://www.nycdotnetdev.com

• “Redmond Review” columnist for Visual Studio Magazine and Redmond Developer News

• brustblog.com, Twitter: @andrewbrust

Meet Andrew



My New Blog (bit.ly/bigondata)

MapReduce, in a Diagram

mapper

mapper

mapper

mapper

mapper

mapper

Input

reducer

reducer

reducer

Input

Input

Input

Input

Input

Input

Output

Output

Output

Output

Output

Output

Output

Input

Input

Input

K1

K2

K3

Output

Output

Output



A MapReduce Example

• Count by suite, on each floor

• Send per-suite, per platform totals to lobby

• Sort totals by platform

• Send two platform packets to 10th, 20th, 30th floor

• Tally up each platform

• Merge tallies into one spreadsheet

• Collect the tallies

What’s a Distributed File System?

• One where data gets distributed over commodity drives on commodity servers

• Data is replicated

• If one box goes down, no data lost– Except the name node = SPOF!

• BUT: HDFS is immutable– Files can only be written to once

– So updates require drop + re-write (slow)



Hadoop = MapReduce + HDFS

• Modeled after Google MapReduce + GFS

• Have more data? Just add more nodes to cluster. – Mappers execute in parallel

– Hardware is commodity

– “Scaling out”

• Use of HDFS means data may well be local to mapper processing

• So, not just parallel, but minimal data movement, which avoids network bottlenecks

The Hadoop Stack

MapReduce, HDFS

Database

RDBMS Import/Export

Query: HiveQL and Pig Latin

Machine Learning/Data Mining

Log file integration



Ways to work

• Amazon Web Services Elastic MapReduce– Create AWS account

– Select Elastic MapReduce in Dashboard

• Microsoft Hadoop on Azure– Visit www.hadooponazure.com

– Request invite

• Cloudera CDH VM image– Download

– Run via VMWare Player

Amazon Elastic MapReduce

• Lots of steps!

• At a high level:– Setup AWS account and S3 “buckets”

– Generate Key Pair and PEM file

– Install Ruby and EMR Command Line Interface

– Provision the cluster using CLI

– Setup and run SSH/PuTTY

– Work interactively at command line



Amazon EMR – Prep Steps

• Create an AWS account

• Create an S3 bucket for log storage– with list permissions for authenticated users

• Create a Key Pair and save PEM file

• Install Ruby

• Install Amazon Web Services Elastic MapReduce Command Line Interface – aka AWS EMR CLI

• Create credentials.json in EMR CLI folder– Associate with same region as where key pair created

Amazon – Security and Startup• Security

– Download PuTTYgen and run it– Click Load and browse to PEM file– Save it in PPK format– Exit PuTTYgen

• In a command window, navigate to EMR CLI folder and enter command:– ruby elastic-mapreduce --create --alive [--num-instance xx]

[--pig-interactive] [--hive-interactive] [--hbase --instance-type m1.large]

• In AWS Console, go to EC2 Dashboard and click Instances on left nav bar

• Wait until instance is running and get its Public DNS name– Use Compatibility View in IE or copy may not work



Connect!• Download and run PuTTY• Paste DNS name of EC2 instance into hostname

field • In Treeview, drill down and navigate to

Connection\SSH\Auth, browse to PPK file• Once EC2 instance(s) running, click Open• Click Yes to “The server’s host key is not cached

in the registry…” PuTTY Security Alert• When prompted for user name, type “hadoop” and

hit Enter• cd bin, then hive, pig, hbase shell• Right-click to paste from clipboard; option to go

full-screen• (Kill EC2 instance(s) from Dashboard when done)

Amazon Elastic MapReduce



Microsoft Hadoop on Azure• Much simpler• Browser-based portal

– Provisioning cluster, managing ports, MapReduce jobs– External data from Azure BLOB storage

• Interactive JavaScript console– HDFS, Pig, light data visualization

• Interactive Hive console– Hive commands and metadata discovery

• From Portal page you can RDP directly to Hadoop head node– Double click desktop shortcut for CLI access– Certain environment variables may need to be set

Microsoft HDInsight



Hadoop commands• HDFS

– hadoop fs filecommand– Create and remove directories:

mkdir, rm, rmr– Upload and download files to/from HDFS

get, put– View directory contents

ls, lsr– Copy, move, view files

cp, mv, cat

• MapReduce– Run a Java jar-file based job

hadoop jar jarname params

Hadoop (directly)



HBase

• Concepts:– Tables, column families

– Columns, rows

– Keys, values

• Commands:– Definition: create, alter, drop, truncate

– Manipulation: get, put, delete, deleteall, scan

– Discovery: list, exists, describe, count

– Enablement: disable, enable

– Utilities: version, status, shutdown, exit

– Reference: http://wiki.apache.org/hadoop/Hbase/Shell

HBase Examples

• create 't1', 'f1', 'f2', 'f3'

• describe 't1'

• alter 't1', {NAME => 'f1', VERSIONS => 5}

• put 't1', 'r1', 'c1', 'value', ts1

• get 't1', 'r1'

• count 't1'



HBase

Hive

• Used by most BI products which connect to Hadoop

• Provides a SQL-like abstraction over Hadoop– Officially HiveQL, or HQL

• Works on own tables, but also on HBase

• Query generates MapReduce job, output of which becomes result set

• Microsoft has Hive ODBC driver– Connects Excel, Reporting Services, PowerPivot,

Analysis Services Tabular Mode (only)



Hive, Continued

• Load data from flat HDFS files– LOAD DATA LOCAL INPATH './examples/files/kv1.txt‘OVERWRITE INTO TABLE pokes;

• SQL Queries– CREATE, ALTER, DROP

– INSERT OVERWRITE (creates whole tables)

– SELECT, JOIN, WHERE, GROUP BY

– SORT BY, but ordering data is tricky!

– USING allows for streaming on map, reduce steps

Hive



Pig• Instead of SQL, employs a language (“Pig

Latin”) that accommodates data flow expressions– Do a combo of Query and ETL

• “10 lines of Pig Latin ≈ 200 lines of Java.”

• Works with structured or unstructured data

• Operations– As with Hive, a MapReduce job is generated

– Unlike Hive, output is only flat file to HDFS

– With MS Hadoop, can easily convert to JavaScript array, then manipulate

• Use command line (“Grunt”) or build scripts

Example

• A = LOAD ‘myfile’AS (x, y, z);

B = FILTER A by x > 0;C = GROUP B BY x;D = FOREACH A GENERATEx, COUNT(B);

STORE D INTO ‘output’;



Pig Latin Examples• Imperative, file system commands

– LOAD, STORESchema specified on LOAD

• Declarative, query commands (SQL-like)– xxx = table/file

– FOREACH xxx GENERATE (SELECT…FROM xxx)

– JOIN (WHERE/INNER JOIN)

– FILTER xxx BY (WHERE)

– ORDER xxx BY (ORDER BY)

– GROUP xxx BY / GENERATE COUNT(xxx)(SELECT COUNT(*) GROUP BY)

– DISTINCT (SELECT DISTINCT)

• Syntax is assignment statement-based:– MyCusts = FILTER Custs BY SalesPerson eq 15

• COGROUP, UDFs

Pig



Sqoop

sqoop import--connect "jdbc:sqlserver://<servername>.database.windows.net:1433;database=<dbname>;user=<username>@<servername>;password=<password>"

--table <from_table>--target-dir <to_hdfs_folder>--split-by <from_table_column>

Sqoop

sqoop export--connect "jdbc:sqlserver://<servername>.database.windows.net:1433;database=<dbname>;user=<username>@<servername>;password=<password>"

--table <to_table>--export-dir <from_hdfs_folder>--input-fields-terminated-by "<delimiter>"



Flume NG

• Source– Avro (data serialization system – can read json-

encoded data files, and can work over RPC)

– Exec (reads from stdout of long-running process)

• Sinks– HDFS, HBase, Avro

• Channels– Memory, JDBC, file

Flume NG• Setup conf/flume.conf# Define a memory channel called ch1 on agent1

agent1.channels.ch1.type = memory

# Define an Avro source called avro-source1 on agent1 and tell it

# to bind to 0.0.0.0:41414. Connect it to channel ch1.

agent1.sources.avro-source1.channels = ch1

agent1.sources.avro-source1.type = avro

agent1.sources.avro-source1.bind = 0.0.0.0

agent1.sources.avro-source1.port = 41414

# Define a logger sink that simply logs all events it receives

# and connect it to the other end of the same channel.

agent1.sinks.log-sink1.channel = ch1

agent1.sinks.log-sink1.type = logger

# Finally, now that we've defined all of our components, tell

# agent1 which ones we want to activate.

agent1.channels = ch1

agent1.sources = avro-source1

agent1.sinks = log-sink1

• From the command line:flume-ng agent --conf ./conf/ -f conf/flume.conf -n agent1



Mahout Algorithms• Recommendation

– Your info + community info– Give users/items/ratings; get user-user/item-item– itemsimilarity

• Classification/Categorization– Drop into buckets– Naïve Bayes, Complementary Naïve Bayes, Decision

Forests

• Clustering– Like classification, but with categories unknown– K-Means, Fuzzy K-Means, Canopy, Dirichlet, Mean-

Shift

Workflow, Syntax• Workflow

– Run the job– Dump the output– Visualize, predict

• mahout algorithm-- input folderspec-- output folderspec-- param1 value1-- param2 value2

…• Example:

– mahout itemsimilarity--input <input-hdfs-path>--output <output-hdfs-path>--tempDir <tmp-hdfs-path>-s SIMILARITY_LOGLIKELIHOOD



Resources• Big On Data blog

– http://www.zdnet.com/blog/big-data

• Apache Hadoop home page– http://hadoop.apache.org/

• Hive & Pig home pages– http://hive.apache.org/– http://pig.apache.org/

• Hadoop on Azure home page– https://www.hadooponazure.com/

• SQL Server 2012 Big Data– http://bit.ly/sql2012bigdata

Thank you

• [email protected]

• @andrewbrust on twitter

• Get Blue Badge’s free briefings– Text “bluebadge” to 22828

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Hadoop and its Ecosystem Components in Action