Hadoop and its Ecosystem Components in Action

SQL Server Live! Orlando 2012

Hadoop and its Ecosystem
Components in Action
Andrew Brust
CEO and Founder
Blue Badge Insights
Level: Intermediate

Meet Andrew
• 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

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


My New Blog (bit.ly/bigondata)

MapReduce, in a Diagram

Input mapper Output

K1

Input mapper Output Input reducer Output

Output
K2
Input mapper Output Input reducer Output
Input
K3
Input mapper Output
Input reducer Output

Input mapper Output

Input mapper 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

• Collect the tallies

• Merge tallies into one spreadsheet

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
Log file integration

Machine Learning/Data Mining

RDBMS Import/Export

Query: HiveQL and Pig Latin

Database

MapReduce, HDFS



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
ConnectionSSHAuth, 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 GENERATE
x, COUNT(B);
STORE D INTO ‘output’;



Pig Latin Examples
• Imperative, file system commands
– LOAD, STORE
Schema 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

• andrew.brust@bluebadgeinsights.com
• @andrewbrust on twitter
• Get Blue Badge’s free briefings
– Text “bluebadge” to 22828


Hadoop and its Ecosystem Components in Action

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