Hadoop and its Ecosystem Hadoop and its Ecosystem Components in ActionComponents in Action

Andrew J. BrustAndrew J. 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 AndrewMeet Andrew

My New Blog (My New Blog (bit.ly/bigondata))

Read All About It!Read All About It!

AgendaAgenda

• Understand:– MapReduce, Hadoop, and Hadoop stack elements

• Review:– Microsoft Hadoop on Azure, Amazon Elastic

MapReduce (EMR), Cloudera CDH4

• Demo:– Hadoop, HBase, Hive, Pig

• Discuss:– Sqoop, Flume

• Demo– Mahout

MapReduce, in a DiagramMapReduce, 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, K4…

K2, K5…

K3, K6…

Output

Output

Output

A MapReduce ExampleA 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?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 + HDFSHadoop = 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 StackThe Hadoop Stack

• Hadoop– MapReduce, HDFS

• Hbase – NoSQL Database– Lesser extent: Cassandra, HyperTable

• Hive, Pig– SQL-like “data warehouse” system– Data transformation language

• Sqoop– Import/export between HDFS, HBase,

Hive and relational data warehouses

• Flume– Log file integration

• Mahout– Data Mining

Ways to workWays to work• Microsoft Hadoop on Azure

– Visit www.hadooponazure.com– Request invite– Free, for now

• Amazon Web Services Elastic MapReduce– Create AWS account– Select Elastic MapReduce in Dashboard– Cheap for experimenting, but not free

• Cloudera CDH VM image– Download as .tar.gz file– “Un-tar” (can use WinRAR, 7zip)– Run via VMWare Player or Virtual Box– Everything’s free

Microsoft Hadoop on AzureMicrosoft Hadoop on Azure• Much simpler than the others• Browser-based portal

– Provisioning cluster, managing ports, MapReduce jobs– Gathering external data

Configure Azure Storage, Amazon S3Import from DataMarket to Hive

• 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 Hadoop on Microsoft Hadoop on AzureAzure

Amazon Elastic MapReduceAmazon 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

A batch file can work very well here– Setup and run SSH/PuTTY

– Work interactively at command line

Amazon Elastic MapReduceAmazon Elastic MapReduce

Cloudera CDH4 Virtual MachineCloudera CDH4 Virtual Machine

• Get it for free, in VMWare and Virtual Box versions.– VMWare player and Virtual Box are free too

• Run it, and configure it to have its own IP on your network.

• Assuming IP of 192.168.1.59, open browser on your own (host ) machine and navigate to:– http://192.168.1.59:8888

• Can also use browser in VM and hit:– http://localhost:8888

• Work in “Hue”…

HueHue• Browser based UI,

with front ends for:– HDFS (w/ upload &

download)– MapReduce job

creation and monitoring

– Hive (“Beeswax”)• And in-browser

command line shells for:– HBase– Pig (“Grunt”)

Cloudera CDH4Cloudera CDH4

Hadoop commandsHadoop 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 jobhadoop jar jarname params

Hadoop (directly)Hadoop (directly)

HBaseHBase• 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

• Moreover,– Interesting HBase work can be done in MapReduce, Pig

HBase ExamplesHBase Examples

• create 't1', 'f1', 'f2', 'f3'• describe 't1'• alter 't1', {NAME => 'f1',

VERSIONS => 5} • put 't1', 'r1', 'c1:f1', 'value'• get 't1', 'r1'• count 't1'

HBaseHBase

HiveHive

• 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, ContinuedHive, Continued

• Load data from flat HDFS files– LOAD DATA [LOCAL] INPATH 'myfile'INTO TABLE mytable;

• SQL Queries– CREATE, ALTER, DROP– INSERT OVERWRITE (creates whole tables)

– SELECT, JOIN, WHERE, GROUP BY– SORT BY, but ordering data is tricky!

– MAP/REDUCE/TRANSFORM…USING allows for custom map, reduce steps utilizing Java or streaming code

HiveHive

PigPig• 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 or text at

command line console– With MS Hadoop, can easily convert to JavaScript array,

then manipulate

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

ExampleExample

• 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 ExamplesPig Latin Examples• Imperative, file system commands

– LOAD, STORESchema specified on LOAD

• Declarative, query commands (SQL-like)– xxx = file or data set– 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;• Access Hbase

– CpuMetrics = LOAD 'hbase://SystemMetrics' USING org.apache.pig.backend.hadoop.hbase.HBaseStorage('cpu:','-loadKey -returnTuple');

PigPig

SqoopSqoop

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>

SqoopSqoop

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 NGFlume 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 (next generation)Flume NG (next generation)• Setup conf/flume.conf# Define a memory channel called ch1 on agent1agent1.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 = ch1agent1.sources.avro-source1.type = avroagent1.sources.avro-source1.bind = 0.0.0.0agent1.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 = ch1agent1.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 = ch1agent1.sources = avro-source1agent1.sinks = log-sink1

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

Mahout AlgorithmsMahout 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, SyntaxWorkflow, 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

MahoutMahout

The Truth About MahoutThe Truth About Mahout

• Mahout is really just an algorithm engine• Its output is almost unusable by non-

statisticians/non-data scientists• You need a staff or a product to visualize, or

make into a usable prediction model• Investigate Predixion Software

– CTO, Jamie MacLennan, used to lead SQL Server Data Mining team

– Excel add-in can use Mahout remotely, visualize its output, run predictive analyses

– Also integrates with SQL Server, Greenplum, MapReduce

– http://www.predixionsoftware.com

ResourcesResources• 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/

• Cloudera CDH 4 download– https://ccp.cloudera.com/display/SUPPORT/CDH+Downloads

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

Thank youThank you

• andrew.brust@bluebadgeinsights.com• @andrewbrust on twitter• Get Blue Badge’s free briefings

– Text “bluebadge” to 22828