Hadoop at JavaZone 2010

Hadoop divide and conquer petabyte-scale data © Matthew McCullough, Ambient Ideas, LLC

Metadata Matthew McCullough Ambient Ideas, LLC matthewm@ambientideas.com http://ambientideas.com/blog @matthewmccull Code http://github.com/matthewmccullough/hadoop-intro

http://delicious.com/matthew.mccullough/hadoop

ado op o ften. I u se H d my Tivo the s oun Th at’s I s kip a very time ma kes e com mer cial. rrency in Practice Ja va Concu n Goetz, author of -Bria

u are using mpu ter yo The co right now ell hav e the very w sor may GHz pro ces fas test u’ll ever own yo

Scale up?

Scale out

Talk Roadmap History MapReduce Filesystem DSLs

History

Do ug Cu tt in g

open source

Lucene

Lucene Nutch

Lucene Nutch Hadoop

Lucene Mahout Nutch Hadoop

Today 0.21.0 current version Dozens of companies contributing Hundreds of companies using

Virtual Machine

VM Sources Yahoo True to the OSS distribution Cloudera Desktop tools Both VMWare based

Set up the Environment • Launch “cloudera-training 0.3.3” VMWare instance • Open a terminal window Lab

Log Files •Attach tail to all the Hadoop logs Lab

processing / Data

Processing Framework Data Storage MapReduce HDFS Hive QL Hive Chuckwa HBase Flume Avro ZooKeeper Hybrid Pig

Hadoop Components

Hadoop Components Tool Purpose Common MapReduce HDFS Filesystem Pig Analyst MapReduce language HBase Column-oriented data storage Hive SQL-like language for HBase ZooKeeper Workﬂow & distributed transactions Chukwa Log ﬁle processing

the Players Comm C hukwa ZooKeeper on H Base Hive HDFS

Motivations

Original Goals Web Indexer Yahoo Search Engine

Pre-Hadoop Shortcomings Search engine update frequency Storage costs Expense of durability Ad-hoc queries

Anti-Patterns Cured RAM-heavy RDBMS boxes Sharding Archiving Ever-smaller-range SQL queries

1 Gigabyte

1 Terabyte

1 Petabyte

16 Petabytes

Near-Linear Hardware Scalability

Applications Protein folding pharmaceutical research Search Engine Indexing walking billions of web pages Product Recommendations based on other customer purchases Sorting terabytes to petabyes in size Classification government intelligence

Contextual Ads

SELECT reccProd.name, reccProd.id FROM products reccProd WHERE purchases.customerId = (SELECT customerId FROM customers WHERE purchases.productId = thisProd) LIMIT 5

30% of Amazon sales are from recommendations

ACID ATOMICITY CONSISTENCY ISOLATION DURABILITY

CAP Consistency Availability Partition Tolerance

MapReduce

MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghem awat jeff@google.com, sanjay@goog le.com Google, Inc. Abstract given day, etc. Most such com MapReduce is a programmin putations are conceptu- g model and an associ- ally straightforward. However ated implementation for proc , the input data is usually essing and generating large large and the computations have data sets. Users specify a map to be distributed across function that processes a hundreds or thousands of mac key/value pair to generate a set hines in order to finish in of intermediate key/value a reasonable amount of time. pairs, and a reduce function that The issues of how to par- merges all intermediate allelize the computation, dist values associated with the sam ribute the data, and handle e intermediate key. Many failures conspire to obscure the real world tasks are expressible original simple compu- in this model, as shown tation with large amounts of in the paper. complex code to deal with these issues. Programs written in this func As a reaction to this complex tional style are automati- ity, we designed a new cally parallelized and executed abstraction that allows us to exp on a large cluster of com- ress the simple computa- modity machines. The run-time tions we were trying to perform system takes care of the but hides the messy de- details of partitioning the inpu tails of parallelization, fault-tol t data, scheduling the pro- erance, data distribution gram’s execution across a set and load balancing in a libra of machines, handling mary. Our abstraction is in- chine failures, and managing spired by the map and reduce the required inter-machine primitives present in Lisp communication. This allows and many other functional lang programmers without any uages. We realized that experience with parallel and most of our computations invo distributed systems to eas- lved applying a map op- ily utilize the resources of a larg eration to each logical “record” e distributed system. in our input in order to Our implementation of Map compute a set of intermediat Reduce runs on a large e key/value pairs, and then cluster of commodity machine applying a reduce operation to s and is highly scalable: all the values that shared a typical MapReduce computa the same key, in order to com tion processes many ter- bine the derived data ap- abytes of data on thousands of propriately. Our use of a func machines. Programmers tional model with user- find the system easy to use: hun specified map and reduce ope dreds of MapReduce pro- rations allows us to paral- grams have been implemente lelize large computations easi d and upwards of one thou- ly and to use re-execution sand MapReduce jobs are exec as the primary mechanism for uted on Google’s clusters fault tolerance. every day. The major contributions of this work are a simple and powerful interface that enables automatic parallelization and distribution of large-scale computations, combined 1 Introduction with an implementation of this interface that achieves high performance on large clus ters of commodity PCs. Over the past five years, the Section 2 describes the basic authors and many others at programming model and Google have implemented hun gives several examples. Sec dreds of special-purpose tion 3 describes an imple- computations that process larg mentation of the MapReduce e amounts of raw data, interface tailored towards such as crawled documents, our cluster-based computing web request logs, etc., to environment. Section 4 de- compute various kinds of deri scribes several refinements of ved data, such as inverted the programming model indices, various representatio that we have found useful. Sec ns of the graph structure tion 5 has performance of web documents, summaries measurements of our implem of the number of pages entation for a variety of crawled per host, the set of tasks. Section 6 explores the most frequent queries in a use of MapReduce within Google including our experie nces in using it as the basis To appear in OSDI 2004 1

mode l and gram ming “ A pro for ment ation imple nd ge nera ting pro cess ing a dat a s ets ” la rg e

MapReduce a word counting conceptual example

The Goal Provide the occurrence count of each distinct word across all documents

Raw Data a folder of documents mydoc1.txt mydoc2.txt mydoc3.txt

Map break documents into words

Shuffle physically group (relocate) by key

Reduce count word occurrences

Reduce Again sort occurrences alphabetically

Grep.java

package org.apache.hadoop.examples; import java.util.Random; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapred.*; import org.apache.hadoop.mapred.lib.*; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; /* Extracts matching regexs from input files and counts them. */ public class Grep extends Configured implements Tool { private Grep() {} // singleton public int run(String[] args) throws Exception { if (args.length < 3) { System.out.println("Grep <inDir> <outDir> <regex> [<group>]");

import org.apache.hadoop.util.ToolRunner; /* Extracts matching regexs from input files and counts them. */ public class Grep extends Configured implements Tool { private Grep() {} // singleton public int run(String[] args) throws Exception { if (args.length < 3) { System.out.println("Grep <inDir> <outDir> <regex> [<group>]"); ToolRunner.printGenericCommandUsage(System.out); return -1; } Path tempDir = new Path("grep-temp-"+ Integer.toString(new Random().nextInt (Integer.MAX_VALUE))); JobConf grepJob = new JobConf(getConf(), Grep.class); try { grepJob.setJobName("grep-search"); FileInputFormat.setInputPaths(grepJob, args[0]);

grepJob.setJobName("grep-search"); FileInputFormat.setInputPaths(grepJob, args[0]); grepJob.setMapperClass(RegexMapper.class); grepJob.set("mapred.mapper.regex", args[2]); if (args.length == 4) grepJob.set("mapred.mapper.regex.group", args[3]); grepJob.setCombinerClass(LongSumReducer.class); grepJob.setReducerClass(LongSumReducer.class); FileOutputFormat.setOutputPath(grepJob, tempDir); grepJob.setOutputFormat(SequenceFileOutputFormat.class); grepJob.setOutputKeyClass(Text.class); grepJob.setOutputValueClass(LongWritable.class); JobClient.runJob(grepJob); JobConf sortJob = new JobConf(Grep.class); sortJob.setJobName("grep-sort"); FileInputFormat.setInputPaths(sortJob, tempDir); sortJob.setInputFormat(SequenceFileInputFormat.class);

FileOutputFormat.setOutputPath(grepJob, tempDir); grepJob.setOutputFormat(SequenceFileOutputFormat.class); grepJob.setOutputKeyClass(Text.class); grepJob.setOutputValueClass(LongWritable.class); JobClient.runJob(grepJob); JobConf sortJob = new JobConf(Grep.class); sortJob.setJobName("grep-sort"); FileInputFormat.setInputPaths(sortJob, tempDir); sortJob.setInputFormat(SequenceFileInputFormat.class); sortJob.setMapperClass(InverseMapper.class); sortJob.setNumReduceTasks(1); // write a single file FileOutputFormat.setOutputPath(sortJob, new Path(args [1])); sortJob.setOutputKeyComparatorClass // sort by decreasing freq (LongWritable.DecreasingComparator.class); JobClient.runJob(sortJob); }

JobClient.runJob(grepJob); JobConf sortJob = new JobConf(Grep.class); sortJob.setJobName("grep-sort"); FileInputFormat.setInputPaths(sortJob, tempDir); sortJob.setInputFormat(SequenceFileInputFormat.class); sortJob.setMapperClass(InverseMapper.class); sortJob.setNumReduceTasks(1); // write a single file FileOutputFormat.setOutputPath(sortJob, new Path(args [1])); sortJob.setOutputKeyComparatorClass // sort by decreasing freq (LongWritable.DecreasingComparator.class); JobClient.runJob(sortJob); } finally { FileSystem.get(grepJob).delete(tempDir, true); } return 0; }

RegExMapper.java

/** * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.hadoop.mapred.lib; import java.io.IOException; import java.util.regex.Matcher; import java.util.regex.Pattern;

/** A {@link Mapper} that extracts text matching a regular expression. */ public class RegexMapper<K> extends MapReduceBase implements Mapper<K, Text, Text, LongWritable> { private Pattern pattern; private int group; public void configure(JobConf job) { pattern = Pattern.compile(job.get("mapred.mapper.regex")); group = job.getInt("mapred.mapper.regex.group", 0); } public void map(K key, Text value, OutputCollector<Text, LongWritable> output, Reporter reporter) throws IOException { String text = value.toString(); Matcher matcher = pattern.matcher(text); while (matcher.find()) { output.collect(new Text(matcher.group(group)), new LongWritable (1)); } } }

Have Code, Will Travel Code travels to the data Opposite of traditional systems

Filesystem

uted file alab le di strib “sc ibuted r large distr s yst em fo icati ons. nsive appl d ata-inte ance fa ult t oler ovides It pr in expensive e runn ing on whil ty h ardw are” com modi

HDFS Basics Open Source implementation of Google BigTable Replicated data store Stored in 64MB blocks

HDFS Rack location aware Configurable redundancy factor Self-healing Looks almost like *NIX filesystem

Why HDFS? Random reads Parallel reads Redundancy

Data Overload

Test HDFS • Upload a ﬁle • List directories Lab

HDFS Upload • Show contents of ﬁle in HDFS • Show vocabulary of HDFS Lab

HDFS Distcp • Copy ﬁle to other nodes • MapReduce job starts Lab

HDFS Challenges Writes are re-writes today Append is planned Block size, alignment Small file inefficiencies NameNode SPOF

FSCK •Check ﬁlesystem Lab

NO SQL

Data Categories

Structured Semi-structured Unstructured

NOSQL is... Semi-structured not death of the RDBMS no JOINing no Normalization big-data tools solving different issues than RDBMSes

Grid Benefits

Scalable Data storage is pipelined Code travels to data Near linear hardware scalability

Optimization Preemptive execution Maximizes use of faster hardware New jobs trump P.E.

Fault Tolerant Configurable data redundancy Minimizes hardware failure impact Automatic job retries Self healing filesystem

Sproinnnng! Bzzzt! Poof!

server Funerals No pagers go off when machines die Report of dead machines once a week Clean out the carcasses

ss attributes Ro bustne eding ed fro m ble p revent plication code into ap Data redundancy Node death Retries Data geography Parallelism Scalability

Node TYPES

NameNode SecondaryNameNode DataNode JobTracker TaskTracker

Robust, Primary 1 Hot Backup 0..1 NameNode NFS Secondary NameNode JobTracker JobTracker Commodity Commodity 1..N 1..N TaskTracker TaskTracker DataNode DataNode

NameNode Catalog of data blocks Prefers large files High memory consumption Journaled file system Saves state to disk

Robust, Primary 1 NameNode JobTracker Commodity 1..N TaskTracker DataNode

Secondary NameNode Near-hot backup for NN Snapshots Missing journal entries Data loss in non-optimal setup Grid reconfig? Big IP device

Robust, Primary 1 Hot Backup 0..1 NameNode Secondary NameNode JobTracker JobTracker

NameNode, Cont’d Backup via SNN Journal to NFS with replication? Single point of failure Failover plan Big IP device, virtual IP

Robust, Primary 1 Hot Backup 0..1 NameNode NFS Secondary NameNode JobTracker JobTracker Commodity 1..N TaskTracker Virtual IP DataNode

DataNode Anonymous Data block storage “No identity” is positive trait Commodity equipment

JobTracker Singleton Commonly co-located with NN Coordinates TaskTracker Load balances FairPlay scheduling Preemptive execution

TaskTracker Runs MapReduce jobs Reports back to JobTracker

Listing Nodes •Use Java’sa JPS tool to get list of nodes on box •sudo jps -l Lab

Processing Nodes Anonymous “No identity” is positive trait Commodity equipment

Master Node Master is a special machine Use high quality hardware Single point of failure Recoverable

Key-value Store

HBase Basics Map-oriented storage Key value pairs Column families Stores to HDFS Fast Usable for synchronous responses

Direct Comparison Amazon SimpleDB Google BigTable (Datastore)

Competitors Facebook Cassandra LinkedIn Project Voldemort

Shortcomings HQL WHERE clause limited to key Other column filters are full-table scans No ad-hoc queries

hbase> help create 'mylittletable', 'mylittlecolumnfamily' describe 'mylittletable' put 'mylittletable', 'r2', 'mylittlecolumnfamily', 'x' get 'mylittletable', 'r2' scan 'mylittletable'

HBase • Create column family • Insert data • Select data Lab

Pig Basics Yahoo-authored add-on High-level language for authoring data analysis programs Console

PIG Questions Ask big questions on unstructured data How many ___? Should we ____? Decide on the questions you want to ask long after you’ve collected the data.

Pig Data 999991,female,Mary,T,Hargrave,600 Quiet Valley Lane,Los Angeles,CA,90017,US,Mary.T.Hargrave@dodgit.com, 999992,male,Harold,J,Candelario,294 Ford Street,OAKLAND,CA,94607,US,Harold.J.Candelario@dodgit.com,ad2U 999993,female,Ruth,G,Carter,4890 Murphy Court,Shakopee,MN,55379,US,Ruth.G.Carter@mailinator.com,uaseu8e 999994,male,Lionel,J,Carter,2701 Irving Road,Saint Clairsville,OH,43950,US,Lionel.J.Carter@trashymail.c 999995,female,Georgia,C,Medina,4541 Cooks Mine Road,CLOVIS,NM,88101,US,Georgia.C.Medina@trashymail.com, 999996,male,Stanley,S,Cruz,1463 Jennifer Lane,Durham,NC,27703,US,Stanley.S.Cruz@pookmail.com,aehoh5rooG 999997,male,Justin,A,Delossantos,3169 Essex Court,MANCHESTER,VT,05254,US,Justin.A.Delossantos@mailinato 999998,male,Leonard,K,Baker,4672 Margaret Street,Houston,TX,77063,US,Leonard.K.Baker@trashymail.com,Aep 999999,female,Charissa,J,Thorne,2806 Cedar Street,Little Rock,AR,72211,US,Charissa.J.Thorne@trashymail. 1000000,male,Michael,L,Powell,2797 Turkey Pen Road,New York,NY,10013,US,Michael.L.Powell@mailinator.com

Pig Sample Person = LOAD 'people.csv' using PigStorage(','); Names = FOREACH Person GENERATE $2 AS name; OrderedNames = ORDER Names BY name ASC; GroupedNames = GROUP OrderedNames BY name; NameCount = FOREACH GroupedNames GENERATE group, COUNT(OrderedNames); store NameCount into 'names.out';

Pig Scripting •Re-column and sort data Lab

Hive Basics Authored by SQL interface to HBase Hive is low-level Hive-specific metadata Data warehousing

hive -e 'select t1.x from mylittletable t1'

hive -S -e 'select a.col from tab1 a' > dump.txt

SELECT * FROM shakespeare WHERE freq > 100 SORT BY freq ASC LIMIT 10;

Load Hive Data • Load and select Hive data Lab

Sync, Async RDBMS SQL is realtime Hive is primarily asynchronous

Sqoop A utility Imports from RDBMS Outputs plaintext, SequenceFile, or Hive

sqoop --connect jdbc:mysql://database.example.com/

Data Warehousing

Sync, Async RDBMS is realtime HBase is near realtime Hive is asynchronous

Monitoring

Web Status Panels NameNode http://localhost:50070/ JobTracker http://localhost:50030/

View Consoles • Start job • Watch consoles • Browse HDFS via web interface Lab

Configuration Files

Grid Execution

Single Node start-all.sh Needs XML config ssh account setup Multiple JVMs List of processes jps -l

Start and Stop Grid •cd /usr/lib/hadoop-0.20/bin •sudo -u hadoop ./stop-all.sh •sudo jps -l •tail •sudo -u hadoop ./start-all.sh •sudo jps -l Lab

SSH Setup create key ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa $ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys

Commodity Robust, Primary 1 1..N TaskTracker start-all.sh NameNode start-dfs.sh start-mapred.sh JobTracker DataNode

Complete Grid start-all.sh Host names, ssh setup Default out-of-box experience

Commodity 1..N TaskTracker h dfs.s Robust, Primary rt- 1 sta h DataNode e d.s -m apr s tart start-all.sh NameNode Commodity 1..N JobTracker sta rt-d fs. sta sh rt- ma TaskTracker pr ed .sh DataNode

Grid on the Cloud

EMR Pricing

Summary

Sapir-WHorF Hypothesis... Remixed The ability to store and process massive data influences what you decide to store.

Metadata Matthew McCullough Ambient Ideas, LLC matthewm@ambientideas.com http://ambientideas.com/blog @matthewmccull Code http://github.com/matthewmccullough/hadoop-intro

Hadoop at JavaZone 2010

by Matthew McCullough on Sep 10, 2010

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Hadoop at JavaZone 2010 — Presentation Transcript