Large-Scale Data Processing with Hadoop and PHP (PHPDAY11 2011-05-14)

LARGE-SCALE DATAPROCESSING WITHHADOOP AND PHP

David Zülke

David Zuelke

http://en.wikipedia.org/wiki/File:München_Panorama.JPG

Founder

Lead Developer

@dzuelke

FROM 30.000 FEETDistributed And Parallel Computing

we want to process data

how much data exactly?

SOME NUMBERS• Facebook • Google • New data per day: • Data processed per month: 400 PB (in 2007!) • 200 GB (March 2008) • Average job size: 180 GB • 2 TB (April 2009) • 4 TB (October 2009) • 12 TB (March 2010)

what if you have that much data?

what if you have just 1% of that amount?

“no problemo”, you say?(or, if youre Italian, “nessun problema a tutti”)

reading 180 GB sequentially off a disk will take ~45 minutes

but you only have 16 GB or so of RAM per computer

data can be processed much faster than it can be read

solution: parallelize your I/O

but now you need to coordinate what you’re doing

and that’s hard

what if a node dies?

is data lost?will other nodes in the grid have to re-start? how do you coordinate this?

ENTER: OUR HERO Introducing MapReduce

in the olden days, the workload was distributed across a grid

but the data was shipped around between nodes

or even stored centrally on something like an SAN

I/O bottleneck

along came a Google publication in 2004

MapReduce: Simpliﬁed Data Processing on Large Clusters http://labs.google.com/papers/mapreduce.html

now the data is distributed

computing happens on the nodes where the data already is

processes are isolated and don’t communicate (share-nothing)

BASIC PRINCIPLE: MAPPER•A Mapper reads records and emits <key, value> pairs • Example: Apache access.log • Each line is a record • Extract client IP address and number of bytes transferred • Emit IP address as key, number of bytes as value• For hourly rotating logs, the job can be split across 24 nodes* * In pratice, it’s a lot smarter than that

BASIC PRINCIPLE: REDUCER•A Reducer is given a key and all values for this speciﬁc key • Even if there are many Mappers on many computers; the results are aggregated before they are handed to Reducers • Example: Apache access.log • The Reducer is called once for each client IP (that’s our key), with a list of values (transferred bytes) • We simply sum up the bytes to get the total trafﬁc per IP!

EXAMPLE OF MAPPED INPUT IP Bytes 212.122.174.13 18271 212.122.174.13 191726 212.122.174.13 198 74.119.8.111 91272 74.119.8.111 8371 212.122.174.13 43

REDUCER WILL RECEIVE THIS IP Bytes 18271 191726 212.122.174.13 198 43 91272 74.119.8.111 8371

AFTER REDUCTION IP Bytes212.122.174.13 210238 74.119.8.111 99643

PSEUDOCODEfunction map($line_number, $line_text) { $parts = parse_apache_log($line_text); emit($parts[ip], $parts[bytes]);}function reduce($key, $values) { $bytes = array_sum($values); emit($key, $bytes);}212.122.174.13 -‐ -‐ [30/Oct/2009:18:14:32 +0100] "GET /foo HTTP/1.1" 200 18271212.122.174.13 -‐ -‐ [30/Oct/2009:18:14:32 +0100] "GET /bar HTTP/1.1" 200 191726212.122.174.13 -‐ -‐ [30/Oct/2009:18:14:32 +0100] "GET /baz HTTP/1.1" 200 19874.119.8.111 -‐ -‐ [30/Oct/2009:18:14:32 +0100] "GET /egg HTTP/1.1" 200 4374.119.8.111 -‐ -‐ [30/Oct/2009:18:14:32 +0100] "GET /moo HTTP/1.1" 200 91272212.122.174.13 -‐ -‐ [30/Oct/2009:18:14:32 +0100] "GET /yay HTTP/1.1" 200 8371212.122.174.13 21023874.119.8.111 99643

A YELLOW ELEPHANT Introducing Apache Hadoop

Hadoop is a MapReduce framework

it allows us to focus on writing Mappers, Reducers etc.

and it works extremely well

how well exactly?

HADOOP AT FACEBOOK (I)• Predominantly used in combination with Hive (~95%)• 8400 cores with ~12.5 PB of total storage•8 cores, 12 TB storage and 32 GB RAM per node• 1x Gigabit Ethernet for each server in a rack• 4x Gigabit Ethernet from rack switch to core Hadoop is aware of racks and locality of nodes http://www.slideshare.net/royans/facebooks-petabyte-scale-data-warehouse-using-hive-and-hadoop

HADOOP AT FACEBOOK (II)• Daily stats: • New data per day: • 25 TB logged by Scribe • I/08: 200 GB • 135 TB of compressed • II/09: 2 TB (compressed) data scanned • III/09: 4 TB (compressed) • 7500+ Hive jobs • I/10: 12 TB (compressed) • ~80k compute hours http://www.slideshare.net/royans/facebooks-petabyte-scale-data-warehouse-using-hive-and-hadoop

HADOOP AT YAHOO!• Over 25,000 computers with over 100,000 CPUs• Biggest Cluster: • 4000 Nodes • 2x4 CPU cores each • 16 GB RAM each• Over 40% of jobs run using Pig http://wiki.apache.org/hadoop/PoweredBy

OTHER NOTABLE USERS• Twitter (storage, logging, analysis. Heavy users of Pig)• Rackspace (log analysis; data pumped into Lucene/Solr)• LinkedIn (friend suggestions)• Last.fm (charts, log analysis, A/B testing)• The New York Times (converted 4 TB of scans using EC2)

there’s just one little problem

you need to write Java code

however, there is hope...

STREAMINGHadoop Won’t Force Us To Use Java

Hadoop Streaming can use any script as Mapper or Reducer

many conﬁguration options (parsers, formats, combining, …)

it works using STDIN and STDOUT

Mappers are streamed the records (usually by line: <line>n)and emit key/value pairs: <key>t<value>n

Reducers are streamed key/value pairs: <keyA>t<value1>n <keyA>t<value2>n <keyA>t<value3>n <keyB>t<value4>n

Caution: no separate Reducer processes per key (but keys are sorted)

HDFSHadoop Distributed File System

HDFS• Stores data in blocks (default block size: 64 MB)• Designed for very large data sets• Designed for streaming rather than random reads• Write-once, read-many (although appending is possible)• Capable of compression and other cool things

HDFS CONCEPTS• Large blocks minimize amount of seeks, maximize throughput• Blocks are stored redundantly (3 replicas as default)• Aware of infrastructure characteristics (nodes, racks, ...)• Datanodes hold blocks• Namenode holds the metadata Critical component for an HDFS cluster (HA, SPOF)

JOB PROCESSING How Hadoop Works

Just like I already described! It’s MapReduce! o/

BASIC RULES• Uses Input Formats to split up your data into single records• You can optimize using combiners to reduce locally on a node • Only possible in some cases, e.g. for max(), but not avg()• You can control partitioning of map output yourself • Rarely useful, the default partitioner (key hash) is enough• And a million other things that really don’t matter right now ;)

oh and, if you’re wondering how Hadoop got its name

The name my kid gave a stuffed yellowelephant. Short, relatively easy to spell andpronounce, meaningless and not used elsewhere:those are my naming criteria. Kids are good atgenerating such. Googol is a kid’s term. Doug Cutting

STREAMING WITH PHP Introducing HadooPHP

HADOOPHP•A little framework to help with writing mapred jobs in PHP• Takes care of input splitting, can do basic decoding et cetera • Automatically detects and handles Hadoop settings such as key length or ﬁeld separators• Packages jobs as one .phar archive to ease deployment • Also creates a ready-to-rock shell script to invoke the job

written by

http://github.com/dzuelke/hadoophp

HANDS-ONHadoop Streaming & PHP in action

THE HADOOP ECOSYSTEM A Little Tour

APACHE AVROEfﬁcient Data Serialization System With Schemas (compare: Facebook’s Thrift)

CLOUDERA FLUMEDistributed Data Collection System (compare: Facebook’s Scribe)

APACHE HBASELike Google’s BigTable, Only That You Can Have It, Too!

HDFSYour Friendly Distributed File System

HIVE Data Warehousing MadeSimple With An SQL Interface

PIGA High-Level Language For Modeling Data Processing Tasks (fulﬁlls the same purpose as Hive)

ZOOKEEPERYour Distributed Applications, Coordinated

!e End

RESOURCES• http://www.cloudera.com/developers/learn-hadoop/• Tom White: Hadoop. The Deﬁnitive Guide. O’Reilly, 2009• http://www.cloudera.com/hadoop/ • Cloudera Distribution for Hadoop is easy to install and has all the stuff included: Hadoop, Hive, Flume, Sqoop, Oozie, …

Questions?

THANK YOU! This was http://joind.in/2996 by @dzuelke. Contact me or hire us:david.zuelke@bitextender.com

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Large-Scale Data Processing with Hadoop and PHP (PHPDAY11 2011-05-14)

by David Zuelke on May 14, 2011

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Large-Scale Data Processing with Hadoop and PHP (PHPDAY11 2011-05-14) — Presentation Transcript