1. Hadoop as a Service Boston Azure / Microsoft DevBoston 07-Feb-2012 Copyright (c) 2011, Bill Wilder – Use allowed under Creative Commons license http://creativecommons.org/licenses/by-nc-sa/3.0/ http://creativecommons.org/licenses/by-nc-sa/3.0/ Boston Azure User Group http://www.bostonazure.org @bostonazure Bill Wilder http://blog.codingoutloud.com http://blog.codingoutloud.com @codingoutloud Big Data tools for the Windows Azure cloud platform

2. Windows Azure MVP Windows Azure Consultant Boston Azure User Group Founder Cloud Architecture Patterns book (due 2012) Bill Wilder

3. We will consider… 1.How might we build a simple Word Frequency Counter? 2.What are Map and Reduce? 3.How do we scale our Word Frequency Counter? – Hint: we might use Hadoop 4.How does Windows Azure make Hadoop easier with “Hadoop as a Service” – CTP

4. Five exabytes of data created every two days - Eric Schmidt (CEO Google at the time) As much as from the dawn of civilization up until 2003

5. Three Vs Volume  lots of it already Velocity  more of it every day Variety  many sources, many formats “Big Data” Challenge

6. Short History of Hadoop ////// 1. Inspired by: Google Map/Reduce paper – http://research.google.com/archive/mapreduce.html http://research.google.com/archive/mapreduce.html Google File System (GFS) – Goals: distributed, fault tolerant, fast enough 2. Born in: Lucene Nutch project Built in Java Hadoop cluster appears as single über- machine

7. Hadoop: batch processing, big data Batch, not real-time or transactional Scale out with commodity hardware Big customers like LinkedIn and Yahoo! – Clusters with 10s of Petabytes (pssst… these fail… daily) Import data from Azure Blob, Data Market, S3 – Or from files, like we will do in our example

8. Word Frequency Counter – how? The “hello world” of Hadoop / MapReduce – But we start without Hadoop / MapReduce Input: large corpus – Wikipedia extract for example – Can handle into PB Output: list of words, ordered by frequency the 31415 be9265 to3589 of 793 and238…

9. Simple Word Frequency Counter const string file = @"e:\dev\azure\hadoop\wordcount\davinci.txt"; var text = File.ReadAllText(file); var matches = Regex.Matches(text, @"\b[\w]*\b"); var words = (from m in matches.Cast () where !string.IsNullOrEmpty(m.Value) orderby m.Value.ToLower() select m.Value.ToLower()).ToArray(); var wordCounts = new Dictionary (); foreach (var word in words) { if (wordCounts.ContainsKey(word)) wordCounts[word]++; else wordCounts.Add(word, 1); } foreach (var wc in wordCounts) Console.WriteLine(wc.Key + " : " + wc.Value);  Read in all text  Parse out words  Normalize & Sort  How many times does each word appear aware : 7 away : 99 awning : 2 awoke : 1 axes : 16 axil : 3 axiom : 2 Output   REDUCE MAP

10. Map “Apply a function to each element in this list and return a new list” Reduce “Apply a function collectively to all elements in this list and return the final answer.”

11. Map Example 1 square(x) { return x*x } { 1, 2, 3, 4 }  { 1, 4, 9, 16 } Reduce Example 1 sum(x, y) { return x + y } { 1, 2, 3, 4 }  10

12. Map Example 1 square(x) { return x*x } { 1, 2, 3, 4 }  { 1, 4, 9, 16 } { square(1), square(2), square(3), square(4) } Reduce Example 1 sum(x, y) { return x + y } { 1, 2, 3, 4 }  10 sum(sum(sum(1,2),3),4)

13. Map Example 2 strlen(s) { return s.Length } { “Celtics”, “Bruins” }  { 7, 6 } Reduce Example 2 strlen_sum(x, y) { return x.Length + y.Length } { “Celtics”, “Bruins” }  13

14. Map Example 3 (the fancy one) fancy_mapper(s) { if (s == “SkipMe”) return null; return ToLower(s) + “, “ + s.Length; } { “Will”, “Dan”, “SkipMe”, “Kevin”, “T.J.” }  { “will, 4”, “dan, 3”, “kevin, 5”, “t.j., 4” }

15. Problems with Word Counter? What happens if our data is… 1 GB, 1 TB, 1 PB, … What happens if our data is… Images, videos, tweets, Facebook updates, … What happens if our processing is… Complex, multiple steps, …

16. Simplified Example Word Frequency Counter Which word appears most frequently, and how many times?

17. Workflow 1.Setup 2.Map 3.Shuffle 4.Reduce 5.Celebrate

18. Hadoop Cluster 1 MASTER NODEMany SLAVE NODES - Job Tracker- Task Tracker on each “the boss” HDFS on all nodes

19. Step 1. Setup (Assumes: you’ve installed Hadoop on a cluster of computers) You supply: 1.Map and Reduce logic – This is “code” – packaged in a Java JAR file – Other language support exists, more coming 2.A big pile of input files – “Get the data here” – For Word Frequency Counter, we might use Wikipedia or Project Gutenberg files 3.Go!

20. Step 2. Map Job Tracker distributes your Mapper and Reducer – To every node Job Tracker distributes your data – To some nodes (at least 3) in 64 MB chunks Task Tracker on each node calls Mapper – Repeatedly until done; lots of parallelism Job Tracker watches for problems – Manages retries, failures, optimistic attempts

21. Mapper’s Job is Simple* Read input, write output – that’s all – Function signature: Map(text) – Parses the text and returns { key, value } – Map(“a b a foo”) returns {a, 1}, {b, 1}, {a, 1}, {foo, 1} * for Word Frequency Counter!

22. Actual Java Map Function public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, “1”); }

23. Step 3. Shuffle Shuffle collects all data from Map, organizes by key, and redistributes data to the nodes for Reduce

24. Step 3. Shuffle – example Mapper input: “the Bruins are the best in the NHL” Mapper output: { the,1 } { the,1 }{ the, 1 } { Bruins,1 } { best,1 }{ NHL, 1 } { are, 1 } { in, 1 } Shuffle transforms this into Reducer input: { are,[ 1 ] } { in, [ 1 ] } { Bruins, [ 1 ] } { best, [ 1 ] } { the, [ 1, 1, 1 ] }{ NHL, [ 1 ] }

25. Step 4. Reduce Output from Step 3. Shuffle has been distributed to datanodes Your “Reducer” is called on local data – Repeatedly, until all complete – Tasks run in parallel on nodes This is very simple for Word Frequency Counter! – Function signature: Reduce(key, values[]) – Adds up all the values and returns { key, sum }

26. Actual Java Reduce Function public void reduce(Text key, Iterable values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } }

27. Step 5. Celebrate! You are done – you have your output In more complex scenario, might repeat – Hadoop tool ecosystem knows how to do this There are other projects in the Hadoop ecosystem for … – Multi-step jobs – Managing a data warehouse – Supporting ad hoc querying – And more!

28. www.hadooponazure.com demo

29. There’s a LOT MORE to the Hadoops… Hadoop streaming interface allows other languages – C# HIVE (HiveQL) Pig (Pig Latin language) Cascading.org Commercial companies dedicated: – HortonWorks

30. Questions? Comments? More information? ?

31. BostonAzure.org Boston Azure cloud user group Focused on Microsoft’s PaaS cloud platform Last Thursday, monthly, 6:00-8:30 PM at NERD – Food; wifi; free; great topics; growing community Boston Azure Boot Camp: June 2012 ( planning ) Follow on Twitter: @bostonazure More info or to join our Meetup.com group: http://www.bostonazure.org

32. Contact Me Looking for … consulting help with Windows Azure Platform? someone to bounce Azure or cloud questions off? a speaker for your user group or company technology event? Just Ask! Bill Wilder @codingoutloud http://blog.codingoutloud.com

33. Hadoop A tool to economically create value from the Three Vs New tool – Complements, rather than displaces, existing data analysis tools There are other approaches, but Hadoop is winning – Emerging as standard (remember XP vs. Scrum?) – Microsoft embracing (http://www.hadooponazure.com/)http://www.hadooponazure.com/ Large Ecosystem – Apache Hadoop project – HBASE, HIVE, Pig, Mahout, … – Hadoop-specific companies: HortonWorks, Cloudera

34. Microsoft working with HortonWorks to port to Windows and enable on Windows Azure Looking to make JavaScript, C# first-class languages – Native language is Java – Community support for Python and others

35. HIVE HIVE QL – SQL-like Creates MapReduce job in the background Remember: Hadoop is BATCH oriented Hive Excel Plugin Hive Interactive Console in Azure

36. MapReduce is Functional Programming Like C#!