Apache Hadoop Talk at QCon

Apache Hadoop, Big Data, and You Philip Zeyliger philip@cloudera.com @philz42 @cloudera November 18, 2009 Wednesday, November 18, 2009

Hi there! Software Engineer Worked at Wednesday, November 18, 2009

I work on stuff... Wednesday, November 18, 2009

Outline Why should you care? (Intro) Challenging yesteryear’s assumptions The MapReduce Model HDFS, Hadoop Map/Reduce The Hadoop Ecosystem Questions Wednesday, November 18, 2009

Data is everywhere. Data is important. Wednesday, November 18, 2009

Wednesday, November 18, 2009

“I keep saying that the sexy job in the next 10 years will be statisticians, and I’m not kidding.” Hal Varian (Google’s chief economist) Wednesday, November 18, 2009

So, what’s Hadoop? The Little Prince, Antoine de Saint-Exupéry, Irene Testot-Ferry Wednesday, November 18, 2009

Apache Hadoop is an open-source system (written in Java!) to store and process gobs of data across many commodity computers. The Little Prince, Antoine de Saint-Exupéry, Irene Testot-Ferry Wednesday, November 18, 2009

Two Big Components HDFS Map/Reduce Self-healing high- bandwidth Fault-tolerant clustered storage. distributed computing. Wednesday, November 18, 2009

Challenging some of yesteryear’s assumptions... Wednesday, November 18, 2009

Assumption 1: Machines can be reliable... Image: MadMan the Mighty CC BY-NC-SA Wednesday, November 18, 2009

Hadoop Goal: Separate distributed system fault-tolerance code from application logic. Systems Programmers Statisticians Wednesday, November 18, 2009

Assumption 2: Machines have identities... Image:Laughing Squid CC BY- NC-SA Wednesday, November 18, 2009

Hadoop Goal: Users should interact with clusters, not machines. Wednesday, November 18, 2009

Assumption 3: A data set ﬁts on one machine... Image: Matthew J. Stinson CC- BY-NC Wednesday, November 18, 2009

Hadoop Goal: System should scale linearly (or better) with data size. Wednesday, November 18, 2009

The M/R Programming Model Wednesday, November 18, 2009

You specify map() and reduce() functions. The framework does the rest. Wednesday, November 18, 2009

map() map: K₁,V₁→list K₂,V₂ public class Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT> { /** * Called once for each key/value pair in the input split. Most applications * should override this, but the default is the identity function. */ protected void map(KEYIN key, VALUEIN value, Context context) throws IOException, InterruptedException { // context.write() can be called many times // this is default “identity mapper” implementation context.write((KEYOUT) key, (VALUEOUT) value); } } Wednesday, November 18, 2009

(the shufﬂe) map output is assigned to a “reducer” map output is sorted by key Wednesday, November 18, 2009

reduce() K₂, iter(V₂)→list(K₃,V₃) public class Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT> { /** * This method is called once for each key. Most applications will define * their reduce class by overriding this method. The default implementation * is an identity function. */ @SuppressWarnings("unchecked") protected void reduce(KEYIN key, Iterable<VALUEIN> values, Context context ) throws IOException, InterruptedException { for(VALUEIN value: values) { context.write((KEYOUT) key, (VALUEOUT) value); } } } Wednesday, November 18, 2009

Putting it together... Logical Physical Flow Physical Wednesday, November 18, 2009

Some samples... Build an inverted index. Summarize data grouped by a key. Build map tiles from geographic data. OCRing many images. Learning ML models. (e.g., Naive Bayes for text classiﬁcation) Augment traditional BI/DW technologies (by archiving raw data). Wednesday, November 18, 2009

There’s more than the Java API Streaming Pig Hive perl, python, Higher-level SQL interface. ruby, whatever. dataﬂow language for easy ad-hoc Great for stdin/stdout/ analysis. analysts. stderr Developed at Developed at Yahoo! Facebook Friday, @10:10 Wednesday, November 18, 2009

A typical look... Commodity servers (8-core, 8-16GB RAM, 4-12 TB, 2x1 gE NIC) 2-level network architecture 20-40 nodes per rack Wednesday, November 18, 2009

The cast... Starring... NameNode (metadata server and database) SecondaryNameNode (assistant to NameNode) JobTracker (scheduler) The Chorus… DataNodes TaskTrackers (block storage) (task execution) Thanks to Zak Stone for earmuff image! Wednesday, November 18, 2009

HDFS 3x64MB file, 3 rep 4x64MB file, 3 rep Namenode Small file, 7 rep Datanodes Wednesday, November 18, 2009 One Rack A Different Rack

HDFS Write Path Wednesday, November 18, 2009

HDFS Failures? Datanode crash? Clients read another copy Background rebalance Namenode crash? uh-oh Wednesday, November 18, 2009

M/R Job on stars Tasktrackers on the same Different job machines as datanodes Idle Wednesday, November 18, 2009 One Rack A Different Rack

M/R Wednesday, November 18, 2009

M/R Failures Task fails Try again? Try again somewhere else? Report failure Retries possible because of idempotence Wednesday, November 18, 2009

Hadoop in the Wild Yahoo! Hadoop Clusters: > 82PB, >25k machines (Eric14, HadoopWorld NYC ’09) Google: 40 GB/s GFS read/write load (Jeff Dean, LADIS ’09) [~3,500 TB/day] Facebook: 4TB new data per day; DW: 4800 cores, 5.5 PB (Dhruba Borthakur, HadoopWorld) Wednesday, November 18, 2009

The Hadoop Ecosystem ETL Tools BI Reporting RDBMS Pig (Data Flow) Hive (SQL) Sqoop Zookeepr (Coordination) Avro (Serialization) MapReduce (Job Scheduling/Execution System) HBase (Key-Value store) HDFS (Hadoop Distributed File System) Wednesday, November 18, 2009

Ok, ﬁne, what next? Get Hadoop! http://hadoop.apache.org/ Cloudera Distribution for Hadoop Try it out! (Locally, or on EC2) Wednesday, November 18, 2009

Just one slide... Software: Cloudera Distribution for Hadoop, Cloudera Desktop, more… Training and certiﬁcation… Free on-line training materials (including video) Support & Professional Services @cloudera, blog, etc. Wednesday, November 18, 2009

Questions? philip@cloudera.com Wednesday, November 18, 2009

Backup Slides Wednesday, November 18, 2009

Important APIs → is 1:many Input Format data→K₁,V₁ Writable Mapper K₁,V₁→K₂,V₂ JobClient M/R Flow Other Combiner K₂,iter(V₂)→K₂,V₂ Partitioner K₂,V₂→int *Context Reducer K₂, iter(V₂)→K₃,V₃ Filesystem Out. Format K₃,V₃→data Wednesday, November 18, 2009

$ cat input.txt adams dunster kirkland dunster kirland dudley dunster adams dunster winthrop $ bin/hadoop jar hadoop-0.18.3- examples.jar grep input.txt output1 'dunster|adams' $ cat output1/part-00000 4 dunster 2 adams Wednesday, November 18, 2009

public int run(String[] args) throws Exception { grepJob.setReducerClass(LongSumRedu FileOutputFormat.setOutputPath(sort if (args.length < 3) { cer.class); Job, new Path(args[1])); System.out.println("Grep // sort by decreasing freq <inDir> <outDir> <regex> [<group>]"); FileOutputFormat.setOutputPath(grep sortJob.setOutputKeyComparatorClass Job, tempDir); (LongWritable.DecreasingComparator. ToolRunner.printGenericCommandUsage class); (System.out); grepJob.setOutputFormat(SequenceFil return -1; eOutputFormat.class); JobClient.runJob(sortJob); } } finally { Path tempDir = new Path("grep- grepJob.setOutputKeyClass(Text.clas temp-"+Integer.toString(new s); FileSystem.get(grepJob).delete(temp Random().nextInt(Integer.MAX_VALUE) Dir, true); )); grepJob.setOutputValueClass(LongWri } JobConf grepJob = new table.class); return 0; JobConf(getConf(), Grep.class); } try { JobClient.runJob(grepJob); grepJob.setJobName("grep- search"); JobConf sortJob = new JobConf(Grep.class); sortJob.setJobName("grep- FileInputFormat.setInputPaths(grepJ sort"); the “grep” ob, args[0]); FileInputFormat.setInputPaths(sortJ grepJob.setMapperClass(RegexMapper. ob, tempDir); class); example sortJob.setInputFormat(SequenceFile grepJob.set("mapred.mapper.regex", InputFormat.class); args[2]); if (args.length == 4) sortJob.setMapperClass(InverseMappe grepJob.set("mapred.mapper.regex.gr r.class); oup", args[3]); // write a single file sortJob.setNumReduceTasks(1); grepJob.setCombinerClass(LongSumRed ucer.class); Wednesday, November 18, 2009

JobConf grepJob = new JobConf(getConf(), Grep.class); try { grepJob.setJobName("grep-search"); FileInputFormat.setInputPaths(grepJob, args[0]); Job 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); 1of 2 FileOutputFormat.setOutputPath(grepJob, tempDir); grepJob.setOutputFormat(SequenceFileOutputFormat.class); grepJob.setOutputKeyClass(Text.class); grepJob.setOutputValueClass(LongWritable.class); JobClient.runJob(grepJob); } ... Wednesday, November 18, 2009

JobConf sortJob = new JobConf(Grep.class); sortJob.setJobName("grep-sort"); FileInputFormat.setInputPaths(sortJob, tempDir); Job sortJob.setInputFormat(SequenceFileInputFormat.class); sortJob.setMapperClass(InverseMapper.class); (implicit identity reducer) // write a single file sortJob.setNumReduceTasks(1); 2 of 2 FileOutputFormat.setOutputPath(sortJob, new Path(args[1])); // sort by decreasing freq sortJob.setOutputKeyComparatorClass( LongWritable.DecreasingComparator.class); JobClient.runJob(sortJob); } finally { FileSystem.get(grepJob).delete(tempDir, true); } return 0; } Wednesday, November 18, 2009

The types there... ?, Text Text, Long Text, list(Long) Text, Long Long, Text Wednesday, November 18, 2009

A Simple Join Id Last First People 1 Washington George 2 Lincoln Abraham Key Entry Log Location Id Time Dunster 1 11:00am Dunster 2 11:02am Kirkland 2 11:08am You want to track individuals throughout the day. How would you do this in M/R, if you had to? Wednesday, November 18, 2009

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Apache Hadoop Talk at QCon

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Apache Hadoop Talk at QCon — Presentation Transcript