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EMC2, Владимир Суворов

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Презентация с технической секции #BitByte - фестиваля профессионального развития, который прошел 19 мая в Санкт-Петербурге. …

Презентация с технической секции #BitByte - фестиваля профессионального развития, который прошел 19 мая в Санкт-Петербурге.

Владимир Суворов, Инженер отраслевых решений компании EMC2: «Практическое использование Apache Hadoop - технологии распределенной обработки данных».

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  • 1. Практическое использование ApacheHadoop - технологии распределенной обработки данных Владимир Суворов EMC
  • 2. UC BerkeleyIntroduction to MapReduce and Hadoop Matei Zaharia UC Berkeley RAD Lab matei@eecs.berkeley.edu
  • 3. What is MapReduce?• Data-parallel programming model for clusters of commodity machines• Pioneered by Google – Processes 20 PB of data per day• Popularized by open-source Hadoop project – Used by Yahoo!, Facebook, Amazon, …
  • 4. What is MapReduce used for?• At Google: – Index building for Google Search – Article clustering for Google News – Statistical machine translation• At Yahoo!: – Index building for Yahoo! Search – Spam detection for Yahoo! Mail• At Facebook: – Data mining – Ad optimization – Spam detection
  • 5. Example: Facebook Lexicon www.facebook.com/lexicon
  • 6. Example: Facebook Lexicon www.facebook.com/lexicon
  • 7. What is MapReduce used for?• In research: – Analyzing Wikipedia conflicts (PARC) – Natural language processing (CMU) – Bioinformatics (Maryland) – Astronomical image analysis (Washington) – Ocean climate simulation (Washington) – <Your application here>
  • 8. Outline• MapReduce architecture• Fault tolerance in MapReduce• Sample applications• Getting started with Hadoop• Higher-level languages on top of Hadoop: Pig and Hive
  • 9. MapReduce Design Goals1. Scalability to large data volumes: – Scan 100 TB on 1 node @ 50 MB/s = 23 days – Scan on 1000-node cluster = 33 minutes2. Cost-efficiency: – Commodity nodes (cheap, but unreliable) – Commodity network – Automatic fault-tolerance (fewer admins) – Easy to use (fewer programmers)
  • 10. Typical Hadoop Cluster Aggregation switch Rack switch• 40 nodes/rack, 1000-4000 nodes in cluster• 1 GBps bandwidth in rack, 8 GBps out of rack• Node specs (Yahoo terasort): 8 x 2.0 GHz cores, 8 GB RAM, 4 disks (= 4 TB?)
  • 11. Typical Hadoop Cluster Image from http://wiki.apache.org/hadoop-data/attachments/HadoopPresentations/attachments/aw-apachecon-eu-2009.pdf
  • 12. Challenges• Cheap nodes fail, especially if you have many – Mean time between failures for 1 node = 3 years – MTBF for 1000 nodes = 1 day – Solution: Build fault-tolerance into system• Commodity network = low bandwidth – Solution: Push computation to the data• Programming distributed systems is hard – Solution: Data-parallel programming model: users write “map” and “reduce” functions, system handles work distribution and fault tolerance
  • 13. Hadoop Components• Distributed file system (HDFS) – Single namespace for entire cluster – Replicates data 3x for fault-tolerance• MapReduce implementation – Executes user jobs specified as “map” and “reduce” functions – Manages work distribution & fault-tolerance
  • 14. Hadoop Distributed File System• Files split into 128MB blocks Namenode• Blocks replicated across File1 1 several datanodes (usually 3) 2 3• Single namenode stores 4 metadata (file names, block locations, etc)• Optimized for large files, sequential reads 1 2 1 3• Files are append-only 2 4 1 3 4 3 2 4 Datanodes
  • 15. MapReduce Programming Model• Data type: key-value records• Map function: (Kin, Vin)  list(Kinter, Vinter)• Reduce function: (Kinter, list(Vinter))  list(Kout, Vout)
  • 16. Example: Word Countdef mapper(line): foreach word in line.split(): output(word, 1)def reducer(key, values): output(key, sum(values))
  • 17. Word Count Execution Input Map Shuffle & Sort Reduce Output the, 1 brown, 1the quick fox, 1 brown, 2brown fox Map fox, 2 Reduce how, 1 now, 1 the, 1 the, 3 fox, 1 the, 1the fox atethe mouse Map quick, 1 how, 1 now, 1 ate, 1 ate, 1 brown, 1 mouse, 1 cow, 1 Reduce mouse, 1 how now quick, 1brown cow Map cow, 1
  • 18. MapReduce Execution Details• Single master controls job execution on multiple slaves as well as user scheduling• Mappers preferentially placed on same node or same rack as their input block – Push computation to data, minimize network use• Mappers save outputs to local disk rather than pushing directly to reducers – Allows having more reducers than nodes – Allows recovery if a reducer crashes
  • 19. An Optimization: The Combiner• A combiner is a local aggregation function for repeated keys produced by same map• For associative ops. like sum, count, max• Decreases size of intermediate data• Example: local counting for Word Count: def combiner(key, values): output(key, sum(values))
  • 20. Word Count with Combiner Input Map & Combine Shuffle & Sort Reduce Output the, 1 brown, 1the quick fox, 1 brown, 2brown fox Map fox, 2 Reduce how, 1 now, 1 the, 3 the, 2 fox, 1the fox atethe mouse Map quick, 1 how, 1 now, 1 ate, 1 ate, 1 brown, 1 mouse, 1 cow, 1 Reduce mouse, 1 how now quick, 1brown cow Map cow, 1
  • 21. Outline• MapReduce architecture• Fault tolerance in MapReduce• Sample applications• Getting started with Hadoop• Higher-level languages on top of Hadoop: Pig and Hive
  • 22. Fault Tolerance in MapReduce1. If a task crashes: – Retry on another node • Okay for a map because it had no dependencies • Okay for reduce because map outputs are on disk – If the same task repeatedly fails, fail the job or ignore that input block (user-controlled)Note: For this and the other fault tolerance features to work, your map and reduce tasks must be side-effect-free
  • 23. Fault Tolerance in MapReduce2. If a node crashes: – Relaunch its current tasks on other nodes – Relaunch any maps the node previously ran • Necessary because their output files were lost along with the crashed node
  • 24. Fault Tolerance in MapReduce3. If a task is going slowly (straggler): – Launch second copy of task on another node – Take the output of whichever copy finishes first, and kill the other one• Critical for performance in large clusters: stragglers occur frequently due to failing hardware, bugs, misconfiguration, etc
  • 25. Takeaways• By providing a data-parallel programming model, MapReduce can control job execution in useful ways: – Automatic division of job into tasks – Automatic placement of computation near data – Automatic load balancing – Recovery from failures & stragglers• User focuses on application, not on complexities of distributed computing
  • 26. Outline• MapReduce architecture• Fault tolerance in MapReduce• Sample applications• Getting started with Hadoop• Higher-level languages on top of Hadoop: Pig and Hive
  • 27. 1. Search• Input: (lineNumber, line) records• Output: lines matching a given pattern• Map: if(line matches pattern): output(line)• Reduce: identify function – Alternative: no reducer (map-only job)
  • 28. 2. Sort• Input: (key, value) records• Output: same records, sorted by key ant, bee• Map: identity function Map Reduce [A-M] zebra• Reduce: identify function aardvark ant cow bee cow Map elephant pig• Trick: Pick partitioning Reduce [N-Z] aardvark, function h such that elephant pig sheep sheep, yak k1<k2 => h(k1)<h(k2) Map yak zebra
  • 29. 3. Inverted Index• Input: (filename, text) records• Output: list of files containing each word• Map: foreach word in text.split(): output(word, filename)• Combine: uniquify filenames for each word• Reduce: def reduce(word, filenames): output(word, sort(filenames))
  • 30. Inverted Index Examplehamlet.txt to, hamlet.txt to be or be, hamlet.txt not to be or, hamlet.txt afraid, (12th.txt) not, hamlet.txt be, (12th.txt, hamlet.txt) greatness, (12th.txt) not, (12th.txt, hamlet.txt) of, (12th.txt) 12th.txt be, 12th.txt or, (hamlet.txt) not, 12th.txt to, (hamlet.txt) be not afraid, 12th.txt afraid of of, 12th.txtgreatness greatness, 12th.txt
  • 31. 4. Most Popular Words• Input: (filename, text) records• Output: the 100 words occurring in most files• Two-stage solution: – Job 1: • Create inverted index, giving (word, list(file)) records – Job 2: • Map each (word, list(file)) to (count, word) • Sort these records by count as in sort job• Optimizations: – Map to (word, 1) instead of (word, file) in Job 1 – Estimate count distribution in advance by sampling
  • 32. Outline• MapReduce architecture• Fault tolerance in MapReduce• Sample applications• Getting started with Hadoop• Higher-level languages on top of Hadoop: Pig and Hive
  • 33. Getting Started with Hadoop• Download from hadoop.apache.org• To install locally, unzip and set JAVA_HOME• Details: hadoop.apache.org/core/docs/current/quickstart.html• Three ways to write jobs: – Java API – Hadoop Streaming (for Python, Perl, etc) – Pipes API (C++)
  • 34. Word Count in Javapublic static class MapClass extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable ONE = new IntWritable(1); public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { String line = value.toString(); StringTokenizer itr = new StringTokenizer(line); while (itr.hasMoreTokens()) { output.collect(new text(itr.nextToken()), ONE); } } }
  • 35. Word Count in Javapublic static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { int sum = 0; while (values.hasNext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); } }
  • 36. Word Count in Javapublic static void main(String[] args) throws Exception { JobConf conf = new JobConf(WordCount.class); conf.setJobName("wordcount"); conf.setMapperClass(MapClass.class); conf.setCombinerClass(Reduce.class); conf.setReducerClass(Reduce.class); FileInputFormat.setInputPaths(conf, args[0]); FileOutputFormat.setOutputPath(conf, new Path(args[1])); conf.setOutputKeyClass(Text.class); // out keys are words (strings) conf.setOutputValueClass(IntWritable.class); // values are counts JobClient.runJob(conf);}
  • 37. Word Count in Python with Hadoop StreamingMapper.py: import sys for line in sys.stdin: for word in line.split(): print(word.lower() + "t" + 1)Reducer.py: import sys counts = {} for line in sys.stdin: word, count = line.split("t") dict[word] = dict.get(word, 0) + int(count) for word, count in counts: print(word.lower() + "t" + 1)
  • 38. Outline• MapReduce architecture• Fault tolerance in MapReduce• Sample applications• Getting started with Hadoop• Higher-level languages on top of Hadoop: Pig and Hive
  • 39. Motivation• MapReduce is great, as many algorithms can be expressed by a series of MR jobs• But it’s low-level: must think about keys, values, partitioning, etc• Can we capture common “job patterns”?
  • 40. Pig• Started at Yahoo! Research• Now runs about 30% of Yahoo!’s jobs• Features: – Expresses sequences of MapReduce jobs – Data model: nested “bags” of items – Provides relational (SQL) operators (JOIN, GROUP BY, etc) – Easy to plug in Java functions – Pig Pen dev. env. for Eclipse
  • 41. An Example ProblemSuppose you have Load Users Load Pagesuser data in one Filter by agefile, website data inanother, and you Join on nameneed to find the top Group on url5 most visitedpages by users Count clicksaged 18 - 25. Order by clicks Take top 5 Example from http://wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
  • 42. In MapReducei m p o r t j a v a . i o . I O E x c e p t i o n ; r e p o r t e r . s e t S t a t u s ( " O K " ) ; l p . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) ;i m p o r t j a v a . u t i l . A r r a y L i s t ; } l p . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a s s ) ;i m p o r t j a v a . u t i l . I t e r a t o r ; l p . s e t M a p p e r C l a s s ( L o a d P a g e s . c l a s s ) ;i m p o r t j a v a . u t i l . L i s t ; / / D o t h e c r o s s p r o d u c t a n d c o l l e c t t F i l e I n p u t F o r m a t . h e v a l u e s a d d I n p u t P a t h ( l p , n e g f o r ( S t r i n s 1 : f i r s t ) { P a t hu s e r / g a t e s / p a g e s " ) ) ; ( " /i m p o r t o r g . a p a c h e . h a d o o p . f s . P a t h ; S f o r ( t r i n g s 2 : s e c o n d ) { F i l e O u t p u t F o r m a t . s e t O u t p u t P a t h ( l p ,i m p o r t o r g . a p a c h e . h a d o o p . i o . L o n g W r i t a b l e ; t S r i n g o u t v a l = k e y + " , " + s 1 + n e w " , " P a t h ( " / u + s 2 ; s e r / g a t e s / t m p / i n d e xi m p o r t o r g . a p a c h e . h a d o o p . i o . T e x t ; c o . c o l l e c t ( n u l l , n e w T e x t ( o u t l p . s e t N u m R e d u c e T v a l ) ) ; a s k s ( 0 ) ;i m p o r t o r g . a p a c h e . h a d o o p . i o . W r i t a b l e ; e r p o r t e r . s e t S t a t u s ( " O K " ) ; J o b l o a d P a g e s = n e w J o b ( l p ) ;im p o r t o r g . a p a c h e . h a d o o p . i o . W r i t a b l e C o m p a r a b l e ; }i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . F i l e I n p u t F o r m a t ; } J o b C o n f l f u = n e w J o b C o n f ( M R E x a m p l ei m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . F i l e O u t p u t F o r m a t ; } e t J o b N a m e ( " L o a d l f u . s a n d F i l t e r U s e r s " ) ;i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . J o b C o n f ; } l f u . s e t I n p u t F o r m a t ( T e x t I n p u t F o r m a t .i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . K e y V a l u e T e x t I p u b l i c r s t a t i c n p u t F o m a t ; c l a s s L o a d J o i n e d e x t e n d s M a p R e d l f u . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) ; u c e B a s ei m p o r t po r g . a h a d o o p . m a p r e d . M a p p e r ; a c h e . i m p l e m e n t s M a p p e r < T e x t , T e x t , T e x t , L o n g W l f u . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a s s ) r i t a b l e > {i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . M a p R e d u c e B a s e ; l f u . s e t M a p p e r C l a s s ( L o a d A n d F i l t e r U s ei m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . O u t p u t C o l l e c t o r ; p u b l i c v o i d m a p ( F i l e I n p u t F o r m a t . a d d , I n p u t P a t h ( l f u n e wi m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . R e c o r d R e a d e r ; T e x t k , P a t h ( " / u s e r / g a t e s / u s e r s " ) ) ;i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . R e d u c e r ; T e x t v a l , F i l e O u t p u t F o r m a t . s e t O u t p u t P a t h ( l f u ,i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . R e p o r t e r ; c tO u t p u t C o l l e n g W r i t a b o r < T e x t , L o l e > o c , n e w P a t h ( " / u s e r / g a t e s / t m p / f i l t ei m op r t o r g . a p a c h e . h a d o o p . m a p r e d . S e q u e n c e F i l e I n p u t F o r m a t ; R e p o r t e r r e p o r t e r ) t h r o w s I O E x c e p l f u . s e t N u m R e d u c e T a s k s ( 0 ) ; t i o n {i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . S e q u e n c e F i l e O u t p u t F o r / / t F i n d m a ; t h e u r l J o b l o a d U s e r s = n e w J o b ( l f u ) ;i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . T e x t I n p u t F o r m a t ; S t r i n g l i n e = v a l . t o S t r i n g ( ) ;i m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . j o b c o n t r o l . J o b ; i n t f i r s t C o m m a = l i n e . i n d e x O f ( , ) ; J o b C o n f j o iM nR E =x a nm ep wl e J. oc bl Ca os ns f) (;i m p o r t o r g . a p a c h e . h a d o o p .o n t r o l ; j o b c o n t r o l . J o b C m a p r e d . i n t s e c o n d C o m m a C o m m a ) ; = l i n e . i n d e x O f ( , , f j o i n . s e t J o b N a m e ( " J o i n i r s t U s e r s a n d P a gi m p o r t o r g . a p a c h e . h a d o o p . m a p r e d . l i b . I d e n t i t y M a p p e r ; S t r i n g k e y = l i n e . s u b s t r i n g ( f i r s t C o m m j o i n . s e t I n p u t F o r m a t ( K e y V a l u e T e x t I n p a , s e c o n d C o m m a ) ; / / d r o p t h e r e s t o f t h e r e c o r d , I d o n j o i n . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) ; t n e e d i t a n y m o r e ,p u b l i c c l a s s M R E x a m p l e { / / j u s t p a s s a 1 f o r t h e c o m b i n e r / r e d j o i n . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a s s u c e r t o s u m i n s t e a d . p u b l i c s t a t i c c l a s s L o a d P a g e s e x t e n d s M a p R e d u c e B a T e x t s e o u t K e y = n e w T e x t ( k e y ) ; j o i n . s e t M a p p e pr eC rl .a cs ls a( sI sd )e ;n t i t y M a p i m p l e m e n t s M a p p e r < L o n g W r i t a b l e , T e x t , T e x t , o c . c o l l e c t ( o u t K e y , T e x t > { n e w L o n g W r i t a b l e ( 1 Lj o i n . s e t R e d u c e r C l a s s ( J o i n . c l a s s ) ; ) ) ; } F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( j o i n , p u b l i c v o i d m a p ( L o n g W r i t a b l e k , T e x t } a l , v P a t h ( " / u s e r / g a t e s / t m p / i n d e x e d _ p a g e s " ) ) ; O u t p u t C o l l e c t o r < T e x t , T e x t > o c , u b l i c p s t a t i c c l a s s R e d u c e U r l s e x t e n d s M a p R e d F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( j o i n , u c e B a s e R e p o r t e r r e p o r t e r ) t h r o w s I O E x c e p i m p l e m e n t s t i o n { R e d u c e r < T e x t , L o n g W r i t P a t h ( " / u s e r / g a t e s / t m p / f i l t e r e d _ u s e r s " ) ) ; a b l e , W r i t a b l e C o m p a r a b l e , / / P u l l t h e k e y o u t W r i t a b l e > { F i l e O u t p u t F o r m a t . s e o i n , t O u t p u t P a t h ( j n e w S t r i n g l i n e = v a l . t o S t r i n g ( ) ; P a t h ( " / u s e r / g a t e s / t m p / j o i n e d " ) ) ; i n t f i r s t C o m m a = l i n e . i n d e x O f ( , ) ; p u b l i c v o i d r e d u c e ( j o i n . s e t N u m R e d u c e T a s k s ( 5 0 ) ; S t r i n g tk e y g= 0l i n e . s u b o m m a ) ; s r i n ( , f i r s t C y , T e x t k e J o b j o i n J o b = n e w J o b ( j o i n ) ; S t r i n g v a l u e = l i n e . s u b s t r i n g ( f i r s t C o m m a + 1 ) I t e r a t o r < L o n g W r i t a b ; l e > i t e r , j o i n J o b . a d d D e p e n d i n g J o b ( l o a d P a g e s ) ; T e x t o u t K e y = n e w T e x t ( k e y ) ; O u t p u t C o l l e c t o r < W r i t a b l e C o m p a r a b l j o i n J o b . a d d D e p e n d i n g J o b ( l o a d U s e r s ) ; e , W r i t a b l e > o c , / / P r e p e n d a n i n d e x t o t h e v a l u e s o w e k n o w w R e p o r t e r e r e p o r t e r ) h i c h f i l t h r o w s I O E x c e p t i o n { / / i t c a m e f r o m . / / A d d u p a l l t h e v a l u e s w e s e e J o b C o n f g r o u p x a= m pn le ew . cJ lo ab sC so )n ;f ( M R E T e x t o u t"V a+l v=a lnueew) ;T e x t ( " 1 g r o u p . s e t J o b N a m e ( " G r o u p U R L s " ) ; o c . c o l l e c t ( o u t K e y , o u t V a l ) ; l o n g s u m = 0 ; g r o u p . s e t I n p u t F o r m a t ( K e y V a l u e T e x t I n } i l e (w h e r . h a s N e x t ( ) ) i t { g r o u p . s e t O u t p u t K e y C l a s s ( T e x t . c l a s s ) } s u m + = i t e r . n e x t ( ) . g e t ( ) ; g r o u p . s e t O u t p u t V a l u e C l a s s ( L o n g W r i t a p u b l i c s t a t i c c l a s s L o a d A n d F i l t e r U s e r s e x t e n d s M a p R e d r e p o r t e r . s e t S t a t u s ( u c e B a s e " O K " ) ; g r o u p . s e t O u t p u t F o r m a t ( S e q u e n c e F i s ) ; l e O u t p u t F o r m a t . c l a s i m p l e m e n t s M a p p e r < L o n g W r i t a b l e , T e x t , T e x t , T } x t > e { g r o u p . s e t M a p p e r C l a s s ( L o a d J o i n e d . c l a g r o u p . s e t C o m b i n e r C l a s s ( R e d u c e U r l s . c p u b l i c v o i d m a p ( L o n g W r i t a b l e k , T e x t v a l , o c . c o l l e c t ( k e y , n e w L o n g W r i t a b l e ( s u m ) g r o u p . s e t R e d u c e r C l a s s ( R e d u c e U r l s . c l ) ; O u t p u t C o l l e c t o r < T e x t , T e x t > o c , } F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( g r o u p , R e p o r t e r r e p o r t e r ) t h r o w s I O E } c e p t i o n x { P a t h ( " / u s e r / g a t e s / t m p / j o i n e d " ) ) ; / / P u l l t h e k e y o u t p u b l i c s t a t i c c l a s s L o a d C l i c k s e x t e n d s M a p RF ei dl ue cO eu Bt ap su et F o r m a t . s e t O u t p u t P a t h ( g r o u p , S t r i n g l i n e = v a l . t o S t r i n g ( ) ; m p l e m e n t s i M a p p e r < W r i t a b l e C o m p a r a b l e , P W r i t a b l e , r L o n g W r i t a b l e , o u p e d " ) ) ; a t h ( " / u s e / g a t e s / t m p / g r i n t f i r s t C o m m a = l i n e . i n d e x O f T e ( x t , > ; { ) g r o u p . s e t N u m R e d u c e T a s k s ( 5 0 ) ; S t r i n g v a l u e r = t l i n e . f i s C o m m a s u + b s t r 1 ) ; i n g ( J o b g r o u p J o b = n e w J o b ( g r o u p ) ; i n t a g e = I n t e g e r . p a r s e I n t ( v a l u e ) ; p u b l i c v o i d m a p ( g r o u p J o b . a d d D e p e n d i n g J o b ( j o i n J o b ) ; i f ( a g e < 1 8 | | a g e > 2 5 ) r e t u r n ; W r i t a b l e C o m p a r a b l e k e y , S t r i n g k e y = l i n e . s u b s t r i n g ( 0 , f i r s t C o m m a ) ; W r i t a b l e v a l , J o b C o n f t o p 1 0 0 = n e w J o b C o n f ( M R E x a m T e x t o u t K e y = n e w T e x t ( k e y ) ; O u t p u t C o l l e c t o r < L o n g W r i t a b l e , T e t o p 1 0 0 . s e t J o b N a m e ( " T o p x t > o c , 1 0 0 s i t e s " ) ; / / P r e p e n d a n e n k n o w i d e x t w h o i c h t h e f v i l e e a l u s o w R e pt oh rr to ew rs rI eO pE ox rc te ep rt )i o n { t o p 1 0 0 . s e t I n p u t F o r m a t ( S e q u e n c e F i l e I / / i t c a m e f r o m . o c . c o l l e c t ( ( L o n g W r i t a b l e ) v a l , ( T e x t ) k t o p 1 0 0 . s e t O u t p u t K e y C l a s s ( L o n g W r i t a b e y ) ; T e x t o u t V a l = n e w T e x t ( " 2 " + v a l u e ) ; } t o p 1 0 0 . s e t O u t p u t V a l u e C l a s s ( T e x t . c l a o c . c o l l e c t ( o u t K e y , o u t V a l ) ; } t o p 1 0 0 . s e t O u t p u t F o r m a t ( S e q u e n c e F i l e o r m a t . c l a s s ) ; } p u b l i c s t a t i c c l a s s L i m i t C l i c k s e x t e n d s M a p R e t o p 1 0 0 . s e t M a p p e r C l a s s ( L o a d C l i c k s . c l d u c e B a s e } i m p l e m e n t s R e d u c e r < L o n g W r i t a b l e , T e x t , L o t o p 1 0 0 . s e t C o m b i n e r C l a s s ( L i m i t C l i c k s n g W r i t a b l e , T e x t > { p u b l i c s t a t i c c l a s s J o i n e x t e n d s M a p R e d u c e B a s e t o p 1 0 0 . s e t R e d u c e r C l a s s ( L i m i t C l i c k s . i m p l e m e n t s R e d u c e r < T e x t , T e x t , T e x t , T e x t i n t > { c o u n t = 0 ; F i l e I n p u t F o r m a t . a d d I n p u t P a t h ( t o p 1 0 0 p u b l i c e d u c e ( v o i d r P a t h ( " / u s e r / g a t e s / t m p / g r o u p e d " ) ) ; p u b l i c v o i d r e d u c e ( T e x t k e y , L o n g W r i t a b l e k e y , F i l e O u t p u t F o r m a t . s e t O u t p u t P a t h ( t o p 1 0 0 , I t e r a t o r < T e x t > i t e r , I t e r a t o r < T e x t > i t e r , P a t h ( " / u s e r / g a t e s / t o p 1 0 0 s i t e s f o r u s e r s 1 8 t o 2 5 O u t p u t C o l l e c t o r < T e x t , T e x t > o c , O u t p u t C o l l e c t o r < L o n g W r i t a b l e , T e x t > o t o p 1 0 0 . s e t N u m R e d u c e T a s k s ( 1 ) ; c , R e p o r t e r r e p o r t e r ) t h r o w s I O E x c e p t i o n R e p o r t e r { r e p o r t e r ) t h r o w s I O E x c e p t i o n J o b { l i m i t = n e w J o b ( t o p 1 0 0 ) ; / / F o r e a c h v a l u e , f i g u r e o u t w h i c h f i l e i t s f r o m a n d l i m i t . a d d D e p e n d i n g J o b ( g r o u p J o b ) ;s t o r e i t / / O n l y o u t p u t t h e f i r s t 1 0 0 r e c o r d s / / a c c o r d i n g l y . w h i l e 0 ( c o u n t e r . h a s N e x t ( ) ) < 1 0 & & i t { J o b C o n t r o l j c = n e w 0J o b C o n t r o l ( " F i n 1 0 s i t e s f o r u s L i s t < S t r i n g > f i r s t = n e w A r r a y L i s t < S t r i n g > ( ) ; o c . c o l l e c t ( k e y , i t e r . n e x t 1 8 ) t o ( ) ; 2 5 " ) ; L i s t < S t r i n g > s e c o n d = n e w A r r a y L i s t < S t r i n g > ( ) c o u n t + + ; ; j c . a d d J o b ( l o a d P a g e s ) ; } j c . a d d J o b ( l o a d U s e r s ) ; w h i l e ( i t e r . h a s N e x t ( ) ) { } j c . a d d J o b ( j o i n J o b ) ; T e x t t = i t e r . n e x t ( ) ; } j c . a d d J o b ( g r o u p J o b ) ; S t rS it nr gi n vg a( l) u; e = t . t o p u b l i c s t a t i c v o i d m a i n ( S t r i n g [ ] a r g s ) t h r o w s j c . a d d J o b ( l i m i t ) ; I O E x c e p t i o n { i f ( v a l u e . c h a r A t ( 0 ) = = 1 ) J o b C o n f l p = n e w J o b C o n f ( M R E x a m p l e . c l a s s ) j c . r u n ( ) ; ;f i r s t . a d d ( v a l u e . s u b s t r i n g ( 1 ) ) ; t J o b N a m e ( " L o a d l p . s e P a g e s " ) ; } e l s e s e c o n d . a d d ( v a l u e . s u b s t r i n g ( 1 l p . s e t I n p u t F o r m a t ( T e x t I n p u t F o r m a t } c l a s s ) ; ) ) ; . Example from http://wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
  • 43. In Pig LatinUsers = load ‘users’ as (name, age);Filtered = filter Users by age >= 18 and age <= 25;Pages = load ‘pages’ as (user, url);Joined = join Filtered by name, Pages by user;Grouped = group Joined by url;Summed = foreach Grouped generate group, count(Joined) as clicks;Sorted = order Summed by clicks desc;Top5 = limit Sorted 5;store Top5 into ‘top5sites’; Example from http://wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
  • 44. Ease of TranslationNotice how naturally the components of the job translate into Pig Latin.Load Users Load Pages Users = load …Filter by age Fltrd = filter … Pages = load … Join on name Joined = join … Group on url Grouped = group … Summed = … count()… Count clicks Sorted = order … Top5 = limit … Order by clicks Take top 5 Example from http://wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
  • 45. Ease of Translation Notice how naturally the components of the job translate into Pig Latin. Load Users Load Pages Users = load … Filter by age Fltrd = filter … Pages = load … Join on name Joined = join …Job 1 Grouped = group … Group on url Job 2 Summed = … count()… Count clicks Sorted = order … Top5 = limit … Order by clicks Job 3 Take top 5 Example from http://wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
  • 46. Hive• Developed at Facebook• Used for majority of Facebook jobs• “Relational database” built on Hadoop – Maintains list of table schemas – SQL-like query language (HQL) – Can call Hadoop Streaming scripts from HQL – Supports table partitioning, clustering, complex data types, some optimizations
  • 47. Creating a Hive Table CREATE TABLE page_views(viewTime INT, userid BIGINT, page_url STRING, referrer_url STRING, ip STRING COMMENT User IP address) COMMENT This is the page view table PARTITIONED BY(dt STRING, country STRING) STORED AS SEQUENCEFILE;• Partitioning breaks table into separate files for each (dt, country) pair Ex: /hive/page_view/dt=2008-06-08,country=US /hive/page_view/dt=2008-06-08,country=CA
  • 48. Simple Query• Find all page views coming from xyz.com on March 31st: SELECT page_views.* FROM page_views WHERE page_views.date >= 2008-03-01 AND page_views.date <= 2008-03-31 AND page_views.referrer_url like %xyz.com;• Hive only reads partition 2008-03-01,* instead of scanning entire table
  • 49. Aggregation and Joins• Count users who visited each page by gender: SELECT pv.page_url, u.gender, COUNT(DISTINCT u.id) FROM page_views pv JOIN user u ON (pv.userid = u.id) GROUP BY pv.page_url, u.gender WHERE pv.date = 2008-03-03;• Sample output: page_url gender count(userid) home.php MALE 12,141,412 home.php FEMALE 15,431,579 photo.php MALE 23,941,451 photo.php FEMALE 21,231,314
  • 50. Using a Hadoop Streaming Mapper ScriptSELECT TRANSFORM(page_views.userid, page_views.date)USING map_script.pyAS dt, uid CLUSTER BY dtFROM page_views;
  • 51. Conclusions• MapReduce’s data-parallel programming model hides complexity of distribution and fault tolerance• Principal philosophies: – Make it scale, so you can throw hardware at problems – Make it cheap, saving hardware, programmer and administration costs (but requiring fault tolerance)• Hive and Pig further simplify programming• MapReduce is not suitable for all problems, but when it works, it may save you a lot of time
  • 52. Yahoo! Super Computer Cluster - M45Yahoo!’s cluster is part of the Open Cirrus’ Testbed created by HP, Intel, and Yahoo! (seepress release at http://research.yahoo.com/node/2328).The availability of the Yahoo! cluster was first announced in November 2007 (see pressrelease at http://research.yahoo.com/node/1879).The cluster has approximately 4,000 processor-cores and 1.5 petabytes of disks.The Yahoo! cluster is intended to run the Apache open source software Hadoop and Pig.Each selected university will share the partition with up to three other universities. Theinitial duration of use is 6 months, potentially renewable for another 6 months uponwritten agreement. For further Information, please contact: http://cloud.citris-uc.org/ Dr. Masoud Nikravesh CITRIS and LBNL, Executive Director, CSE Nikravesh@eecs.berkeley.edu Phone: (510) 643-4522
  • 53. Resources• Hadoop: http://hadoop.apache.org/core/• Hadoop docs: http://hadoop.apache.org/core/docs/current/• Pig: http://hadoop.apache.org/pig• Hive: http://hadoop.apache.org/hive• Hadoop video tutorials from Cloudera: http://www.cloudera.com/hadoop-training

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