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# Lecture 4: Data-Intensive Computing for Text Analysis (Fall 2011)

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co-taught with Jason Baldridge, topic for the day: practical Hadoop

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### Lecture 4: Data-Intensive Computing for Text Analysis (Fall 2011)

1. 1. Data-Intensive Computing for Text Analysis CS395T / INF385T / LIN386M University of Texas at Austin, Fall 2011 Lecture 4 September 15, 2011 Jason Baldridge Matt Lease Department of Linguistics School of Information University of Texas at Austin University of Texas at AustinJasonbaldridge at gmail dot com ml at ischool dot utexas dot edu
2. 2. Acknowledgments Course design and slides based on Jimmy Lin’s cloud computing courses at the University of Maryland, College ParkSome figures courtesy of the followingexcellent Hadoop books (order yours today!)• Chuck Lam’s Hadoop In Action (2010)• Tom White’s Hadoop: The Definitive Guide, 2nd Edition (2010)
3. 3. Today’s Agenda• Practical Hadoop – Input/Ouput – Splits: small file and whole file operations – Compression – Mounting HDFS – Hadoop Workflow and EC2/S3
5. 5. “Hello World”: Word Countmap ( K( K1=String, V1=String ) → list ( K2=String, V2=Integer )reduce ( K2=String, list(V2=Integer) ) → list ( K3=String, V3=Integer) Map(String docid, String text): for each word w in text: Emit(w, 1); Reduce(String term, Iterator<Int> values): int sum = 0; for each v in values: sum += v; Emit(term, sum);
6. 6. Courtesy of Chuck Lam’s Hadoop In Action (2010), p. 17
7. 7. Courtesy of Chuck Lam’s Hadoop In Action (2010), pp. 48-49
8. 8. Courtesy of Chuck Lam’s Hadoop In Action (2010), p. 51
9. 9. Courtesy of Tom White’s Hadoop: The Definitive Guide, 2nd Edition (2010), p. 191
10. 10. Command-Line Parsing Courtesy of Tom White’s Hadoop: The Definitive Guide, 2nd Edition (2010), p. 135
11. 11. Data Types in Hadoop Writable Defines a de/serialization protocol. Every data type in Hadoop is a Writable. WritableComparable Defines a sort order. All keys must be of this type (but not values). IntWritable Concrete classes for different data types. LongWritable Text … SequenceFiles Binary encoded of a sequence of key/value pairs
12. 12. Hadoop basic types Courtesy of Chuck Lam’s Hadoop In Action (2010), p. 46
13. 13. Complex Data Types in Hadoop How do you implement complex data types? The easiest way:  Encoded it as Text, e.g., (a, b) = “a:b”  Use regular expressions to parse and extract data  Works, but pretty hack-ish The hard way:  Define a custom implementation of WritableComprable  Must implement: readFields, write, compareTo  Computationally efficient, but slow for rapid prototyping Alternatives:  Cloud9 offers two other choices: Tuple and JSON  (Actually, not that useful in practice)
14. 14. InputFormat &RecordReader Courtesy of Tom White’s Hadoop: The Definitive Guide, 2nd Edition (2010), pp. 198-199 Split is logical; atomic records are never split Note re-use key & value objects!
15. 15. Courtesy of Tom White’sHadoop: The Definitive Guide,2nd Edition (2010), p. 201
16. 16. Input Courtesy of Chuck Lam’s Hadoop In Action (2010), p. 53
17. 17. Output Courtesy of Chuck Lam’s Hadoop In Action (2010), p. 58
18. 18. OutputFormat Reducer Reducer Reduce RecordWriter RecordWriter RecordWriter Output File Output File Output FileSource: redrawn from a slide by Cloduera, cc-licensed
19. 19. Creating Input Splits (White p. 202-203) FileInputFormat: large files split into blocks  isSplitable() – default TRUE  computeSplitSize() = max(minSize, min(maxSize,blockSize) )  getSplits()… How to prevent splitting?  Option 1: set mapred.min.splitsize=Long.MAX_VALUE  Option 2: subclass FileInputFormat, set isSplitable()=FALSE
20. 20. How to process whole file as a single record? e.g. file conversion Preventing splitting is necessary, but not sufficient  Need a RecordReader that delivers entire file as a record Implement WholeFile input format & record reader recipe  See White pp. 206-209  Overrides getRecordReader() in FileInputFormat  Defines new WholeFileRecordReader
21. 21. Small Files Files < Hadoop block size are never split (by default)  Note this is with default mapred.min.splitsize = 1 byte  Could extend FileInputFormat to override this behavior Using many small files inefficient in Hadoop  Overhead for TaskTracker, JobTracker, Map object, …  Requires more disk seeks  Wasteful for NameNode memory How to deal with small files??
22. 22. Dealing with small files  Pre-processing: merge into one or more bigger files  Doubles disk space, unless clever (can delete after merge)  Create Hadoop Archive (White pp. 72-73) • Doesn’t solve splitting problem, just reduces NameNode memory  Simple text: just concatenate (e.g. each record on a single line)  XML: concatenate, specify start/end tags StreamXmlRecordReader (as newline is end tag for Text)  Create a SequenceFile (see White pp. 117-118) • Sequence of records, all with same (key,value) type • E.g. Key=filename, Value=text or bytes of original file • Can also use for larger files, e.g. if block processing is really fast  Use CombineFileInputFormat  Reduces map overhead, but not seeks or NameNode memory…  Only an abstract class provided, you get to implement it… :-<  Could use to speed up the pre-processing above…
23. 23. Multiple File Formats?  What if you have multiple formats for same content type?  MultipleInputs (White pp. 214-215)  Specify InputFormat & Mapper to use on a per-path basis • Path could be a directory or a single file • Even a single file could have many records (e.g. Hadoop archive or SequenceFile)  All mappers must have the same output signature! • Same reducer used for all (only input format is different, not the logical records being processed by the different mappers)  What about multiple file formats stored in the same Archive or SequenceFile?  Multiple formats stored in the same directory?  How are multiple file types typically handled in general?  e.g. factory pattern, White p. 80
24. 24. White 77-86, Lam 153-155Data Compression Big data = big disk space & I/O (bound) transfer times  Affects both intermediate (mapper output) and persistent data Compression makes big data less big (but still cool)  Often 1/4th size of original data Main issues  Does the compression format support splitting? • What happens to parallelization if an entire 8GB compressed file has to be decompressed before we can access the splits?  Compression/decompression ratio vs. speed • More compression reduces disk space and transfer times, but… • Slow compression can take longer than reduced transfer time savings • Use native libraries!
25. 25. Courtesy of Tom White’s Hadoop: The Definitive Guide, 2nd Edition (2010), Ch. 4Slow; decompression can’t keep pace disk reads
26. 26. Compression Speed  LZO 2x faster than gzip  LZO ~15-20x faster than bzip2http://www.cloudera.com/blog/2009/11/hadoop-at-twitter-part-1-splittable-lzo-compression/ http://arunxjacob.blogspot.com/2011/04/rolling-out-splittable-lzo-on-cdh3.html
27. 27. Splittable LZO to the rescue LZO format not internally splittable, but we can create a separate, accompanying index of split pointsRecipe Get LZO from Cloudera or elsewhere, and setup  See URL on last slide for instructions LZO compress files, copy to HDFS at /path Index them: \$ hadoop jar /path/to/hadoop-lzo.jar com.hadoop.compression.lzo.LzoIndexer /path Use hadoop-lzo’s LzoTextInputFormat instead of TextInputFormat Voila!
28. 28. Compression API for persistent data JobConf helper functions –or– set properties Input  conf.setInputFormatClass(LzoTextInputFormat.class); Persistent (reducer) output  FileOutputFormat.setCompressOutput(conf, true)  FileOutputFormat.setOutputCompressorClass(conf, LzopCodec.class) Courtesy of Tom White’s Hadoop: The Definitive Guide, 2nd Edition (2010), p. 85
29. 29. Compression API for intermediate data  Similar JobConf helper functions –or– set properties  conf.setCompressMapOutput()  Conf.setMapOutputCompressClass(LzopCodec.class) Courtesy of Chuck Lam’s Hadoop In Action(2010), pp. 153-155
30. 30. SequenceFile & compression Use SequenceFile for passing data between Hadoop jobs  Optimized for this usage case  conf.setOutputFormat(SequenceFileOutputFormat.class) With compression, one more parameter to set  Default compression per-record; almost always preferable to compress on a per-block basis
31. 31. From “hadoop fs X” -> Mounted HDFS See White p. 50; hadoop: src/contrib/fuse-dfs
32. 32. Hadoop Workflow 1. Load data into HDFS 2. Develop code locally 3. Submit MapReduce job 3a. Go back to Step 2 Hadoop Cluster You 4. Retrieve data from HDFS
33. 33. On Amazon: With EC2 0. Allocate Hadoop cluster 1. Load data into HDFS EC2 2. Develop code locally 3. Submit MapReduce job 3a. Go back to Step 2 You Your Hadoop Cluster 4. Retrieve data from HDFS 5. Clean up!Uh oh. Where did the data go?
34. 34. On Amazon: EC2 and S3 Copy from S3 to HDFS EC2 S3 (The Cloud) (Persistent Store) Your Hadoop Cluster Copy from HFDS to S3