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Map reduce in Hadoop BIG DATA ANALYTICS
- 1. Myself Archana R Assistant Professor In Dept Of CS SACWC. I am here because I love to give presentations.
- 2. MapReduce MapReduce is a programming model for efficient distributed computing It works like a Unix pipeline cat input | grep | sort | uniq -c | cat > output Input | Map | Shuffle & Sort | Reduce | Output Efficiency from Streaming through data, reducing seeks Pipelining A good fit for a lot of applications Log processing Web index building
- 4. MapReduce - Features Fine grained Map and Reduce tasks Improved load balancing Faster recovery from failed tasks Automatic re-execution on failure In a large cluster, some nodes are always slow or flaky Framework re-executes failed tasks Locality optimizations With large data, bandwidth to data is a problem Map-Reduce + HDFS is a very effective solution Map-Reduce queries HDFS for locations of input data Map tasks are scheduled close to the inputs when possible
- 5. Word Count Example Mapper Input: value: lines of text of input Output: key: word, value: 1 Reducer Input: key: word, value: set of counts Output: key: word, value: sum Launching program Defines this job Submits job to cluster
- 7. Word Count Mapper public static class Map extends MapReduceBase implements Mapper<LongWritable,Text,Text,IntWritable> { private static final IntWritable one = new IntWritable(1); private Text word = new Text(); public static void map(LongWritable key, Text value, OutputCollector<Text,IntWritable> output, Reporter reporter) throws IOException { String line = value.toString(); StringTokenizer = new StringTokenizer(line); while(tokenizer.hasNext()) { word.set(tokenizer.nextToken()); output.collect(word,one); } } }
- 8. Word Count Example Jobs are controlled by configuring JobConfs JobConfs are maps from attribute names to string values The framework defines attributes to control how the job is executed conf.set(“mapred.job.name”, “MyApp”); Applications can add arbitrary values to the JobConf conf.set(“my.string”, “foo”); conf.set(“my.integer”, 12); JobConf is available to all tasks
- 9. Putting it all together Create a launching program for your application The launching program configures: The Mapper and Reducer to use The output key and value types (input types are inferred from the InputFormat) The locations for your input and output The launching program then submits the job and typically waits for it to complete
- 10. Input and Output Formats A Map/Reduce may specify how it’s input is to be read by specifying an InputFormat to be used A Map/Reduce may specify how it’s output is to be written by specifying an OutputFormat to be used These default to TextInputFormat and TextOutputFormat, which process line-based text data Another common choice is SequenceFileInputFormat and SequenceFileOutputFormat for binary data These are file-based, but they are not required to be
- 11. How many Maps and Reduces Maps Usually as many as the number of HDFS blocks being processed, this is the default Else the number of maps can be specified as a hint The number of maps can also be controlled by specifying the minimum split size The actual sizes of the map inputs are computed by: max(min(block_size,data/#maps), min_split_size Reduces Unless the amount of data being processed is small 0.95*num_nodes*mapred.tasktracker.tasks.maximum
- 12. Some handy tools Partitioners Combiners Compression Counters Speculation Zero Reduces Distributed File Cache Tool
- 13. Partitioners Partitioners are application code that define how keys are assigned to reduces Default partitioning spreads keys evenly, but randomly Uses key.hashCode() % num_reduces Custom partitioning is often required, for example, to produce a total order in the output Should implement Partitioner interface Set by calling conf.setPartitionerClass(MyPart.class) To get a total order, sample the map output keys and pick values to divide the keys into roughly equal buckets and use that in your partitioner
- 14. Compression Compressing the outputs and intermediate data will often yield huge performance gains Can be specified via a configuration file or set programmatically Set mapred.output.compress to true to compress job output Set mapred.compress.map.output to true to compress map outputs Compression Types (mapred(.map)?.output.compression.type) “block” - Group of keys and values are compressed together “record” - Each value is compressed individually Block compression is almost always best Compression Codecs (mapred(.map)?.output.compression.codec) Default (zlib) - slower, but more compression LZO - faster, but less compression
- 15. Counters Often Map/Reduce applications have countable events For example, framework counts records in to and out of Mapper and Reducer To define user counters: static enum Counter {EVENT1, EVENT2}; reporter.incrCounter(Counter.EVENT1, 1); Define nice names in a MyClass_Counter.properties file CounterGroupName=MyCounters EVENT1.name=Event 1 EVENT2.name=Event 2