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In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished. Note: The reduce phase has 3 steps: shuffle, sort, reduce. Shuffle is where the data is collected by the reducer from each mapper. This can happen while mappers are generating data since it is only a data transfer. On the other hand, sort and reduce can only start once all the mappers are done.Why is starting the reducers early a good thing? Because it spreads out the data transfer from the mappers to the reducers over time, which is a good thing if your network is the bottleneck. Why is starting the reducers early a bad thing? Because they "hog up" reduce slots while only copying data. Another job that starts later that will actually use the reduce slots now can't use them. You can customize when the reducers startup by changing the default value of mapred.reduce.slowstart.completed.maps in mapred-site.xml. A value of 1.00 will wait for all the mappers to finish before starting the reducers. A value of 0.0 will start the reducers right away. A value of 0.5 will start the reducers when half of the mappers are complete. You can also change mapred.reduce.slowstart.completed.maps on a job-by-job basis. Typically, keep mapred.reduce.slowstart.completed.maps above 0.9 if the system ever has multiple jobs running at once. This way the job doesn't hog up reducers when they aren't doing anything but copying data. If you only ever have one job running at a time, doing 0.1 would probably be appropriate. Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, When is the reducers are started in a MapReduce job?

The Client communication to HDFS happens using Hadoop HDFS API. Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add/copy/move/delete a file on HDFS. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives. Client applications can talk directly to a DataNode, once the NameNode has provided the location of the data. Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, How the Client communicates with HDFS?

Hadoop streaming is a utility that comes with the Hadoop distribution. The utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer. Reference: http://hadoop.apache.org/common/docs/r0.20.1/streaming.html (Hadoop Streaming, second sentence)

Reducer has 3 primary phases:1. Shuffle The Reducer copies the sorted output from each Mapper using HTTP across the network. 2. Sort The framework merge sorts Reducer inputs by keys (since different Mappers may have output the same key). The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are merged. SecondarySort To achieve a secondary sort on the values returned by the value iterator, the application should extend the key with the secondary key and define a grouping comparator. The keys will be sorted using the entire key, but will be grouped using the grouping comparator to decide which keys and values are sent in the same call to reduce. 3. Reduce In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of values)> in the sorted inputs. The output of the reduce task is typically written to a RecordWriter via TaskInputOutputContext.write(Object, Object). The output of the Reducer is not re-sorted. Reference: org.apache.hadoop.mapreduce, Class Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

Reducer has 3 primary phases:1. Shuffle The Reducer copies the sorted output from each Mapper using HTTP across the network. 2. Sort The framework merge sorts Reducer inputs by keys (since different Mappers may have output the same key). The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they are merged. SecondarySort To achieve a secondary sort on the values returned by the value iterator, the application should extend the key with the secondary key and define a grouping comparator. The keys will be sorted using the entire key, but will be grouped using the grouping comparator to decide which keys and values are sent in the same call to reduce. 3. Reduce In this phase the reduce(Object, Iterable, Context) method is called for each <key, (collection of values)> in the sorted inputs. The output of the reduce task is typically written to a RecordWriter via TaskInputOutputContext.write(Object, Object). The output of the Reducer is not re-sorted. Reference: org.apache.hadoop.mapreduce, Class Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

There will be four failed task attempts for each of the five file splits. Note:    

See 3) below. Here is an illustrative example on how to use the DistributedCache:     // Setting up the cache for the application            1. Copy the requisite files to the FileSystem:           $ bin/hadoop fs -copyFromLocal lookup.dat /myapp/lookup.dat        $ bin/hadoop fs -copyFromLocal map.zip /myapp/map.zip        $ bin/hadoop fs -copyFromLocal mylib.jar /myapp/mylib.jar      $ bin/hadoop fs -copyFromLocal mytar.tar /myapp/mytar.tar      $ bin/hadoop fs -copyFromLocal mytgz.tgz /myapp/mytgz.tgz      $ bin/hadoop fs -copyFromLocal mytargz.tar.gz /myapp/mytargz.tar.gz            2. Setup the application's JobConf:           JobConf job = new JobConf();      DistributedCache.addCacheFile(new URI("/myapp/lookup.dat#lookup.dat"),                                     job);      DistributedCache.addCacheArchive(new URI("/myapp/map.zip", job);      DistributedCache.addFileToClassPath(new Path("/myapp/mylib.jar"), job);      DistributedCache.addCacheArchive(new URI("/myapp/mytar.tar", job);      DistributedCache.addCacheArchive(new URI("/myapp/mytgz.tgz", job);      DistributedCache.addCacheArchive(new URI("/myapp/mytargz.tar.gz", job);            3. Use the cached files in the Mapper      or Reducer:           public static class MapClass extends MapReduceBase        implements Mapper<K, V, K, V> {              private Path[] localArchives;        private Path[] localFiles;                public void configure(JobConf job) {          // Get the cached archives/files          localArchives = DistributedCache.getLocalCacheArchives(job);          localFiles = DistributedCache.getLocalCacheFiles(job);        }                public void map(K key, V value,                         OutputCollector<K, V> output, Reporter reporter)         throws IOException {          // Use data from the cached archives/files here          // ...          // ...          output.collect(k, v);        }      }       Reference: org.apache.hadoop.filecache , Class DistributedCache

Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, What are combiners? When should I use a combiner in my MapReduce Job?

Note: * Join Algorithms in MapReduce A) Reduce-side join B) Map-side join C) In-memory join / Striped Striped variant variant / Memcached variant * Which join to use? / In-memory join > map-side join > reduce-side join / Limitations of each? In-memory join: memoryMap-side join: sort order and partitioningReduce-side join: general purpose