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Indentify the utility that allows you to create and run MapReduce jobs with any executable or script as the mapper and/or the reducer?
- Hadoop Streaming
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.
http://hadoop.apache.org/common/docs/r0.20.1/streaming.html (Hadoop Streaming, second sentence)
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Which describes how a client reads a file from HDFS?
- The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the client. The client reads the data directory off the DataNode(s).
- The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode.
- The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client then reads the data directly off the DataNode.
- The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.
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.
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Questions & Answers for Hadoop MapReduce developers, When is the reducers are started in a MapReduce job?
How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?
- Keys are presented to reducer in sorted order; values for a given key are not sorted.
- Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order.
- Keys are presented to a reducer in random order; values for a given key are not sorted.
- Keys are presented to a reducer in random order; values for a given key are sorted in ascending order.
Reducer has 3 primary phases:
The Reducer copies the sorted output from each Mapper using HTTP across the network.
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.
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.
In this phase the reduce(Object, Iterable, Context) method is called for each
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.
org.apache.hadoop.mapreduce, Class Reducer