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What is the use of grouping comparator in hadoop map reduce

In support of the chosen answer I add: Following on from this explanation **Input**: symbol time price a 1 10 a 2 20 b 3 30 **Map output**: create composite key\values like so: > symbol-time time-price > >**a-1**         1-10 > >**a-2**         2-20 > >**b-3**         3-30 The Partitioner: will route the a-1 and a-2 keys to the same reducer … Read more

Change File Split size in Hadoop

The parameter mapred.max.split.size which can be set per job individually is what you looking for. Don’t change dfs.block.size because this is global for HDFS and can lead to problems.

How does Hadoop perform input splits?

The InputFormat is responsible to provide the splits. In general, if you have n nodes, the HDFS will distribute the file over all these n nodes. If you start a job, there will be n mappers by default. Thanks to Hadoop, the mapper on a machine will process the part of the data that is … Read more

Where does hadoop mapreduce framework send my System.out.print() statements ? (stdout)

Actually stdout only shows the System.out.println() of the non-map reduce classes. The System.out.println() for map and reduce phases can be seen in the logs. Easy way to access the logs is http://localhost:50030/jobtracker.jsp->click on the completed job->click on map or reduce task->click on tasknumber->task logs->stdout logs. Hope this helps

Change output filename prefix for DataFrame.write()

You cannot change the “part” prefix while using any of the standard output formats (like Parquet). See this snippet from ParquetRelation source code: private val recordWriter: RecordWriter[Void, InternalRow] = { val outputFormat = { new ParquetOutputFormat[InternalRow]() { // … override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = { // .. // prefix is hard-coded here: … Read more

MultipleOutputFormat in hadoop

Each reducer uses an OutputFormat to write records to. So that’s why you are getting a set of odd and even files per reducer. This is by design so that each reducer can perform writes in parallel. If you want just a single odd and single even file, you’ll need to set mapred.reduce.tasks to 1. … Read more

Is gzip format supported in Spark?

From the Spark Scala Programming guide’s section on “Hadoop Datasets”: Spark can create distributed datasets from any file stored in the Hadoop distributed file system (HDFS) or other storage systems supported by Hadoop (including your local file system, Amazon S3, Hypertable, HBase, etc). Spark supports text files, SequenceFiles, and any other Hadoop InputFormat. Support for … Read more

Java8: HashMap to HashMap using Stream / Map-Reduce / Collector

Map<String, String> x; Map<String, Integer> y = x.entrySet().stream() .collect(Collectors.toMap( e -> e.getKey(), e -> Integer.parseInt(e.getValue()) )); It’s not quite as nice as the list code. You can’t construct new Map.Entrys in a map() call so the work is mixed into the collect() call.

Remove Duplicates from MongoDB

The “dropDups” syntax for index creation has been “deprecated” as of MongoDB 2.6 and removed in MongoDB 3.0. It is not a very good idea in most cases to use this as the “removal” is arbitrary and any “duplicate” could be removed. Which means what gets “removed” may not be what you really want removed. … Read more

Why is Spark faster than Hadoop Map Reduce

bafna’s answer provides the memory-side of the story, but I want to add other two important facts：DAG and ecosystem Spark uses “lazy evaluation” to form a directed acyclic graph (DAG) of consecutive computation stages. In this way, the execution plan can be optimized, e.g. to minimize shuffling data around. In contrast, this should be done … Read more