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computations into the client and write them to stdout.įor (Pair wordCount : unt().top(20). We then read the results of the MapReduce jobs that performed the a map of the top 20 unique words in the input PCollection to their counts.
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#Jav library hunter series
The count method applies a series of Crunch primitives and returns Public static void main(String args) throws Exception, Writables.strings()) // Indicates the serialization format
#Jav library hunter code
Here is the source code for the WordCount application in Crunch: The source code ships with a number of example applications.
#Jav library hunter download
You can download the source or the binaries of latest version of Crunch from the website, or you can use the dependencies that are published at Maven Central. Crunch developers can also use the Crunch primitives to define APIs that provide clients with advanced ETL, machine learning, and scientific computing functionality that involves a series of complex MapReduce jobs. User-defined functions in Crunch are designed to be lightweight while still providing complete access to the underlying MapReduce APIs for applications that require it. Crunch also supports an in-memory execution engine that can be used to test and debug pipelines on local data.Ĭrunch was designed for problems that benefit from lots of user-defined functions operating on custom data types. The Crunch job planner takes in the graph of operations defined by the pipeline developer, breaks the operations up into a series of dependent MapReduce jobs, and then executes them on a Hadoop cluster. union: Treat two or more PCollections as a single, virtual PCollection.Īll of Crunch's higher-order operations (joins, cogroups, set operations, etc.) are implemented in terms of these primitives.combineValues: Perform an associative operation to aggregate the values from a groupByKey operation.groupByKey: Sort and group the elements of a PTable by their keys (equivalent to the shuffle phase of a MapReduce job).parallelDo: Apply a user-defined function to a given PCollection and return a new PCollection as a result.These two core classes support four primitive operations: Crunch ConceptsĬrunch's core abstractions are a PCollection, which represents a distributed, immutable collection of objects, and a PTable, which is a sub-interface of PCollection that contains additional methods for working with key-value pairs.
MapReduce, for all of its virtues, is the wrong level of abstraction for many problems: most interesting computations are made up of multiple MapReduce jobs, and it is often the case that we need to compose logically independent operations (e.g., data filtering, data projection, data transformation) into a single physical MapReduce job for performance reasons.Įssentially, Crunch is designed to be a thin veneer on top of MapReduce - with the intention being not to diminish MapReduce's power (or the developer's access to the MapReduce APIs) but rather to make it easy to work at the right level of abstraction for the problem at hand.Īlthough Crunch is reminiscent of the venerable Cascading API, their respective data models are very different: one simple common-sense summary would be that folks who think about problems as data flows prefer Crunch and Pig, and people who think in terms of SQL-style joins prefer Cascading and Hive. Instead, Crunch uses a customizable type system that is flexible enough to work directly with complex data such as time series, HDF5 files, Apache HBase tables, and serialized objects like protocol buffers or Avro records.Ĭrunch does not try to discourage developers from thinking in MapReduce, but it does try to make thinking in MapReduce easier to do. Unlike those other tools, Crunch does not impose a single data type that all of its inputs must conform to. Like other high-level tools for creating MapReduce jobs, such as Apache Hive, Apache Pig, and Cascading, Crunch provides a library of patterns to implement common tasks like joining data, performing aggregations, and sorting records. Apache Crunch (incubating) is a Java library for creating MapReduce pipelines that is based on Google's FlumeJava library.
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