Category Archives: MapReduce

Untangling Apache Hadoop YARN, Part 1

Categories: Hadoop MapReduce YARN

In this multipart series, fully explore the tangled ball of thread that is YARN.

YARN (Yet Another Resource Negotiator) is the resource management layer for the Apache Hadoop ecosystem. YARN has been available for several releases, but many users still have fundamental questions about what YARN is, what it’s for, and how it works. This new series of blog posts is designed with the following goals in mind:

  • Provide a basic understanding of the components that make up YARN
  • Illustrate how a MapReduce job fits into the YARN model of computation.

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How-to: Translate from MapReduce to Apache Spark (Part 2)

Categories: How-to MapReduce Spark

The conclusion to this series covers Combiner-like aggregation functionality, counters, partitioning, and serialization.

Apache Spark is rising in popularity as an alternative to MapReduce, in a large part due to its expressive API for complex data processing. A few months ago, my colleague, Sean Owen wrote a post describing how to translate functionality from MapReduce into Spark, and in this post, I’ll extend that conversation to cover additional functionality.

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Apache Hive on Apache Spark: The First Demo

Categories: Community Hive MapReduce Spark

The community effort to make Apache Spark an execution engine for Apache Hive is making solid progress.

Apache Spark is quickly becoming the programmatic successor to MapReduce for data processing on Apache Hadoop. Over the course of its short history, it has become one of the most popular projects in the Hadoop ecosystem, and is now supported by multiple industry vendors—ensuring its status as an emerging standard.

Two months ago Cloudera,

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How-to: Translate from MapReduce to Apache Spark

Categories: How-to MapReduce Spark

The key to getting the most out of Spark is to understand the differences between its RDD API and the original Mapper and Reducer API.

Venerable MapReduce has been Apache Hadoop‘s work-horse computation paradigm since its inception. It is ideal for the kinds of work for which Hadoop was originally designed: large-scale log processing, and batch-oriented ETL (extract-transform-load) operations.

As Hadoop’s usage has broadened,

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How-to: Use Parquet with Impala, Hive, Pig, and MapReduce

Categories: Hive How-to Impala MapReduce Parquet Pig

The CDH software stack lets you use your tool of choice with the Parquet file format – – offering the benefits of columnar storage at each phase of data processing. 

An open source project co-founded by Twitter and Cloudera, Parquet was designed from the ground up as a state-of-the-art, general-purpose, columnar file format for the Apache Hadoop ecosystem. In particular, Parquet has several features that make it highly suited to use with Cloudera Impala for data warehouse-style operations:

  • Columnar storage layout: A query can examine and perform calculations on all values for a column while reading only a small fraction of the data from a data file or table.

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