Category Archives: Hadoop

Untangling Apache Hadoop YARN, Part 5: Using FairScheduler queue properties

Categories: Hadoop YARN

Previously in Part 4, we described the most commonly used FairScheduler properties in Apache Hadoop.  In Part 5, we’ll provide some examples to show how properties can be used, individually and in combination, to achieve commonly desired behavior such as application prioritization and organizing queues.

Example: Best Effort Queue

Summary: Create a “best effort” queue that runs applications when the cluster is underutilized.  

Implementation: In FairScheduler,

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Performance comparison of different file formats and storage engines in the Apache Hadoop ecosystem

Categories: Avro Guest Hadoop HBase Kudu Parquet

Zbigniew Baranowski is a database systems specialist and a member of a group which provides and supports central database and Hadoop-based services at CERN. This blog was originally released on CERN’s “Databases at CERN” blog, and is syndicated here with CERN’s permission.

 

TOPIC

This post presents a performance comparison of few popular data formats and storage engines available in the Apache Hadoop ecosystem: Apache Avro,

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New in Cloudera Enterprise 5.10: Hue SQL Editor and Security Improvements

Categories: Hadoop Hue Oozie

Cloudera Enterprise 5.10 includes the latest updates of Hue, the intelligent editor for SQL Developers and Analysts.

As part of Cloudera’s continuing investments in user experience and productivity, Cloudera Enterprise 5.10 includes an updated version of Hue. We provide a summary of the main enhancements in the following part of this blog post. (Hue from C5.10 is also available for a quick try in one click on demo.gethue.com.)

SQL Improvements

The Hue editor keeps getting better with these major improvements:

Row Count

The number of rows returned is displayed so you can quickly see the size of the dataset.

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Analyzing US flight data on Amazon S3 with sparklyr and Apache Spark 2.0

Categories: CDH Data Science Hadoop Spark Use Case

We posted several blog posts about sparklyr (introduction, automation), which enables you to analyze big data leveraging Apache Spark seamlessly with R. sparklyr, developed by RStudio, is an R interface to Spark that allows users to use Spark as the backend for dplyr, which is the popular data manipulation package for R.

If you are interested in sparklyr, you can learn how to use it with the official document,

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Working with UDFs in Apache Spark

Categories: Hadoop Spark

User-defined functions (UDFs) are a key feature of most SQL environments to extend the system’s built-in functionality.  UDFs allow developers to enable new functions in higher level languages such as SQL by abstracting their lower level language implementations.  Apache Spark is no exception, and offers a wide range of options for integrating UDFs with Spark SQL workflows.

In this blog post, we’ll review simple examples of Apache Spark UDF and UDAF (user-defined aggregate function) implementations in Python,

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