Author Archives: Sandy Ryza

How-to: Tune Your Apache Spark Jobs (Part 2)

Categories: How-to Spark

In the conclusion to this series, learn how resource tuning, parallelism, and data representation affect Spark job performance.

In this post, we’ll finish what we started in “How to Tune Your Apache Spark Jobs (Part 1)”. I’ll try to cover pretty much everything you could care to know about making a Spark program run fast. In particular, you’ll learn about resource tuning, or configuring Spark to take advantage of everything the cluster has to offer.

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How-to: Tune Your Apache Spark Jobs (Part 1)

Categories: How-to Spark

Learn techniques for tuning your Apache Spark jobs for optimal efficiency.

When you write Apache Spark code and page through the public APIs, you come across words like transformation, action, and RDD. Understanding Spark at this level is vital for writing Spark programs. Similarly, when things start to fail, or when you venture into the web UI to try to understand why your application is taking so long,

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New in CDH 5.1: Apache Spark 1.0

Categories: CDH Spark

Spark 1.0 reflects a lot of hard work from a very diverse community.

Cloudera’s latest platform release, CDH 5.1, includes Apache Spark 1.0, a milestone release for the Spark project that locks down APIs for Spark’s core functionality. The release reflects the work of hundreds of contributors (including our own Diana Carroll, Mark Grover, Ted Malaska, Colin McCabe, Sean Owen, Hari Shreedharan, Marcelo Vanzin, and me).

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Estimating Financial Risk with Apache Spark

Categories: Data Science Spark Use Case

Learn how Spark facilitates the calculation of computationally-intensive statistics such as VaR via the Monte Carlo method.

Under reasonable circumstances, how much money can you expect to lose? The financial statistic value at risk (VaR) seeks to answer this question. Since its development on Wall Street soon after the stock market crash of 1987, VaR has been widely adopted across the financial services industry. Some organizations report the statistic to satisfy regulations,

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Apache Spark Resource Management and YARN App Models

Categories: Spark YARN

A concise look at the differences between how Spark and MapReduce manage cluster resources under YARN

The most popular Apache YARN application after MapReduce itself is Apache Spark. At Cloudera, we have worked hard to stabilize Spark-on-YARN (SPARK-1101), and CDH 5.0.0 added support for Spark on YARN clusters.

In this post, you’ll learn about the differences between the Spark and MapReduce architectures, why you should care,

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