Cloudera Engineering Blog · Spark Posts

This Month in the Ecosystem (May 2014)

Welcome to our ninth edition of “This Month in the Ecosystem,” a digest of highlights from May/early June 2014 (never intended to be comprehensive; for that, see the excellent Hadoop Weekly).

More good news!

Apache Spark 1.0 is Released

Spark 1.0 is its biggest release yet, with a list of new features for enterprise customers.

Congratulations to the Apache Spark community for today’s release of Spark 1.0, which includes contributions from more than 100 people (including Cloudera’s own Diana Carroll, Mark Grover, Ted Malaska, Sean Owen, Sandy Ryza, and Marcelo Vanzin). We think this release is an important milestone in the continuing rapid uptake of Spark by enterprises — which is supported by Cloudera via Cloudera Enterprise 5 — as a modern, general-purpose processing engine for Apache Hadoop.

Apache Spark Resource Management and YARN App Models

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.

Making Apache Spark Easier to Use in Java with Java 8

Our thanks to Prashant Sharma and Matei Zaharia of Databricks for their permission to re-publish the post below about future Java 8 support in Apache Spark. Spark is now generally available inside CDH 5.

One of Apache Spark‘s main goals is to make big data applications easier to write. Spark has always had concise APIs in Scala and Python, but its Java API was verbose due to the lack of function expressions. With the addition of lambda expressions in Java 8, we’ve updated Spark’s API to transparently support these expressions, while staying compatible with old versions of Java. This new support will be available in Spark 1.0.

A Few Examples

How-to: Run a Simple Apache Spark App in CDH 5

Getting started with Spark (now shipping inside CDH 5) is easy using this simple example.

(Editor’s note – this post has been updated to reflect CDH 5.1/Spark 1.0)

Letting It Flow with Spark Streaming

Our thanks to Russell Cardullo and Michael Ruggiero, Data Infrastructure Engineers at Sharethrough, for the guest post below about its use case for Spark Streaming.

At Sharethrough, which offers an advertising exchange for delivering in-feed ads, we’ve been running on CDH for the past three years (after migrating from Amazon EMR), primarily for ETL. With the launch of our exchange platform in early 2013 and our desire to optimize content distribution in real time, our needs changed, yet CDH remains an important part of our infrastructure.

Apache Spark: A Delight for Developers

Sure, Spark is fast, but it also gives developers a positive experience they won’t soon forget.

Apache Spark is well known today for its performance benefits over MapReduce, as well as its versatility. However, another important benefit – the elegance of the development experience – gets less mainstream attention.

Why Apache Spark is a Crossover Hit for Data Scientists

Spark is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.

Data science is a broad church. I am a data scientist — or so I’ve been told — but what I do is actually quite different from what other “data scientists” do. For example, there are those practicing “investigative analytics” and those implementing “operational analytics.” (I’m in the second camp.)

Spark is Now Generally Available for Cloudera Enterprise

Cloudera is announcing the general availability of support for Spark, bringing interactive machine learning and stream processing to enterprise data hubs.

Cloudera is pleased to announce the immediate availability of its first release of Apache Spark for Cloudera Enterprise (comprising CDH and Cloudera Manager).

This Month (and Year) in the Ecosystem (December 2013)

Welcome to our sixth edition of “This Month in the Ecosystem,” a digest of highlights from December 2013 (never intended to be comprehensive; for completeness, see the excellent Hadoop Weekly).

With the close of 2013, we also thought it appropriate to include some high points from across the year (not listed in any particular order):

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