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,
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.
Getting started with Apache Spark in CDH 5.x is easy using this simple example.
Apache Spark is a general-purpose, cluster computing framework that, like MapReduce in Apache Hadoop, offers powerful abstractions for processing large datasets. For various reasons pertaining to performance, functionality, and APIs, Spark is already becoming more popular than MapReduce for certain types of workloads. (For more background about Spark, read this post.)
In this how-to,
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,
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.
In this post, you’ll learn just a few of the features in Spark that make development purely a pleasure.