Cloudera Engineering Blog
Big Data best practices, how-to's, and internals from Cloudera Engineering and the community
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. Then we’ll move to tuning parallelism, the most difficult as well as most important parameter in job performance. Finally, you’ll learn about representing the data itself, in the on-disk form which Spark will read (spoiler alert: use Apache Avro or Apache Parquet) as well as the in-memory format it takes as it’s cached or moves through the system.
Tuning Resource Allocation
Following these best practices can make your upgrade path to CDH 5 relatively free of obstacles.
Upgrading the software that powers mission-critical workloads can be challenging in any circumstance. In the case of CDH, however, Cloudera Manager makes upgrades easy, and the built-in Upgrade Wizard, available with Cloudera Manager 5, further simplifies the upgrade process. The wizard performs service-specific upgrade steps that, previously, you had to run manually, and also features a rolling restart capability that reduces downtime for minor and maintenance version upgrades. (Please refer to this blog post or webinar to learn more about the Upgrade Wizard).
Thanks to Sam Shuster, Software Engineer at Edmunds.com, for the guest post below about his company’s use case for Spark Streaming, SparkOnHBase, and Morphlines.
Every year, the Super Bowl brings parties, food and hopefully a great game to appease everyone’s football appetites until the fall. With any event that brings in around 114 million viewers with larger numbers each year, Americans have also grown accustomed to commercials with production budgets on par with television shows and with entertainment value that tries to rival even the game itself.
Use the scripts and screenshots below to configure a Kerberized cluster in minutes.
Kerberos is the foundation of securing your Apache Hadoop cluster. With Kerberos enabled, user authentication is required. Once users are authenticated, you can use projects like Apache Sentry (incubating) for role-based access control via GRANT/REVOKE statements.
Thanks to Big Data Solutions Architect Matthieu Lieber for allowing us to republish the post below.
A customer of mine wants to take advantage of both worlds: work with his existing Apache Avro data, with all of the advantages that it confers, but take advantage of the predicate push-down features that Parquet provides. How to reconcile the two?
Set up your own, or even a shared, environment for doing interactive analysis of time-series data.
Although software engineering offers several methods and approaches to produce robust and reliable components, a more lightweight and flexible approach is required for data analysts—who do not build “products” per se but still need high-quality tools and components. Thus, recently, I tried to find a way to re-use existing libraries and datasets stored already in HDFS with Apache Spark.
Thanks to Cody Koeninger, Senior Software Engineer at Kixer, for the guest post below about Apache Kafka integration points in Apache Spark 1.3. Spark 1.3 will ship in CDH 5.4.
The new release of Apache Spark, 1.3, includes new experimental RDD and DStream implementations for reading data from Apache Kafka. As the primary author of those features, I’d like to explain their implementation and usage. You may be interested if you would benefit from:
Security architecture is complex, but these testing strategies help Cloudera customers rely on production-ready results.
Among other things, good security requires user authentication and that authenticated users and services be granted access to those things (and only those things) that they’re authorized to use. Across Apache Hadoop and Apache Solr (which ships in CDH and powers Cloudera Search), authentication is accomplished using Kerberos and SPNego over HTTP and authorization is accomplished using Apache Sentry (the emerging standard for role-based fine grain access control, currently incubating at the ASF).
Having a good grasp of HDFS recovery processes is important when running or moving toward production-ready Apache Hadoop. In the conclusion to this two-part post, pipeline recovery is explained.
An important design requirement of HDFS is to ensure continuous and correct operations that support production deployments. For that reason, it’s important for operators to understand how HDFS recovery processes work. In Part 1 of this post, we looked at lease recovery and block recovery. Now, in Part 2, we explore pipeline recovery.
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, you’re confronted with a new vocabulary of words like job, stage, and task. Understanding Spark at this level is vital for writing good Spark programs, and of course by good, I mean fast. To write a Spark program that will execute efficiently, it is very, very helpful to understand Spark’s underlying execution model.