Apache Spark Pipelines in the Cloud with Alluxio

Spark Summit Europe 2017 *

In this talk, we discuss how Alluxio can be deployed and used with a Spark data processing pipeline in the cloud. We show how pipeline stages can share data with Alluxio memory for improved performance benefits, and how Alluxio can improves completion times and reduces performance variability for Spark pipelines in the cloud.

Unified Big Data Analytics – Any stack, Any Cloud

Boston Meetup *

This presentation focuses on how Alluxio helps the big data analytics stack to be cloud-native. The trending Cloud object storage systems provide more cost-effective and scalable storage solutions but also different semantics and performance implications compared to HDFS. Applications like Spark or Presto will not benefit from the node-level locality or cross-job caching when retrieving data from the cloud object storage. Deploying Alluxio to access cloud solves these problems because data will be retrieved and cached in Alluxio instead of the underlying cloud or object storage repeatedly.

Getting Started with Spark Caching using Alluxio in 5 Minutes

Apache Spark has brought significant innovation to Big Data computing, but its results are even more extraordinary when paired with Alluxio. Alluxio, provides Spark with a reliable data sharing layer, enabling Spark to excel at performing application logic while Alluxio handles storage. Bazaarvoice uses the combination of Spark and Alluxio to provide a real time big data platform that has the ability to not only handle the intake of 1.5 billion page views during peak events like Black Friday, but also provide real time analytics against it (read more). At this scale, the gain in speed is an enabler for new workloads. We’ve established a clean and simple way to integrate Alluxio and Spark.

Achieving 10x acceleration of Spark and Hive Jobs on AWS S3 with Alluxio Tiered Storage

 

The data engineering team at Bazaarvoice, a software-as-a-service digital marketing company based in Austin, Texas, must handle data at massive Internet-scale to serve its customers. Facing challenges with scaling their storage capacity up and provisioning hardware, they turned to Alluxio’s tiered storage system and saw 10x acceleration of their Spark and Hive jobs running on AWS S3.

In this whitepaper you’ll learn:

  • How to build a big data analytics platform on AWS that includes technologies like Hive, Spark, Kafka, Storm, Cassandra, and more
  • How to setup a Hive metastore using a storage tier for hot tables
  • How to leverage tiered storage for maximized read performance

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One Click to Benchmark Spark + Alluxio + S3 Stack with TPC-DS queries on AWS

The Alluxio sandbox is the easiest way to test drive the popular data analytics stack of Spark, Alluxio, and S3 deployed in a multi-node cluster in a public cloud environment. The sandbox cluster is fully configured and ready for users to run applications ranging from the hello-world example to the TPC-DS benchmark suite. Don’t take our word for it; kick off the benchmark yourself to see the performance benefits of running Spark jobs that interface through Alluxio on S3 compared to running Spark jobs directly on S3. It is extremely easy to request and launch a sandbox cluster as a playground for 24 hours at no cost to you.

Top 10 Tips for Making the Spark + Alluxio Stack Blazing Fast

The Apache Spark + Alluxio stack is getting quite popular particularly for the unification of data access across S3 and HDFS. In addition, compute and storage are increasingly being separated causing larger latencies for queries. Alluxio is leveraged as compute-side virtual storage to improve performance. But to get the best performance, like any technology stack, you need to follow the best practices. This article provides the top 10 tips for performance tuning for real-world workloads when running Spark on Alluxio with data locality giving the most bang for the buck.

Developer Tip: Why Did My Job Fail with Error Message “Class alluxio.hadoop.FileSystem not found”?

From time to time, a question pops up on the user mailing list referencing job failures with the error message “java.lang.ClassNotFoundException: Class alluxio.hadoop.FileSystem not found”. This post explains the reason for the failure and the solution to the issue when it occurs.
This error indicates the Alluxio client is not available at runtime. This causes an exception when the job tries to access the Alluxio filesystem but fails to find the implementation of Alluxio client to connect to the service.