Running Spark with QFS on a Docker Swarm

In my last post, we put together a quick build that runs Apache Spark on the Personal Compute Cluster. The goal of that build was to simply demonstrate how to run Spark on a Docker Swarm. What it did not address well was how Spark would access data. I did set up GlusterFS shared volume that did allow Spark to read and write data files. However, this was not fast because the set up did not allow Spark to take advantage of data locality when scheduling task. As a result, almost all data reads and writes are done across the ethernet backbone. The goal in this post is to set up a Spark build on Docker Swarm that is paired with a distributed file system set up in a manner where data locality can be leveraged to speed up processing. There are choices for distributed file systems, the most notable being Hadoop’s HDFS and the Quantcast File System (QFS). For Read More …

Quantcast File System 1.2 for ARM71

NOTE – This article has been updated. It now assumes you have set up the cluster with Ubuntu 16.04, and it has the latest builds of QFS v1.2.1 and Spark v2.2.0. I have been using the Quantcast File System (QFS) as my primary distributed file system on my ODROID XU4 cluster.  Due to QFS’s low memory footprint, it works well with Spark, allowing me to assign as much of the ODROID XU4’s limited 2 GB RAM footprint to the Spark executor running on a node. Recently, QFS 1.2 was released. This version brings many features and updates, many not relevant to my ODROID cluster use case. However, the most notable updates relevant to the ODROID XU4 cluster include: Correct Spark’s ability to create a hive megastore on a new QFS instance (QFS-332) Improved error reporting in the QFS/HDFS shim HDFS shim for the Hadoop 2.7.2 API, which the latest versions of Spark use. In this post, I will update the ODROID XU4 Read More …

Using the Quantcast File System with Spark on the ODROID XU4 Cluster

I used to work at a company named Quantcast, which is known for processing petabyte-scale data sets. When operating at that scale, any inefficiency in your data infrastructure gets multiplied many times over. Early on, Quantcast found that the inefficiencies in Hadoop literally cost them by requiring more hardware to obtain certain levels of performance. So Quantcast set out to write a highly efficient big data stack. At the bottom of that stack was Quantcast’s distributed file system called QFS. Quantcast open-sourced QFS, and performed several studies to show the efficiency gains QFS had over HDFS. Given the first hand experience I have had with QFS performance, I thought it would interesting to get it running on the ODROID XU4 cluster in combination with Spark to see how much QFS would improve processing speeds in our set up. Spoiler alert: there was no time improvements, but QFS required less memory than HDFS, allowing me to assign more RAM to Spark. Installing Read More …