Tag: QFS

Upgrading the Compute Cluster to 2.5G Ethernet

michael

I recently updated my Personal Compute Cluster to use faster ethernet interconnect for the cluster network. After putting together a PySpark Benchmark, I was intrigued to see how faster networking within the cluster would help. Note: All product links below are affiliate links. Upgrading the Cluster Networking Hardware To upgrade the networking for the EGLOBAL S200 computers that I use within my cluster, my only real option was to use USB ethernet dongles. This is because the S200 computer has no PCIe expansion slots, but it does have a USB 3 bus. This gives me a few options. There are no 10 Gbps ethernet dongles for USB 3, but there are several 5 and 2.5 Gbps ethernet dongles. These are part fo the more recent NBASE-T ethernet standard which allows faster than 1 Gbps ethernet over Cat 5e and Cat 6 cabling. The first option I investigated was the StarTech USB 3 to 5 Gbps Ethernet Adapter. I am going Read More …

Airline Flight Data Analysis – Part 1 – Data Preparation (Reprise)

michael

As some of you know, I previously explored building a Spark cluster using ODROID XU-4 single board computers. I was able to demonstrate some utility with this cluster, but it was limited. One analysis I attempted looking at was the Airline On-Time Performance data set collected by the United States Department of Transportation. The XU-4 cluster allowed me to make summarization graphs of the data, but not much more. The primary constraint was the RAM pool the cluster had, which was 8 GB total RAM across the four nodes, and the 10 year data set was greater than 30 GB uncompressed. Now that I have built the Personal Compute Cluster, I decided to revise this data set to see if I could do more sophisticated analysis. The short answer is I can. But before I do that we need to prepare the raw data that we download from the Department of Transportation’s website into a format. Specifically we need to Read More …

Running Spark with QFS on a Docker Swarm

michael

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

michael

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

michael

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 …