Mesos, Omega, Borg: A Survey

Google recently unveiled one of their crown jewels of system infrastructure: Borg, their cluster scheduler. This prompted me to re-read the Mesos and Omega papers, which deal with the same topic. I thought it’d be interested to do a compare and contrast of these systems. Mesos gets credit for the groundbreaking idea of two-level scheduling, Omega improved upon this with an analogy from databases, and Borg can sort of be seen as the culmination of all these ideas.

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Posted by andrew in Reviews, 0 comments

Bucket list: Catch a fish and eat it

I checked off one of my bucket list items yesterday: catching a fish, cleaning it, and eating it.

This was the last day of a family vacation in Port St. Lucie in Florida. My original plans to go deep sea fishing fell through, so I went to the surprisingly well-stocked local Walmart to pick up some freshwater gear. I was lucky enough to nab a healthy-looking 15″ largemouth bass with a silver Mepps spinner from the lake behind our timeshare.

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Posted by andrew in Personal, 0 comments

Paper review: Facebook f4

It’s been a while since I did one of these! I did a previous review of Facebook Haystack, which was designed as an online blob storage system. f4 is a sister system that works in conjunction with Haystack, and is intended for storage of warm rather than hot blobs. As is usual for Facebook, they came up with a system that is both eminently practical and tailored for their exact use case.

This paper, “f4: Facebook’s Warm BLOB Storage System” by Muralidhar et al., was published at OSDI ’14.

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In-memory Caching in HDFS: Lower latency, same great taste

My coworker Colin McCabe and I recently gave a talk at Hadoop Summit Amsterdam titled “In-memory Caching in HDFS: Lower latency, same great taste.” I’m very pleased with how this feature turned out, since it was approximately a year-long effort going from initial design to production system. Combined with Impala, we showed up to a 6x performance improvement by running on cached data, and that number will only improve with time.

Slides: pptx

Video: Youtube

Posted by andrew in Talks, 0 comments

Two engineering principles

I received two interesting pieces of advice at the AMP Lab retreat this past week, which concisely state some of my favorite software engineering principles:

  1. Don’t be a zealot. Understand in technical detail why a given language, framework, or design should be preferred, not because of technological fascination or fanboy-ism. The canonical examples here are programming language flamewars, e.g. Java vs. C++.
  2. Ruthlessly optimize for your requirements. This means first, carefully defining said requirements, but then being completely unafraid to buck conventional wisdom if it’s not a good match. This often means intentionally pruning out features, even common ones implemented by other systems.
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Apache Hadoop committer

A quick post celebrating that I recently was made a committer on the Apache Hadoop project. I owe a big thanks to everyone who’s reviewed my patches and helped me along the way (especially my colleagues ATM, Todd, and Colin here at Cloudera).

My very first patch was HDFS-1952 in May 2011, via a Hadoop hackathon hosted at Cloudera. It was the most promising newbie HDFS JIRA on the list, and I still remember all the basic issues I had checking out the repo, setting up Eclipse, using Ant, and generating the diff. Two years later, these things have gotten easier 🙂

Here’s to many more contributions in the future!

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Grad school four months out

Here’s my account of leaving the PhD program at Berkeley to work at Cloudera. My experience might not be representative or generalize beyond my own situation, but I’m writing this because a number of people have asked me about the differences between grad school and industry. Choosing to leave Berkeley was a very personal decision, but fortunately I’m happy with how it’s turned out.

This also serves as my “Year in review: 2012” post, since this was the major change in my life last year.

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Hadoop 101 slides

I gave a guest lecture on the Hadoop stack last week at Tapan Parikh’s INFO 206: Distributed Computing Applications and Infrastructure course at Berkeley. I took a more academic approach than most, talking about the original motivating problem of Google search before moving into a deep dive of HDFS and MapReduce and an overview of the rest of the Hadoop ecosystem.

A couple students came up afterwards to say they enjoyed the talk, so I think it was well-received.

Slides: pptx and pdf

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Highly-available audio in HDFS

Here on the HDFS team at Cloudera, we believe in eating our own dogfood. Since we value our (substantial) MP3 collections quite dearly, it’s only natural to store them in a high performance, highly-available, enterprise-quality distributed filesystem like HDFS. Today, I’m announcing the next generation in aural HDFS enjoyment: listening to music directly from the Namenode web UI.

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