Replicating SQLite using Raft Consensus

SQLite is a “self-contained, serverless, zero-configuration, transactional SQL database engine”.  However, it doesn’t come with replication built in, so if you want to store mission-critical data in it, you better back it up. The usual approach is to continually copy the SQLite file on every change.

I wanted SQLite, I wanted it distributed, and I really wanted a more elegant solution for replication. So rqlite was born.
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Call me Definitely

The creator of the network monitoring system Riemann, Kyle Kingsbury, has put together a comprehensive series of blog posts, on the fault-tolerance, high-availability, and general correctness of number of database and storage technologies. Of the technologies discussed I am most familiar with — elasticsearch and Apache Kafka — I found the posts to be a great read.

If you haven’t read them yet, you should check them out on his site.

What I wish I’d been told about the JVM

Java is the predominant language of Big Data technologies. HBase, Lucene, elasticsearch, Cassandra – all are written in Java and, of course, run inside a Java Virtual Machine (JVM). There are some other important Big Data technologies, while not written in Java, also run inside a JVM. Examples include Apache Storm, which is written in Clojure, and Apache Kafka, which is written in Scala. This makes basic knowledge of the JVM quite important when it comes to deploying and operating Big Data technologies.

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Why you should write software design documents

Many software engineers never write design documents. Design documentation takes time, and implementations often proceed so far without any documentation that if it happens, it’s an act of recording what has been done — a tedious task at the best times. Many software engineers argue “the code exists, it’s running, it’s working, let’s move on and build the next thing.”

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Speaking at AWS re:Invent 2013

This past week I had the opportunity to speak, with my colleague Jim Nisbet, at AWS re:Invent 2013. Titled “Unmeltable Infrastructure at Scale: Using Apache Kafka, Twitter Storm, and Elastic Search on AWS“, Jim and I described the architecture of Loggly’s next-generation log aggregation and analytics Infrastructure, which went live 3 months ago, and runs on AWS EC2.

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Avoiding elasticsearch split-brain

Loggly recently held an elasticsearch meetup, which was a great success. One question that was repeatedly asked was how to ensure elasticsearch does not suffer a partition — known as a split-brain. This can be a particular problem in AWS EC2, where the network is subject to interruptions. It can also happen if the elasticsearch master node performs long garbage collection cycles.

One configuration that is very effective at preventing this problem is described in this post.

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Loggly Generation 2 Released!

After 14 months of hard work, the next generation of Loggly has been released. It’s been a great time to be part of the Software Infrastructure team at Loggly and we have put together a superb log aggregation & real-time analytics platform.

We used a combination of custom log Collectors, Apache Kafka, Twitter StormElasticSearch, and lots of secret sauce. You can find more details about the technology stack from my Loggly blog post.

Technical Leadership through Testing

As technical lead at Loggly, responsibility for a well-engineered infrastructure ends with me. And one way to ensure the system is designed and implemented well is to stay as close as possible to the code, ensuring that the team and I write quality software.

But it can be difficult to complete the design and implementation of the features I am responsible for, ensure that what the team produces is well-implemented, and understand every line of code — there is only so much time in the day.

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