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.
Over 16 years, I’ve written software up-and-down the entire stack. Earliest in my career I wrote boot ROM software for specialized embedded devices. This kind of programming taught me so much about how computers really work.
Continue reading A Prayer for Distributed Systems Developers
This blog describes working with InfluxDB 0.8. InfluxDB 0.8 is no longer supported, and has been superseded by the 1.0 release.
I recently came across InfluxDB — it’s a time-series database built on LevelDB. It’s designed to support horizontal as well as vertical scaling and, best of all, it’s not written in Java — it’s written in Go. I was intrigued to say the least.
Continue reading InfluxDB and Grafana HOWTO
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.
Continue reading What I wish I’d been told about the JVM
In my last blog post I explained why writing design documents is such a powerful approach to building well-engineered systems. But what should one document? When it comes to software, if one documents too much, the content of the documentation can become inaccurate very quickly, and inaccurate documentation is quickly ignored.
Continue reading How 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.”
Continue reading Why you should write software design documents
My father worked for many years in QA at Beckman, an American medical instruments firm. His job was to ensure that newly-manufactured centrifuge rotors would hold up when spun at thousands of RPMs. He used to tell me that the Beckman philosophy could be summarised in one sentence — “There is no substitute for quality”.
Continue reading Always thinking of the next guy
After 2 years at Loggly, tomorrow I start a new role at Jut. While I will miss the team at Loggly very much, and the wonderful product we built during my team there, I’m looking forward very much to working again with some old colleagues from Riverbed Technology.
I came across a very readable paper on distributed systems — Distributed systems for fun and profit. I recommend it for anyone interested in learning more about distributed systems, and the challenges involved with designing, building, and operating distributed systems.
Packt recently asked me to review their new publication Mastering ElasticSearch by Rafał Kuć and Marek Rogoziński. Since most of my experience with elasticsearch has been from a systems points of view — index management, cluster maintenance, indexing performance — I paid most attention to the chapters about those parts of elasticsearch.
Continue reading Book Review: Mastering ElasticSearch
AWS have posted the video online of Jim Nisbet’s and my talk at AWS:reinvent 2013. In it, Jim and I describe the system we built at Loggly, which uses Apache Kafka, Twitter Storm, and elasticseach, to build a high-performance log aggregation and analytics SaaS solution, running on AWS EC2.
Continue reading Infrastructure at Scale: Apache Kafka, Twitter Storm and elasticsearch
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.
Continue reading Avoiding elasticsearch split-brain
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 Storm, ElasticSearch, and lots of secret sauce. You can find more details about the technology stack from my Loggly blog post.
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.
Continue reading Technical Leadership through Testing
I have written another post for the Loggly blog — all about our guidelines for choosing and integrating open-source software and technology in your next project.
Check it out here.
I recently wrote my first post for the Loggly blog. It illustrates why host machines are often the worst place to store the logs those machines are generating.
You can check it out here.
When running a large real-time processing system, monitoring is critical. But it does more than allow you to keep an eye on your system. During development it allows you test hypotheses about how it works, how it performs when certain parameters are changed, and takes the guessing out of working with dynamic systems.
Storm, a real-time computational framework open-sourced by Twitter, is such a system and comes with a Spout, allowing messages to be streamed from a Kafka Broker.
Continue reading Monitoring Storm Kafka Spouts using Python
The Boost ASIO Library is a wonderful piece of software. I’ve built high-performance event-driven IO C++ programs that just scream — it works very well. However, there is one subtlety when it comes to timers — specifically when it comes to cancelling expired timers.
Continue reading Boost ASIO timers — errors are never enough
Cassandra is an open-source, distributed database, informally known as a NoSQL database. It is designed to store large amounts of data, offer high-write performance, and provide fault-tolerance. I recently needed some hands-on experience with Cassandra, and being relatively new to Java programming, needed a simple set-up with which I would experiment.