Search is everywhere. Once you’ve built search systems, you see its potential application in many places. So when I came across bleve, an open-source search library written in Go, I was interested in learning more about its feature set and its indexing performance. And I could see immediately one might be able to shard it to improve performance.
Recently at InfluxDB we discussed how code reviews fit in during the various stages of development. It’s great to see the team reach consensus about how we should develop software. It made me think more deeply about why I remain a big believer in the code review process.
I recently came across a talk on YouTube titled History of Software Engineering, given by Paolo Perrotta. Normally I find online videos to have a low information-to-time ratio, but this one was excellent. It’s not too long, with plenty of humour, and makes many serious points that resonated with me.
Continue reading History of Software Engineering
Bjarne Stroustrup has another very interesting paper on his website. Titled Software Development for Infrastructure, it discusses some key ideas for building software that has “…more stringent correctness, reliability, efficiency, and maintainability requirements than non-essential applications.” It is not a long paper, but offers useful observations and guidelines for building such software systems.
The Eudyptula Challenge is a series of programming tasks, with the goal of getting one up-to-speed on Linux kernel programming. When I first heard about it, it immediately intrigued me. I’ve written a few production Linux kernel modules in my time — mostly device drivers — so I started the challenge today.
Bjarne Stroustrup has great paper on his website titled Evolving a language in and for the real world: C++ 1991-2006. It provides fascinating insights on the development of the language, the challenges involved, and discusses interesting design ideas. If you have even a basic understanding of C++, it’s a such a worthwhile read.
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.
Continue reading Replicating SQLite using Raft Consensus
So far coding in Go has been fun. It comes with nice functionality that lets you know that the Go team really have been writing system software (useful stuff like this, and this). And then I read about the Go Memory Model, and had my consciousness raised.
I’ve started coding in Go (golang), and I received some advice recently from Robert Griesemer, whom I was fortunate enough to sit beside at a recent Go Meetup. To learn Go, Robert suggested that I code a solution in Go for a problem I had previously solved in a different language.
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.
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.
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.
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.”
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”.
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.
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.
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.
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.