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?
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.