In the last post we examined the design and implementation of Ekanite, a system for indexing log data, and making that data available for search in near-real-time. Is this final post let’s see Ekanite in action.
In the previous post I outlined some of the high-level requirements for a system that indexed log data, and makes that data available for search, all in near-real-time. Satisfying these requirements involves making trade-offs, and sometimes there are no easy answers.
For the past few years, I’ve been building indexing and search systems, for various types of data, and often at scale. It’s fascinating work — only at scale does O(n) really come alive. Developing embedded systems teaches you how computers really work, but working on search systems and databases teaches you that algorithms really do matter.
Hashicorp provide a nice implementation of the Raft consensus protocol, and it’s at the heart of InfluxDB (amongst other systems). I wanted to experiment with a simple system built using this particular Raft implementation, so was inspired by raftd to built hraftd.
“Run into an obstacle in what you’re working on? Hmm, I wonder what’s new online. Better check.”
If you haven’t already, you should start reading Paul Graham’s essays. In one on philosophy, Graham believes that many of the answers provided by philosophy are useless because “…of how little effect they have”. By that standard another of his essays is of high utility because it has affected the way I program. John Stuart Mill would be pleased.
This past week I attended Gophercon 2015, in Denver, CO. It was also a chance to get together with the rest of the InfluxDB team. And because the Go community is still relatively young and small, it was a great chance to meet, in person, some of the best people working with Go today.
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.
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.
Real-time — or near real-time — data pipelines are all the rage these days. I’ve built one myself, and they are becoming key components of many SaaS platforms. SaaS Analytics, Operations, and Business Intelligence systems often involve moving large amounts of data, received over the public Internet, into complex backend systems. And managing the incoming flow of data to these pipelines is key.
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.