I recently presented at the InfluxDB San Francisco Meetup, on InfluxDB and the Raft consensus protocol. My talk was about the fundamental problems of distributed systems, and how InfluxDB uses Raft to solve these issues.
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.
This is the first part of a 3-part series “Designing and building a search system for log data”. Part 2 is here, and part 3 is here.
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.