What I learned from programming databases

databaseProgramming a database is fascinating work. I’ve been deeply involved with developing open source databases for the past two years and programming a database is possibly the most instructive project one can ever complete as a software developer.

What’s really striking however, is how much my attitude towards databases has changed over the past 6 years. From a state of disinterest, I’ve come to think of these systems as a pinnacle of software engineering.

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rqlite – replicated SQLite with new Raft consensus and API

Raft consensus protocolrqlite provides robust replication for SQLite databases using the Raft consensus protocol. Coded in Go it ensures that all changes made to the leader SQLite database are replicated to all other nodes in the cluster, providing fault-tolerance and reliability.

It’s been 18 months since development of rqlite first started and it’s time for version 2.

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rqlite and Hashicorp Raft Consensus

Hashicorp RaftI’ve started replacing go-raft within rqlite with the implementation from Hashicorp. go-raft is no longer maintained, and I’ve good experience with the Hashicorp code, due to my work with InfluxDB and hraftd. I’m also going to change the API, so it’s more useful. The existing implementation and API has been tagged as v1.0, so it’s still available.

You can follow the work on this branch, and I hope to merge it to master in the near future.

Revisiting syslog-gollector

It’s been 18 months since the first commit to my first significant Go project — syslog-gollector. After an initial burst of activity to create a functional Syslog Collector that streamed to Apache Kafka, the source code hadn’t been updated much since. But today I received a report that it no longer built, so I spent some time porting the code to the latest Shopify Sarama framework.

It was amusing to see how naive much of my early Go code was.

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Designing a search system for log data — part 3

This is the last part of a 3-part series “Designing and building a search system for log data”. Be sure to check out part 1 and part 2.

ekanite-cubeIn 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.

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Designing a search system for log data — part 2

This is the second part of a 3-part series “Designing and building a search system for log data”. Be sure to check out part 1. Part 3 follows this post.

ekanite-cubeIn 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.

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Designing a search system for log data — part 1

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.

ekanite-cubeFor 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.

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Philip O'Toole