Tag Archives: go

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 go-raft is no longer maintained, and I’ve good experience with the Hashicorp code, due to my work with InfluxDB and hraftd.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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Coding like it’s 1999

“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.

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Increasing bleve indexing performance with sharding

bleve-logoSearch 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.

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Software Development for Infrastructure

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.

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Drop, Throttle, or Buffer

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.

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