Tag Archives: cloud

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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Come for the Features, Stay for the Uptime

I’ve been thinking¬† a lot recently about what makes computer services and products sticky — what makes users and customers come back again and again to what you’ve built. There are lots of ways to summarize it, but when it comes to systems that help technical people run their own systems, they come for the features, but they stay for the uptime.

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InfluxDB and Grafana HOWTO

This blog describes working with InfluxDB 0.8. InfluxDB 0.8 is no longer supported, and has been superseded by the 1.0 release.

grafanaI recently came across InfluxDB — it’s a time-series database built on LevelDB. It’s designed to support horizontal as well as vertical scaling and, best of all, it’s not written in Java — it’s written in Go. I was intrigued to say the least.

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Infrastructure at Scale: Apache Kafka, Twitter Storm and elasticsearch

storm_logoAWS have posted the video online of Jim Nisbet’s and my talk at AWS:reinvent 2013. In it, Jim and I describe the system we built at Loggly, which uses Apache Kafka, Twitter Storm, and elasticseach, to build a high-performance log aggregation and analytics SaaS solution, running on AWS EC2.

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Speaking at AWS re:Invent 2013

amazon.com_web_servicesThis past week I had the opportunity to speak, with my colleague Jim Nisbet, at AWS re:Invent 2013. Titled “Unmeltable Infrastructure at Scale: Using Apache Kafka, Twitter Storm, and Elastic Search on AWS“, Jim and I described the architecture of Loggly’s next-generation log aggregation and analytics Infrastructure, which went live 3 months ago, and runs on AWS EC2.

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Avoiding elasticsearch split-brain

elasticsearchLoggly recently held an elasticsearch meetup, which was a great success. One question that was repeatedly asked was how to ensure elasticsearch does not suffer a partition — known as a split-brain. This can be a particular problem in AWS EC2, where the network is subject to interruptions. It can also happen if the elasticsearch master node performs long garbage collection cycles.

One configuration that is very effective at preventing this problem is described in this post.

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