Life in the Valley
This 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.
Loggly 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.
After 14 months of hard work, the next generation of Loggly has been released. It’s been a great time to be part of the Software Infrastructure team at Loggly and we have put together a superb log aggregation & real-time analytics platform.
We used a combination of custom log Collectors, Apache Kafka, Twitter Storm, ElasticSearch, and lots of secret sauce. You can find more details about the technology stack from my Loggly blog post.
As technical lead at Loggly, responsibility for a well-engineered infrastructure ends with me. And one way to ensure the system is designed and implemented well is to stay as close as possible to the code, ensuring that the team and I write quality software.
But it can be difficult to complete the design and implementation of the features I am responsible for, ensure that what the team produces is well-implemented, and understand every line of code — there is only so much time in the day.
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I have written another post for the Loggly blog — all about our guidelines for choosing and integrating open-source software and technology in your next project.
Check it out here.
When running a large real-time processing system, monitoring is critical. But it does more than allow you to keep an eye on your system. During development it allows you test hypotheses about how it works, how it performs when certain parameters are changed, and takes the guessing out of working with dynamic systems.
Storm, a real-time computational framework open-sourced by Twitter, is such a system and comes with a Spout, allowing messages to be streamed from a Kafka Broker.
The Boost ASIO Library is a wonderful piece of software. I’ve built high-performance event-driven IO C++ programs that just scream — it works very well. However, there is one subtlety when it comes to timers — specifically when it comes to cancelling expired timers.
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Cassandra is an open-source, distributed database, informally known as a NoSQL database. It is designed to store large amounts of data, offer high-write performance, and provide fault-tolerance. I recently needed some hands-on experience with Cassandra, and being relatively new to Java programming, needed a simple set-up with which I would experiment.
I needed some C++ code to generate Type-1 time-based UUIDs. The Boost libraries, while offering support for other types, don’t have support for time-based UUIDs.
A cut of my code can be found in github.