When you start a company – or a third company as is the case for Lee and me – you start with a problem statement, a product you believe in, and a lot of hope. This means when growth goes as planned or exceeds expectations, you shouldn’t be surprised. This is what is supposed to happen. Great Product + Market Opportunity + Great Team = Successful Business. Intellectually, I know all this, but it is still exciting to see it come to fruition.

In a previous post, I explained the role Apache Kafka plays in production-grade ELK deployments, as a message broker and a transport layer deployed in front of Logstash. As I mentioned in that piece, Redis is another common option. I recently found out that it is even more popular than Kafka! Known for its flexibility, performance and wide language support, Redis is used both as a database and cache but also as a message broker.

So you have been adding more and more logs to your Graylog instance, gathering up your server, network, application logs and throwing in anything else you can think of. This is exactly what Graylog is designed for, to collect all the logs and have them ready for you to search through in one place. Unfortunately, during your administration of Graylog you go the System -> Overview screen and see the big bad red box, saying you are having indexing failures.

There used to be a distinct, technical separation between terms such as AI and machine learning (ML) – but only while these technologies remained largely theoretical. As soon as they became practical in the real world, and then commodifiable into products, the marketers stepped in. Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words.

I have been super fortunate to work with incredibly innovative, talented teams that create powerful technology to help manage the world's data. When I met with Thomas Hazel, CHAOSSEARCH founder and CTO, and Les Yetton, CHAOSSEARCH co-founder and CEO to talk about CHAOSSEARCH — scalable, performant text search to your object storage without having to move any data — I knew it was special and was eager to join.

The next major version of the LogDNA agent is right around the corner, and we’re introducing some significant improvements. Most notably, we’ve completely rewritten the agent using Rust instead of Node.js. As we gear up for the release, we wanted to explain why we chose Rust for the LogDNA agent, the benefits that it offers over other languages, and how it can help you log faster. Rust is a systems programming language that aims to offer both performance and safety.