Picture a perfect world where all logs shared the same layout, format, and structure. Every application, programming language, and logging framework created logs that were verbose, yet easily parsable. Of course, we don’t live in this ideal world, and so we’re stuck with dozens or even hundreds of various log formats. While LogDNA supports a large number of common log formats, there are formats out there that our automatic parsing engine won’t recognize.

While enterprise leaders are constantly looking to innovate, there’s one area where “business as usual” should be a focus — spotting anomalies in your data. When it comes to time series data, “business as usual” is the baseline or expected behavior of the KPIs you track. Any unexpected deviations in those patterns can be classified as anomalies. However it’s important to keep in mind that anomalies can be either negative or positive.

Over the past several months, our team has been hard at work building the best log management solution out there. Introducing new features like Views, reporting, and script alerts, alongside updates to content packs, the Sidecar, and pipeline rules, Version 3.0 will knock your socks off. Read on for the nitty-gritty details.

We hope you’re enjoying your time at IBM Think 2019 – thank you for dropping by to chat with our team (at booth 598) and now checking our blog. As promised, setting up modern logging for your Kubernetes clusters on IBM Cloud is really easy and in this article we’ll take a closer log at IBM Log Analysis with LogDNA and how to use it to log your cloud Kubernetes clusters.

In a previous article, we explained the importance of monitoring the performance of your servers. Keeping tabs on metrics such as CPU, memory, disk usage, uptime, network traffic and swap usage will help you gauge the general health of your environment as well as provide the context you need to troubleshoot and solve production issues.

Many online applications use a web server as the primary point of contact for their clients. At least 43% of those systems are running the Apache HTTP Server. If you’re responsible for one of those systems, you need to work with the Apache error log.

Akamai provides the Content Delivery Network (CDN) which is a highly-distributed platform of servers optimized to deliver contents including web and media applications. These networks enable applications to easily serve content from closer to their end users. Centralizing Akamai logs increases the ability to observe the end to end application and service delivery. LogDNA is proud to enable integration with Akamai to provide better observability and a unified view for our customers.

Lucene has a lot of options for configuring similarity. By extension, Solr and Elasticsearch have the same options. Similarity makes the base of your relevancy score: how similar is this document (actually, this field in this document) to the query? I’m saying the base of the score because, on top of this score, you can apply per-field boosts, function scoring (e.g. boost more recent documents) and re-ranking (e.g. Learning to Rank).