Searches are integral parts of any application. Performing searches on terabytes and petabytes of data can be challenging when speed, performance, and high availability are core requirements. This blog post will pit Solr vs Elasticsearch, two of the most popular open source search engines whose fortunes over the years have gone in different directions. Both of them are built on top of Apache Lucene, so the features they support are very similar.

Production environment stability and high availability are the holy grail of every SaaS company. R&D organizations put a lot of effort into achieving these goals by implementing different monitoring and alert methodologies and by utilizing a variety of systems and tools. Mean-time-to-detect (MTTD) and mean-time-to-repair (MTTR) are two crucial KPIs that help R&D management personnel determine the efficiency and proficiency of their teams’ responses to production incidents.

Today’s monitoring investments align more often with automation than any other technology. Automation is one of the principal objectives of DevOps to reduce toil, i.e. manual work. This helps keep engineers happy and engaged, allowing for better scale in building and operating applications. Automation typically spans infrastructure and application technologies. The challenge is that many organizations just have too many automation tools.

Logz.io Infrastructure Monitoring just got some substantive upgrades to kick off the summer. The major change is the Grafana 7 update, specifically to version 7.0.3. This includes several new features. The Logz.io deployment will also include applying Grafana to logs. The Transform tab gives users the option to quickly add non-time series data to tables and then pair it with other data (time series or not) in your Metrics deployment.

Netflix, Amazon, Google, Facebook, and a host of other companies have adopted chaos engineering, which encourages designing systems to proactively ward off potential issues through testing and the anticipation of failure. When it comes to container orchestration tools like Kubernetes, chaos engineering is a vital tactic for enhancing security.

Metrics and logs have been around for a long time, yet we haven’t adopted common standards for them. Sure, there have been attempts on the metric side with OpenMetrics. Similarly, tracing only got a standardization effort with OpenTracing just a few years ago. There was no effort in a unified approach to standardize all observability signals until OpenTelemetry began a little less than two years ago. And there has been a need.

Like everyone else, my life for the last few months has become a never-ending stream of video calls. With Zoom calls, and the occasional Skype, Google Meet, or Microsoft Teams, becoming the norm I’ve noticed that the fans on my Macbook have been kicking in and sounding like a tiny jet trying to take off.

Many enterprises have already adopted Kubernetes or have a Kubernetes migration plan in place, making it clear that the platform is here to stay. While it provides a lot of benefits to its users, to take advantage of them, you need to thoroughly learn Kubernetes and how it works in production. Typically, the most difficult aspects of Kubernetes are learned through experience solving real-world problems.

Managing a complex microservice-based architecture requires defending multiple endpoints. Automating security covers a vast amount of tools and methodologies, so making sure they all communicate is critical. Additionally, tool sprawl in any aspect of DevOps requires putting automation to good use. The Logz.io Cloud SIEM focuses on identifying threats. To optimize its effectiveness, we have negotiated and built out multiple integrations tying complementary tools together.

On May 27, the first OpenObservability Conference was held to bring together leaders, practitioners, and users of leading open source observability tools for sessions on the experiences, strategies, and future of the industry. For the Logz.io team, as long-time proponents of open source, it was rewarding to see everyone come together to explore the challenges and opportunities of open source observability.