Everyone’s infrastructure is growing – today mostly in the container space. As we learned in Part 1 of this series – Docker Container Monitoring and Management Challenges, monitoring for containers is different from traditional server monitoring. In Part 2 we had a glance at key container metrics and in Part 3 we compared several open source tools for container monitoring.
As shown in Elasticsearch Key Metrics, the setup, tuning, and operations of Elasticsearch require deep insights into the performance metrics such as index rate, query rate, query latency, merge times, and many more. Sematext provides an excellent alternative to other Elasticsearch monitoring tools.
Over the years the adoption of Elasticsearch and its ecosystem of tools positioned them as the leaders in the time series data management and analysis market. With strong search capabilities, great analytical engine, Kibana as the flexible frontend and a number of data shippers enable building of end to end data processing pipeline using components designed to work with each other. Very simple setup and configuration resulted in high adoption rates and the whole stack gaining more and more users.
If you google “DevOps tools,” you’ll see a dizzying litany of software applications, all promising to simplify your life as a DevOps engineer. This can be an intimidating experience — not only because there are so many DevOps solutions available that it can be difficult to know which ones are the best for your needs, but also because the idea of having to learn and “carry around” so many tools is itself unnerving.
Don’t you just hate it when APIs are failing and you have absolutely no clue why? Now imagine you don’t have access to the VM, cluster or container where your software is running. Want me to continue with this nightmare? Yes, that’s what debugging AWS Lambda functions tends to seem like. A horrid nightmare of not knowing what is happening nor why things are failing. This article will show you a way of logging function invocations.
One of the unintended side-effects of the public cloud is the enablement of a platform-agnostic cloud architecture. This means that it’s now easier than ever to mix offerings from multiple vendors (cloud vendors in this case) to form a single application or service-based solution. There is one gotcha, however: log management.
Entity extraction is the process of figuring out which fields a query should target, as opposed to always hitting all fields. For example: how to tell, when the user typed in Apple iPhone, that the intent was to run company:Apple AND product:iPhone?
Elasticsearch, Logstash, and Kibana — the trio better known as Elastic Stack (or ELK, if you prefer a term that is now going out of style), make up a powerful set of tools for searching and analyzing data. Their power derives not just from their technical features, but also the fact that Elastic Stack is an open source platform that anyone can download and set up anywhere.
Elastic Stack Features (formerly X-Pack) is an Elastic Stack extension that bundles security, alerting, monitoring, reporting, and graph capabilities. One could use either all or specific components.
Recent Linux kernel releases are coming weaponized with built-in instrumentation framework that has its roots in what historically was approached as BPF (Berkeley Packet Filter) – a very efficient network packet filtering mechanism which aims to avoid unnecessary user space allocations and operate on packet’s data directly in kernel land. The most familiar application of BPF powers is related to filter expressions used in tcpdump tool.
