“Security is always seen as too much until the day it’s not enough.” – William H. Webster, former FBI Director As we all know, every year, thousands of new vulnerabilities are discovered, requiring organizations to patch operating systems, update applications, and reconfigure security settings throughout the entirety of their IT environments, including the cloud.
Engineering teams hoping to gain full-stack observability into their environment need access to the relevant logs, metrics, and traces generated by their cloud infrastructure and applications. Accessing the relevant data quickly is essential – not just because it is more convenient, but because faster engineers are also business-critical for many organizations.
OpenTelemetry is the recommended path today for instrumenting applications with tracing in a standard, vendor-agnostic and future-proof way. In fact, OpenTelemetry (nicknamed OTEL) encompasses all three pillars of observability: tracing, metrics, and logs. The tracing element of the specification is now stable with the rest following. This is innovative stuff! You can read more on OpenTelemetry and the current release state on this guide.
We just announced the creation of a new RemoteWrite SDK to support custom metrics from applications using several different languages. This tutorial will give a quick rundown of how to use the Java SDK. This SDK – like the others – is completely free and open source, and is meant to apply to any output destination, not just Logz.io.
In this tutorial, we will go through a working example of a Python application auto-instrumented with OpenTelemetry. To keep things simple, we will create a basic “Hello World” application using Flask, instrument it with OpenTelemetry’s Python client library to generate trace data and send it to an OpenTelemetry Collector. The Collector will then export the trace data to an external distributed tracing analytics tool of our choice.
We all know the importance of cataloging, organizing, and breaking down the data in your logs. That process, parsing, makes information easier to find and simplifies subsequent analysis. Now, with Logz.io’s upgraded self-parsing tool, custom parsing rules, and log organization is easier than ever. What’s important is parsing that data out correctly. The better parsed, the easier to query.
We recently announced the release of our RemoteWrite SDK to support custom metrics from applications using several different languages – The first SDKs allow shipping of metrics from Golang (Go), Python, Java, Node.js, and.NET. This tutorial will cover the Golang SDK. The SDKs cover not just Logz.io, but can be used by any platform that supports the Prometheus remote write endpoint.
We are proud to announce the launch of direct dashboard uploads with Logz.io. These new metrics dashboard templates are available for 25 different tools and more to come. Each of these templates is now available to Logz.io customers and covers the gamut of popular monitoring tools used by DevOps teams. Some of these tools also include multiple options. The process is simple. Head into the Logz.io app and head to your metrics account.
Logz.io is proud to announce a slew of new integrations via Telegraf. Logz.io utilizes Prometheus in its product, but aims to support compatibility across common DevOps tools. A number of our customers, and the community in general, are strong users of Telegraf and its companion apps in the TICK Stack (which includes InfluxDB). Telegraf is not as popular as Prometheus, but it’s a strong element in the DevOps toolbox.
Our first tutorial gave a general introduction to OpenSearch installation and configuration. We recently also published a comparative introduction for OpenSearch queries (and how they parallel or contrast with Elasticsearch). Now, we’ll continue that series with an intro to OpenSearch clusters. This is a very simple tutorial with straight-forward examples, but we will try to cover some detail and common advanced settings.
