Trends in the infrastructure and software space have changed the way we build and run software. As a result, we have started treating our infrastructure as code, which has helped us lower costs and get our products to market more quickly. These new architectures also give us the ability to test our software faster in production-like deployments, and generally deliver more stable and reproducible deployments.
In our previous two blogs, we provided an overview of the architecture and design of the Elasticsearch Go client and explored how to configure and customize the client. In doing so, we pointed to a number of examples available in the GitHub repository. The goal of these examples is to provide executable "scripts" for common operations, so it's a good idea to look there whenever you're trying to solve a specific problem with the client.
In this two-part blog post, we’ll use Elastic Observability to monitor a sample Java application. In the first blog post, we started by looking at how Elastic Observability monitors Java applications. We built and instrumented a sample Java Spring application composed of a data-access microservice supported by a MySQL backend. In this part, we’ll use Java ECS logging and APM log correlation to link transactions with their logs.
For public IPs, it is possible to create tables that will specify which city specific ranges of IPs belong to. However, a big portion of the internet is different. There are company private networks with IP addresses of the form 10.0.0.0/8, 172.16.0.0/12 or 192.168.0.0/16 scattered in every country in the world. These IP addresses tend to have no real information for the geographic locations.
OpenID Connect (OIDC) is an authentication layer based on OAuth 2.0 protocol that provides a way to identify and authenticate users via an authorization server. OAuth 2.0 authorization servers are managed by identity providers. In the domain associated with OIDC, they are also called OpenID Connect Providers (OPs). OIDC allows users to connect to web applications in a simplistic single sign-on (SSO) manner thanks to the identity provider.
As work from home has ballooned in 2020, virtual methods for communicating with colleagues have become more critical than ever. Same goes for all the useful productivity and collaboration tools at our disposal. The emerging downside is the difficulty of finding needed information among so many tools. Compounding the problem is the tendency for info to get siloed off by department.
We recently announced the general availability of our Elasticsearch Service API. APIs help to automate tasks such as creating and scaling deployments, integrating with existing workflows, and testing. The Elasticsearch Service API supports the Open API Specification, which allows you to use tools like Swagger to generate software development kits (SDKs) in any programming language. You can import the API spec onto Postman and create a Postman Collection to create a test suite.
In a previous blog, we saw that the seemingly simple job of an Elasticsearch client — moving data between the calling code and the cluster — is actually quite complicated under the hood. Naturally, as much as we try to make the default behaviour of the client optimal for the majority of scenarios, there are situations where you want to configure, customize, or enable/disable certain features.
We love maps at Elastic. In the Elastic Stack, there is one core component of all data we visualize using maps: Location. Location can mean reporting real-time positions of fleet vehicles, using a geofence for limiting search results, gauging application performance metrics from a geographic area, or identifying security threats by attaching geographic coordinates to IP addresses.
In two previous posts, we covered structuring data with grok and building custom grok patterns. But what happens if you just can’t get your grok patterns to work? In this article, we’re going to use Kibana’s Grok Debugger to help us debug a broken grok pattern. The divide-and-conquer method described below should help you to quickly find the reason that a given grok pattern is not matching your data.
