![]() Input metadata JSON structure:ĭeploying the Observability Dashboard Services In the table below, you’ll see a sample of the structure of both the input metadata and the input data source. Docker: You’ll need to install the docker agent software on the host in order to use this containerized service deployment.Couchbase stores your data as JSON documents, or you can have it store required fields as separate documents for historic trends while preparing your health or trend data. Couchbase: You might already be using this NoSQL document database, but if not, you can install it through a container or directly on a different host. ![]() ![]() The Alert Manager has custom alert rules that receive alerts when certain thresholds are met.This is the regular Prometheus tool itself that holds your metrics as time-series data.This is the regular Grafana tool itself that you use to create panels and display as dashboards.Note that this service is needed only when the data source is to be maintained for time series (many trends need this). At a high level, this acts as a bridge between the Prometheus and datasources. This is a custom Prometheus exporter (say prometheus.py) Flask web app service that connects to the data sources and serves the requests from Prometheus itself.You can develop a similar one to have a generic template (JSON) for the panels on Grafana and attach the graph data points and tabular-data points as target JSON to display on your Grafana dashboard. This is a generic Python Flask web app service ( dashboard.py) that interacts with the Grafana service to serve the tabular data and other APIs like /query, /add, /import and /export endpoints. ![]() Let’s take a closer look at the different facets and services included in the architecture diagram: The central part of the above diagram shows the different services in the collection that support the creation of the dashboards. The expected output will be a Grafana dashboard UI and Prometheus time-series collected metrics from the two inputs listed above. The diagram below shows the generic architecture of the observability dashboards we’ll build together: With this approach, it’s quick and easy to develop future dashboards. In order to build a reusable and scalable tool, it’s better to work from common designs and templates as a first step. Generic Observability Dashboard: Design & Architecture However, the steps we’ll show you today should be applicable across a number of tools and deployments. Your in-house data source pipelines may vary – as might your data visualization software. In this article, we’ll walk you through how to build your own observability dashboard using Prometheus, Grafana and Couchbase. In addition, we used Couchbase to store historical data for later use with its Full-Text Search and Analytics tools. We used a combination of Prometheus, which simplifies storing and querying time-series data, and Grafana, which can be used to make stunning data visualizations. Then repeat the cycle until you’ve sufficiently improved.Īt Couchbase, we needed to improve some of our day-to-day operations, so we created observability dashboards to help us identify issues and track improvement. Using those metrics, you can tune the underlying work and analyze the effectiveness of any changes. The key to any improvement is to first identify what to measure and then collect the related metrics. It’s true: what you observe and measure is what you can improve. You’ve certainly heard it before: “What gets measured gets done.”
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