Opsani Ignite analyzes applications running on a Kubernetes cluster in order to identify performance and reliability risks, as well as inefficient configurations. It then identifies specific corrective actions that align the application's configuration with deployment best practices for production environments and may also reduce the application's resource footprint.
CAUTION: Opsani Ignite is a new tool, still in alpha. We appreciate feedback and suggestions.
To install opsani-ignite, download the binary for your OS (macOS, Linux or Windows) from the latest release and place it somewhere along your shell's path. Check back often as we release updated analysis capabilities frequently; if your version is more than a week old, please see if a newer version is available before using it.
To run Opsani Ignite, you will first want to set up port forwarding to the Prometheus API on your cluster. A typical command looks like this (assuming your Prometheus is called prometheus-server and runs in the prometheus namespace):
kubectl port-forward service/prometheus-server 9090:80 -n prometheus
Once port forwarding is active, run the opsani-ignite executable, providing the URL to the port-forwarded Prometheus API:
opsani-ignite -p http://localhost:9090
Opsani Ignite works in three phases: discovery, analysis and recommendations.
On startup, Ignite discovers the applications running on the Kubernetes cluster. By querying your Prometheus monitoring system, Ignite finds all non-system namespaces and the deployment workloads running in them; it then obtain their key settings and metrics.
By default, Ignite looks at the last 7 days of metrics for each application to capture most daily and weekly load and performance variations.
Ignite analyzes each application, looking at pods and containers that make up the application in order to uncover specific omissions of best practices for reliable production deployments. It looks at important characteristics such as the pod's quality of service (QoS), replica count, resource allocation, usage, limits, and processed load. Ignite then identifies areas requiring attention that are either causing or can cause performance and reliability issues.
In addition, Ignite determines whether the application is overprovisioned and has a higher-than-necessary cloud spend. In these cases, it also estimates the likely savings that can be obtained through optimization.
When an application is selected (pressing Enter in the table of apps), Ignite produces a set of actionable recommendations for improving the efficiency, performance, and reliability of the application. The recommendations fall into several categories, including production best practices (for example, setting resource requests and limits), as well as optimal and resilient operation optimization recommendations. Applying these recommendations results in improved performance and efficiency, as well as increased resilience of their applications under load.
Opsani Ignite provides analysis and a number of additional recommendations to improve performance, reliability and efficiency.
Best practices require correctly setting resource requirements in a way that meets the performance and reliability requirements of an application (typically, latency and error rate service level objectives), while using assigned resources efficiently to control cloud costs. These values can be discovered manually, often through an onerous and repetitive manual tuning process.
They can also be automatically identified using automatic optimization services, such as the Opsani optimization-as-a-service tool. Those who are interested in how continuous optimization can remediate these issues can go to the Opsani website, set up a free trial account and attach the optimizer to their application. Connecting an application to the optimizer typically takes 10-15 minutes and, in a few hours, produces concrete, tested resource specifications that can be applied using a simple kubectl command.
By default, Ignite is text-based interactive tool (using the fantastic tview package, familiar to those who use the equally magnificent k9s tool). Ignite's command line options can change the output to simple stdout text view and even full-detail YAML output that can be used to integrate Ignite into your dashboards and higher level tools.
Here are Ignite's command line options:
Usage:
opsani-ignite [<namespace> [<deployment>]] [flags]
Flags:
--config string config file (default is $HOME/.opsani-ignite.yaml)
-p, --prometheus-url string URI to Prometheus API (typically port-forwarded to localhost using kubectl)
--start string Analysis start time, in RFC3339 or relative form (default "-7d")
--end string Analysis end time, in RFC3339 or relative form (default "-0d")
--step string Time resolution, in relative form (default "1d")
-o, --output string Output format (interactive|table|detail|yaml|servo.yaml)
-b, --hide-blocked Hide applications that don't meet optimization prerequisites
--debug Display tracing/debug information to stderr
-q, --quiet Suppress warning and info level messages
-h, --help help for opsani-ignite
The Ignite tool is the result of analyzing thousands of applications as part of our work at Opsani. We released it as an open source tool in order to share our experience and learning with the Kubernetes community and help improve application reliability and efficiency. The source code is available to review and to contribute.
We appreciate your feedback. Please send us a few lines about your experience--or, even better--a screenshot 📷 with the results (be they good or not so good) at 
. Issues and PRs are also a great way to help improve Ignite for everyone.
Opsani Ignite records diagnostic information in opsani-ignite.log. You can increase the logging level by adding the --debug option to the command line; running the YAML output option (-o yaml) is also a great way to see the full details.
You can reach out to Opsani Technical support at or, for faster response, use the chat bot 💬 on the Opsani web site.
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