openshift/insights-operator

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# Insights Operator This cluster operator gathers anonymized system configuration and reports it to Red Hat Insights. It is a part of the standard OpenShift distribution. The data collected allows for debugging in the event of cluster failures or unanticipated errors. # Table of Contents - [Insights Operator](#insights-operator) - [Table of Contents](#table-of-contents) - [Building](#building) - [Testing](#testing) - [Documentation](#documentation) - [Getting metrics from Prometheus](#getting-metrics-from-prometheus) - [Generate the certificate and key](#generate-the-certificate-and-key) - [Prometheus metrics provided by Insights Operator](#prometheus-metrics-provided-by-insights-operator) - [Running IO locally](#running-io-locally) - [Running IO on K8s](#running-io-on-k8s) - [Getting the data directly from Prometheus](#getting-the-data-directly-from-prometheus) - [Debugging Prometheus metrics without valid CA](#debugging-prometheus-metrics-without-valid-ca) - [Debugging](#debugging) - [Using the profiler](#using-the-profiler) - [Starting IO with the profiler](#starting-io-with-the-profiler) - [Collect profiling data](#collect-profiling-data) - [Analyzing profiling data](#analyzing-profiling-data) - [Changelog](#changelog) - [Updating the changelog](#updating-the-changelog) - [Reported data](#reported-data) - [Insights Operator Archive](#insights-operator-archive) - [Sample IO archive](#sample-io-archive) - [Generating a sample archive](#generating-a-sample-archive) - [Formatting archive json files](#formatting-archive-json-files) - [Obfuscating an archive](#obfuscating-an-archive) - [Updating the sample archive](#updating-the-sample-archive) - [Conditional Gathering](#conditional-gathering) - [Contributing](#contributing) - [Support](#support) - [License](#license) # Building To build the operator, install Go 1.11 or above and run: ```shell script make build ``` To test the operator against a remote cluster, run: ```shell script bin/insights-operator start --config=config/local.yaml --kubeconfig=$KUBECONFIG ``` where `$KUBECONFIG` has sufficiently high permissions against the target cluster. # Testing Unit tests can be started by the following command: ```shell script make test ``` It is also possible to specify CLI options for Go test. For example, if you need to disable test results caching, use the following command: ```shell script VERBOSE=-count=1 make test ``` > Integration (e2e) tests are not part of this repository, you can find it [here](https://gitlab.cee.redhat.com/ccx/insights-operator-tests). # Documentation The document [docs/gathered-data](docs/gathered-data.md) contains the list of collected data and the API that is used to collect it. This documentation is generated by the command bellow, by collecting the comment tags located above each Gather method. To start generating the document run: ```shell script make docs ``` The configuration and functionality of the Insights operator is described more in the [architecture document](docs/arch.md). # Getting metrics from Prometheus ## Generate the certificate and key Certificate and key are required to access Prometheus metrics (instead 404 Forbidden is returned). It is possible to generate these two files from Kubernetes config file. Certificate is stored in `users/admin/client-cerfificate-data` and key in `users/admin/client-key-data`. Please note that these values are encoded by using Base64 encoding, so it is needed to decode them, for example by `base64 -d`. There's a tool named `gen_cert_key.py` that can be used to automatically generate both files. It is stored in `tools` subdirectory. ```shell script gen_cert_file.py kubeconfig.yaml ``` ## Prometheus metrics provided by Insights Operator It is possible to read Prometheus metrics provided by Insights Operator. Example of metrics exposed by Insights Operator can be found at [metrics.txt](docs/metrics.txt), also there is a list of possible metrics available in the [architecture document](docs/arch.md). Depending on how or where the IO is running you may have different ways to retrieve the metrics. Here is a list of some options, so you can find the one that fits you: ### Running IO locally If the IO runs locally, the following command might be used: ```shell script curl --cert k8s.crt --key k8s.key -k https://localhost:8443/metrics ``` ### Running IO on K8s Get the token ```shell script oc whoami -t ``` Read metrics from Pod ```shell script oc exec \ -it deployment/insights-operator \ -n openshift-insights -- \ curl -k -H "Authorization: Bearer YOUR-TOKEN-HERE" 'https://localhost:8443/metrics' ``` ## Getting the data directly from Prometheus ```shell script sudo kubefwd svc -n openshift-monitoring -d openshift-monitoring.svc -l prometheus=k8s curl --cert k8s.crt --key k8s.key -k 'https://prometheus-k8s.openshift-monitoring.svc:9091/metrics' ``` ## Debugging Prometheus metrics without valid CA 1. Forward the service ```bash sudo kubefwd svc -n openshift-monitoring -d openshift-monitoring.svc -l prometheus=k8s ``` 2. Set `INSECURE_PROMETHEUS_TOKEN` environment variable: ```bash export INSECURE_PROMETHEUS_TOKEN=$(oc sa get-token prometheus-k8s -n openshift-monitoring) ``` 3. Run the operator. # Debugging ## Using the profiler ### Starting IO with the profiler IO starts a profiler if given the correct environment. Set the `OPENSHIFT_PROFILE` env variable to "web". ```shell script export OPENSHIFT_PROFILE=web ``` ### Collect profiling data After IO starts the profiling can be accessed at `http://localhost:6060`, you can use the `pprof` tool to connect to it. Some profiling examples: ```shell script # CPU profiling for 30 seconds go tool pprof http://localhost:6060/debug/pprof/profile?seconds=30 ``` ```shell script # heap profiling go tool pprof http://localhost:6060/debug/pprof/heap ``` These commands will create a compressed file that can be visualized using a variety of tools, one of them is the `pprof` tool. ### Analyzing profiling data Starting a web ui at `localhost:8080` to visualize/analyze the profiling data: ```shell script go tool pprof -http=:8080 /path/to/profiling.out ``` For extra info: [check this link](https://jvns.ca/blog/2017/09/24/profiling-go-with-pprof/) # Changelog You can find the project changelog by [clicking here](CHANGELOG.md). ## Updating the changelog At `./cmd/changelog/main.go` there is a script that can update the changelog for you. It uses both the local git and GitHub`s API to update the file so: - To get info from GitHub you will need to set the `GITHUB_TOKEN` envvar to a GitHub access-token. - Make sure that you have a local, up-to-date copy of each release-branch that might be in the changelog. It can be used 2 ways: 1. Providing no command line arguments the script will update the current `CHANGELOG.md` with the latest changes 2. according to the local git state. > 🚨 IMPORTANT: It will only work with changelogs created with this script ```shell script go run cmd/changelog/main.go ``` 2. Providing 2 command line arguments, `AFTER` and `UNTIL` dates the script will generate a new `CHANGELOG.md` within the provided time frame. ```shell script go run cmd/changelog/main.go 2021-01-10 2021-01-20 ``` In case of changelog not being generated properly, try switching to release branch you want to generate. # Reported data * ClusterVersion * ClusterOperator objects * All non-secret global config (hostnames and URLs anonymized) The list of all collected data with description, location in produced archive and link to Api and some examples is at [docs/gathered-data.md](docs/gathered-data.md) The resulting data is packed in `.tar.gz` archive with folder structure indicated in the document. Example of such archive is at [docs/insights-archive-sample](docs/insights-archive-sample). For more information on which data points are anonymized by the Insights Operator, please see [this list](docs/anonymized-data.md). ## Insights Operator Archive ### Sample IO archive There is a sample IO archive maintained in this repo to use as a quick reference. (can be found at [docs/insights-archive-sample](https://github.com/openshift/insights-operator/tree/master/docs/insights-archive-sample)) To keep it up-to-date it is **required** to update this manually when developing a new data enhancement. Make sure the `.json` files are in a humanly readable format in the sample archive. By doing this its easier to review a data enhancement PR, and rule developers can easily check what data it collects. ### Generating a sample archive Run the insights-operator on a test cluster (from `cluster-bot` or `Quicklab` or etc). ### Formatting archive json files This formats `.json` files from folder with extracted archive. ```shell script find . -type f -name '*.json' -print | while read line; do cat "$line" | jq > "$line.tmp" && mv "$line.tmp" "$line"; done ``` ### Obfuscating an archive You can run obfuscation with an archive by running the next command: ```shell script go run ./cmd/obfuscate-archive/main.go YOUR_ARCHIVE.tar.gz ``` where `YOUR_ARCHIVE.tar.gz` is the path to the archive. The obfuscated version will be created in the same directory and called `YOUR_ARCHIVE-obfuscated.tar.gz` ### Updating the sample archive The `docs/insights-archive-sample/` directory contains an example of an Insights Operator archive, extracted and with pretty-formatted JSON files. In case of any changes that affect multiple files in the archive, it is a good idea to regenerate the sample archive to make sure it remains up-to-date. There are two ways of updating the sample archive directory automatically. Both of them require running the Insights Operator, letting it generate an archive and extracting the archive into an otherwise empty directory. The script will automatically replace existing files in the sample archive with their respective counterparts from the supplied extracted IO archive. In case of files with (partially) randomized names, such as pods or nodes, the entire directory is deleted and replaced with a matching directory from the new archive if possible. Changes made by the script can be checked and reverted using Git. The updated JSON files will be automatically pretty-formatted using `jq`, which is the only dependency required for running the script. All existing files in the sample archive can be updated using the following command: ```sh ./scripts/update_sample_archive.sh <Path of directory with the NEW extracted IO archive> ``` If you only want to update files containing a certain string pattern, you can supply a regular expression as a second optional argument. For example, the following command was used to replace JSON files containing the `managedFields` field when it was removed from the IO archive to save space: ```sh ./scripts/update_sample_archive.sh <Path of directory with the NEW extracted IO archive> '"managedFields":' ``` The path of the sample archive directory should be constant relative to the path of the script and therefore does not have to be specified explicitly. # Conditional Gathering See [docs/conditional-gatherer/README.md](https://github.com/openshift/insights-operator/blob/master/docs/conditional-gatherer/README.md) # Contributing See [CONTRIBUTING](CONTRIBUTING.md) for workflow & convention details. See [STYLEGUIDE](STYLEGUIDE.md) for file format and coding style guide. # Support Insights Operator is part of Red Hat OpenShift Container Platform. For product-related issues, please file a ticket in [Red Hat JIRA](https://issues.redhat.com/secure/CreateIssue.jspa?pid=12332330&issuetype=1) for "Insights Operator" component. # License This project is licensed by the Apache License 2.0. For more information check the LICENSE file.

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create-gatherer

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Create a new gathering function including implementation, tests, sample archive data, registration, and docs regeneration

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2026-08-21

write-tests

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Write unit tests for Go code following the insights-operator project conventions

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2026-04-27

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