We'd love your help!
Reporting bugs is an important contribution. Please make sure to include:
- Expected and actual behavior
- OpenTelemetry version you are running
- If possible, steps to reproduce
Please read project contribution guide for general practices for OpenTelemetry project.
Select a good issue from the links below (ordered by difficulty/complexity):
Comment on the issue that you want to work on so we can assign it to you and clarify anything related to it.
If you would like to work on something that is not listed as an issue (e.g. a new feature or enhancement) please first read our vision and roadmap to make sure your proposal aligns with the goals of the Collector, then create an issue and describe your proposal. It is best to do this in advance so that maintainers can decide if the proposal is a good fit for this repository. This will help avoid situations when you spend significant time on something that maintainers may decide this repo is not the right place for.
Follow the instructions below to create your PR.
In the interest of keeping this repository clean and manageable, you should
work from a fork. To create a fork, click the 'Fork' button at the top of the
repository, then clone the fork locally using git clone [email protected]:USERNAME/opentelemetry-service.git.
You should also add this repository as an "upstream" repo to your local copy, in order to keep it up to date. You can add this as a remote like so:
git remote add upstream https://github.com/open-telemetry/opentelemetry-collector.git
Verify that the upstream exists:
git remote -v
To update your fork, fetch the upstream repo's branches and commits, then merge your master with upstream's master:
git fetch upstream
git checkout master
git merge upstream/master
Remember to always work in a branch of your local copy, as you might otherwise have to contend with conflicts in master.
Please also see GitHub workflow section of general project contributing guide.
Working with the project sources requires the following tools:
Fork the repo, checkout the upstream repo to your GOPATH by:
$ GO111MODULE="" go get -d github.com/open-telemetry/opentelemetry-collector
Add your fork as an origin:
$ cd $(go env GOPATH)/src/github.com/open-telemetry/opentelemetry-collector
$ git remote add fork [email protected]:YOUR_GITHUB_USERNAME/opentelemetry-service.gitRun tests, fmt and lint:
$ make install-tools # Only first time.
$ makeNote: the default build target requires tools that are installed at $(go env GOPATH)/bin, ensure that $(go env GOPATH)/bin is included in your PATH.
Checkout a new branch, make modifications, build locally, and push the branch to your fork to open a new PR:
$ git checkout -b feature
# edit
$ make
$ git commit
$ git push fork featureThis project uses Go 1.12.5 and Travis for CI.
Travis CI uses the Makefile with the default target, it is recommended to
run it before submitting your PR. It runs gofmt -s (simplify) and golint.
The dependencies are managed with go mod if you work with the sources under your
$GOPATH you need to set the environment variable GO111MODULE=on.
Although OpenTelemetry project as a whole is still in Alpha stage we consider OpenTelemetry Collector to be close to production quality and the quality bar for contributions is set accordingly. Contributions must have readable code written with maintainability in mind (if in doubt check Effective Go for coding advice). The code must adhere to the following robustness principles that are important for software that runs autonomously and continuously without direct interaction with a human (such as this Collector).
Verify configuration during startup and fail fast if the configuration is invalid. This will bring the attention of a human to the problem as it is more typical for humans to notice problems when the process is starting as opposed to problems that may arise sometime (potentially long time) after process startup. Monitoring systems are likely to automatically flag processes that exit with failure during startup, making it easier to notice the problem. The Collector should print a reasonable log message to explain the problem and exit with a non-zero code. It is acceptable to crash the process during startup if there is no good way to exit cleanly but do your best to log and exit cleanly with a process exit code.
Do not crash or exit outside the main() function, e.g. via log.Fatal or os.Exit,
even during startup. Instead, return detailed errors to be handled appropriately
by the caller. The code in packages other than main may be imported and used by
third-party applications, and they should have full control over error handling
and process termination.
Do not crash or exit the Collector process after the startup sequence is finished. A running Collector typically contains data that is received but not yet exported further (e.g. is stored in the queues and other processors). Crashing or exiting the Collector process will result in losing this data since typically the receiver has already acknowledged the receipt for this data and the senders of the data will not send that data again.
Do not crash on bad input in receivers or elsewhere in the pipeline. Crash-only software is valid in certain cases; however, this is not a correct approach for Collector (except during startup, see above). The reason is that many senders from which Collector receives data have built-in automatic retries of the same data if no acknowledgment is received from the Collector. If you crash on bad input chances are high that after the Collector is restarted it will see the same data in the input and will crash again. This will likely result in infinite crashing loop if you have automatic retries in place.
Typically bad input when detected in a receiver should be reported back to the sender. If it is elsewhere in the pipeline it may be too late to send a response to the sender (particularly in processors which are not synchronously processing data). In either case it is recommended to keep a metric that counts bad input data.
Be rigorous in error handling. Don't ignore errors. Think carefully about each error and decide if it is a fatal problem or a transient problem that may go away when retried. Fatal errors should be logged or recorded in an internal metric to provide visibility to users of the Collector. For transient errors come up with a retrying strategy and implement it. Typically you will want to implement retries with some sort of exponential back-off strategy. For connection or sending retries use jitter for back-off intervals to avoid overwhelming your destination when network is restored or the destination is recovered. Exponential Backoff is a good library that provides all this functionality.
Log your component startup and shutdown, including successful outcomes (but don't overdo it, keep the number of success message to a minimum). This can help to understand the context of failures if they occur elsewhere after your code is successfully executed.
Use logging carefully for events that can happen frequently to avoid flooding the logs. Avoid outputting logs per a received or processed data item since this can amount to very large number of log entries (Collector is designed to process many thousands of spans and metrics per second). For such high-frequency events instead of logging consider adding an internal metric and increment it when the event happens.
Make log message human readable and also include data that is needed for easier understanding of what happened and in what context.
Limit usage of CPU, RAM or other resources that the code can use. Do not write code that consumes resources in an uncontrolled manner. For example if you have a queue that can contain unprocessed messages always limit the size of the queue unless you have other ways to guarantee that the queue will be consumed faster than items are added to it.
Performance test the code for both normal use-cases under acceptable load and also for abnormal use-cases when the load exceeds acceptable many times. Ensure that your code performs predictably under abnormal use. For example if the code needs to process received data and cannot keep up with the receiving rate it is not acceptable to keep allocating more memory for received data until the Collector runs out of memory. Instead have protections for these situations, e.g. when hitting resource limits drop the data and record the fact that it was dropped in a metric that is exposed to users.
Collector does not yet support graceful shutdown but we plan to add it. All components
must be ready to shutdown gracefully via Shutdown() function that all component
interfaces require. If components contain any temporary data they need to process
and export it out of the Collector before shutdown is completed. The shutdown process
will have a maximum allowed duration so put a limit on how long your shutdown
operation can take.
Cover important functionality with unit tests. We require that contributions do not decrease overall code coverage of the codebase - this is aligned with our goal to increase coverage over time. Keep track of execution time for your unit tests and try to keep them as short as possible.
If you implement a new component add end-to-end tests for the component using the automated Testbed.