CONTRIBUTING.md

Contributing

First, thank you for contributing to Vector! The goal of this document is to provide everything you need to start contributing to Vector. The following TOC is sorted progressively, starting with the basics and expanding into more specifics.

1. Introduction
2. Your First Contribution
   1. New sources, sinks, and transforms
3. Change Control
   1. Git Branches
   2. Git Commits
      1. Style
      2. Signing-off
   3. Github Pull Requests
      1. Title
      2. Reviews & Approvals
      3. Merge Style
   4. CI
      1. Releasing
      2. Testing
         1. Skipping tests
         2. Daily tests
      3. Flakey tests
         1. Test harness
4. Development
   1. Setup
      1. Using a Docker or Podman environment
      2. Bring your own toolbox
   2. The Basics
      1. Directory Structure
      2. Makefile
      3. Code Style
         1. Logging style
      4. Feature flags
      5. Dependencies
   3. Guidelines
      1. Sink Healthchecks
      2. Metric naming convention
      3. Option naming
   4. Testing
      1. Unit Tests
      2. Integration Tests
      3. Blackbox Tests
      4. Tips and Tricks
         1. Testing Specific Components
         2. Generating Sample Logs
   5. Benchmarking
   6. Profiling
   7. Kubernetes
      1. Kubernetes Dev Flow
         1. Requirements
         2. The dev flow
         3. Troubleshooting
         4. Going through the dev flow manually
      2. Kubernetes E2E tests
         1. Requirements
         2. Running the E2E tests
      3. Kubernetes Architecture
         1. The operation logic
         2. Where to find things
5. Humans
   1. Documentation
      1. How the docs work
      2. Formatting
      3. Validation
      4. Development flow
   2. Changelog
      1. What makes a highlight noteworthy?
      2. How is a highlight different from a blog post?
6. Security
7. Legal
   1. DCO
      1. Trivial changes
   2. Granted rights and copyright assignment
8. FAQ
   1. Why a DCO instead of a CLA?
   2. If I’m contributing while an employee, do I still need my employer to sign something?
   3. What if I forgot to sign my commits?
9. Contact

Introduction

1. You're familiar with Github and the pull request workflow.
2. You've read Vector's docs.
3. You know about the Vector community. Please use this for help.

Your First Contribution

1. Ensure your change has an issue! Find an existing issue or open a new issue.
   • This is where you can get a feel if the change will be accepted or not. Changes that are questionable will have a needs: approval label.
2. Once approved, fork the Vector repository in your own Github account.
3. Create a new Git branch.
4. Review the Vector change control and development workflows.
5. Make your changes.
6. Submit the branch as a pull request to the main Vector repo. A Vector team member should comment and/or review your pull request within a few days. Although, depending on the circumstances, it may take longer.

New sources, sinks, and transforms

If you're contributing a new source, sink, or transform to Vector, thank you that's way cool! There's a few steps you need think about if you want to make sure we can merge your contribution. We're here to help you along with these steps but they are a blocker to getting a new integration released.

To merge a new source, sink, or transform, you need to:

• Add tests, especially integration tests if your contribution connects to an external service.
• Add instrumentation so folks using your integration can get insight into how it's working and performing. You can see some example of instrumentation in existing integrations.
• Add documentation. You can see examples in the docs directory.
• Update .github/CODEOWNERS or talk to us about identifying someone on the team to help look after the new integration.

Change Control

Git Branches

All changes must be made in a branch and submitted as pull requests. Vector does not adopt any type of branch naming style, but please use something descriptive of your changes.

Git Commits

Style

Please ensure your commits are small and focused; they should tell a story of your change. This helps reviewers to follow your changes, especially for more complex changes.

Signing-off

Your commits must include a DCO signature. This is simpler than it sounds; it just means that all of your commits must contain:

Signed-off-by: Joe Smith <[email protected]>

Git makes this easy by adding the -s or --signoff flags when you commit:

git commit -sm 'My commit message'

We also included a make signoff target that handles this for you if you forget.

Github Pull Requests

Once your changes are ready you must submit your branch as a pull
request.

Title

The pull request title must follow the format outlined in the conventional
commits spec. Conventional commits is a standardized format for commit messages. Vector only requires this format for commits on the master branch. And because Vector squashes commits before merging branches, this means that only the pull request title must conform to this format. Vector performs a pull request check to verify the pull request title in case you forget.

A list of allowed sub-categories is defined here.

The following are all good examples of pull request titles:

feat(new sink): new `xyz` sink
feat(tcp source): add foo bar baz feature
fix(tcp source): fix foo bar baz bug
chore: improve build process
docs: fix typos

Reviews & Approvals

All pull requests should be reviewed by:

• No review required for cosmetic changes like whitespace, typos, and spelling by a maintainer
• One Vector team member for minor changes or trivial changes from contributors
• Two Vector team members for major changes
• Three Vector team members for RFCs

If there are any CODEOWNERs automatically assigned, you should also wait for their review.

Merge Style

All pull requests are squashed and merged. We generally discourage large pull requests that are over 300-500 lines of diff. If you would like to propose a change that is larger we suggest coming onto our Discord server and discuss it with one of our engineers. This way we can talk through the solution and discuss if a change that large is even needed! This will produce a quicker response to the change and likely produce code that aligns better with our process.

CI

Currently Vector uses Github Actions to run tests. The workflows are defined in .github/workflows.

Releasing

Github Actions is responsible for releasing updated versions of Vector through various channels.

Testing

Skipping tests

Tests are run for all changes except those that have the label:

ci-condition: skip

Daily tests

Some long running tests are only run daily, rather than on every pull request. If needed, an administrator can kick off these tests manually via the button on the nightly build action page

Flakey tests

Historically, we've had some trouble with tests being flakey. If your PR does not have passing tests:

• Ensure that the test failures are unrelated to your change
  • Is it failing on master?
  • Does it fail if you rerun CI?
  • Can you reproduce locally?
• Find or open an issue for the test failure (example)
• Link the PR in the issue for the failing test so that there are more examples

Test harness

You can invoke the test harness by commenting on any pull request with:

/test -t <name>

Development

Setup

We're super excited to have you interested in working on Vector! Before you start you should pick how you want to develop.

For small or first-time contributions, we recommend the Docker method. Prefer to do it yourself? That's fine too!

Using a Docker or Podman environment

Targets: You can use this method to produce AARCH64, Arm6/7, as well as x86/64 Linux builds.

Since not everyone has a full working native environment, we took our environment and stuffed it into a Docker (or Podman) container!

This is ideal for users who want it to "Just work" and just want to start contributing. It's also what we use for our CI, so you know if it breaks we can't do anything else until we fix it. 😉

Before you go farther, install Docker or Podman through your official package manager, or from the Docker or Podman sites.

# Optional: Only if you use `podman`
export CONTAINER_TOOL="podman"

By default, make environment style tasks will do a docker pull from Github's container repository, you can optionally build your own environment while you make your morning coffee ☕:

# Optional: Only if you want to go make a coffee
make environment-prepare

Now that you have your coffee, you can enter the shell!

# Enter a shell with optimized mounts for interactive processes.
# Inside here, you can use Vector like you have full toolchain (See below!)
make environment
# Try out a specific container tool. (Docker/Podman)
make environment CONTAINER_TOOL="podman"
# Add extra cli opts
make environment CLI_OPTS="--publish 3000:2000"

Now you can use the jobs detailed in "Bring your own toolbox" below.

Want to run from outside of the environment? Clever. Good thinking. You can run any of the following:

# Validate your code can compile
make check ENVIRONMENT=true
# Validate your code actually does compile (in dev mode)
make build-dev ENVIRONMENT=true
# Validate your test pass
make test SCOPE="sources::example" ENVIRONMENT=true
# Validate tests (that do not require other services) pass
make test ENVIRONMENT=true
# Validate your tests pass (starting required services in Docker)
make test-integration SCOPE="sources::example" ENVIRONMENT=true
# Validate your tests pass against a live service.
make test-integration SCOPE="sources::example" AUTOSPAWN=false ENVIRONMENT=true
# Validate all tests pass (starting required services in Docker)
make test-integration ENVIRONMENT=true
# Run your benchmarks
make bench SCOPE="transforms::example" ENVIRONMENT=true
# Format your code before pushing!
make fmt ENVIRONMENT=true

We use explicit environment opt-in as many contributors choose to keep their Rust toolchain local.

Bring your own toolbox

Targets: This option is required for MSVC/Mac/FreeBSD toolchains. It can be used to build for any environment or OS.

To build Vector on your own host will require a fairly complete development environment!

Loosely, you'll need the following:

• To build Vector: Have working Rustup, Protobuf tools, C++/C build tools (LLVM, GCC, or MSVC), Python, and Perl, make (the GNU one preferably), bash, cmake, and autotools.
• To run integration tests: Have docker available, or a real live version of that service. (Use AUTOSPAWN=false)
• To run make check-component-features: Have remarshal installed.

If you find yourself needing to run something inside the Docker environment described above, that's totally fine, they won't collide or hurt each other. In this case, you'd just run make environment-generate.

We're interested in reducing our dependencies if simple options exist. Got an idea? Try it out, we'd to hear of your successes and failures!

In order to do your development on Vector, you'll primarily use a few commands, such as cargo and make tasks you can use ordered from most to least frequently run:

# Validate your code can compile
cargo check
make check
# Validate your code actually does compile (in dev mode)
cargo build
make build-dev
# Validate your test pass
cargo test sources::example
make test scope="sources::example"
# Validate tests (that do not require other services) pass
cargo test
make test
# Validate your tests pass (starting required services in Docker)
make test-integration scope="sources::example" autospawn=false
# Validate your tests pass against a live service.
make test-integration scope="sources::example" autospawn=false
cargo test --features docker sources::example
# Validate all tests pass (starting required services in Docker)
make test-integration
# Run your benchmarks
make bench scope="transforms::example"
cargo bench transforms::example
# Format your code before pushing!
make fmt
cargo fmt

If you run make you'll see a full list of all our tasks. Some of these will start Docker containers, sign commits, or even make releases. These are not common development commands and your mileage may vary.

The Basics

Directory Structure

• /benches - Internal benchmarks.
• /config - Public facing Vector config, included in releases.
• /distribution - Distribution artifacts for various targets.
• /docs - Structured data used to generate documentation.
• /lib - External libraries that do not depend on vector but are used within the project.
• /proto - Protobuf definitions.
• /scripts - Scripts used to generate docs and maintain the repo.
• /src - Vector source.
• /tests - Various high-level test cases.

Makefile

Vector includes a Makefile in the root of the repo. This serves as a high-level interface for common commands. Running make will produce a list of make targets with descriptions. These targets will be referenced throughout this document.

Code Style

We use rustfmt on stable to format our code and CI will verify that your code follows this format style. To run the following command make sure rustfmt has been installed on the stable toolchain locally.

# To install rustfmt
rustup component add rustfmt

# To format the code
make fmt

Logging style

• Always use the Tracing crate's key/value style for log events.
• Events should be capitalized and end with a period, ..
• Never use e or err - always spell out error to enrich logs and make it clear what the output is.
• Prefer Display over Debug, %error and not ?error.

Nope!

warn!("Failed to merge value: {}.", err);

Yep!

warn!(message = "Failed to merge value.", %error);

Feature flags

When a new component (a source, transform, or sink) is added, it has to be put behind a feature flag with the corresponding name. This ensures that it is possible to customize Vector builds. See the features section in Cargo.toml for examples.

In addition, during development of a particular component it is useful to disable all other components to speed up compilation. For example, it is possible to build and run tests only for console sink using

cargo test --lib --no-default-features --features sinks-console sinks::console

In case if the tests are already built and only the component file changed, it is around 4 times faster than rebuilding tests with all features.

Dependencies

Dependencies should be carefully selected and avoided if possible. You can see how dependencies are reviewed in the Reviewing guide.

If a dependency is required only by one or multiple components, but not by Vector's core, make it optional and add it to the list of dependencies of the features corresponding to these components in Cargo.toml.

Guidelines

Sink Healthchecks

Sinks may implement a health check as a means for validating their configuration against the environment and external systems. Ideally, this allows the system to inform users of problems such as insufficient credentials, unreachable endpoints, non-existent tables, etc. They're not perfect, however, since it's impossible to exhaustively check for issues that may happen at runtime.

When implementing health checks, we prefer false positives to false negatives. This means we would prefer that a health check pass and the sink then fail than to have the health check fail when the sink would have been able to run successfully.

A common cause of false negatives in health checks is performing an operation that the sink itself does not need. For example, listing all of the available S3 buckets and checking that the configured bucket is on that list. The S3 sink doesn't need the ability to list all buckets, and a user that knows that may not have permitted it to do so. In that case, the health check will fail due to bad credentials even through its credentials are sufficient for normal operation.

This leads to a general strategy of mimicking what the sink itself does. Unfortunately, the fact that health checks don't have real events available to them leads to some limitations here. The most obvious example of this is with sinks where the exact target of a write depends on the value of some field in the event (e.g. an interpolated Kinesis stream name). It also pops up for sinks where incoming events are expected to conform to a specific schema. In both cases, random test data is reasonably likely to trigger a potentially false-negative result. Even in simpler cases, we need to think about the effects of writing test data and whether the user would find that surprising or invasive. The answer usually depends on the system we're interfacing with.

In some cases, like the Kinesis example above, the right thing to do might be nothing at all. If we require dynamic information to figure out what entity (i.e. Kinesis stream in this case) that we're even dealing with, odds are very low that we'll be able to come up with a way to meaningfully validate that it's in working order. It's perfectly valid to have a health check that falls back to doing nothing when there is a data dependency like this.

With all that in mind, here is a simple checklist to go over when writing a new health check:

• Does this check perform different fallible operations from the sink itself?
• Does this check have side effects the user would consider undesirable (e.g. data pollution)?
• Are there situations where this check would fail but the sink would operate normally?

Not all of the answers need to be a hard "no", but we should think about the likelihood that any "yes" would lead to false negatives and balance that against the usefulness of the check as a whole for finding problems. Because we have the option to disable individual health checks, there's an escape hatch for users that fall into a false negative circumstance. Our goal should be to minimize the likelihood of users needing to pull that lever while still making a good effort to detect common problems.

Metric naming convention

For metrics naming, Vector broadly follows the Prometheus metric naming standards. Hence, a metric name:

• Must only contain valid characters, which are ASCII letters and digits, as well as underscores. It should match the regular expression: [a-z_][a-z0-9_]*.

• Metrics have a broad template:

  <namespace>_<name>_<unit>_[total]

  • The namespace is a single word prefix that groups metrics from a specific source, for example host-based metrics like CPU, disk, and memory are prefixed with host, Apache metrics are prefixed with apache, etc.
  • The name describes what the metric measures.
  • The unit is a single base unit, for example seconds, bytes, metrics.
  • The suffix should describe the unit in plural form: seconds, bytes. Accumulating counts, both with units or without, should end in total, for example disk_written_bytes_total and http_requests_total.

• Where required, use tags to differentiate the characteristic of the measurement. For example, whilst host_cpu_seconds_total is name of the metric, we also record the mode that is being used for each CPU. The mode and the specific CPU then become tags on the metric:

host_cpu_seconds_total{cpu="0",mode="idle"}
host_cpu_seconds_total{cpu="0",mode="idle"}
host_cpu_seconds_total{cpu="0",mode="nice"}
host_cpu_seconds_total{cpu="0",mode="system"}
host_cpu_seconds_total{cpu="0",mode="user"}
host_cpu_seconds_total

Option naming

When naming options for sinks, sources, and transforms it's important to keep in mind these guidelines:

• Suffix options with their unit. Ex: _seconds, _bytes, etc.
• Don't repeat the name space in the option name, ex. fingerprinting.fingerprint_bytes.
• Normalize around time units where relevant and possible, for example using seconds consistently rather than seconds and milliseconds.
• Use nouns as category names, for example fingerprint instead of fingerprinting.

Testing

Testing is very important since Vector's primary design principle is reliability. You can read more about how Vector tests in our testing blog post.

Unit Tests

Unit tests refer to the majority of inline tests throughout Vector's code. A defining characteristic of unit tests is that they do not require external services to run, therfore they should be much quicker. You can run them with:

cargo test

Integration Tests

Integration tests verify that Vector actually works with the services it integrates with. Unlike unit tests, integration tests require external services to run. A few rules when setting up integration tests:

• To ensure all contributors can run integration tests, the service must run in a Docker container.
• The service must be configured on a unique port that is configured through an environment variable.
• Add a test-integration-<name> to Vector's Makefile and ensure that it starts the service before running the integration test.
• Add a test-integration-<name> job to Vector's .github/workflows/test.yml workflow and call your make target accordingly.

Once complete, you can run your integration tests with:

make test-integration-<name>

Blackbox Tests

Vector also offers blackbox testing via Vector's test harness. This is a complex testing suite that tests Vector's performance in real-world environments. It is typically used for benchmarking, but also correctness testing.

You can run these tests within a PR as described in the CI section.

Tips and Tricks

Testing Specific Components

If you are developing a particular component and want to quickly iterate on unit tests related only to this component, the following approach can reduce waiting times:

1. Install cargo-watch.

2. (Only for GNU/Linux) Install LLVM 9 (for example, package llvm-9 on Debian) and set RUSTFLAGS environment variable to use lld as the linker:

   export RUSTFLAGS='-Clinker=clang-9 -Clink-arg=-fuse-ld=lld'

3. Run in the root directory of Vector's source

   cargo watch -s clear -s \
     'cargo test --lib --no-default-features --features=<component type>-<component name> <component type>::<component name>'

   For example, if the component is add_fields transform, the command above turns into

   cargo watch -s clear -s \
     'cargo test --lib --no-default-features --features=transforms-add_fields transforms::add_fields'

Generating Sample Logs

We use flog to build a sample set of log files to test sending logs from a file. This can be done with the following commands on mac with homebrew. Installation instruction for flog can be found here.

flog --bytes $((100 * 1024 * 1024)) > sample.log

This will create a 100MiB sample log file in the sample.log file.

Benchmarking

All benchmarks are placed in the /benches folder. You can run benchmarks via the make bench command. In addition, Vector maintains a full test harness for complex end-to-end integration and performance testing.

Profiling

If you're trying to improve Vector's performance (or understand why your change made it worse), profiling is a useful tool for seeing where time is being spent.

While there are a bunch of useful profiling tools, a simple place to get started is with Linux's perf. Before getting started, you'll likely need to give yourself access to collect stats:

echo -1 | sudo tee /proc/sys/kernel/perf_event_paranoid

You'll also want to edit Cargo.toml and make sure that Vector is being built with debug symbols in release mode. This ensures that you'll get human-readable info in the eventual output:

[profile.release]
debug = true

Then you can start up a release build of Vector with whatever config you're interested in profiling.

cargo run --release -- --config my_test_config.toml

Once it's started, use the ps tool (or equivalent) to make a note of its PID. We'll use this to tell perf which process we would like it to collect data about.

The next step is somewhat dependent on the config you're testing. For this example, let's assume you're using a simple TCP-mode socket source listening on port 9000. Let's also assume that you have a large file of example input in access.log (you can use a tool like flog to generate this).

With all that prepared, we can send our test input to Vector and collect data while it is under load:

perf record -F99 --call-graph dwarf -p $VECTOR_PID socat -dd OPEN:access.log TCP:localhost:9000

This instructs perf to collect data from our already-running Vector process for the duration of the socat command. The -F argument is the frequency at which perf should sample the Vector call stack. Higher frequencies will collect more data and produce more detailed output, but can produce enormous amounts of data that take a very long time to process. Using -F99 works well when your input data is large enough to take a minute or more to process, but feel free to adjust both input size and sampling frequency for your setup.

It's worth noting that this is not the normal way to profile programs with perf. Usually you would simply run something like perf record my_program and not have to worry about PIDs and such. We differ from this because we're only interested in data about what Vector is doing while under load. Running it directly under perf would collect data for the entire lifetime of the process, including startup, shutdown, and idle time. By telling perf to collect data only while the load generation command is running we get a more focused dataset and don't have to worry about timing different commands in quick succession.

You'll now find a perf.data file in your current directory with all of the information that was collected. There are different ways to process this, but one of the most useful is to create a flamegraph. For this we can use the inferno tool (available via cargo install):

perf script | inferno-collapse-perf > stacks.folded
cat stacks.folded | inferno-flamegraph > flamegraph.svg

And that's it! You now have a flamegraph SVG file that can be opened and navigated in your favorite web browser.

Kubernetes

Kubernetes Dev Flow

There is a special flow for when you develop portions of Vector that are designed to work with Kubernetes, like kubernetes_logs source or the deployment/kubernetes/*.yaml configs.

This flow facilitates building Vector and deploying it into a cluster.

Requirements

There are some extra requirements besides what you'd normally need to work on Vector:

• linux system (create an issue if you want to work with another OS and we'll help);
• skaffold
• docker
• kubectl
• kustomize
• minikube-powered or other k8s cluster
• cargo watch

The dev flow

Once you have the requirements, use the scripts/skaffold.sh dev command.

That's it, just one command should take care of everything!

It will:

1. build the vector binary in development mode,
2. build a docker image from this binary via skaffold/docker/Dockerfile,
3. deploy vector into the Kubernetes cluster at your current kubectl context using the built docker image and a mix of our production deployment configuration from the distribution/kubernetes/*.yaml and the special dev-flow configuration at skaffold/manifests/*.yaml; see kustomization.yaml for the exact specification.

As the result of invoking the scripts/skaffold.sh dev, you should see a skaffold process running on your local machine, printing the logs from the deployed vector instance.

To stop the process, press Ctrl+C, and wait for skaffold to clean up the cluster state and exit.

scripts/skaffold.sh wraps skaffold, you can use other skaffold subcommands if it fits you better.

Troubleshooting

You might need to tweak skaffold, here are some hints:

• skaffold will try to detect whether a local cluster is used; if a local cluster is used, skaffold won't push the docker images it builds to a registry. See this page for how you can troubleshoot and tweak this behavior.

• skaffold can rewrite the image name so that you don't try to push a docker image to a repo that you don't have access to. See this page for more info.

• For the rest of the skaffold tweaks you might want to apply check out this page.

Going through the dev flow manually

Is some cases skaffold may not work. It's possible to go through the dev flow manually, without skaffold.

One of the important thing skaffold does is it patches the configuration to tie things together. If you want to go without it, you'll have to take care of that yourself, thus some additional knowledge of Kubernetes inner workings is required.

Essentially, the steps you have to take to deploy manually are the same that skaffold will perform, and they're outlined at the previous section.

Kubernetes E2E tests

Kubernetes integration has a lot of parts that can go wrong.

To cope with the complexity and ensure we maintain high quality, we use E2E (end-to-end) tests.

E2E tests normally run at CI, so there's typically no need to run them manually.

Requirements

• kubernetes cluster (minikube has special support, but any cluster should work)
• docker
• kubectl
• bash

Vector release artifacts are prepared for E2E tests, so the ability to do that is required too, see Vector docs for more details.

Note: minikube had a bug in the versions 1.12.x that affected our test process - see kubernetes/minikube#8799. Use version 1.13.0+ that has this bug fixed.

Also:

Note: minikube has troubles running on ZFS systems. If you're using ZFS, we suggest using a cloud cluster or minik8s with local registry.

Running the E2E tests

To run the E2E tests, use the following command:

CONTAINER_IMAGE_REPO=<your name>/vector-test make test-e2e-kubernetes

Where CONTAINER_IMAGE_REPO is the docker image repo name to use, without part after the :. Replace <your name> with your Docker Hub username.

You can also pass additional parameters to adjust the behavior of the test:

• QUICK_BUILD=true - use development build and a skaffold image from the dev flow instead of a production docker image. Significantly speeds up the preparation process, but doesn't guarantee the correctness in the release build. Useful for development of the tests or Vector code to speed up the iteration cycles.

• USE_MINIKUBE_CACHE=true - instead of pushing the built docker image to the registry under the specified name, directly load the image into a minikube-controlled cluster node. Requires you to test against a minikube cluster. Eliminates the need to have a registry to run tests. When USE_MINIKUBE_CACHE=true is set, we provide a default value for the CONTAINER_IMAGE_REPO so it can be omitted. Can be set to auto (default) to automatically detect whether to use minikube cache or not, based on the current kubectl context. To opt-out, set USE_MINIKUBE_CACHE=false.

• CONTAINER_IMAGE=<your name>/vector-test:tag - completely skip the step of building the Vector docker image, and use the specified image instead. Useful to speed up the iterations speed when you already have a Vector docker image you want to test against.

• SKIP_CONTAINER_IMAGE_PUBLISHING=true - completely skip the image publishing step. Useful when you want to speed up the iteration speed and when you know the Vector image you want to test is already available to the cluster you're testing against.

• SCOPE - pass a filter to the cargo test command to filter out the tests, effectively equivalent to cargo test -- $SCOPE.

Passing additional commands is done like so:

QUICK_BUILD=true USE_MINIKUBE_CACHE=true make test-e2e-kubernetes

or

QUICK_BUILD=true CONTAINER_IMAGE_REPO=<your name>/vector-test make test-e2e-kubernetes

Kubernetes Architecture

Kubernetes integration architecture is largely inspired by the RFC 2221, so this is a concise outline of the effective design, rather than a deep dive into the concepts.

The operation logic

With kubernetes_logs source, Vector connects to the Kubernetes API doing a streaming watch request over the Pods executing on the same Node that Vector itself runs at. Once Vector gets the list of all the Pods that are running on the Node, it starts collecting logs for the logs files corresponding to each of the Pod. Only plaintext (as in non-gzipped) files are taken into consideration. The log files are then parsed into events, and the said events are annotated with the metadata from the corresponding Pods, correlated via the file path of the originating log file. The events are then passed to the topology.

Where to find things

We use custom Kubernetes API client and machinery, that lives at src/kubernetes. The kubernetes_logs source lives at src/sources/kubernetes_logs. There is also an end-to-end (E2E) test framework that resides at lib/k8s-test-framework, and the actual end-to-end tests using that framework are at lib/k8s-e2e-tests.

The Kubernetes-related distribution bit that are at distribution/docker, distribution/kubernetes and distribution/helm. There are also snapshot tests for Helm at tests/helm-snapshots.

The development assistance resources are located at skaffold.yaml and skaffold dir.

Humans

After making your change, you'll want to prepare it for Vector's users (mostly humans). This usually entails updating documentation and announcing your feature.

Documentation

Documentation is very important to the Vector project! The official docs at https://vector.dev/docs are built using structured data written in [CUE], a language designed for data templating and validation. All of Vector's CUE sources are in the /docs folder.

Vector is currently using CUE version 0.3.2. Be sure to use precisely this version, as CUE is evolving quickly and you can expect breaking changes in each release.

How the docs work

When the HTML output for the Vector docs is built, the vector repo is cloned (in another repo) and these CUE sources are converted into one big JSON object using the cue export command. That JSON is then used as an input to the site build.

Formatting

Vector has some CUE-related CI checks that are run whenever changes are made to the docs directory. This includes checks to make sure that the CUE sources are properly formatted. To run CUE's autoformatting, run this command from the vector root:

cue fmt ./docs/**/*.cue

If that rewrites any files, make sure to commit your changes or else you'll see CI failures.

Validation

In addition to proper formatting, the CUE sources need to be valid, that is, the provided data needs to conform to various CUE schemas. To check the validity of the CUE sources:

make check-docs

Development flow

A good practice for writing CUE is to make small, incremental changes and to frequently check to ensure that those changes are valid. If you introduce larger changes that introduce multiple errors, you may have difficulty interpreting CUE's verbose (and not always super helpful) log output. In fact, we recommend using a tool like [watchexec] to validate the sources every time you save a change:

# From the root
watchexec "make check-docs"

Changelog

Developers do not need to maintain the Changelog. This is automatically generated via the make release command. This is made possible by the use of conventional commit titles.

What makes a highlight noteworthy?

It should offer meaningful value to users. This is inherently subjective and it is impossible to define exact rules for this distinction. But we should be cautious not to dilute the meaning of a highlight by producing low values highlights.

How is a highlight different from a blog post?

Highlights are not blog posts. They are short one, maybe two, paragraph announcements. Highlights should allude to, or link to, a blog post if relevant.

For example, this performance increase announcement is noteworthy, but also deserves an in-depth blog post covering the work that resulted in the performance benefit. Notice that the highlight alludes to an upcoming blog post. This allows us to communicate a high-value performance improvement without being blocked by an in-depth blog post.

Security

Please see the SECURITY.md file.

Legal

To protect all users of Vector, the following legal requirements are made. If you have additional questions, please contact us.

DCO

Vector requires all contributors to agree to the DCO. DCO stands for Developer Certificate of Origin and is maintained by the Linux Foundation. It is an attestation attached to every commit made by every developer. All contributions are covered by, and fall under, the DCO.

Trivial changes

Trivial changes, such as spelling fixes, do not need to be signed.

Granted rights and copyright assignment

This is covered by the DCO. Contributions are covered by the DCO and do not require a CLA.

FAQ

Why a DCO instead of a CLA?

It's simpler, clearer, and still protects users of Vector. We believe the DCO more accurately embodies the principles of open-source. More info can be found here:

• Gitlab's switch to DCO
• DCO vs CLA

If I’m contributing while an employee, do I still need my employer to sign something?

Nope! The DCO confirms that you are entitled to submit the code, which assumes that you are authorized to do so. It treats you like an adult and relies on your accurate statement about your rights to submit a contribution.

What if I forgot to sign my commits?

No problem! We made this simple with the signoff Makefile target:

make signoff

If you prefer to do this manually:

git commit --amend --signoff

Contact

If you have questions about this document or the project as a whole, please contact us at [email protected].