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Pod Utilities

Pod utilities are small, focused Go programs used by plank to decorate user-provided PodSpecs in order to increase the ease of integration for new jobs into the entire CI infrastructure. The utilities today wrap the execution of the test code to ensure that the tests run against correct versions of the source code, that test commands run in the appropriate environment and that output from the test (in the form of status, logs and artifacts) is correctly uploaded to the cloud.

These utilities are integrated into a test run by adding InitContainers and sidecar Containers to the user-provided PodSpec, as well as by overwriting the Container entrypoint for the test Container provided by the user. The following utilities exist today:

  • clonerefs: clones source code under test
  • initupload: records the beginning of a test in cloud storage and reports the status of the clone operations
  • entrypoint: is injected into the test Container, wraps the test code to capture logs and exit status
  • sidecar: runs alongside the test Container, uploads status, logs and test artifacts to cloud storage once the test is finished

Why use Pod Utilities?

Writing a ProwJob that uses the Pod Utilities is much easier than writing one that doesn't because the Pod Utilities will transparently handle many of the tasks the job would otherwise need to do in order to prepare its environment and output more than pass/fail. Historically, this was achieved by wrapping every job with a bootstrap.py script that handled cloning source code, preparing the test environment, and uploading job metadata, logs, and artifacts. This was cumbersome to configure and required every job to be wrapped with the script in the job image. The pod utilities achieve the same goals with less configuration and much simpler job images that are easier to develop and less coupled to Prow.

Writing a ProwJob that uses Pod Utilities

What the test container can expect

Example test container script:

pwd # my repo root
ls path/to/file/in/my/repo.txt # access repo file
ls ../other-repo # access repo file in another repo
echo success > ${ARTIFACTS}/results.txt # result info that will be uploaded to GCS.
# logs, and job metadata are automatically uploaded.

More specifically, a ProwJob using the Pod Utilities can expect the following:

  • Source Code - Jobs can expect to begin execution with their working directory set as the root of the checked out repo. The commit that is checked out depends on the type of job:
    • presubmit jobs will have the relevant PR checked out and merged with the base branch.
    • postsubmit jobs will have the upstream commit that triggered the job checked out.
    • periodic jobs will have the working directory set to the root of the repo specified by the first ref in extra_refs (if specified). See the extra_refs field if you need to clone more than one repo.
  • Metadata and Logs - Jobs can expect metadata about the job to be uploaded before the job starts, and additional metadata and logs to be uploaded when the job completes.
  • Artifact Directory - Jobs can expect an $ARTIFACTS environment variable to be specified. It indicates an existent directory where job artifacts can be dumped for automatic upload to GCS upon job completion.

How to configure

In order to use the pod utilities, you will need to configure plank with some settings first. See plank's README for reference.

ProwJobs may request Pod Utility decoration by setting decorate: true in their config. Example ProwJob configuration:

  - name: pull-job
    always_run: true
    decorate: true
    spec:
      containers:
      - image: alpine
        command:
        - "echo"
        args:
        - "The artifacts dir is $(ARTIFACTS)"

In addition to normal ProwJob configuration, ProwJobs using the Pod Utilities must specify the command field in the container specification instead of using the Dockerfile's ENTRYPOINT directive. Note that the command field is a string array not just a string. It should point to the test binary location in the container.

Additional fields may be required for some use cases:

  • Private repos need to do two things:
    • Add an ssh secret that gives the bot access to the repo to the build cluster and specify the secret name in the ssh_key_secrets field of the job decoration config.
    • Set the clone_uri field of the job spec to git@github.com:{{.Org}}/{{.Repo}}.git.
  • Repos requiring a non-standard clone path can use the path_alias field to clone the repo to different go import path than the default of /home/prow/go/src/github.com/{{.Org}}/{{.Repo}}/ (e.g. path_alias: k8s.io/test-infra -> /home/prow/go/src/k8s.io/test-infra).
  • Jobs that require additional repos to be checked out can arrange for that with the exta_refs field. If the cloned path of this repo must be used as a default working dir the workdir: true must be specified.
  • Jobs that do not want submodules to be cloned should set skip_submodules to true
  • Jobs that want to perform shallow cloning can use clone_depth field. It can be set to desired clone depth. By default, clone_depth get set to 0 which results in full clone of repo.
- name: post-job
  decorate: true
  decoration_config:
    ssh_key_secrets:
    - ssh-secret
  clone_uri: "git@github.com:<YOUR_ORG>/<YOUR_REPO>.git"
  extra_refs:
  - org: kubernetes
    repo: other-repo
    base_ref: master
    workdir: false
  skip_submodules: true
  clone_depth: 0
  spec:
    containers:
    - image: alpine
      command:
      - "echo"
      args:
      - "The artifacts dir is $(ARTIFACTS)"

Migrating from bootstrap.py to Pod Utilities

Jobs using the deprecated bootstrap.py should switch to the Pod Utilities at their earliest convenience. @dims has created a handy migration guide.

Automatic Censoring of Secret Data

Many jobs exist that must touch third-party systems in order to be productive. Whether the job provisions resources in a cloud IaaS like GCP, reports results to an aggregation service like coveralls.io, or simply clones private repositories, jobs require sensitive credentials to achieve their goals. Even with the best intentions, it is possible for end-user code running in a test Pod for a ProwJob to accidentally leak the content of those credentials. If Prow is configured to push job logs and artifacts to a public cloud storage bucket, that leak is immediately immortalized in plain text for the world to read. The sidecar utility can infer what secrets a job has access to and censor those secrets from the output. The following job turns on censoring:

- name: censored-job
  decorate: true
  decoration_config:
    censor_secrets: true

Censoring Process

The automatic censoring process is written to be as useful as possible while having a bounded impact on the execution cost in resources and time for the job. In order to censor every possible leak, all keys in all Secrets that are mounted into the test Pod are treated as sensitive data. For each of these keys, the value of the key as well as the base-64 encoded value are censored from the job's log as well as any artifacts the job produces. If any archives (e.g. .tar.gz) are found in the output artifacts for a job, they are unarchived in order to censor their contents on the fly before being re-archived and pushed up to cloud storage.

In order to bound the impact in runtime and resource cost for censoring on the job, both the concurrency and buffer size of the censoring algorithm are tunable. The overall steady-state memory footprint of the censoring algorithm is simply the buffer size times the maximum concurrency. The buffer must be as large as twice the length of the largest secret to be censored, but may be tuned to very small values in order to decrease the memory footprint. Keep mind that this will increase overall disk I/O and therefore increase the runtime of censoring. Therefore, in order to decrease censoring runtime the buffer should be increased.

Configuring Censoring

A number of aspects of the censoring algorithm are tunable with configuration option at the per-job level or for entire repositories or organizations. Under the decoration_config stanza, the following options are available to tune censoring:

decoration_config:
  censoring_options:
    censoring_concurrency: 0 # the number of files to censor concurrently; each allocates a buffer
    censoring_buffer_size: 0 # the size of the censoring buffer, in bytes
    include_directories:
    - path/**/to/*something.txt # globs relative to $ARTIFACTS that should be censored; everything censored if unset
    exclude_directories:
    - path/**/to/*other.txt # globs relative to $ARTIFACTS that should not be censored