python-heptapod-runner 0.6.4

Heptapod Runner: Python utilities and subsystems

Python utilities and subsytems for Heptapod Runner

Heptapod Runner is the fork of GitLab Runner meant to work for Heptapod and GitLab instances.

The facilities provided in this package are part of the Heptapod Runner extended distribution, aiming to provide additional options. They also share the same source code repository with Heptapod Runner.

They are not to be confused with the main Heptapod Runner program, which is a standalone programm written in Go (like GitLab Runner) that does not have any dependency on python-heptapod-runner.

Python was selected for fast prototyping and because it is lingua franca in the Mercurial community, but It is possible that some of facilities end up being rewritten in another language.

heptapod-paas-runner-register

This interactive program can be used to bootstrap the configuration for heptapod-paas-runner.

Warning: in all cases, the configuration has to be manually completed afterwards.

It takes care notably of the registration against the GitLab or Heptapod instance, hence playing a similar role as the gitlab-runner register command.

It will just append a new [[runners] section if the configuration file already exists.

Caveats for configuration file creation

• The parent directory of the configuration file path has to exist, be writable by the system user doing the registration
• The resulting configuration file has to be readable by the system user for heptapod-paas-runner.
• The parent directory of the state file path has to be writable by the system user for heptapod-paas-runner.

heptapod-paas-runner

The purpose of this program is to provide on-demand provisioning while following the expected workflows of PAAS systems:

• Docker hosts are provisioned only after jobs have been acquired from the coordinator
• actual job launch involves pushing a Dockerfile to a Git repository that controls the Docker host.

Process and state management

Simple invocation

heptapod-paas-runner takes a single positional argument: the path to its configuration file (see the section about heptapod-paas-runner-register above)

Several options are available and can be displayed with:

heptapod-paas-runner --help

Graceful restart and state management

Heptapod PAAS Runner tracks the jobs it has launched in order to deprovision the resources once they are finished.

To that end, it implements a graceful shutdown when SIGTERM is received (this is the default stop signal used by many process managers, including systemd).

Upon signal reception, it will

• stop acquiring new jobs
• finish operations that can't be interrupted (launchings, decommissionings)
• write all needed information about currently tracked jobs in a file
• exit

This shutdown sequence is not instantaneous. A waiting time of 2 minutes should be enough before resorting to more drastic means (this includes margin).

We will probably improve the interruption of the launch sequence, but we can't go as far as to interrupt a currently running request, as it could provision untrackable resources.

Graceful reload

Currently not supported, a configuration change can be done without losing information with the grateful restart.

In theory, a proper reload would be less dangerous.

Sample systemd unit file

[Unit]
Description=Heptapod PAAS Runner
After=network.target

[Install]
WantedBy=multi-user.target

[Service]
User=heptapod-runner
WorkingDirectory=/srv/heptapod-runner
ExecStart=/srv/heptapod-runner/venv/bin/heptapod-paas-runner /etc/heptapod-runner/pass-runner.toml
# We don't need a specific ExecStop, as systemd has a cascading system
# of defaults for the stop signal, with the
# SIGTERM being the ultimate default.

TimeoutStopSec=120
Restart=always
RestartSec=125

The configuration file

heptapod-paas-runner uses the same configuration file as the normal Heptapod (or GitLab) Runner.

Each runners section has an executor entry that must be one of the PAAS executors, and consists otherwise of a mix of

• specific PAAS Runner configuration, depending on the executor.
• standard GitLab Runner configuration, forwarded to the final executor, unless forced by the PAAS Runner.

The PAAS executors are currently:

• clever-docker (see below)
• local-docker, for testing and development purposes only

Note: heptapod-paas-runner does not have a registration facility yet. In practice, you can use any heptapod-runner or gitlab-runner executable to create a configuration file with an appropriate coordinator token, and then modify it for heptapod-paas-runner. This does not have to be done on the actual target system.

Global configuration

The global configuration is not forwarded to the PAAS resources. It is instead used to tweak heptapod-paas-runner itself.

• concurrent (default 50): same meaning as in standard GitLab Runner. This is the maximum number of jobs across all defined runners.

• check_interval (default 3): similar meaning as in standard GitLab Runner. This is the time in seconds between full coordinator polls, given that:

  • if a coordinator grants a job, it is repolled immediately unless limits on jobs are reached (number of jobs, weighted quota, and concurrent provisioning)
  • if limits on jobs are reached, the waiting time is given by job_progress_poll_interval (see below)

• job_progress_interval (default 30): time in seconds to wait between coordinator requests to check if launched jobs are finished.

• quota_computation (required): allows to set the weighted quota by taking a runner section, one of its associated flavor and a number of job as reference. Example:

  [quota_computation]
    reference_runner = "my-runner"
    reference_flavor = "M"
    reference_jobs_count = 4
  
  [[runners]]
    name = "my-runner"
    url = "https://heptapod.example"
    ...
  

  This sets the quota to be the weight of 4 jobs with flavor M of the my-runner runner defined afterwards.

• state_file: path of the file used to keep tracking running jobs after a graceful restart.

• paas_max_concurrent_provisioning (default 10): maximum number of concurrent jobs currently queued at the PAAS infrastructure that haven't been acknowledged as really running by their coordinators yet.

• paas_finished_jobs_keep_resources (default false): if true, no decommissioning will occur.

• pass_decommision_launched_failures (default true): if false, in the case where resource was supposedly created in the PAAS infrastructure but did not actually start, no attempt to remove it will be done, so that it can be inspected to understand the issue.

Global footprint: the weight system

Some heptapod-paas-runner executors have the capability to use different resources ("flavors") according to job details. The job details are usually available only once the job is acquired, which can currently not be reverted.

A concurrency control system accounting only for the number of jobs would hence need to cripple its capability to run small jobs because of the possibility to run large jobs. This is what happened before the introduction of the weight system.

The weights used by the various configured runners are simply summed up. Beware that the weights of different executors are usually very much not comparable, hence it is a very bad idea to mix several executor types in a single heptapod-paas-runner service. For instance, at the time of this writing, the clever-docker executor has weights ranging roughly from 2 to 75, whereas an executor not implementing weights would have all values equal to 1.

It is perfectly admissible, and even recommended, to smooth out those limits to put several runners in a single service, as long as they all have the same executor. The typical use-case for this would be to perform CI for several Heptapod (or pure upstream GitLab) instances.

Common properties of the Docker executors

• All standard features (images, services) are supporteed

Image management and Dependency Proxy

We don't currently have the means to reuse PAAS resources, hence all jobs start with fresh downloads of all necessary Docker images.

Partly because of this, the Heptapod PAAS Runner uses the Dependency Proxy automatically, diverging in that from the standard GitLab Runner Docker executor.

Practical consequences for job authors:

• do not disable the Dependency Proxy for your Group

• if a service has no alias defined in the job, it will be accessible with only one of the two syntaxes normally supported by GitLab Runner:

  • if the service image is defined as postgres:13, the service container can be accessed as usual as postgres
  • if the image is defined as tutum/wordpress, the service container can be accessed as tutum-wordpress, but not tutum__wordpress

The clever-docker executor

This runs the job in Clever Cloud.

Clever Cloud is also the company hosting the public Heptapod instance for Free and Open Source Software, where Heptapod is self-hosted and the commercial Heptapod instance.

There are two modes of operation: single organization and multi-tenant.

Common configuration

Required:

• executor: clever-docker
• cc_extra_env subsection. Can be used to pass extra environment to the sub-runner spawned on the provisioned resource. Currently CC_ENABLE_HEPTAPOD_RUNNER = "true" is necessary

Optional:

• cc_api_url (defaults to https://api.clever-cloud.com/v2): Clever Cloud base API URL
• cc_zone (defaults to par): any zone can be in theory used. In practice, the zone better be close to the GitLab / Heptapod instance (the coordinator).
• cc_default_flavor (defaults to M): the flavor (size) of instances that will be launched on Clever Cloud if not specified by the job.
• [job_trace_watch] (strongly recommended): this section enables watching the user-level job log (internally called "the job trace") to limit the number of concurrent jobs being in provisioning and not fully started yet, and to provide quicker user feedback in case of problems. Items are:
  • token: sadly, has to be an Administrator token with read-api scope. A future version of Heptapod should be able to avoid that, but probably not upstream GitLab
  • timout_seconds (default 300): the time after which the job launch is considered to have failed.
  • poll_step (default 10): the time in seconds between checks of the job trace
• cc_deployment_repo_timeout (default 20): maximum time in seconds to wait for the Clever Cloud's deployment Git repository to be ready
• cc_deployment_repo_wait_step (default 2): time between requests to assess if the Clever Cloud's deployment Git repository is ready

Running for a single Clever Cloud Organization

Put these in the Runner configuration

• cc_multi_tenant: unspecified or false.
• cc_orga_id: specify the id of your Clever Cloud Organization, as seen, e.g, in its Overview page in the Clever Cloud console.
• cc_token: token for the Clever Cloud API, with enough rights to create, deploy and delete applications and instances

It doesn't matter whether the Runner is tied to specific Projects, a Group or a whole GitLab / Heptapod instance: all resources will be attached (and billed) to the specified Organization.

This is a good fit for a self-hosted Heptapod instance.

Full example:

state_file = "/srv/heptapod-runner/paas-runner-state.json"

[quota_computation]
  reference_runner = "clever-cloud"
  reference_flavor = "3XL"
  reference_jobs_count = 8

[[runners]]
  name = "clever-cloud"
  url = "https://heptapod.example.com"
  token = "D3adNQYu8OCjkYDbwDaG"
  executor = "clever-docker"

  cc_orga_id = "orga_07cf2ef0-c9ad-4f04-b492-94c164f95c76"
  cc_token = "bb52e490-d47e-47a4-b190-73e23eb17111"

  [runners.cc_extra_env]
    CC_ENABLE_HEPTAPOD_RUNNER = "true"
  [runners.custom_build_dir]
  [runners.cache]
    [runners.cache.s3]
    # A future version of Heptapod PAAS Runner may fill this in automatically.
    # Meanwhile it is possible to use any S3 configuration. Using Clever's
    # Cellar for your CI caches in the same zone is the best for
    # network proximity and bandwidth.
    # (replace with your credentials and bucket of choice)
    ServerAddress = "cellar-c2.services.clever-cloud.com"
    AccessKey = "dEA7gjmYM98gobVi6Y1x"
    SecretKey = "v0tdpjgpsDRqaSvIndvHAXFmjbpEd958gbZuO7yv"
    BucketName = "heptapod-ci"
    [runners.cache.gcs]
  [runners.docker]
    helper_image = "registry.heptapod.net/heptapod/heptapod-runner/helper:x86_64-latest"
    tls_verify = false
    image = "debian:bullseye"
    privileged = false
    disable_entrypoint_overwrite = false
    oom_kill_disable = false
    disable_cache = false
    volumes = ["/cache"]
    shm_size = 0

(all tokens and uuids in this example are random values freshly obtained for this documentation)

The multi-tenant mode

In this mode, the runner determines the Clever Cloud Organization and the associated API token from attributes of the top-level Heptapod Group to which the Project belongs.

It assumes that something populates those attributes.

This mode of operation is intended for instances such as heptapod.host.

Required Runner configuration:

• cc_multi_tenant: true
• cc_gitlab_namespace_attributes_token: a GitLab / Heptapod private token with enough rights to query Group custom attributes.

Optional Runner configuration:

• cc_orga_id_attribute (defaults to cc_orga_id). Name of the custom attribute on top-level Groups to use for the Organization ID.
• cc_orga_token_attribute (defaults to cc_orga_token`). Name of the custom attribute on top-level Groups to use for the Clever API token of the Organization.

Full example:

state_file = "/srv/heptapod-runner/paas-runner-state.json"

[quota_computation]
  reference_runner = "clever-cloud"
  reference_flavor = "3XL"
  reference_jobs_count = 8

[[runners]]
  name = "clever-cloud"
  url = "https://heptapod.example.com"
  token = "D3adNQYu8OCjkYDbwDaG"
  executor = "clever-docker"

  cc_multi_tenant = true
  cc_gitlab_namespace_attributes_token = "D7aY5I5SygxA5oyZ11vB"

  [runners.cc_extra_env]
    CC_ENABLE_HEPTAPOD_RUNNER = "true"

  [runners.custom_build_dir]
  [runners.cache]
    [runners.cache.s3]
    # Do not fill in this: it would use the same bucket for all tenants.
    # A future version of Heptapod PAAS Runner will fill this automatically,
    # using the Cellar add-on of each tenant for proper separation.
    [runners.cache.gcs]
  [runners.docker]
    helper_image = "registry.heptapod.net/heptapod/heptapod-runner/helper:x86_64-latest"
    tls_verify = false
    image = "debian:bullseye"
    privileged = false
    disable_entrypoint_overwrite = false
    oom_kill_disable = false
    disable_cache = false
    volumes = ["/cache"]
    shm_size = 0

(all tokens in this example are random values freshly obtained for this documentation)

Flavors and their weights

End users can control the flavors (sizes) of the Clever Cloud instances running their jobs. This is done with the CI_CLEVER_CLOUD_FLAVOR job variable, with admissible values currently ranging from XS to 3XL.

The clever-docker executors implement the weight system described above to limit the maximum footprint of the whole runner service in the Clever while taking differences between the flavors into account.

If one wishes to allow as much as exactly N jobs with flavor F, the simplest is to use the quota_computation section (see above). Alternatively, one may set the concurrent global configuration parameter to N*F.weight. The weight system will then automatically adjust what it means for the typical payload, consisting of bigger and or smaller instances than F.

The weight computation is expected to change in the future, as it is merely an approximation of actual infrastructure constraints, hence it is better to use quota_computation rather than concurrent.

Currently, the weight of a flavor is computed as M^1.25 where M is the RAM footprint of the flavor, expressed in GiB. The non linearity of this formula will allow in practice to spawn more smaller flavors with an equivalent total memory footprint.