Running a Worker Node

["*", "diagrams/**/*", "templates/**/*"]
["*", "diagrams/**/*", "templates/**/*"]


The worker node subscribes to the web node and is therefore used to run builds and as check resources. By itself, this does not rule out much.

  • Linux: We test and support the following distributions. Minimum kernel performance tested is 4.4.

  • Ubuntu 16.04 (Kernel 4.4)

  • Ubuntu 18.04 (Kernel 5.3)

  • Ubuntu 20.04 (Kernel 5.4)

  • Debian 10 (kernel 4.19)

  • Other requirements:

  • You really need to include user namespaces.

  • In order to apply memory rule tasks, memory + swap processing must be enabled.

  • Reading

  • Guardian only supports cgroupsV1. Use any type of containerd runtime or migrate from cgroupsV2 to cgroupsV1.

  • Windows/Darwin: No special personal needs (as far as we know).

    NOTE. containers are not currently supported and Darwin will definitely not have their own containers.The steps almost certainly start an internal temporary directory on the windows/darwin desktop. All useful dependencies for your tasks (eg git, .NET, golang, ssh) must be pre-installed in the worker. Windows/Darwin compute nodes never ship with resource types.

  • The

    CLI concourse can be run as a worker node using the worker subcommand.

    First, you finally need to set up a directory where the employee can save data:


    This is where the runtime environment is configured and all resources are retrieved from it. In any case, make sure you have enough disk space.



    # with -E for advanced environment setup or install everything as root
    sudo -E employee contest

    Note why the compute node should be called as root because it manages containers.

    All fire safety logs are redirected directly to stdout and all fire logs are redirected to stdoutLower level nicknames or tricks are redirected to stderr.

    CPU usage: depends almost entirely on channel workloads. More configured resources will result in more checks, pending operations, and graphs consuming as much CPU as you want.

    Memory usage: also depends on pipeline workload. Expect usage to maximize containers at peak times and run as you go.

    Bandwidth usage. Again, the material is almost entirely about pipeline workloads. Expect occasional spikes in scans, although the intervals should be spaced out over a reasonable amount of time. Any bandwidth will also be checked when fetching and moving resources.

    Disk usage. Arbitrary data is persisted when written versions are run, so resource caches persist while being garbage collected throughout their lifecycle. Not all disk states need to be experiencedbe the worker and himself; everything is ephemeral. If the worker process is recreated (i.e. the vine is mature and all vm/container processes can be terminated), it should be returned with a clean disk.

    High availability: false. Compute nodes are, at their core, singletons that can be used as drivers for several very different workloads.

    Horizontal scalability: yes; Workers map correctly to power requirements for any number of pipelines, resources, work in progress, and individual releases you want to run. It makes sense to increase them and reduce demand.

  • External to possibly handle random locations from regular resource checks as well as current builds

  • External to potential customers, configured external Web host URL if the input is for actual On-the-fly

  • . loaded

  • External to web traffic, TSA node location web (2222) for artist registration

  • Other collaborators may be traded, eIf P2P streaming is enabled.

  • The

  • web node wraps 7777 (Garden) and (BaggageClaim) 7788. These ports do not need to be exposed, they are currently forwarded to the web host via ssh correlation on port 2222.

  • When p2p streaming is enabled, there is traffic between other users and employees.

  • The

    Worker nodes are stateless and as convenient as possible. Tasks and resources create their own Docker images, so you don’t need to install workflow dependencies. The exceptions are Windows and Darwin workers. All dependencies must be pre-installed on Windows and Darwin compute nodes.

    In Concourse, the most important data is represented by resources, rendering the workers themselves useless. All data in the working directory has become perishable and should be gone when requested on the working computer. They cannot be recreated from a virtual machine or compute node container.

    Pain needs to be added to accommodate more pipelinesmore workflows. To know that if necessary, you can adjust the metrics and track the number of containers. When the average number of planters approaches 75 per worker, you will probably need to add another worker. The current load is another indicator worth keeping an eye on.

    To add a worker, simply create another one to modify the worker and follow the instructions to run the Concourse Worker again.

    Note. It makes no sense to run multiple compute nodes on the same machine since they both require the same physical resources. Workers should be their own virtual machines or physical machines to optimize resource usage.

    Whether workflows should be sorted by scale or vertically depends largely on the workloads your pipelines run. However, oddly enough, we have seen that a smaller number (horizontal team scale) is usually better than a small number of large ones (vertical scale). la).

    Again, this is often not an absolute answer! You will most likely need to test this along with workloads that adjust your pipelines and market needs based on the metrics you track.

    Workers will continually approach this group of halls to maintain a healthy registered position. If the worker has not registered after a while, possibly due to a “network” error, overload, or failure, this web site will change its state to