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Loom is a server-daemon that provides concurrent task-scheduling for clusters and servers. It consists of a Twisted daemon that reads in target-node and scheduling information from flexible YAML configuration files. As job schedules come to bear, task-work is applied to remote nodes via the Paramiko/SSH API, Fabric. Loom relies upon the Twisted library, Ampoule to achieve concurrency and tasks applied to multiple nodes are each applied seperately in a child-process.


in a location of your choice, clone the Loom git repository:

cd ~/
git clone git://
cd loom
sudo python install

configuration files

You can configure whatever specific directories you want for each of the configuration or manifest locations that Loom searches. The suggested place to put them however is in your home directory under the directory ~/.loom:

mkdir .loom
touch .loom/loom.yaml
touch .loom/nodes.yaml
mkdir .loom/jobs/


The main configuration format for Loom is YAML. When Loom starts it will search a few paths for the main configuration file, usually ~/.loom.yaml If you have a special need to place this file elsewhere you can specify it with the -c/--config commandline option. Some configuration options are required and tell Loom where to find your node and job manifests. Other options are optional, like datafile which contains YAML that will be prepended to each of your job-manifests. We'll describe the datafile option later. Here are some of the important options you can specify:

  • nodesfile : Path to your node-manifest file
  • jobspath : Path containing your job-manifests
  • datafile : Path to your data-manifest containing YAML to be prepended to each job-manifest
  • min_workers: Minimum amount of child worker-processes to maintain (default: 0)
  • max_workers: Maximum amount of child worker-processes to maintain (default: 10)


nodes that can be used in job definitions are declared in a file specified by the nodesfile setting. The definitions are quite simple. The YAML key should be the locally resolvable hostname of the node and is the name used to refer to the node in the job manifests. In this example it it is staging:

  user: root
  password: useidentinstead

If your loom user on the server that hosts your loom instance is already setup for passwordless SSH access to your nodes and the server has locally configured hostnames (/etc/hosts) for the nodes then a node-manifest can be as simple as:

staging: # node name will resolve to ip
  user: root # user still needs to be supplied

node attributes

here are a list of all possible node attributes:

  • YAML key: the locally resolvable hostname
  • ip: override hostname with specified IP address
  • user: user under which node work will take place
  • password: password for user above
  • identity: a locally accessible ssh identity to use instead of user/password


In Loom, jobs are the combination of a cron-schedule, a list of target nodes and a python import path that designates the callable that should be applied to each node. The jobspath configuration option should specify a path where your job-manifests can be found. This path will be searched recursively for any files ending in the extension .yaml that contain a top-level YAML dictionary key jobs. The value should be a dictionary with each key being the name of a job. The job definition should follow. Here is the example job manifest that ships with Loom:

        task: loom.system.add_sshfs_mount
        args: [*storage]
        schedule: "* * * * *"
        targets: [staging, production]
        description: Mount storage on forge and hive
        args: ["touch /tmp/loom_was_here"]
        schedule: "* * * * *"
        targets: *dbservers
        description: Leave our mark

The structure is quite simple and allows you to describe the parameters of a scheduled job. You'll notice that, say in the case of the mountstorage job that the args parameter contains a YAML reference. The testtask job's targets are also specified as a YAML reference. These references may be defined in your datafile and referred to in multiple places in your manifests. The datafile will be described shortly. First though, let's go through the job parameters that you can specify:

  • YAML key : job name
  • task : a Python import path pointing to a Python ''callable'' (usually a Fabric task)
  • args : a list of values to pass as arguments to the task callable
  • kwargs : a dictionary of values to pass as keyword-arguments to the task callable
  • schedule : a regular cron scheduling definition
  • targets : a list of node names, defined in your nodesfile
  • description : textual description of the job


The datafile is a special file that contains YAML data that will be prepended to all of your job-manifests before being processed. This allows you to define certain complex data as YAML Anchors that can be resued througout your manifests. If you need to pass complex values to your task callables this is a good place to define it. Of course you are not limited to putting your complex data here. Each job-manifest can contain it's own YAML Anchors however they will only be available from that specific manifest. Here is the contents of the example datafile shipped with loom:

    - &cloudhosts [staging, production]
    - &dbservers [db1, db2, db3, db4]
    - &storage
        host: root@staging
        remotepath: /mnt/storage
        mountpoint: /mnt/storage
        excludes: [staging]
    - &rootutils
        host: root@staging
        remotepath: /mnt/rootutils
        mountpoint: /mnt/rootutils
        excludes: [staging]

You can see that we have defined collections for our different node types and a couple structures claiming to be mountpoints. The hosts anchors are easy to understand. They allow you to categorically refer to your defined nodes with a single identifier. The mountpoints however are not data that is specifically relevant to Loom. It is data that is passed to your job tasks as argument values. If you scroll up, you can see that we refer to the storage mountpoint as the args parameter in the mountstorage job-manifest. Pretty handy.

starting the daemon

Loom is built and packaged as a Twisted plugin and so the 'twistd' command is used to interact with it. After writing your loom config and node and job manifests you can start loom with the following command:

twistd loom -c <path-to-your-loom-config>

Or if you'd like to see the output on stdout:

twistd -n loom -c <path-to-your-loom-config>

updating configurations

Loom will automatically reload, going through the entire configuration and manifest parsing process anytime you change a file it already has loaded. This includes both the main configuration file and the node and job manifests. However note that Loom will not automatically detect new job manifests and load them. A restart of the daemon will be needed for that.

current outstanding issues


  • Ampoule spams the twisted log and doesn't use the "system" keyword so that its messages can be filtered. Ampoule will probably need to be patched to fix this.
  • Each job manifest should take a log option and we should route job output there
  • Logging in general is unhandled and not investigated at this point


  • The scheduler is extremely basic. It makes no effort to track job success or failure, does not try to make up missed work and other things you might expect of a capable scheduler. We'll get there.


  • Loom needs a lot more exception handling


  • Loom needs a lot more of them ;P

things to look forward to

reusable tasks

  • Loom should provide a standard-library of sorts for system-management related Fabric tasks. They'd need to generic enough to be used widely.
  • Perhaps we could even provide namespace packages like loom.apache, loom.debian, loom.whatever for categorically related generic Fabric libraries!

twistd subcommands

  • I would like to support subcommands that you can provide to the 'twistd loom' command-line. Things like invoking jobs directly, turning jobs on and off by name, querying the scheduler in various ways, etc.

external interface

  • at some point I would also like to expose similar abilities to interact with the scheduler via a web-api of some sort. Either REST or something like Amazon notifications
  • web-interface!?

remote loom utility

  • This will probably rely some amount on the external interface noted above but I'd like a utility that I can run on my local workstation that can interact with the loom server remotely. Mmmm.

resource manifests

  • Being able to enforce resource manifests similar to other tools like Puppet or Chef using Fabric as the transport would be some fancy pants. I can only imagine that a good resource manifest layer would highly increase loom's general utility.

bash completion

  • this is more of a wish-list item but being able to automatically tab-complete job and node names and their attributes would be handy


A Twisted/Fabric based centralized task-scheduler for clusters




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