Basic Principles

This section covers general principles that should be kept in mind when writing any workflow. More advanced topics are covered later: Efficiency And Maintainability and Portable Workflows.

UTC Mode

Cylc has full timezone support if needed, but real time NWP workflows should use UTC mode to avoid problems at the transition between local standard time and daylight saving time, and to enable the same workflow to run the same way in different timezones.

[scheduler]
    UTC mode = True

Fine Or Coarse-Grained Workflows

Workflows can have many small simple tasks, fewer large complex tasks, or anything in between. A task that runs many distinct processes can be split into many distinct tasks. The fine-grained approach is more transparent and it allows more task level concurrency and quicker failure recovery - you can rerun just what failed without repeating anything unnecessarily.

rose bunch

One caveat to our fine-graining advice is that submitting a large number of small tasks at once may be a problem on some platforms. If you have many similar concurrent jobs you can use rose bunch to pack them into a single task with incremental rerun capability: retriggering the task will rerun just the component jobs that did not successfully complete earlier.

Monolithic Or Interdependent Workflows

When writing workflows from scratch you may need to decide between putting multiple loosely connected sub-workflows into a single large workflow, or constructing a more modular system of smaller workflows that depend on each other through inter-workflow triggering. Each approach has its pros and cons, depending on your requirements and preferences with respect to the complexity and manageability of the resulting system.

Self-Contained Workflows

All files generated by Cylc during a workflow run are confined to the workflow run directory $HOME/cylc-run/<workflow-id>. However, Cylc has no control over the locations of the programs, scripts, and files, that are executed, read, or generated by your tasks at runtime. It is up to you to ensure that all of this is confined to the run directory too, as far as possible.

Self-contained workflows are more robust, easier to work with, and more portable. Multiple instances of the same workflow (with different workflow names) should be able to run concurrently under the same user account without mutual interference.

Avoiding External Files

Workflows that use external scripts, executables, and files beyond the essential system libraries and utilities are vulnerable to external changes: someone else might interfere with these files without telling you.

In some case you may need to symlink to large external files anyway, if space or copy speed is a problem, but otherwise workflows with private copies of all the files they need are more robust.

Confining Output To The Run Directory

Output files should be confined to the run directory tree. Then all output is easy to find, multiple instances of the same workflow can run concurrently without interference, and other users should be able to copy and run your workflow with few modifications. Cylc provides a share directory for generated files that are used by several tasks in a workflow (see Shared Task IO Paths). Archiving tasks can use rose arch to copy or move selected files to external locations as needed (see Workflow Housekeeping).

Task Host Selection

The rose host-select command is now deprecated. Workflows should migrate to using platforms which provide a more efficient solution. See Platforms for details.

Task Scripting

Non-trivial task scripting should be held in separate script files rather than inlined in flow.cylc. This keeps the workflow definition tidy, and it allows proper shell-mode text editing and independent testing of task scripts.

For automatic access by task jobs, task-specific scripts should be kept in Rose app bin directories, and shared scripts kept in (or installed to) the workflow bin directory.

Coding Standards

When writing your own task scripts make consistent use of appropriate coding standards such as:

Basic Functionality

In consideration of future users who may not be expert on the internals of your workflow and its tasks, all task scripts should:

  • Print clear usage information if invoked incorrectly (and via the standard options -h, --help).

  • Print useful diagnostic messages in case of error. For example, if a file was not found, the error message should contain the full path to the expected location.

  • Always return correct shell exit status - zero for success, non-zero for failure. This is used by Cylc job wrapper code to detect success and failure and report it back to the scheduler.

  • In shell scripts use set -u to abort on any reference to an undefined variable. If you really need an undefined variable to evaluate to an empty string, make it explicit: FOO=${FOO:-}.

  • In shell scripts use set -e to abort on any error without having to failure-check each command explicitly.

  • In shell scripts use set -o pipefail to abort on any error within a pipe line. Note that all commands in the pipe line will still run, it will just exit with the right most non-zero exit status.

Note

Examples and more details are available for the above three set commands.

Rose Apps

Rose apps allow all non-shared task configuration - which is not relevant to workflow automation - to be moved from the workflow definition into app config files. This makes workflows tidier and easier to understand, and it allows rose edit to provide a unified metadata-enhanced view of the workflow and its apps (see Rose Metadata Compliance).

Rose apps are a clear winner for tasks with complex configuration requirements. It matters less for those with little configuration, but for consistency and to take full advantage of rose edit it makes sense to use Rose apps for most tasks.

When most tasks are Rose apps, set the app-run command as a root-level default, and override it for the occasional non Rose app task:

[runtime]
    [[root]]
        script = rose task-run -v
    [[rose-app1]]
        #...
    [[rose-app2]]
        #...
    [[hello-world]]  # Not a Rose app.
        script = echo "Hello World"

Rose Metadata Compliance

Rose metadata drives page layout and sort order in rose edit, plus help information, input validity checking, macros for advanced checking and app version upgrades, and more.

To ensure the workflow and its constituent applications are being run as intended it should be valid against any provided metadata: launch the rose edit GUI or run rose macro --validate on the command line to highlight any errors, and correct them prior to use. If errors are flagged incorrectly you should endeavour to fix the metadata.

When writing a new workflow or application, consider creating metadata to facilitate ease of use by others.

Task Independence

Essential dependencies must be encoded in the workflow graph, but tasks should not rely unnecessarily on the action of other tasks. For example, tasks should create their own output directories if they don’t already exist, even if they would normally be created by an earlier task in the workflow. This makes it is easier to run tasks alone during development and testing.

Clock-Triggered Tasks

Tasks that wait on real time data should use clock triggers to delay job submission until the expected data arrival time:

[scheduling]
    initial cycle point = now
    [[xtriggers]]
        # Trigger 5 min after wallclock time is equal to cycle point.
        clock = wall_clock(offset=PT5M)
    [[graph]]
        T00 = @clock => get-data => process-data

Clock-triggered tasks typically have to handle late data arrival. Task execution retry delays can be used to simply retrigger the task at intervals until the data is found, but frequently retrying small tasks is inefficient, and multiple task failures will be logged for what is a essentially a normal condition (at least it is normal until the data is really late).

Rather than using task execution retry delays to repeatedly trigger a task that checks for a file, it may be better to have the task itself repeatedly poll for the data (see Custom Trigger Functions).

Rose App File Polling

Rose apps have built-in polling functionality to check repeatedly for the existence of files before executing the main app. See the [poll] section in Rose app config documentation. This is a good way to implement check-and-wait functionality in clock-triggered tasks (Clock-Triggered Tasks), for example.

It is important to note that frequent polling may be bad for some filesystems, so be sure to configure a reasonable interval between polls.

Task Execution Time Limits

Instead of setting job wallclock limits directly in job runner directives, use flow.cylc[runtime][<namespace>]execution time limit. Cylc automatically derives the correct job runner directives from this, and it is also used to run background and at jobs via the timeout command, and to poll tasks that haven’t reported in finished by the configured time limit.

Restricting Workflow Activity

It may be possible for large workflows to overwhelm a job host by submitting too many jobs at once:

  • Large workflows that are not sufficiently limited by real time clock triggering or intercycle dependence may generate a lot of runahead (this refers to Cylc’s ability to run multiple cycles at once, restricted only by the dependencies of individual tasks).

  • Some workflows may have large families of tasks whose members all become ready at the same time.

These problems can be avoided with runahead limiting and internal queues, respectively.

Runahead Limiting

By default Cylc allows a maximum of five cycle points to be active at the same time, but this value is configurable:

[scheduling]
    initial cycle point = 2020-01-01T00
    # Don't allow any cycle interleaving:
    runahead limit = P0

Internal Queues

Tasks can be assigned to named internal queues that limit the number of members that can be active (i.e. submitted or running) at the same time:

[scheduling]
    initial cycle point = 2020-01-01T00
    [[queues]]
        # Allow only 2 members of BIG_JOBS to run at once:
        [[[big_jobs_queue]]]
            limit = 2
            members = BIG_JOBS
    [[graph]]
        T00 = pre => BIG_JOBS
[runtime]
    [[BIG_JOBS]]
    [[foo, bar, baz, ...]]
        inherit = BIG_JOBS

Workflow Housekeeping

Ongoing cycling workflows can generate an enormous number of output files and logs so regular housekeeping is very important. Special housekeeping tasks, typically the last tasks in each cycle, should be included to archive selected important files and then delete everything at some offset from the current cycle point.

The Rose built-in apps rose_arch and rose_prune provide an easy way to do this. They can be configured easily with file-matching patterns and cycle point offsets to perform various housekeeping operations on matched files.

Complex Jinja2 Code

The Jinja2 template processor provides general programming constructs, extensible with custom Python filters, that can be used to generate the workflow definition. This makes it possible to write flexible multi-use workflows with structure and content that varies according to various input switches. There is a cost to this flexibility however: excessive use of Jinja2 can make a workflow hard to understand and maintain. It is difficult to say exactly where to draw the line, but we recommend erring on the side of simplicity and clarity: write workflows that are easy to understand and therefore easy to modify for other purposes, rather than extremely complicated workflows that attempt do everything out of the box but are hard to maintain and modify.

Note that use of Jinja2 loops for generating tasks is now deprecated in favour of built-in parameterized tasks - see Task Parameters.

Shared Configuration

Configuration that is common to multiple tasks should be defined in one place and used by all, rather than duplicated in each task. Duplication is a maintenance risk because changes have to be made consistently in several places at once.

Jinja2 Variables

In simple cases you can share by passing a Jinja2 variable to all the tasks that need it:

{% set JOB_VERSION = 'A23' %}
[runtime]
    [[foo]]
        script = run-foo --version={{JOB_VERSION}}
    [[bar]]
        script = run-bar --version={{JOB_VERSION}}

Inheritance

Sharing by inheritance of task families is recommended when more than a few configuration items are involved.

The simplest application of inheritance is to set global defaults in the [runtime][root] namespace that is inherited by all tasks. However, this should only be done for settings that really are used by the vast majority of tasks. Over-sharing of via root, particularly of environment variables, is a maintenance risk because it can be very difficult to be sure which tasks are using which global variables.

Any [runtime] settings can be shared - scripting, platform configuration, environment variables, and so on - from single items up to complete task or app configurations. At the latter extreme, it is quite common to have several tasks that inherit the same complete job configuration followed by minor task-specific additions:

[runtime]
    [[FILE-CONVERT]]
        script = convert-netcdf
        #...
    [[convert-a]]
        inherit = FILE-CONVERT
        [[[environment]]]
              FILE_IN = file-a
    [[convert-b]]
        inherit = FILE-CONVERT
        [[[environment]]]
              FILE_IN = file-b

Inheritance is covered in more detail from an efficiency perspective in The Task Family Hierarchy.

Shared Task IO Paths

If one task uses files generated by another task (and both see the same filesystem) a common IO path should normally be passed to both tasks via a shared environment variable. As far as Cylc is concerned this is no different to other shared configuration items, but there are some additional aspects of usage worth addressing here.

Primarily, for self-containment (see Self-Contained Workflows) shared IO paths should be under the workflow share directory, the location of which is passed to all tasks as $CYLC_WORKFLOW_SHARE_DIR.

The rose task-env utility can provide additional environment variables that refer to static and cyclepoint-specific locations under the workflow share directory.

[runtime]
    [[my-task]]
        env-script = $(eval rose task-env -T P1D -T P2D)

For a current cycle point of 20170105 this will make the following variables available to tasks:

ROSE_DATA=$CYLC_WORKFLOW_SHARE_DIR/data
ROSE_DATAC=$CYLC_WORKFLOW_SHARE_DIR/cycle/20170105
ROSE_DATACP1D=$CYLC_WORKFLOW_SHARE_DIR/cycle/20170104
ROSE_DATACP2D=$CYLC_WORKFLOW_SHARE_DIR/cycle/20170103

Subdirectories of $ROSE_DATAC etc. should be agreed between different sub-systems of the workflow; typically they are named for the file-generating tasks, and the file-consuming tasks should know to look there.

The share-not-duplicate rule can be relaxed for shared files whose names are agreed by convention, so long as their locations under the share directory are proper shared workflow variables. For instance the Unified Model uses a large number of files whose conventional names (glu_snow, for example) can reasonably be expected not to change, so they are typically hardwired into app configurations (as $ROSE_DATA/glu_snow, for example) to avoid cluttering the workflow definition.

Here two tasks share a workspace under the workflow share directory by inheritance:

# Sharing an I/O location via inheritance.
[scheduling]
    [[graph]]
        R1 = write_data => read_data
[runtime]
    [[root]]
        env-script = $(eval rose task-env)
    [[WORKSPACE]]
        [[[environment]]]
            DATA_DIR = ${ROSE_DATA}/png
    [[write_data]]
        inherit = WORKSPACE
        script = """
            mkdir -p $DATA_DIR
            write-data.exe -o ${DATA_DIR}
        """
    [[read_data]]
        inherit = WORKSPACE
        script = read-data.exe -i ${DATA_DIR}

In simple cases where an appropriate family does not already exist paths can be shared via Jinja variables:

# Sharing an I/O location with Jinja2.
{% set DATA_DIR = '$ROSE_DATA/stuff' %}
[scheduling]
    [[graph]]
        R1 = write_data => read_data
[runtime]
    [[write_data]]
        script = """
            mkdir -p {{DATA_DIR}}
            write-data.exe -o {{DATA_DIR}}
        """
    [[read_data]]
        script = read-data.exe -i {{DATA_DIR}}

For completeness we note that it is also possible to configure multiple tasks to use the same work directory so they can all share files in $PWD. (Cylc executes task jobs in special work directories that by default are unique to each task). This may simplify the workflow slightly, and it may be useful if you are unfortunate enough to have executables that are designed for IO in $PWD, but it is not recommended. There is a higher risk of interference between tasks; it will break rose task-run incremental file creation mode; and rose task-run --new will in effect delete the work directories of tasks other than its intended target.

# Shared work directory: tasks can read and write in $PWD - use with caution!
[scheduling]
    initial cycle point = 2018
    [[graph]]
        P1Y = write_data => read_data
[runtime]
    [[WORKSPACE]]
        work sub-directory = $CYLC_TASK_CYCLE_POINT/datadir
    [[write_data]]
        inherit = WORKSPACE
        script = write-data.exe
    [[read_data]]
        inherit = WORKSPACE
        script = read-data.exe

Varying Behaviour By Cycle Point

To make a cycling job behave differently at different cycle points you could use a single task with scripting that reacts to the cycle point it finds itself running at, but it is better to use different tasks (in different cycling sections) that inherit the same base job configuration. This results in a more transparent workflow that can be understood just by inspecting the graph:

# Run the same job differently at different cycle points.
[scheduling]
    initial cycle point = 2020-01-01T00
    [[graph]]
        T00 = pre => long_fc => post
        T12 = pre => short_fc => post
[runtime]
    [[MODEL]]
        script = run-model.sh
    [[long_fc]]
        inherit = MODEL
        execution time limit = PT30M
        [[[environment]]]
            RUN_LEN = PT48H
    [[short_fc]]
        inherit = MODEL
        execution time limit = PT10M
        [[[environment]]]
            RUN_LEN = PT12H

The few differences between short_fc and long_fc, including job runner resource requests, can be configured after common settings are inherited.

At Start-Up

Similarly, if a cycling job needs special behaviour at the initial (or any other) cycle point, just use a different logical task in an R1 graph and have it inherit the same job as the general cycling task, not a single task with scripting that behaves differently if it finds itself running at the initial cycle point.

Automating Failure Recovery

Job Submission Retries

When submitting jobs to a remote host, use job submission retries to automatically resubmit tasks in the event of network outages.

Note that this is distinct from job retries for job execution failure (just below).

Job Execution Retries

Automatic retry on job execution failure is useful if you have good reason to believe that a simple retry will usually succeed. This may be the case if the job host is known to be flaky, or if the job only ever fails for one known reason that can be fixed on a retry. For example, if a model fails occasionally with a numerical instability that can be remedied with a short timestep rerun, then an automatic retry may be appropriate.

[runtime]
    [[model]]
        script = """
            if [[ $CYLC_TASK_TRY_NUMBER > 1 ]]; then
                SHORT_TIMESTEP=true
            else
                SHORT_TIMESTEP=false
            fi
            model.exe
        """
        execution retry delays = 1*PT0M

Failure Recovery Workflows

For recovery from failures that require explicit diagnosis you can configure alternate graph branches. In the following example, if the model fails a diagnosis task will trigger; if it determines the cause of the failure is a known numerical instability (e.g. by parsing model job logs) it will succeed, triggering a short timestep run. Postprocessing can proceed from either the original or the short-step model run.

../_images/failure-recovery.png
[scheduling]
    [[graph]]
        R1 = """
            model | model_short => postproc
            model:fail => diagnose => model_short
        """

Include Files

Include-files should not be overused, but they can sometimes be useful (e.g. see Portable Workflows):

#...
{% include 'inc/foo.cylc' %}

(Technically this inserts a Jinja2-rendered file template). Cylc also has a native include mechanism that pre-dates Jinja2 support and literally inlines the include-file:

#...
%include 'inc/foo.cylc'

The two methods normally produce the same result, but use the Jinja2 version if you need to construct an include-file name from a variable (because Cylc include-files get inlined before Jinja2 processing is done):

#...
{% include 'inc/' ~ SITE ~ '.cylc' %}