When developing a workflow, developers often run the same workflow with incremental changes over and over. Often large fragments of the workflow have not been changed yet are computed again, wasting valuable developer time and computation resources. AppCaching solves this problem by caching results from apps that have completed so that they can be re-used. By default individual apps are set to not cache, and must be enabled explicitly like:

@app('bash', dfk, cache=True)
def hello (msg, stdout=None):
    return 'echo {}'.format(msg)


Here are some important considerations before using AppCaching:


AppCaching can be useful for interactive workflows such as when developing on a Jupyter notebook where cells containing apps are often rerun as partof the development flow.


AppCaching is generally useful only when the apps are deterministic. If the outputs may be different for identical inputs, caching will hide this non-deterministic behavior. For instance caching an app that returns a random number will result in every invocation returning the same result.


If several identical calls to previously defined app hello are made for the first time, several apps will be launched since no cached result is available. Once one such app completes and the result is cached all subsequent calls will return immediately with the cached result.


If AppCaching is enabled, some minor performance penalty will be seen especially when thousands of subsecond tasks are launched rapidly.


The performance penalty has not yet been quantified.


The appCache option in the config is the master switch, which if set to False disables all AppCaching. By default the global appCache is enabled, and AppCaching is disabled for each app individually, which can be enabled to pick and choose what apps are to be cached.

Disabling AppCaching globally :

  1. Disabling AppCaching globally via config:

    config = {
        "sites": [{ ... }],
        "globals": {
             "appCache": False # <-- Disable AppCaching globally
    dfk = DataFlowKernel(config=config)
  2. Disabling AppCaching globally via option to DataFlowKernel:

    workers = ThreadPoolExecutor(max_workers=4)
    dfk = DataFlowKernel(executors=[workers], appCache=False)