custom uptodate

The basics of uptodate was already introduced. Here we look in more detail into some implementations shipped with doit. And the API used by those.


In some cases you can not determine if a task is “up-to-date” only based on input files, the input could come from a database or an external process. doit defines a “result-dependency” to deal with these cases without need to create an intermediate file with the results of the process.

i.e. Suppose you want to send an email every time you run doit on a mercurial repository that contains a new revision number.

from import result_dep

def task_version():
	return {'actions': ["hg tip --template '{rev}:{node}'"]}

def task_send_email():
	return {'actions': ['echo "TODO: send an email"'],
	        'uptodate': [result_dep('version')]}

Note the result_dep with the name of the task (‘version’). doit will keep track of the output of the task version and will execute send_email only when the mercurial repository has a new version since last time doit was executed.

The “result” from the dependent task compared between different runs is given by its last action. The content for python-action is the value of the returned string or dict. For cmd-actions it is the output send to stdout plus stderr.

result_dep also supports group-tasks. In this case it will check that the result of all subtasks did not change. And also the existing sub-tasks are the same.


Sometimes there is no dependency for a task but you do not want to execute it all the time. With “run_once” the task will not be executed again after the first successful run. This is mostly used together with targets.

Suppose you need to download something from internet. There is no dependency, but you do not want to download it many times.

from import run_once

def task_get_pylogo():
    url = ""
    return {'actions': ["wget %s" % url],
            'targets': ["python-logo.gif"],
            'uptodate': [run_once],

Note that even with run_once the file will be downloaded again in case the target is removed.

$ doit
.  get_pylogo
$ doit
-- get_pylogo
$ rm python-logo.gif
$ doit
.  get_pylogo


timeout is used to expire a task after a certain time interval.

i.e. You want to re-execute a task only if the time elapsed since the last time it was executed is bigger than 5 minutes.

import datetime
from import timeout

def task_expire():
    return {
            'actions': ['echo test expire; date'],
            'uptodate': [timeout(datetime.timedelta(minutes=5))],
            'verbosity': 2,

timeout is function that takes an int (seconds) or timedelta as a parameter. It returns a callable suitable to be used as an uptodate callable.


config_changed is used to check if any “configuration” value for the task has changed. Config values can be a string or dict.

For dict’s the values are converted to string (actually it uses python’s repr()) and only a digest/checksum of the dictionaries keys and values are saved.

from import config_changed

option = "AB"
def task_with_params():
    return {'actions': ['echo %s' % option],
            'uptodate': [config_changed(option)],
            'verbosity': 2,


check_timestamp_unchanged is used to check if specified timestamp of a given file/dir is unchanged since last run.

The timestamp field to check defaults to mtime, but can be selected by passing time parameter which can be one of: atime, ctime, mtime (or their aliases access, status, modify).

Note that ctime or status is platform dependent. On Unix it is the time of most recent metadata change, on Windows it is the time of creation. See Python library documentation for os.stat and Linux man page for stat(2) for details.

It also accepts an cmp_op parameter which defaults to operator.eq (==). To use it pass a callable which takes two parameters (prev_time, current_time) and returns True if task should be considered up-to-date, False otherwise. Here prev_time is the time from the last successful run and current_time is the time obtained in current run.

If the specified file does not exist, an exception will be raised. If a file is a target of another task you should probably add task_dep on that task to ensure the file is created before it is checked.

from import check_timestamp_unchanged

def task_create_foo():
    return {
        'actions': ['touch foo', 'chmod 750 foo'],
        'targets': ['foo'],
        'uptodate': [True],

def task_on_foo_changed():
    # will execute if foo or its metadata is modified
    return {
        'actions': ['echo foo modified'],
        'task_dep': ['create_foo'],
        'uptodate': [check_timestamp_unchanged('foo', 'ctime')],

uptodate API

This section will explain how to extend doit writing an uptodate implementation. So unless you need to write an uptodate implementation you can skip this.

Let’s start with trivial example. uptodate is a function that returns a boolean value.

def fake_get_value_from_db():
    return 5

def check_outdated():
    total = fake_get_value_from_db()
    return total > 10

def task_put_more_stuff_in_db():
    def put_stuff(): pass
    return {'actions': [put_stuff],
            'uptodate': [check_outdated],

You could also execute this function in the task-creator and pass the value to to uptodate. The advantage of just passing the callable is that this check will not be executed at all if the task was not selected to be executed.

Example: run-once implementation

Most of the time an uptodate implementation will compare the current value of something with the value it had last time the task was executed.

We already saw how tasks can save values by returning dict on its actions. But usually the “value” we want to check is independent from the task actions. So the first step is to add a callable to the task so it can save some extra values. These values are not used by the task itself, they are only used for dependency checking.

The Task has a property called value_savers that contains a list of callables. These callables should return a dict that will be saved together with other task values. The value_savers will be executed after all actions.

The second step is to actually compare the saved value with its “current” value.

The uptodate callable can take two positional parameters task and values. The callable can also be represented by a tuple (callable, args, kwargs).

  • task parameter will give you access to task object. So you have access to its metadata and opportunity to modify the task itself!

  • values is a dictionary with the computed values saved in the last

    successful execution of the task.

Let’s take a look in the run_once implementation.

def run_once(task, values):
    def save_executed():
        return {'run-once': True}
    return values.get('run-once', False)

The function save_executed returns a dict. In this case it is not checking for any value because it just checks it the task was ever executed.

The next line we use the task parameter adding save_executed to task.value_savers.So whenever this task is executed this task value ‘run-once’ will be saved.

Finally the return value should be a boolean to indicate if the task is up-to-date or not. Remember that the ‘values’ parameter contains the dict with the values saved from last successful execution of the task. So it just checks if this task was executed before by looking for the run-once entry in `values.

Example: timeout implementation

Let’s look another example, the timeout. The main difference is that we actually pass the parameter timeout_limit. Here we present a simplified version that only accepts integers (seconds) as a parameter.

class timeout(object):
    def __init__(self, timeout_limit):
        self.limit_sec = timeout_limit

    def __call__(self, task, values):
        def save_now():
            return {'success-time': time_module.time()}
        last_success = values.get('success-time', None)
        if last_success is None:
            return False
        return (time_module.time() - last_success) < self.limit_sec

This is a class-based implementation where the objects are made callable by implementing a __call__ method.

On __init__ we just save the timeout_limit as an attribute.

The __call__ is very similar with the run-once implementation. First it defines a function (save_now) that is registered into task.value_savers. Than it compares the current time with the time that was saved on last successful execution.

Example: result_dep implementation

The result_dep is more complicated due to two factors. It needs to modify the task’s task_dep. And it needs to check the task’s saved values and metadata from a task different from where it is being applied.

A result_dep implies that its dependency is also a task_dep. We have seen that the callable takes a task parameter that we used to modify the task object. The problem is that modifying task_dep when the callable gets called would be “too late” according to the way doit works. When an object is passed uptodate and this object’s class has a method named configure_task it will be called during the task creation.

The base class dependency.UptodateCalculator gives access to an attribute named tasks_dict containing a dictionary with all task objects where the key is the task name (this is used to get all sub-tasks from a task-group). And also a method called get_val to access the saved values and results from any task.

See the result_dep source.