Scheduling Policies
===================

This document describes the policies used to select the preference of tasks and
to select the preference of workers used by Dask's distributed scheduler.  For
more information on how this these policies are enacted efficiently see
:doc:`Scheduling State<scheduling-state>`.


.. _decide-worker:

Choosing Workers
----------------

When a task transitions from waiting to a processing state, we decide a suitable
worker for that task. If the task has significant data dependencies or if the
workers are under heavy load, then this choice of worker can strongly impact
global performance. Similarly, the placement of root tasks affects performance
of downstream computations, since it can determine how much data will need to be
transferred between workers in the future. Different heuristics are used for these
different scenarios:

Initial Task Placement
~~~~~~~~~~~~~~~~~~~~~~

We want neighboring root tasks to run on the same worker, since there's a
good chance those neighbors will be combined in a downstream operation::

      i       j
     / \     / \
    e   f   g   h
    |   |   |   |
    a   b   c   d
    \   \  /   /
         X

In the above case, we want ``a`` and ``b`` to run on the same worker,
and ``c`` and ``d`` to run on the same worker, reducing future
data transfer. We can also ignore the location of ``X``, because assuming
we split the ``a b c d`` group across all workers to maximize parallelism,
then ``X`` will eventually get transferred everywhere.
(Note that wanting to co-locate ``a b`` and ``c d`` would still apply even if
``X`` didn't exist.)

Calculating these cousin tasks directly by traversing the graph would be expensive.
Instead, we use the task's TaskGroup, which is the collection of all tasks with the
same key prefix. (``(random-a1b2c3, 0)``, ``(random-a1b2c3, 1)``, ``(random-a1b2c3, 2)``
would all belong to the TaskGroup ``random-a1b2c3``.)

To identify the root(ish) tasks, we use this heuristic:

1.  The TaskGroup has 2x more tasks than there are threads in the cluster
2.  The TaskGroup has fewer than 5 unique dependencies across *all* tasks in the group.

    We don't just say "The task has no dependencies", because real-world cases like
    :obj:`dask.array.from_zarr` and :obj:`dask.array.from_array` produce graphs like the one
    above, where the data-creation tasks (``a b c d``) all share one dependency
    (``X``)---the Zarr dataset, for example. Though ``a b c d`` are not technically
    root tasks, we want to treat them as such, hence allowing a small number of trivial
    dependencies shard by all tasks.

Then, we use the same priority described in :ref:`priority-break-ties` to
determine which tasks are related. This depth-first-with-child-weights metric
can usually be used to properly segment the leaves of a graph into decently
well-separated sub-graphs with relatively low inter-sub-graph connectedness.

Iterating through tasks in this priority order, we assign a batch of subsequent tasks
to a worker, then select a new worker (the least-busy one) and repeat.

Though this does not provide perfect initial task assignment (a handful of sibling
tasks may be split across workers), it does well in most cases, while adding
minimal scheduling overhead.

Initial task placement is a forward-looking decision. By colocating related root tasks,
we ensure that their downstream tasks are set up for success.

Downstream Task Placement
~~~~~~~~~~~~~~~~~~~~~~~~~

When initial tasks are well-placed, placing subsequent tasks is backwards-looking:
where can the task run the soonest, considering both data transfer and worker busyness?

Tasks that don't meet the root-ish criteria described above are selected as follows:

First, we identify the pool of viable workers:

1.  If the task has no dependencies and no restrictions, then we find the
    least-occupied worker.
2.  Otherwise, if a task has user-provided restrictions (for example it must
    run on a machine with a GPU) then we restrict the available pool of workers
    to just that set. Otherwise, we consider all workers.
3.  We restrict the above set to just workers that hold at least one dependency
    of the task.

From among this pool of workers, we then determine the worker where we think the task will
start running the soonest, using :meth:`Scheduler.worker_objective`. For each worker:

1.  We consider the estimated runtime of other tasks already queued on that worker.
    Then, we add how long it will take to transfer any dependencies to that worker that
    it doesn't already have, based on their size, in bytes, and the measured network
    bandwith between workers. Note that this does *not* consider (de)serialization
    time, time to retrieve the data from disk if it was spilled, or potential differences
    between size in memory and serialized size. In practice, the
    queue-wait-time (known as *occupancy*) usually dominates, so data will usually be
    transferred to a different worker if it means the task can start any sooner.
2.  It's possible for ties to occur with the "start soonest" metric, though uncommon
    when all workers are busy. We break ties by choosing the worker that has the
    fewest number of bytes of Dask data stored (including spilled data). Note that
    this is the same as :ref:`managed <memtypes>` plus :ref:`spilled <memtypes>`
    memory, not the :ref:`process <memtypes>` memory.

This process is easy to change (and indeed this document may be outdated).  We
encourage readers to inspect the ``decide_worker`` and ``worker_objective``
functions in ``scheduler.py``.

.. currentmodule:: distributed.scheduler

.. autosummary:: decide_worker

.. autosummary:: Scheduler.decide_worker_non_rootish

.. autosummary:: Scheduler.decide_worker_rootish_queuing_disabled

.. autosummary:: Scheduler.decide_worker_rootish_queuing_enabled

.. autosummary:: Scheduler.worker_objective


Choosing Tasks
--------------

We often have a choice between running many valid tasks.  There are a few
competing interests that might motivate our choice:

1.  Run tasks on a first-come-first-served basis for fairness between
    multiple clients
2.  Run tasks that are part of the critical path in an effort to
    reduce total running time and minimize straggler workloads
3.  Run tasks that allow us to release many dependencies in an effort to keep
    the memory footprint small
4.  Run tasks that are related so that large chunks of work can be completely
    eliminated before running new chunks of work

Accomplishing all of these objectives simultaneously is impossible.  Optimizing
for any of these objectives perfectly can result in costly overhead.  The
heuristics with the scheduler do a decent but imperfect job of optimizing for
all of these (they all come up in important workloads) quickly.

Last in, first out
~~~~~~~~~~~~~~~~~~

When a worker finishes a task, the immediate dependencies of that task get top
priority.  This encourages a behavior of finishing ongoing work immediately
before starting new work (depth-first graph traversal). This often conflicts with
the first-come-first-served objective, but often results in significantly reduced
memory footprints and, due to avoiding data spillage to disk, better overall runtimes.

.. _priority-break-ties:

Break ties with children and depth
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Often a task has multiple dependencies and we need to break ties between them
with some other objective.  Breaking these ties has a surprisingly strong
impact on performance and memory footprint.

When a client submits a graph we perform a few linear scans over the graph to
determine something like the number of descendants of each node (not quite,
because it's a DAG rather than a tree, but this is a close proxy).  This number
can be used to break ties and helps us to prioritize nodes with longer critical
paths and nodes with many children.  The actual algorithms used are somewhat
more complex and are described in detail in `dask/order.py`_

.. _`dask/order.py`: https://github.com/dask/dask/blob/main/dask/order.py

First-Come-First-Served, Coarsely
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The last-in-first-out behavior used by the workers to minimize memory footprint
can distort the task order provided by the clients.  Tasks submitted recently
may run sooner than tasks submitted long ago because they happen to be more
convenient given the current data in memory.  This behavior can be *unfair* but
improves global runtimes and system efficiency, sometimes quite significantly.

However, workers inevitably run out of tasks that were related to tasks they
were just working on and the last-in-first-out policy eventually exhausts
itself.  In these cases workers often pull tasks from the common task pool.
The tasks in this pool *are* ordered in a first-come-first-served basis and so
workers do behave in a scheduling manner that's fair to multiple submissions
at a *coarse* level, if not a fine-grained one.

Dask's scheduling policies are short-term-efficient and long-term-fair
to multiple clients.


Where these decisions are made
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The objectives above are mostly followed by small decisions made by the client,
scheduler, and workers at various points in the computation.

1.  As we submit a graph from the client to the scheduler we assign a numeric
    priority to each task of that graph.  This priority focuses on
    computing deeply before broadly, preferring critical paths, preferring
    nodes with many dependencies, etc..  This is the same logic used by the
    single-machine scheduler and lives in `dask/order.py
    <https://github.com/dask/dask/blob/main/dask/order.py>`_.
2.  When the graph reaches the scheduler the scheduler changes each of these
    numeric priorities into a tuple of two numbers, the first of which is an
    increasing counter, the second of which is the client-generated priority
    described above.  This per-graph counter encourages a first-in-first-out
    policy between computations.  All tasks from a previous call to compute
    have a higher priority than all tasks from a subsequent call to compute (or
    submit, persist, map, or any operation that generates futures).
3.  Whenever a task is ready to run (its dependencies, if any, are complete),
    the scheduler assigns it to a worker. When multiple tasks are ready at once,
    they are all submitted to workers, in priority order.
4.  However, when the worker receives these tasks, it considers their priorities
    when determining which tasks to prioritize for fetching data or for
    computation.  The worker maintains a heap of all ready-to-run tasks ordered
    by this priority.