Data Locality ============= *Data movement often needlessly limits performance.* This is especially true for analytic computations. Dask.distributed minimizes data movement when possible and enables the user to take control when necessary. This document describes current scheduling policies and user API around data locality. Current Policies ---------------- Task Submission ``````````````` In the common case distributed runs tasks on workers that already hold dependent data. If you have a task ``f(x)`` that requires some data ``x`` then that task will very likely be run on the worker that already holds ``x``. If a task requires data split among multiple workers, then the scheduler chooses to run the task on the worker that requires the least data transfer to it. The size of each data element is measured by the workers using the ``sys.getsizeof`` function, which depends on the ``__sizeof__`` protocol generally available on most relevant Python objects. Data Scatter ```````````` When a user scatters data from their local process to the distributed network this data is distributed in a round-robin fashion grouping by number of cores. So for example If we have two workers ``Alice`` and ``Bob``, each with two cores and we scatter out the list ``range(10)`` as follows: .. code-block:: python futures = client.scatter(range(10)) Then Alice and Bob receive the following data * Alice: ``[0, 1, 4, 5, 8, 9]`` * Bob: ``[2, 3, 6, 7]`` User Control ------------ Complex algorithms may require more user control. For example the existence of specialized hardware such as GPUs or database connections may restrict the set of valid workers for a particular task. In these cases use the ``workers=`` keyword argument to the ``submit``, ``map``, or ``scatter`` functions, providing a hostname, IP address, or alias as follows: .. code-block:: python future = client.submit(func, *args, workers=['Alice']) * Alice: ``[0, 1, 4, 5, 8, 9, new_result]`` * Bob: ``[2, 3, 6, 7]`` Required data will always be moved to these workers, even if the volume of that data is significant. If this restriction is only a preference and not a strict requirement, then add the ``allow_other_workers`` keyword argument to signal that in extreme cases such as when no valid worker is present, another may be used. .. code-block:: python future = client.submit(func, *args, workers=['Alice'], allow_other_workers=True) Additionally the ``scatter`` function supports a ``broadcast=`` keyword argument to enforce that all the data is sent to all workers rather than round-robined. If new workers arrive they will not automatically receive this data. .. code-block:: python futures = client.scatter([1, 2, 3], broadcast=True) # send data to all workers * Alice: ``[1, 2, 3]`` * Bob: ``[1, 2, 3]`` Valid arguments for ``workers=`` include the following: * A single IP addresses, IP/Port pair, or hostname like the following:: 192.168.1.100, 192.168.1.100:8989, alice, alice:8989 * A list or set of the above:: ['alice'], ['192.168.1.100', '192.168.1.101:9999'] If only a hostname or IP is given then any worker on that machine will be considered valid. Additionally, you can provide aliases to workers upon creation.:: $ dask worker scheduler_address:8786 --name worker_1 And then use this name when specifying workers instead. .. code-block:: python client.map(func, sequence, workers='worker_1') Specify workers with Compute/Persist ------------------------------------ The ``workers=`` keyword in ``scatter``, ``submit``, and ``map`` is fairly straightforward, taking either a worker hostname, host:port pair or a sequence of those as valid inputs: .. code-block:: python client.submit(f, x, workers='127.0.0.1') client.submit(f, x, workers='127.0.0.1:55852') client.submit(f, x, workers=['192.168.1.101', '192.168.1.100']) For more complex computations, such as occur with dask collections like dask.dataframe or dask.delayed, we sometimes want to specify that certain parts of the computation run on certain workers while other parts run on other workers. .. code-block:: python x = delayed(f)(1) y = delayed(f)(2) z = delayed(g)(x, y) future = client.compute(z, workers={z: '127.0.0.1', x: '192.168.0.1'}) Here the values of the dictionary are of the same form as before, a host, a host:port pair, or a list of these. The keys in this case are either dask collections or tuples of dask collections. All of the *final* keys of these collections will run on the specified machines; dependencies can run anywhere unless they are also listed in ``workers=``. We explore this through a set of examples: The computation ``z = f(x, y)`` runs on the host ``127.0.0.1``. The other two computations for ``x`` and ``y`` can run anywhere. .. code-block:: python future = client.compute(z, workers={z: '127.0.0.1'}) The computations for both ``z`` and ``x`` must run on ``127.0.0.1`` .. code-block:: python future = client.compute(z, workers={z: '127.0.0.1', x: '127.0.0.1'}) Use a tuple to group collections. This is shorthand for the above. .. code-block:: python future = client.compute(z, workers={(x, y): '127.0.0.1'}) Recall that all options for ``workers=`` in ``scatter/submit/map`` hold here as well. .. code-block:: python future = client.compute(z, workers={(x, y): ['192.168.1.100', '192.168.1.101:9999']}) Set ``allow_other_workers=True`` to make these loose restrictions rather than hard requirements. .. code-block:: python future = client.compute(z, workers={(x, y): '127.0.0.1'}, allow_other_workers=True) Provide a collection to ``allow_other_workers=[...]`` to say that the keys for only some of the collections are loose. In the case below ``z`` *must* run on ``127.0.0.1`` while ``x`` *should* run on ``127.0.0.1`` but can run elsewhere if necessary: .. code-block:: python future = client.compute(z, workers={(x, y): '127.0.0.1'}, allow_other_workers=[x]) This works fine with ``persist`` and with any dask collection (any object with a ``.__dask_graph__()`` method): .. code-block:: python df = dd.read_csv('s3://...') df = client.persist(df, workers={df: ...}) See the :doc:`efficiency ` page to learn about best practices.