Custom Collections ================== For many problems, the built-in Dask collections (``dask.array``, ``dask.dataframe``, ``dask.bag``, and ``dask.delayed``) are sufficient. For cases where they aren't, it's possible to create your own Dask collections. Here we describe the required methods to fulfill the Dask collection interface. .. note:: This is considered an advanced feature. For most cases the built-in collections are probably sufficient. Before reading this you should read and understand: - :doc:`overview ` - :doc:`graph specification ` - :doc:`custom graphs ` **Contents** - :ref:`Description of the Dask collection interface ` - :ref:`How this interface is used to implement the core Dask methods ` - :ref:`How to add the core methods to your class ` - :ref:`example-dask-collection` - :ref:`How to check if something is a Dask collection ` - :ref:`How to make tokenize work with your collection ` .. _collection-interface: The Dask Collection Interface ----------------------------- To create your own Dask collection, you need to fulfill the interface defined by the :py:class:`dask.typing.DaskCollection` protocol. Note that there is no required base class. It is recommended to also read :ref:`core-method-internals` to see how this interface is used inside Dask. Collection Protocol ~~~~~~~~~~~~~~~~~~~~ .. autoclass:: dask.typing.DaskCollection :members: __dask_graph__, __dask_keys__, __dask_postcompute__, __dask_postpersist__, __dask_tokenize__, __dask_optimize__, __dask_scheduler__, compute, persist, visualize HLG Collection Protocol ~~~~~~~~~~~~~~~~~~~~~~~ Collections backed by Dask's :ref:`high-level-graphs` must implement an additional method, defined by this protocol: .. autoclass:: dask.typing.HLGDaskCollection :members: __dask_layers__ Scheduler ``get`` Protocol ~~~~~~~~~~~~~~~~~~~~~~~~~~ The ``SchedulerGetProtocol`` defines the signature that a Dask collection's ``__dask_scheduler__`` definition must adhere to. .. autoclass:: dask.typing.SchedulerGetCallable :members: __call__ Post-persist Callable Protocol ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Collections must define a ``__dask_postpersist__`` method which returns a callable that adheres to the ``PostPersistCallable`` interface. .. autoclass:: dask.typing.PostPersistCallable :members: __call__ .. _core-method-internals: Internals of the Core Dask Methods ---------------------------------- Dask has a few *core* functions (and corresponding methods) that implement common operations: - ``compute``: Convert one or more Dask collections into their in-memory counterparts - ``persist``: Convert one or more Dask collections into equivalent Dask collections with their results already computed and cached in memory - ``optimize``: Convert one or more Dask collections into equivalent Dask collections sharing one large optimized graph - ``visualize``: Given one or more Dask collections, draw out the graph that would be passed to the scheduler during a call to ``compute`` or ``persist`` Here we briefly describe the internals of these functions to illustrate how they relate to the above interface. Compute ~~~~~~~ The operation of ``compute`` can be broken into three stages: 1. **Graph Merging & Optimization** First, the individual collections are converted to a single large graph and nested list of keys. How this happens depends on the value of the ``optimize_graph`` keyword, which each function takes: - If ``optimize_graph`` is ``True`` (default), then the collections are first grouped by their ``__dask_optimize__`` methods. All collections with the same ``__dask_optimize__`` method have their graphs merged and keys concatenated, and then a single call to each respective ``__dask_optimize__`` is made with the merged graphs and keys. The resulting graphs are then merged. - If ``optimize_graph`` is ``False``, then all the graphs are merged and all the keys concatenated. After this stage there is a single large graph and nested list of keys which represents all the collections. 2. **Computation** After the graphs are merged and any optimizations performed, the resulting large graph and nested list of keys are passed on to the scheduler. The scheduler to use is chosen as follows: - If a ``get`` function is specified directly as a keyword, use that - Otherwise, if a global scheduler is set, use that - Otherwise fall back to the default scheduler for the given collections. Note that if all collections don't share the same ``__dask_scheduler__`` then an error will be raised. Once the appropriate scheduler ``get`` function is determined, it is called with the merged graph, keys, and extra keyword arguments. After this stage, ``results`` is a nested list of values. The structure of this list mirrors that of ``keys``, with each key substituted with its corresponding result. 3. **Postcompute** After the results are generated, the output values of ``compute`` need to be built. This is what the ``__dask_postcompute__`` method is for. ``__dask_postcompute__`` returns two things: - A ``finalize`` function, which takes in the results for the corresponding keys - A tuple of extra arguments to pass to ``finalize`` after the results To build the outputs, the list of collections and results is iterated over, and the finalizer for each collection is called on its respective results. In pseudocode, this process looks like the following: .. code:: python def compute(*collections, **kwargs): # 1. Graph Merging & Optimization # ------------------------------- if kwargs.pop('optimize_graph', True): # If optimization is turned on, group the collections by # optimization method, and apply each method only once to the merged # sub-graphs. optimization_groups = groupby_optimization_methods(collections) graphs = [] for optimize_method, cols in optimization_groups: # Merge the graphs and keys for the subset of collections that # share this optimization method sub_graph = merge_graphs([x.__dask_graph__() for x in cols]) sub_keys = [x.__dask_keys__() for x in cols] # kwargs are forwarded to ``__dask_optimize__`` from compute optimized_graph = optimize_method(sub_graph, sub_keys, **kwargs) graphs.append(optimized_graph) graph = merge_graphs(graphs) else: graph = merge_graphs([x.__dask_graph__() for x in collections]) # Keys are always the same keys = [x.__dask_keys__() for x in collections] # 2. Computation # -------------- # Determine appropriate get function based on collections, global # settings, and keyword arguments get = determine_get_function(collections, **kwargs) # Pass the merged graph, keys, and kwargs to ``get`` results = get(graph, keys, **kwargs) # 3. Postcompute # -------------- output = [] # Iterate over the results and collections for res, collection in zip(results, collections): finalize, extra_args = collection.__dask_postcompute__() out = finalize(res, **extra_args) output.append(out) # `dask.compute` always returns tuples return tuple(output) Persist ~~~~~~~ Persist is very similar to ``compute``, except for how the return values are created. It too has three stages: 1. **Graph Merging & Optimization** Same as in ``compute``. 2. **Computation** Same as in ``compute``, except in the case of the distributed scheduler, where the values in ``results`` are futures instead of values. 3. **Postpersist** Similar to ``__dask_postcompute__``, ``__dask_postpersist__`` is used to rebuild values in a call to ``persist``. ``__dask_postpersist__`` returns two things: - A ``rebuild`` function, which takes in a persisted graph. The keys of this graph are the same as ``__dask_keys__`` for the corresponding collection, and the values are computed results (for the single-machine scheduler) or futures (for the distributed scheduler). - A tuple of extra arguments to pass to ``rebuild`` after the graph To build the outputs of ``persist``, the list of collections and results is iterated over, and the rebuilder for each collection is called on the graph for its respective results. In pseudocode, this looks like the following: .. code:: python def persist(*collections, **kwargs): # 1. Graph Merging & Optimization # ------------------------------- # **Same as in compute** graph = ... keys = ... # 2. Computation # -------------- # **Same as in compute** results = ... # 3. Postpersist # -------------- output = [] # Iterate over the results and collections for res, collection in zip(results, collections): # res has the same structure as keys keys = collection.__dask_keys__() # Get the computed graph for this collection. # Here flatten converts a nested list into a single list subgraph = {k: r for (k, r) in zip(flatten(keys), flatten(res))} # Rebuild the output dask collection with the computed graph rebuild, extra_args = collection.__dask_postpersist__() out = rebuild(subgraph, *extra_args) output.append(out) # dask.persist always returns tuples return tuple(output) Optimize ~~~~~~~~ The operation of ``optimize`` can be broken into two stages: 1. **Graph Merging & Optimization** Same as in ``compute``. 2. **Rebuilding** Similar to ``persist``, the ``rebuild`` function and arguments from ``__dask_postpersist__`` are used to reconstruct equivalent collections from the optimized graph. In pseudocode, this looks like the following: .. code:: python def optimize(*collections, **kwargs): # 1. Graph Merging & Optimization # ------------------------------- # **Same as in compute** graph = ... # 2. Rebuilding # ------------- # Rebuild each dask collection using the same large optimized graph output = [] for collection in collections: rebuild, extra_args = collection.__dask_postpersist__() out = rebuild(graph, *extra_args) output.append(out) # dask.optimize always returns tuples return tuple(output) Visualize ~~~~~~~~~ Visualize is the simplest of the 4 core functions. It only has two stages: 1. **Graph Merging & Optimization** Same as in ``compute``. 2. **Graph Drawing** The resulting merged graph is drawn using ``graphviz`` and outputs to the specified file. In pseudocode, this looks like the following: .. code:: python def visualize(*collections, **kwargs): # 1. Graph Merging & Optimization # ------------------------------- # **Same as in compute** graph = ... # 2. Graph Drawing # ---------------- # Draw the graph with graphviz's `dot` tool and return the result. return dot_graph(graph, **kwargs) .. _adding-methods-to-class: Adding the Core Dask Methods to Your Class ------------------------------------------ Defining the above interface will allow your object to used by the core Dask functions (``dask.compute``, ``dask.persist``, ``dask.visualize``, etc.). To add corresponding method versions of these, you can subclass from ``dask.base.DaskMethodsMixin`` which adds implementations of ``compute``, ``persist``, and ``visualize`` based on the interface above. .. _example-dask-collection: Example Dask Collection ----------------------- Here we create a Dask collection representing a tuple. Every element in the tuple is represented as a task in the graph. Note that this is for illustration purposes only - the same user experience could be done using normal tuples with elements of ``dask.delayed``: .. code:: python # Saved as dask_tuple.py import dask from dask.base import DaskMethodsMixin, replace_name_in_key from dask.optimization import cull def tuple_optimize(dsk, keys, **kwargs): # We cull unnecessary tasks here. See # https://docs.dask.org/en/stable/optimize.html for more # information on optimizations in Dask. dsk2, _ = cull(dsk, keys) return dsk2 # We subclass from DaskMethodsMixin to add common dask methods to # our class (compute, persist, and visualize). This is nice but not # necessary for creating a Dask collection (you can define them # yourself). class Tuple(DaskMethodsMixin): def __init__(self, dsk, keys): # The init method takes in a dask graph and a set of keys to use # as outputs. self._dsk = dsk self._keys = keys def __dask_graph__(self): return self._dsk def __dask_keys__(self): return self._keys # use the `tuple_optimize` function defined above __dask_optimize__ = staticmethod(tuple_optimize) # Use the threaded scheduler by default. __dask_scheduler__ = staticmethod(dask.threaded.get) def __dask_postcompute__(self): # We want to return the results as a tuple, so our finalize # function is `tuple`. There are no extra arguments, so we also # return an empty tuple. return tuple, () def __dask_postpersist__(self): # We need to return a callable with the signature # rebuild(dsk, *extra_args, rename: Mapping[str, str] = None) return Tuple._rebuild, (self._keys,) @staticmethod def _rebuild(dsk, keys, *, rename=None): if rename is not None: keys = [replace_name_in_key(key, rename) for key in keys] return Tuple(dsk, keys) def __dask_tokenize__(self): # For tokenize to work we want to return a value that fully # represents this object. In this case it's the list of keys # to be computed. return self._keys Demonstrating this class: .. code:: python >>> from dask_tuple import Tuple >>> from operator import add, mul # Define a dask graph >>> dsk = {"k0": 1, ... ("x", "k1"): 2, ... ("x", 1): (add, "k0", ("x", "k1")), ... ("x", 2): (mul, ("x", "k1"), 2), ... ("x", 3): (add, ("x", "k1"), ("x", 1))} # The output keys for this graph. # The first element of each tuple must be the same across the whole collection; # the remainder are arbitrary, unique str, bytes, int, or floats >>> keys = [("x", "k1"), ("x", 1), ("x", 2), ("x", 3)] >>> x = Tuple(dsk, keys) # Compute turns Tuple into a tuple >>> x.compute() (2, 3, 4, 5) # Persist turns Tuple into a Tuple, with each task already computed >>> x2 = x.persist() >>> isinstance(x2, Tuple) True >>> x2.__dask_graph__() {('x', 'k1'): 2, ('x', 1): 3, ('x', 2): 4, ('x', 3): 5} >>> x2.compute() (2, 3, 4, 5) # Run-time typechecking >>> from dask.typing import DaskCollection >>> isinstance(x, DaskCollection) True .. _is-dask-collection: Checking if an object is a Dask collection ------------------------------------------ To check if an object is a Dask collection, use ``dask.base.is_dask_collection``: .. code:: python >>> from dask.base import is_dask_collection >>> from dask import delayed >>> x = delayed(sum)([1, 2, 3]) >>> is_dask_collection(x) True >>> is_dask_collection(1) False .. _deterministic-hashing: Implementing Deterministic Hashing ---------------------------------- Dask implements its own deterministic hash function to generate keys based on the value of arguments. This function is available as ``dask.base.tokenize``. Many common types already have implementations of ``tokenize``, which can be found in ``dask/base.py``. When creating your own custom classes, you may need to register a ``tokenize`` implementation. There are two ways to do this: 1. The ``__dask_tokenize__`` method Where possible, it is recommended to define the ``__dask_tokenize__`` method. This method takes no arguments and should return a value fully representative of the object. It is a good idea to call ``dask.base.normalize_token`` from it before returning any non-trivial objects. 2. Register a function with ``dask.base.normalize_token`` If defining a method on the class isn't possible or you need to customize the tokenize function for a class whose super-class is already registered (for example if you need to sub-class built-ins), you can register a tokenize function with the ``normalize_token`` dispatch. The function should have the same signature as described above. In both cases the implementation should be the same, where only the location of the definition is different. .. note:: Both Dask collections and normal Python objects can have implementations of ``tokenize`` using either of the methods described above. Example ~~~~~~~ .. code:: python >>> from dask.base import tokenize, normalize_token # Define a tokenize implementation using a method. >>> class Point: ... def __init__(self, x, y): ... self.x = x ... self.y = y ... ... def __dask_tokenize__(self): ... # This tuple fully represents self ... # Wrap non-trivial objects with normalize_token before returning them ... return normalize_token(Point), self.x, self.y >>> x = Point(1, 2) >>> tokenize(x) '5988362b6e07087db2bc8e7c1c8cc560' >>> tokenize(x) == tokenize(x) # token is idempotent True >>> tokenize(Point(1, 2)) == tokenize(Point(1, 2)) # token is deterministic True >>> tokenize(Point(1, 2)) == tokenize(Point(2, 1)) # tokens are unique False # Register an implementation with normalize_token >>> class Point3D: ... def __init__(self, x, y, z): ... self.x = x ... self.y = y ... self.z = z >>> @normalize_token.register(Point3D) ... def normalize_point3d(x): ... return normalize_token(Point3D), x.x, x.y, x.z >>> y = Point3D(1, 2, 3) >>> tokenize(y) '5a7e9c3645aa44cf13d021c14452152e' For more examples, see ``dask/base.py`` or any of the built-in Dask collections.