Custom Collections¶
For many problems, the builtin 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 fullfill the Dask collection interface.
Warning
The custom collection API is experimental and subject to change without going through a deprecation cycle.
Note
This is considered an advanced feature. For most cases the builtin collections are probably sufficient.
Before reading this you should read and underestand:
Contents
 Description of the Dask collection interface
 How this interface is used to implement the core Dask methods
 How to add the core methods to your class
 Example Dask Collection
 How to check if something is a Dask collection
 How to make tokenize work with your collection
The Dask Collection Interface¶
To create your own Dask collection, you need to fullfill the following interface. Note that there is no required base class.
It is recommended to also read Internals of the Core Dask Methods to see how this interface is used inside Dask.

__dask_graph__
(self)¶ The Dask graph.
 dsk : MutableMapping, None
 The Dask graph. If
None
, this instance will not be interpreted as a Dask collection, and none of the remaining interface methods will be called.

__dask_keys__
(self)¶ The output keys for the Dask graph.
 keys : list
 A possibly nested list of keys that represent the outputs of the graph. After computation, the results will be returned in the same layout, with the keys replaced with their corresponding outputs.

static
__dask_optimize__
(dsk, keys, **kwargs)¶ Given a graph and keys, return a new optimized graph.
This method can be either a
staticmethod
or aclassmethod
, but not aninstancemethod
.Note that graphs and keys are merged before calling
__dask_optimize__
; as such, the graph and keys passed to this method may represent more than one collection sharing the same optimize method.If not implemented, defaults to returning the graph unchanged.
 dsk : MutableMapping
 The merged graphs from all collections sharing the same
__dask_optimize__
method.  keys : list
 A list of the outputs from
__dask_keys__
from all collections sharing the same__dask_optimize__
method.  **kwargs
 Extra keyword arguments forwarded from the call to
compute
orpersist
. Can be used or ignored as needed.
 optimized_dsk : MutableMapping
 The optimized Dask graph.

static
__dask_scheduler__
(dsk, keys, **kwargs)¶ The default scheduler
get
to use for this object.Usually attached to the class as a staticmethod, e.g.:
>>> import dask.threaded >>> class MyCollection(object): ... # Use the threaded scheduler by default ... __dask_scheduler__ = staticmethod(dask.threaded.get)

__dask_postcompute__
(self)¶ Return the finalizer and (optional) extra arguments to convert the computed results into their inmemory representation.
Used to implement
dask.compute
. finalize : callable
 A function with the signature
finalize(results, *extra_args)
. Called with the computed results in the same structure as the corresponding keys from__dask_keys__
, as well as any extra arguments as specified inextra_args
. Should perform any necessary finalization before returning the corresponding inmemory collection fromcompute
. For example, thefinalize
function fordask.array.Array
concatenates all the individual array chunks into one large numpy array, which is then the result ofcompute
.  extra_args : tuple
 Any extra arguments to pass to
finalize
afterresults
. If no extra arguments should be an empty tuple.

__dask_postpersist__
(self)¶ Return the rebuilder and (optional) extra arguments to rebuild an equivalent Dask collection from a persisted graph.
Used to implement
dask.persist
. rebuild : callable
 A function with the signature
rebuild(dsk, *extra_args)
. Called with a persisted graph containing only the keys and results from__dask_keys__
, as well as any extra arguments as specified inextra_args
. Should return an equivalent Dask collection with the same keys asself
, but with the results already computed. For example, therebuild
function fordask.array.Array
is just the__init__
method called with the new graph but the same metadata.  extra_args : tuple
 Any extra arguments to pass to
rebuild
afterdsk
. If no extra arguments should be an empty tuple.
Note
It’s also recommended to define __dask_tokenize__
,
see Implementing Deterministic Hashing.
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 inmemory counterpartspersist
: Convert one or more Dask collections into equivalent Dask collections with their results already computed and cached in memoryoptimize
: Convert one or more Dask collections into equivalent Dask collections sharing one large optimized graphvisualize
: Given one or more Dask collections, draw out the graph that would be passed to the scheduler during a call tocompute
orpersist
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:
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
isTrue
(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
isFalse
, 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.
 If
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 ofkeys
, with each key substituted with its corresponding result. If a
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.
 A
In pseudocode, this process looks like the following:
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
# subgraphs.
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:
Graph Merging & Optimization
Same as in
compute
.Computation
Same as in
compute
, except in the case of the distributed scheduler, where the values inresults
are futures instead of values.Postpersist
Similar to
__dask_postcompute__
,__dask_postpersist__
is used to rebuild values in a call topersist
.__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. A
In pseudocode, this looks like the following:
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:
Graph Merging & Optimization
Same as in
compute
.Rebuilding
Similar to
persist
, therebuild
function and arguments from__dask_postpersist__
are used to reconstruct equivalent collections from the optimized graph.
In pseudocode, this looks like the following:
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:
Graph Merging & Optimization
Same as in
compute
.Graph Drawing
The resulting merged graph is drawn using
graphviz
and outputs to the specified file.
In pseudocode, this looks like the following:
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 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¶
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
:
# Saved as dask_tuple.py
from dask.base import DaskMethodsMixin
from dask.optimization import cull
# We subclass from DaskMethodsMixin to add common dask methods to our
# class. This is nice but not necessary for creating a dask collection.
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
@staticmethod
def __dask_optimize__(dsk, keys, **kwargs):
# We cull unnecessary tasks here. Note that this isn't necessary,
# dask will do this automatically, this just shows one optimization
# you could do.
dsk2, _ = cull(dsk, keys)
return dsk2
# 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):
# Since our __init__ takes a graph as its first argument, our
# rebuild function can just be the class itself. For extra
# arguments we also return a tuple containing just the keys.
return Tuple, (self._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 tuple(self._keys)
Demonstrating this class:
>>> from dask_tuple import Tuple
>>> from operator import add, mul
# Define a dask graph
>>> dsk = {'a': 1,
... 'b': 2,
... 'c': (add, 'a', 'b'),
... 'd': (mul, 'b', 2),
... 'e': (add, 'b', 'c')}
# The output keys for this graph
>>> keys = ['b', 'c', 'd', 'e']
>>> 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__()
{'b': 2,
'c': 3,
'd': 4,
'e': 5}
>>> x2.compute()
(2, 3, 4, 5)
Checking if an object is a Dask collection¶
To check if an object is a Dask collection, use
dask.base.is_dask_collection
:
>>> 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
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:
The
__dask_tokenize__
methodWhere 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.Register a function with
dask.base.normalize_token
If defining a method on the class isn’t possible, 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¶
>>> from dask.base import tokenize, normalize_token
# Define a tokenize implementation using a method.
>>> class Foo(object):
... def __init__(self, a, b):
... self.a = a
... self.b = b
...
... def __dask_tokenize__(self):
... # This tuple fully represents self
... return (Foo, self.a, self.b)
>>> x = Foo(1, 2)
>>> tokenize(x)
'5988362b6e07087db2bc8e7c1c8cc560'
>>> tokenize(x) == tokenize(x) # token is deterministic
True
# Register an implementation with normalize_token
>>> class Bar(object):
... def __init__(self, x, y):
... self.x = x
... self.y = y
>>> @normalize_token.register(Bar)
... def tokenize_bar(x):
... return (Bar, x.x, x.x)
>>> y = Bar(1, 2)
>>> tokenize(y)
'5a7e9c3645aa44cf13d021c14452152e'
>>> tokenize(y) == tokenize(y)
True
>>> tokenize(y) == tokenize(x) # tokens for different objects aren't equal
False
For more examples, see dask/base.py
or any of the builtin Dask collections.