from __future__ import annotations import abc import copy import html from collections.abc import Hashable, Iterable, KeysView, Mapping, MutableMapping, Set from typing import Any, cast import tlz as toolz from dask import config from dask.base import clone_key, flatten, is_dask_collection from dask.core import keys_in_tasks, reverse_dict from dask.utils import ensure_dict, ensure_set, import_required, key_split, stringify from dask.widgets import get_template def compute_layer_dependencies(layers): """Returns the dependencies between layers""" def _find_layer_containing_key(key): for k, v in layers.items(): if key in v: return k raise RuntimeError(f"{repr(key)} not found") all_keys = {key for layer in layers.values() for key in layer} ret = {k: set() for k in layers} for k, v in layers.items(): for key in keys_in_tasks(all_keys - v.keys(), v.values()): ret[k].add(_find_layer_containing_key(key)) return ret class Layer(Mapping): """High level graph layer This abstract class establish a protocol for high level graph layers. The main motivation of a layer is to represent a collection of tasks symbolically in order to speedup a series of operations significantly. Ideally, a layer should stay in this symbolic state until execution but in practice some operations will force the layer to generate all its internal tasks. We say that the layer has been materialized. Most of the default implementations in this class will materialize the layer. It is up to derived classes to implement non-materializing implementations. """ annotations: Mapping[str, Any] | None collection_annotations: Mapping[str, Any] | None def __init__( self, annotations: Mapping[str, Any] | None = None, collection_annotations: Mapping[str, Any] | None = None, ): """Initialize Layer object. Parameters ---------- annotations : Mapping[str, Any], optional By default, None. Annotations are metadata or soft constraints associated with tasks that dask schedulers may choose to respect: They signal intent without enforcing hard constraints. As such, they are primarily designed for use with the distributed scheduler. See the dask.annotate function for more information. collection_annotations : Mapping[str, Any], optional. By default, None. Experimental, intended to assist with visualizing the performance characteristics of Dask computations. These annotations are *not* passed to the distributed scheduler. """ self.annotations = annotations or copy.copy(config.get("annotations", None)) self.collection_annotations = collection_annotations or copy.copy( config.get("collection_annotations", None) ) @abc.abstractmethod def is_materialized(self) -> bool: """Return whether the layer is materialized or not""" return True @abc.abstractmethod def get_output_keys(self) -> Set: """Return a set of all output keys Output keys are all keys in the layer that might be referenced by other layers. Classes overriding this implementation should not cause the layer to be materialized. Returns ------- keys: Set All output keys """ return self.keys() # this implementation will materialize the graph def cull( self, keys: set, all_hlg_keys: Iterable ) -> tuple[Layer, Mapping[Hashable, set]]: """Remove unnecessary tasks from the layer In other words, return a new Layer with only the tasks required to calculate `keys` and a map of external key dependencies. Examples -------- >>> inc = lambda x: x + 1 >>> add = lambda x, y: x + y >>> d = MaterializedLayer({'x': 1, 'y': (inc, 'x'), 'out': (add, 'x', 10)}) >>> _, deps = d.cull({'out'}, d.keys()) >>> deps {'out': {'x'}, 'x': set()} Returns ------- layer: Layer Culled layer deps: Map Map of external key dependencies """ if len(keys) == len(self): # Nothing to cull if preserving all existing keys return ( self, {k: self.get_dependencies(k, all_hlg_keys) for k in self.keys()}, ) ret_deps = {} seen = set() out = {} work = keys.copy() while work: k = work.pop() out[k] = self[k] ret_deps[k] = self.get_dependencies(k, all_hlg_keys) for d in ret_deps[k]: if d not in seen: if d in self: seen.add(d) work.add(d) return MaterializedLayer(out, annotations=self.annotations), ret_deps def get_dependencies(self, key: Hashable, all_hlg_keys: Iterable) -> set: """Get dependencies of `key` in the layer Parameters ---------- key: Hashable The key to find dependencies of all_hlg_keys: Iterable All keys in the high level graph. Returns ------- deps: set A set of dependencies """ return keys_in_tasks(all_hlg_keys, [self[key]]) def __dask_distributed_annotations_pack__( self, annotations: Mapping[str, Any] | None = None ) -> Mapping[str, Any] | None: """Packs Layer annotations for transmission to scheduler Callables annotations are fully expanded over Layer keys, while other values are simply transmitted as is Parameters ---------- annotations : Mapping[str, Any], optional A top-level annotations. Returns ------- packed_annotations : dict Packed annotations. """ annotations = cast( "dict[str, Any]", toolz.merge(self.annotations or {}, annotations or {}) ) packed = {} for a, v in annotations.items(): if callable(v): packed[a] = {stringify(k): v(k) for k in self} packed[a]["__expanded_annotations__"] = True else: packed[a] = v return packed @staticmethod def __dask_distributed_annotations_unpack__( annotations: MutableMapping[str, Any], new_annotations: Mapping[str, Any] | None, keys: Iterable[Hashable], ) -> None: """ Unpack a set of layer annotations across a set of keys, then merge those expanded annotations for the layer into an existing annotations mapping. This is not a simple shallow merge because some annotations like retries, priority, workers, etc need to be able to retain keys from different layers. Parameters ---------- annotations: MutableMapping[str, Any], input/output Already unpacked annotations, which are to be updated with the new unpacked annotations new_annotations: Mapping[str, Any], optional New annotations to be unpacked into `annotations` keys: Iterable All keys in the layer. """ if new_annotations is None: return expanded = {} keys_stringified = False # Expand the new annotations across the keyset for a, v in new_annotations.items(): if type(v) is dict and "__expanded_annotations__" in v: # Maybe do a destructive update for efficiency? v = v.copy() del v["__expanded_annotations__"] expanded[a] = v else: if not keys_stringified: keys = [stringify(k) for k in keys] keys_stringified = True expanded[a] = dict.fromkeys(keys, v) # Merge the expanded annotations with the existing annotations mapping for k, v in expanded.items(): v.update(annotations.get(k, {})) annotations.update(expanded) def clone( self, keys: set, seed: Hashable, bind_to: Hashable = None, ) -> tuple[Layer, bool]: """Clone selected keys in the layer, as well as references to keys in other layers Parameters ---------- keys Keys to be replaced. This never includes keys not listed by :meth:`get_output_keys`. It must also include any keys that are outside of this layer that may be referenced by it. seed Common hashable used to alter the keys; see :func:`dask.base.clone_key` bind_to Optional key to bind the leaf nodes to. A leaf node here is one that does not reference any replaced keys; in other words it's a node where the replacement graph traversal stops; it may still have dependencies on non-replaced nodes. A bound node will not be computed until after ``bind_to`` has been computed. Returns ------- - New layer - True if the ``bind_to`` key was injected anywhere; False otherwise Notes ----- This method should be overridden by subclasses to avoid materializing the layer. """ from dask.graph_manipulation import chunks is_leaf: bool def clone_value(o): """Variant of distributed.utils_comm.subs_multiple, which allows injecting bind_to """ nonlocal is_leaf typ = type(o) if typ is tuple and o and callable(o[0]): return (o[0],) + tuple(clone_value(i) for i in o[1:]) elif typ is list: return [clone_value(i) for i in o] elif typ is dict: return {k: clone_value(v) for k, v in o.items()} else: try: if o not in keys: return o except TypeError: return o is_leaf = False return clone_key(o, seed) dsk_new = {} bound = False for key, value in self.items(): if key in keys: key = clone_key(key, seed) is_leaf = True value = clone_value(value) if bind_to is not None and is_leaf: value = (chunks.bind, value, bind_to) bound = True dsk_new[key] = value return MaterializedLayer(dsk_new), bound def __dask_distributed_pack__( self, all_hlg_keys: Iterable[Hashable], known_key_dependencies: Mapping[Hashable, Set], client, client_keys: Iterable[Hashable], ) -> Any: """Pack the layer for scheduler communication in Distributed This method should pack its current state and is called by the Client when communicating with the Scheduler. The Scheduler will then use .__dask_distributed_unpack__(data, ...) to unpack the state, materialize the layer, and merge it into the global task graph. The returned state must be compatible with Distributed's scheduler, which means it must obey the following: - Serializable by msgpack (notice, msgpack converts lists to tuples) - All remote data must be unpacked (see unpack_remotedata()) - All keys must be converted to strings now or when unpacking - All tasks must be serialized (see dumps_task()) The default implementation materialize the layer thus layers such as Blockwise and ShuffleLayer should implement a specialized pack and unpack function in order to avoid materialization. Parameters ---------- all_hlg_keys: Iterable[Hashable] All keys in the high level graph known_key_dependencies: Mapping[Hashable, Set] Already known dependencies client: distributed.Client The client calling this function. client_keys : Iterable[Hashable] List of keys requested by the client. Returns ------- state: Object serializable by msgpack Scheduler compatible state of the layer """ from distributed.client import Future from distributed.utils import CancelledError from distributed.utils_comm import subs_multiple, unpack_remotedata from distributed.worker import dumps_task dsk = dict(self) # Find aliases not in `client_keys` and substitute all matching keys # with its Future future_aliases = { k: v for k, v in dsk.items() if isinstance(v, Future) and k not in client_keys } if future_aliases: dsk = subs_multiple(dsk, future_aliases) # Remove `Future` objects from graph and note any future dependencies dsk2 = {} fut_deps = {} for k, v in dsk.items(): dsk2[k], futs = unpack_remotedata(v, byte_keys=True) if futs: fut_deps[k] = futs dsk = dsk2 # Check that any collected futures are valid unpacked_futures = set.union(*fut_deps.values()) if fut_deps else set() for future in unpacked_futures: if future.client is not client: raise ValueError( "Inputs contain futures that were created by another client." ) if stringify(future.key) not in client.futures: raise CancelledError(stringify(future.key)) # Calculate dependencies without re-calculating already known dependencies # - Start with known dependencies dependencies = ensure_dict(known_key_dependencies, copy=True) # - Remove aliases for any tasks that depend on both an alias and a future. # These can only be found in the known_key_dependencies cache, since # any dependencies computed in this method would have already had the # aliases removed. if future_aliases: alias_keys = set(future_aliases) dependencies = {k: v - alias_keys for k, v in dependencies.items()} # - Add in deps for any missing keys missing_keys = dsk.keys() - dependencies.keys() dependencies.update( (k, keys_in_tasks(all_hlg_keys, [dsk[k]], as_list=False)) for k in missing_keys ) # - Add in deps for any tasks that depend on futures for k, futures in fut_deps.items(): if futures: d = ensure_set(dependencies[k], copy=True) d.update(f.key for f in futures) dependencies[k] = d # The scheduler expect all keys to be strings dependencies = { stringify(k): {stringify(dep) for dep in deps} for k, deps in dependencies.items() } merged_hlg_keys = all_hlg_keys | dsk.keys() dsk = { stringify(k): stringify(v, exclusive=merged_hlg_keys) for k, v in dsk.items() } dsk = toolz.valmap(dumps_task, dsk) return {"dsk": dsk, "dependencies": dependencies} @classmethod def __dask_distributed_unpack__( cls, state: Any, dsk: Mapping[str, Any], dependencies: Mapping[str, set], ) -> dict: """Unpack the state of a layer previously packed by __dask_distributed_pack__() This method is called by the scheduler in Distributed in order to unpack the state of a layer and merge it into its global task graph. The method can use `dsk` and `dependencies`, which are the already materialized state of the preceding layers in the high level graph. The layers of the high level graph are unpacked in topological order. See Layer.__dask_distributed_pack__() for packing detail. Parameters ---------- state: Any The state returned by Layer.__dask_distributed_pack__() dsk: Mapping, read-only The materialized low level graph of the already unpacked layers dependencies: Mapping, read-only The dependencies of each key in `dsk` Returns ------- unpacked-layer: dict layer_dsk: Mapping[str, Any] Materialized (stringified) graph of the layer layer_deps: Mapping[str, set] Dependencies of each key in `layer_dsk` """ return {"dsk": state["dsk"], "deps": state["dependencies"]} def __reduce__(self): """Default serialization implementation, which materializes the Layer""" return (MaterializedLayer, (dict(self),)) def __copy__(self): """Default shallow copy implementation""" obj = type(self).__new__(self.__class__) obj.__dict__.update(self.__dict__) return obj def _repr_html_(self, layer_index="", highlevelgraph_key="", dependencies=()): if highlevelgraph_key != "": shortname = key_split(highlevelgraph_key) elif hasattr(self, "name"): shortname = key_split(self.name) else: shortname = self.__class__.__name__ svg_repr = "" if ( self.collection_annotations and self.collection_annotations.get("type") == "dask.array.core.Array" ): chunks = self.collection_annotations.get("chunks") if chunks: from dask.array.svg import svg svg_repr = svg(chunks) return get_template("highlevelgraph_layer.html.j2").render( materialized=self.is_materialized(), shortname=shortname, layer_index=layer_index, highlevelgraph_key=highlevelgraph_key, info=self.layer_info_dict(), dependencies=dependencies, svg_repr=svg_repr, ) def layer_info_dict(self): info = { "layer_type": type(self).__name__, "is_materialized": self.is_materialized(), "number of outputs": f"{len(self.get_output_keys())}", } if self.annotations is not None: for key, val in self.annotations.items(): info[key] = html.escape(str(val)) if self.collection_annotations is not None: for key, val in self.collection_annotations.items(): # Hide verbose chunk details from the HTML table if key != "chunks": info[key] = html.escape(str(val)) return info class MaterializedLayer(Layer): """Fully materialized layer of `Layer` Parameters ---------- mapping: Mapping The mapping between keys and tasks, typically a dask graph. """ def __init__(self, mapping: Mapping, annotations=None): super().__init__(annotations=annotations) self.mapping = mapping def __contains__(self, k): return k in self.mapping def __getitem__(self, k): return self.mapping[k] def __iter__(self): return iter(self.mapping) def __len__(self): return len(self.mapping) def is_materialized(self): return True def get_output_keys(self): return self.keys() class HighLevelGraph(Mapping): """Task graph composed of layers of dependent subgraphs This object encodes a Dask task graph that is composed of layers of dependent subgraphs, such as commonly occurs when building task graphs using high level collections like Dask array, bag, or dataframe. Typically each high level array, bag, or dataframe operation takes the task graphs of the input collections, merges them, and then adds one or more new layers of tasks for the new operation. These layers typically have at least as many tasks as there are partitions or chunks in the collection. The HighLevelGraph object stores the subgraphs for each operation separately in sub-graphs, and also stores the dependency structure between them. Parameters ---------- layers : Mapping[str, Mapping] The subgraph layers, keyed by a unique name dependencies : Mapping[str, set[str]] The set of layers on which each layer depends key_dependencies : Mapping[Hashable, set], optional Mapping (some) keys in the high level graph to their dependencies. If a key is missing, its dependencies will be calculated on-the-fly. Examples -------- Here is an idealized example that shows the internal state of a HighLevelGraph >>> import dask.dataframe as dd >>> df = dd.read_csv('myfile.*.csv') # doctest: +SKIP >>> df = df + 100 # doctest: +SKIP >>> df = df[df.name == 'Alice'] # doctest: +SKIP >>> graph = df.__dask_graph__() # doctest: +SKIP >>> graph.layers # doctest: +SKIP { 'read-csv': {('read-csv', 0): (pandas.read_csv, 'myfile.0.csv'), ('read-csv', 1): (pandas.read_csv, 'myfile.1.csv'), ('read-csv', 2): (pandas.read_csv, 'myfile.2.csv'), ('read-csv', 3): (pandas.read_csv, 'myfile.3.csv')}, 'add': {('add', 0): (operator.add, ('read-csv', 0), 100), ('add', 1): (operator.add, ('read-csv', 1), 100), ('add', 2): (operator.add, ('read-csv', 2), 100), ('add', 3): (operator.add, ('read-csv', 3), 100)} 'filter': {('filter', 0): (lambda part: part[part.name == 'Alice'], ('add', 0)), ('filter', 1): (lambda part: part[part.name == 'Alice'], ('add', 1)), ('filter', 2): (lambda part: part[part.name == 'Alice'], ('add', 2)), ('filter', 3): (lambda part: part[part.name == 'Alice'], ('add', 3))} } >>> graph.dependencies # doctest: +SKIP { 'read-csv': set(), 'add': {'read-csv'}, 'filter': {'add'} } See Also -------- HighLevelGraph.from_collections : typically used by developers to make new HighLevelGraphs """ layers: Mapping[str, Layer] dependencies: Mapping[str, Set] key_dependencies: dict[Hashable, Set] _to_dict: dict _all_external_keys: set def __init__( self, layers: Mapping[str, Mapping], dependencies: Mapping[str, Set], key_dependencies: dict[Hashable, Set] | None = None, ): self.dependencies = dependencies self.key_dependencies = key_dependencies or {} # Makes sure that all layers are `Layer` self.layers = { k: v if isinstance(v, Layer) else MaterializedLayer(v) for k, v in layers.items() } @classmethod def _from_collection(cls, name, layer, collection): """`from_collections` optimized for a single collection""" if not is_dask_collection(collection): raise TypeError(type(collection)) graph = collection.__dask_graph__() if isinstance(graph, HighLevelGraph): layers = ensure_dict(graph.layers, copy=True) layers[name] = layer deps = ensure_dict(graph.dependencies, copy=True) deps[name] = set(collection.__dask_layers__()) else: key = _get_some_layer_name(collection) layers = {name: layer, key: graph} deps = {name: {key}, key: set()} return cls(layers, deps) @classmethod def from_collections(cls, name, layer, dependencies=()): """Construct a HighLevelGraph from a new layer and a set of collections This constructs a HighLevelGraph in the common case where we have a single new layer and a set of old collections on which we want to depend. This pulls out the ``__dask_layers__()`` method of the collections if they exist, and adds them to the dependencies for this new layer. It also merges all of the layers from all of the dependent collections together into the new layers for this graph. Parameters ---------- name : str The name of the new layer layer : Mapping The graph layer itself dependencies : List of Dask collections A list of other dask collections (like arrays or dataframes) that have graphs themselves Examples -------- In typical usage we make a new task layer, and then pass that layer along with all dependent collections to this method. >>> def add(self, other): ... name = 'add-' + tokenize(self, other) ... layer = {(name, i): (add, input_key, other) ... for i, input_key in enumerate(self.__dask_keys__())} ... graph = HighLevelGraph.from_collections(name, layer, dependencies=[self]) ... return new_collection(name, graph) """ if len(dependencies) == 1: return cls._from_collection(name, layer, dependencies[0]) layers = {name: layer} deps = {name: set()} for collection in toolz.unique(dependencies, key=id): if is_dask_collection(collection): graph = collection.__dask_graph__() if isinstance(graph, HighLevelGraph): layers.update(graph.layers) deps.update(graph.dependencies) deps[name] |= set(collection.__dask_layers__()) else: key = _get_some_layer_name(collection) layers[key] = graph deps[name].add(key) deps[key] = set() else: raise TypeError(type(collection)) return cls(layers, deps) def __getitem__(self, key): # Attempt O(1) direct access first, under the assumption that layer names match # either the keys (Scalar, Item, Delayed) or the first element of the key tuples # (Array, Bag, DataFrame, Series). This assumption is not always true. try: return self.layers[key][key] except KeyError: pass try: return self.layers[key[0]][key] except (KeyError, IndexError, TypeError): pass # Fall back to O(n) access for d in self.layers.values(): try: return d[key] except KeyError: pass raise KeyError(key) def __len__(self) -> int: # NOTE: this will double-count keys that are duplicated between layers, so it's # possible that `len(hlg) > len(hlg.to_dict())`. However, duplicate keys should # not occur through normal use, and their existence would usually be a bug. # So we ignore this case in favor of better performance. # https://github.com/dask/dask/issues/7271 return sum(len(layer) for layer in self.layers.values()) def __iter__(self): return iter(self.to_dict()) def to_dict(self) -> dict: """Efficiently convert to plain dict. This method is faster than dict(self).""" try: return self._to_dict except AttributeError: out = self._to_dict = ensure_dict(self) return out def keys(self) -> KeysView: """Get all keys of all the layers. This will in many cases materialize layers, which makes it a relatively expensive operation. See :meth:`get_all_external_keys` for a faster alternative. """ return self.to_dict().keys() def get_all_external_keys(self) -> set: """Get all output keys of all layers This will in most cases _not_ materialize any layers, which makes it a relative cheap operation. Returns ------- keys: set A set of all external keys """ try: return self._all_external_keys except AttributeError: keys: set = set() for layer in self.layers.values(): # Note: don't use `keys |= ...`, because the RHS is a # collections.abc.Set rather than a real set, and this will # cause a whole new set to be constructed. keys.update(layer.get_output_keys()) self._all_external_keys = keys return keys def items(self): return self.to_dict().items() def values(self): return self.to_dict().values() def get_all_dependencies(self) -> dict[Hashable, Set]: """Get dependencies of all keys This will in most cases materialize all layers, which makes it an expensive operation. Returns ------- map: Mapping A map that maps each key to its dependencies """ all_keys = self.keys() missing_keys = all_keys - self.key_dependencies.keys() if missing_keys: for layer in self.layers.values(): for k in missing_keys & layer.keys(): self.key_dependencies[k] = layer.get_dependencies(k, all_keys) return self.key_dependencies @property def dependents(self): return reverse_dict(self.dependencies) def copy(self): return HighLevelGraph( ensure_dict(self.layers, copy=True), ensure_dict(self.dependencies, copy=True), self.key_dependencies.copy(), ) @classmethod def merge(cls, *graphs): layers = {} dependencies = {} for g in graphs: if isinstance(g, HighLevelGraph): layers.update(g.layers) dependencies.update(g.dependencies) elif isinstance(g, Mapping): layers[id(g)] = g dependencies[id(g)] = set() else: raise TypeError(g) return cls(layers, dependencies) def visualize(self, filename="dask-hlg.svg", format=None, **kwargs): """ Visualize this dask high level graph. Requires ``graphviz`` to be installed. Parameters ---------- filename : str or None, optional The name of the file to write to disk. If the provided `filename` doesn't include an extension, '.png' will be used by default. If `filename` is None, no file will be written, and the graph is rendered in the Jupyter notebook only. format : {'png', 'pdf', 'dot', 'svg', 'jpeg', 'jpg'}, optional Format in which to write output file. Default is 'svg'. color : {None, 'layer_type'}, optional (default: None) Options to color nodes. - None, no colors. - layer_type, color nodes based on the layer type. **kwargs Additional keyword arguments to forward to ``to_graphviz``. Examples -------- >>> x.dask.visualize(filename='dask.svg') # doctest: +SKIP >>> x.dask.visualize(filename='dask.svg', color='layer_type') # doctest: +SKIP Returns ------- result : IPython.diplay.Image, IPython.display.SVG, or None See dask.dot.dot_graph for more information. See Also -------- dask.dot.dot_graph dask.base.visualize # low level variant """ from dask.dot import graphviz_to_file g = to_graphviz(self, **kwargs) graphviz_to_file(g, filename, format) return g def _toposort_layers(self): """Sort the layers in a high level graph topologically Parameters ---------- hlg : HighLevelGraph The high level graph's layers to sort Returns ------- sorted: list List of layer names sorted topologically """ degree = {k: len(v) for k, v in self.dependencies.items()} reverse_deps = {k: [] for k in self.dependencies} ready = [] for k, v in self.dependencies.items(): for dep in v: reverse_deps[dep].append(k) if not v: ready.append(k) ret = [] while len(ready) > 0: layer = ready.pop() ret.append(layer) for rdep in reverse_deps[layer]: degree[rdep] -= 1 if degree[rdep] == 0: ready.append(rdep) return ret def cull(self, keys: Iterable) -> HighLevelGraph: """Return new HighLevelGraph with only the tasks required to calculate keys. In other words, remove unnecessary tasks from dask. Parameters ---------- keys iterable of keys or nested list of keys such as the output of ``__dask_keys__()`` Returns ------- hlg: HighLevelGraph Culled high level graph """ from dask.layers import Blockwise keys_set = set(flatten(keys)) all_ext_keys = self.get_all_external_keys() ret_layers: dict = {} ret_key_deps: dict = {} for layer_name in reversed(self._toposort_layers()): layer = self.layers[layer_name] # Let's cull the layer to produce its part of `keys`. # Note: use .intersection rather than & because the RHS is # a collections.abc.Set rather than a real set, and using & # would take time proportional to the size of the LHS, which # if there is no culling can be much bigger than the RHS. output_keys = keys_set.intersection(layer.get_output_keys()) if output_keys: culled_layer, culled_deps = layer.cull(output_keys, all_ext_keys) # Update `keys` with all layer's external key dependencies, which # are all the layer's dependencies (`culled_deps`) excluding # the layer's output keys. external_deps = set() for d in culled_deps.values(): external_deps |= d external_deps -= culled_layer.get_output_keys() keys_set |= external_deps # Save the culled layer and its key dependencies ret_layers[layer_name] = culled_layer if ( isinstance(layer, Blockwise) or isinstance(layer, MaterializedLayer) or (layer.is_materialized() and (len(layer) == len(culled_deps))) ): # Don't use culled_deps to update ret_key_deps # unless they are "direct" key dependencies. # # Note that `MaterializedLayer` is "safe", because # its `cull` method will return a complete dict of # direct dependencies for all keys in its subgraph. # See: https://github.com/dask/dask/issues/9389 # for performance motivation ret_key_deps.update(culled_deps) # Converting dict_keys to a real set lets Python optimise the set # intersection to iterate over the smaller of the two sets. ret_layers_keys = set(ret_layers.keys()) ret_dependencies = { layer_name: self.dependencies[layer_name] & ret_layers_keys for layer_name in ret_layers } return HighLevelGraph(ret_layers, ret_dependencies, ret_key_deps) def cull_layers(self, layers: Iterable[str]) -> HighLevelGraph: """Return a new HighLevelGraph with only the given layers and their dependencies. Internally, layers are not modified. This is a variant of :meth:`HighLevelGraph.cull` which is much faster and does not risk creating a collision between two layers with the same name and different content when two culled graphs are merged later on. Returns ------- hlg: HighLevelGraph Culled high level graph """ to_visit = set(layers) ret_layers = {} ret_dependencies = {} while to_visit: k = to_visit.pop() ret_layers[k] = self.layers[k] ret_dependencies[k] = self.dependencies[k] to_visit |= ret_dependencies[k] - ret_dependencies.keys() return HighLevelGraph(ret_layers, ret_dependencies) def validate(self): # Check dependencies for layer_name, deps in self.dependencies.items(): if layer_name not in self.layers: raise ValueError( f"dependencies[{repr(layer_name)}] not found in layers" ) for dep in deps: if dep not in self.dependencies: raise ValueError(f"{repr(dep)} not found in dependencies") for layer in self.layers.values(): assert hasattr(layer, "annotations") # Re-calculate all layer dependencies dependencies = compute_layer_dependencies(self.layers) # Check keys dep_key1 = self.dependencies.keys() dep_key2 = dependencies.keys() if dep_key1 != dep_key2: raise ValueError( f"incorrect dependencies keys {set(dep_key1)!r} " f"expected {set(dep_key2)!r}" ) # Check values for k in dep_key1: if self.dependencies[k] != dependencies[k]: raise ValueError( f"incorrect dependencies[{repr(k)}]: {repr(self.dependencies[k])} " f"expected {repr(dependencies[k])}" ) def __dask_distributed_pack__( self, client, client_keys: Iterable[Hashable], annotations: Mapping[str, Any] | None = None, ) -> dict: """Pack the high level graph for Scheduler -> Worker communication The approach is to delegate the packaging to each layer in the high level graph by calling .__dask_distributed_pack__() and .__dask_distributed_annotations_pack__() on each layer. Parameters ---------- client : distributed.Client The client calling this function. client_keys : Iterable[Hashable] List of keys requested by the client. annotations : Mapping[str, Any], optional A top-level annotations. Returns ------- data: dict Packed high level graph layers """ # Dump each layer (in topological order) layers = [] for layer in (self.layers[name] for name in self._toposort_layers()): layers.append( { "__module__": layer.__module__, "__name__": type(layer).__name__, "state": layer.__dask_distributed_pack__( self.get_all_external_keys(), self.key_dependencies, client, client_keys, ), "annotations": layer.__dask_distributed_annotations_pack__( annotations ), } ) return {"layers": layers} @staticmethod def __dask_distributed_unpack__(hlg: dict) -> dict: """Unpack the high level graph for Scheduler -> Worker communication The approach is to delegate the unpackaging to each layer in the high level graph by calling ..._unpack__() and ..._annotations_unpack__() on each layer. Parameters ---------- hlg: dict Packed high level graph layers Returns ------- unpacked-graph: dict dsk: dict[str, Any] Materialized (stringified) graph of all nodes in the high level graph deps: dict[str, set] Dependencies of each key in `dsk` annotations: dict[str, Any] Annotations for `dsk` """ from distributed.protocol.serialize import import_allowed_module dsk: dict = {} deps: dict = {} anno: dict = {} # Unpack each layer (in topological order) for layer in hlg["layers"]: # Find the unpack functions if layer["__module__"] is None: # Default implementation unpack_state = Layer.__dask_distributed_unpack__ unpack_anno = Layer.__dask_distributed_annotations_unpack__ else: mod = import_allowed_module(layer["__module__"]) cls = getattr(mod, layer["__name__"]) unpack_state = cls.__dask_distributed_unpack__ unpack_anno = cls.__dask_distributed_annotations_unpack__ # Unpack state into a graph and key dependencies unpacked_layer = unpack_state(layer["state"], dsk, deps) dsk.update(unpacked_layer["dsk"]) for k, v in unpacked_layer["deps"].items(): deps[k] = deps.get(k, set()) | v # Unpack the annotations unpack_anno(anno, layer["annotations"], unpacked_layer["dsk"].keys()) return {"dsk": dsk, "deps": deps, "annotations": anno} def __repr__(self) -> str: representation = f"{type(self).__name__} with {len(self.layers)} layers.\n" representation += f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}>\n" for i, layerkey in enumerate(self._toposort_layers()): representation += f" {i}. {layerkey}\n" return representation def _repr_html_(self): return get_template("highlevelgraph.html.j2").render( type=type(self).__name__, layers=self.layers, toposort=self._toposort_layers(), layer_dependencies=self.dependencies, n_outputs=len(self.get_all_external_keys()), ) def to_graphviz( hg, data_attributes=None, function_attributes=None, rankdir="BT", graph_attr=None, node_attr=None, edge_attr=None, **kwargs, ): from dask.dot import label, name graphviz = import_required( "graphviz", "Drawing dask graphs with the graphviz visualization engine requires the `graphviz` " "python library and the `graphviz` system library.\n\n" "Please either conda or pip install as follows:\n\n" " conda install python-graphviz # either conda install\n" " python -m pip install graphviz # or pip install and follow installation instructions", ) data_attributes = data_attributes or {} function_attributes = function_attributes or {} graph_attr = graph_attr or {} node_attr = node_attr or {} edge_attr = edge_attr or {} graph_attr["rankdir"] = rankdir node_attr["shape"] = "box" node_attr["fontname"] = "helvetica" graph_attr.update(kwargs) g = graphviz.Digraph( graph_attr=graph_attr, node_attr=node_attr, edge_attr=edge_attr ) n_tasks = {} for layer in hg.dependencies: n_tasks[layer] = len(hg.layers[layer]) min_tasks = min(n_tasks.values()) max_tasks = max(n_tasks.values()) cache = {} color = kwargs.get("color") if color == "layer_type": layer_colors = { "DataFrameIOLayer": ["#CCC7F9", False], # purple "ShuffleLayer": ["#F9CCC7", False], # rose "SimpleShuffleLayer": ["#F9CCC7", False], # rose "ArrayOverlayLayer": ["#FFD9F2", False], # pink "BroadcastJoinLayer": ["#D9F2FF", False], # blue "Blockwise": ["#D9FFE6", False], # green "BlockwiseLayer": ["#D9FFE6", False], # green "MaterializedLayer": ["#DBDEE5", False], # gray } for layer in hg.dependencies: layer_name = name(layer) attrs = data_attributes.get(layer, {}) node_label = label(layer, cache=cache) node_size = ( 20 if max_tasks == min_tasks else int(20 + ((n_tasks[layer] - min_tasks) / (max_tasks - min_tasks)) * 20) ) layer_type = str(type(hg.layers[layer]).__name__) node_tooltips = ( f"A {layer_type.replace('Layer', '')} Layer with {n_tasks[layer]} Tasks.\n" ) layer_ca = hg.layers[layer].collection_annotations if layer_ca: if layer_ca.get("type") == "dask.array.core.Array": node_tooltips += ( f"Array Shape: {layer_ca.get('shape')}\n" f"Data Type: {layer_ca.get('dtype')}\n" f"Chunk Size: {layer_ca.get('chunksize')}\n" f"Chunk Type: {layer_ca.get('chunk_type')}\n" ) if layer_ca.get("type") == "dask.dataframe.core.DataFrame": dftype = {"pandas.core.frame.DataFrame": "pandas"} cols = layer_ca.get("columns") node_tooltips += ( f"Number of Partitions: {layer_ca.get('npartitions')}\n" f"DataFrame Type: {dftype.get(layer_ca.get('dataframe_type'))}\n" f"{len(cols)} DataFrame Columns: {str(cols) if len(str(cols)) <= 40 else '[...]'}\n" ) attrs.setdefault("label", str(node_label)) attrs.setdefault("fontsize", str(node_size)) attrs.setdefault("tooltip", str(node_tooltips)) if color == "layer_type": node_color = layer_colors.get(layer_type)[0] layer_colors.get(layer_type)[1] = True attrs.setdefault("fillcolor", str(node_color)) attrs.setdefault("style", "filled") g.node(layer_name, **attrs) for layer, deps in hg.dependencies.items(): layer_name = name(layer) for dep in deps: dep_name = name(dep) g.edge(dep_name, layer_name) if color == "layer_type": legend_title = "Key" legend_label = ( '<' "" ) for layer_type, color in layer_colors.items(): if color[1]: legend_label += f'' legend_label += "
Legend: Layer types
{layer_type}
>" attrs = data_attributes.get(legend_title, {}) attrs.setdefault("label", str(legend_label)) attrs.setdefault("fontsize", "20") attrs.setdefault("margin", "0") g.node(legend_title, **attrs) return g def _get_some_layer_name(collection) -> str: """Somehow get a unique name for a Layer from a non-HighLevelGraph dask mapping""" try: (name,) = collection.__dask_layers__() return name except (AttributeError, ValueError): # collection does not define the optional __dask_layers__ method # or it spuriously returns more than one layer return str(id(collection))