Source code for dask.highlevelgraph

from __future__ import annotations

import abc
import copy
import html
from import (
from typing import Any

import tlz as toolz

import dask
from dask import config
from dask.base import clone_key, flatten, is_dask_collection, normalize_token
from dask.core import keys_in_tasks, reverse_dict
from dask.typing import DaskCollection, Graph, Key
from dask.utils import ensure_dict, import_required, key_split
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()):
    return ret

class Layer(Graph):
    """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

    annotations: Mapping[str, Any] | None
    collection_annotations: Mapping[str, Any] | None

    def __init__(
        annotations: Mapping[str, Any] | None = None,
        collection_annotations: Mapping[str, Any] | None = None,
        """Initialize Layer object.

        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 dask.get_annotations().copy() or None
        self.collection_annotations = collection_annotations or copy.copy(
            config.get("collection_annotations", None)

    def is_materialized(self) -> bool:
        """Return whether the layer is materialized or not"""
        return True

    def get_output_keys(self) -> Set[Key]:
        """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.

        keys: Set
            All output keys
        return self.keys()  # this implementation will materialize the graph

    def cull(
        self, keys: set[Key], all_hlg_keys: Collection[Key]
    ) -> tuple[Layer, Mapping[Key, set[Key]]]:
        """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.

        >>> 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()}

        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 (
                {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:

        return MaterializedLayer(out, annotations=self.annotations), ret_deps

    def get_dependencies(self, key: Key, all_hlg_keys: Collection[Key]) -> set:
        """Get dependencies of `key` in the layer

            The key to find dependencies of
            All keys in the high level graph.

        deps: set
            A set of dependencies
        return keys_in_tasks(all_hlg_keys, [self[key]])

    def clone(
        keys: set,
        seed: Hashable,
        bind_to: Key | None = None,
    ) -> tuple[Layer, bool]:
        """Clone selected keys in the layer, as well as references to keys in other

            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.
            Common hashable used to alter the keys; see :func:`dask.base.clone_key`
            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.

        - New layer
        - True if the ``bind_to`` key was injected anywhere; False otherwise

        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
            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()}
                    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 __copy__(self):
        """Default shallow copy implementation"""
        obj = type(self).__new__(self.__class__)
        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(
            shortname = self.__class__.__name__

        svg_repr = ""
        if (
            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(

    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`

    mapping: Mapping
        The mapping between keys and tasks, typically a dask graph.

    def __init__(self, mapping: Mapping, annotations=None, collection_annotations=None):
            annotations=annotations, collection_annotations=collection_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()

[docs]class HighLevelGraph(Graph): """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 : dict[Key, 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[ == '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[ == 'Alice'], ('add', 0)), ('filter', 1): (lambda part: part[ == 'Alice'], ('add', 1)), ('filter', 2): (lambda part: part[ == 'Alice'], ('add', 2)), ('filter', 3): (lambda part: part[ == '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[str]] key_dependencies: dict[Key, set[Key]] _to_dict: dict _all_external_keys: set def __init__( self, layers: Mapping[str, Graph], dependencies: Mapping[str, set[str]], key_dependencies: dict[Key, set[Key]] | 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)
[docs] @classmethod def from_collections( cls, name: str, layer: Graph, dependencies: Sequence[DaskCollection] = (), ) -> HighLevelGraph: """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} name_dep: set[str] = set() deps: dict[str, set[str]] = {name: name_dep} 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) name_dep |= set(collection.__dask_layers__()) else: key = _get_some_layer_name(collection) layers[key] = graph name_dep.add(key) deps[key] = set() else: raise TypeError(type(collection)) return cls(layers, deps)
def __getitem__(self, key: Key) -> Any: # 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] # type: ignore except KeyError: pass try: return self.layers[key[0]][key] # type: ignore 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. # return sum(len(layer) for layer in self.layers.values()) def __iter__(self) -> Iterator[Key]: return iter(self.to_dict())
[docs] def to_dict(self) -> dict[Key, Any]: """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
[docs] 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()
[docs] def get_all_external_keys(self) -> set[Key]: """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 # 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
[docs] def items(self) -> ItemsView[Key, Any]: return self.to_dict().items()
[docs] def values(self) -> ValuesView[Any]: return self.to_dict().values()
[docs] def get_all_dependencies(self) -> dict[Key, set[Key]]: """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) -> dict[str, set[str]]: return reverse_dict(self.dependencies) def copy(self) -> HighLevelGraph: return HighLevelGraph( ensure_dict(self.layers, copy=True), ensure_dict(self.dependencies, copy=True), self.key_dependencies.copy(), ) @classmethod def merge(cls, *graphs: Graph) -> HighLevelGraph: layers: dict[str, Graph] = {} dependencies: dict[str, set[str]] = {} for g in graphs: if isinstance(g, HighLevelGraph): layers.update(g.layers) dependencies.update(g.dependencies) elif isinstance(g, Mapping): layers[str(id(g))] = g dependencies[str(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.display.Image, IPython.display.SVG, or None See for more information. See Also -------- dask.base.visualize # low level variant """ from import graphviz_to_file g = to_graphviz(self, **kwargs) graphviz_to_file(g, filename, format) return g def _toposort_layers(self) -> list[str]: """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: dict[str, list[str]] = {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
[docs] def cull(self, keys: Iterable[Key]) -> 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 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: # 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)
[docs] 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) -> None: # 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 __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) -> str: 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 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 = ( '<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="5">' "<TR><TD><B>Legend: Layer types</B></TD></TR>" ) for layer_type, color in layer_colors.items(): if color[1]: legend_label += f'<TR><TD BGCOLOR="{color[0]}">{layer_type}</TD></TR>' legend_label += "</TABLE>>" 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)) @normalize_token.register(HighLevelGraph) def register_highlevelgraph(hlg): return normalize_token(list(hlg.layers.keys()))