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graphframes_client.py
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1222 lines (1117 loc) · 44.9 KB
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from __future__ import annotations
from typing import final
from pyspark.sql.connect import functions as F
from pyspark.sql.connect import proto
from pyspark.sql.connect.client import SparkConnectClient
from pyspark.sql.connect.column import Column
from pyspark.sql.connect.dataframe import DataFrame
from pyspark.sql.connect.plan import LogicalPlan
from pyspark.sql.connect.session import SparkSession
from pyspark.storagelevel import StorageLevel
from typing_extensions import override
try:
from typing import Self
except ImportError:
from typing_extensions import Self
from .proto import graphframes_pb2 as pb
from .utils import (
dataframe_to_proto,
make_column_or_expr,
make_str_or_long_id,
storage_level_to_proto,
)
# Spark 4 removed the withPlan method in favor of the constructor, but Spark 3
# does not have the plan as an arg in the constructor, so we need to handle
# both cases.
def _dataframe_from_plan(plan: LogicalPlan, session: SparkSession) -> DataFrame:
if hasattr(DataFrame, "withPlan"):
# Spark 3
return DataFrame.withPlan(plan, session)
# Spark 4
return DataFrame(plan, session)
@final
class PregelConnect:
def __init__(self, graph: "GraphFrameConnect") -> None:
self.graph = graph
self._max_iter = 10
self._checkpoint_interval = 2
self._col_name = None
self._initial_expr = None
self._update_after_agg_msgs_expr = None
self._send_msg_to_src: list[Column | str] = []
self._send_msg_to_dst: list[Column | str] = []
self._agg_msg = None
self._early_stopping = False
self._use_local_checkpoints = False
self._storage_level = StorageLevel.MEMORY_AND_DISK_DESER
self._initial_active_expr: Column | str | None = None
self._update_active_expr: Column | str | None = None
self._stop_if_all_non_active = False
self._skip_messages_from_non_active = False
self._required_src_columns: list[str] = []
self._required_dst_columns: list[str] = []
def setMaxIter(self, value: int) -> Self:
self._max_iter = value
return self
def setCheckpointInterval(self, value: int) -> Self:
self._checkpoint_interval = value
return self
def setEarlyStopping(self, value: bool) -> Self:
self._early_stopping = value
return self
def withVertexColumn(
self,
colName: str,
initialExpr: Column | str,
updateAfterAggMsgsExpr: Column | str,
) -> Self:
self._col_name = colName
self._initial_expr = initialExpr
self._update_after_agg_msgs_expr = updateAfterAggMsgsExpr
return self
def sendMsgToSrc(self, msgExpr: Column | str) -> Self:
self._send_msg_to_src.append(msgExpr)
return self
def sendMsgToDst(self, msgExpr: Column | str) -> Self:
self._send_msg_to_dst.append(msgExpr)
return self
def aggMsgs(self, aggExpr: Column) -> Self:
self._agg_msg = aggExpr
return self
def setStopIfAllNonActiveVertices(self, value: bool) -> Self:
self._stop_if_all_non_active = value
return self
def setInitialActiveVertexExpression(self, value: Column | str) -> Self:
self._initial_active_expr = value
return self
def setUpdateActiveVertexExpression(self, value: Column | str) -> Self:
self._update_active_expr = value
return self
def setSkipMessagesFromNonActiveVertices(self, value: bool) -> Self:
self._skip_messages_from_non_active = value
return self
def setUseLocalCheckpoints(self, value: bool) -> Self:
self._use_local_checkpoints = value
return self
def setIntermediateStorageLevel(self, storage_level: StorageLevel) -> Self:
self._storage_level = storage_level
return self
def required_src_columns(self, col_name: str, *col_names: str) -> Self:
"""Specifies which source vertex columns are required when constructing triplets.
By default, all source vertex columns are included in triplets, which can create large
intermediate datasets for algorithms with significant state. Use this method to reduce
memory usage by specifying only the columns that are actually needed.
:param col_name: the first required source vertex column name
:param col_names: additional required source vertex column names
"""
self._required_src_columns = [col_name] + list(col_names)
return self
def required_dst_columns(self, col_name: str, *col_names: str) -> Self:
"""Specifies which destination vertex columns are required when constructing triplets.
By default, all destination vertex columns are included in triplets, which can create large
intermediate datasets for algorithms with significant state. Use this method to reduce
memory usage by specifying only the columns that are actually needed.
:param col_name: the first required destination vertex column name
:param col_names: additional required destination vertex column names
"""
self._required_dst_columns = [col_name] + list(col_names)
return self
def run(self) -> DataFrame:
@final
class Pregel(LogicalPlan):
def __init__(
self,
max_iter: int,
checkpoint_interval: int,
early_stopping: bool,
vertex_col_name: str,
agg_msg: Column | str,
send2dst: list[Column | str],
send2src: list[Column | str],
vertex_col_init: Column | str,
vertex_col_upd: Column | str,
use_local_checkpoints: bool,
storage_level: StorageLevel,
initial_active_col: Column | str | None,
update_active_col: Column | str | None,
stop_if_all_non_active: bool,
skip_message_from_non_active: bool,
required_src_columns: list[str],
required_dst_columns: list[str],
vertices: DataFrame,
edges: DataFrame,
) -> None:
super().__init__(None)
self.max_iter = max_iter
self.checkpoint_interval = checkpoint_interval
self.early_stopping = early_stopping
self.vertex_col_name = vertex_col_name
self.agg_msg = agg_msg
self.send2dst = send2dst
self.send2src = send2src
self.vertex_col_init = vertex_col_init
self.vertex_col_upd = vertex_col_upd
self.use_local_checkpoints = use_local_checkpoints
self.storage_level = storage_level
self.initial_active_expr = initial_active_col
self.update_active_expr = update_active_col
self.stop_if_all_non_active = stop_if_all_non_active
self.skip_message_from_non_active = skip_message_from_non_active
self.required_src_columns = required_src_columns
self.required_dst_columns = required_dst_columns
self.vertices = vertices
self.edges = edges
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
pregel = pb.Pregel(
agg_msgs=make_column_or_expr(self.agg_msg, session),
send_msg_to_dst=[
make_column_or_expr(c_or_e, session) for c_or_e in self.send2dst
],
send_msg_to_src=[
make_column_or_expr(c_or_e, session) for c_or_e in self.send2src
],
checkpoint_interval=self.checkpoint_interval,
max_iter=self.max_iter,
additional_col_name=self.vertex_col_name,
additional_col_initial=make_column_or_expr(self.vertex_col_init, session),
additional_col_upd=make_column_or_expr(self.vertex_col_upd, session),
early_stopping=self.early_stopping,
use_local_checkpoints=self.use_local_checkpoints,
storage_level=storage_level_to_proto(self.storage_level),
stop_if_all_non_active=self.stop_if_all_non_active,
skip_messages_from_non_active=self.skip_message_from_non_active,
initial_active_expr=make_column_or_expr(self.initial_active_expr, session)
if self.initial_active_expr is not None
else None,
update_active_expr=make_column_or_expr(self.update_active_expr, session)
if self.update_active_expr is not None
else None,
required_src_columns=",".join(self.required_src_columns)
if self.required_src_columns
else None,
required_dst_columns=",".join(self.required_dst_columns)
if self.required_dst_columns
else None,
)
pb_message = pb.GraphFramesAPI(
vertices=dataframe_to_proto(self.vertices, session),
edges=dataframe_to_proto(self.edges, session),
)
pb_message.pregel.CopyFrom(pregel)
plan = self._create_proto_relation()
plan.extension.Pack(pb_message)
return plan
if (
(self._col_name is None)
or (self._initial_expr is None)
or (self._update_after_agg_msgs_expr is None)
):
raise ValueError("Initial vertex column is not initialized!")
if self._agg_msg is None:
raise ValueError("AggMsg is not initialized!")
return _dataframe_from_plan(
Pregel(
max_iter=self._max_iter,
checkpoint_interval=self._checkpoint_interval,
vertex_col_name=self._col_name,
vertex_col_init=self._initial_expr,
vertex_col_upd=self._update_after_agg_msgs_expr,
agg_msg=self._agg_msg,
send2dst=self._send_msg_to_dst,
send2src=self._send_msg_to_src,
early_stopping=self._early_stopping,
use_local_checkpoints=self._use_local_checkpoints,
initial_active_col=self._initial_active_expr,
update_active_col=self._update_active_expr,
stop_if_all_non_active=self._stop_if_all_non_active,
skip_message_from_non_active=self._skip_messages_from_non_active,
required_src_columns=self._required_src_columns,
required_dst_columns=self._required_dst_columns,
storage_level=self._storage_level,
vertices=self.graph._vertices,
edges=self.graph._edges,
),
session=self.graph._spark,
)
@staticmethod
def msg() -> Column:
return F.col("_pregel_msg_")
@staticmethod
def src(colName: str) -> Column:
return F.col("src." + colName)
@staticmethod
def dst(colName: str) -> Column:
return F.col("dst." + colName)
@staticmethod
def edge(colName: str) -> Column:
return F.col("edge." + colName)
@final
class GraphFrameConnect:
_ID: str = "id"
_SRC: str = "src"
_DST: str = "dst"
_EDGE: str = "edge"
def __init__(self, v: DataFrame, e: DataFrame) -> None:
self._vertices = v
self._edges = e
self._spark = v.sparkSession
@staticmethod
def _get_pb_api_message(
vertices: DataFrame, edges: DataFrame, client: SparkConnectClient
) -> pb.GraphFramesAPI:
return pb.GraphFramesAPI(
vertices=dataframe_to_proto(vertices, client),
edges=dataframe_to_proto(edges, client),
)
@property
def triplets(self) -> DataFrame:
@final
class Triplets(LogicalPlan):
def __init__(self, v: DataFrame, e: DataFrame) -> None:
super().__init__(None)
self.v = v
self.e = e
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.triplets.CopyFrom(pb.Triplets())
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
return _dataframe_from_plan(Triplets(self._vertices, self._edges), self._spark)
@property
def pregel(self):
return PregelConnect(self)
def find(self, pattern: str) -> DataFrame:
@final
class Find(LogicalPlan):
def __init__(self, v: DataFrame, e: DataFrame, pattern: str) -> None:
super().__init__(None)
self.v = v
self.e = e
self.p = pattern
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.find.CopyFrom(pb.Find(pattern=self.p))
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
return _dataframe_from_plan(Find(self._vertices, self._edges, pattern), self._spark)
def filterVertices(self, condition: str | Column) -> "GraphFrameConnect":
@final
class FilterVertices(LogicalPlan):
def __init__(self, v: DataFrame, e: DataFrame, condition: str | Column) -> None:
super().__init__(None)
self.v = v
self.e = e
self.c = condition
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
col_or_expr = make_column_or_expr(self.c, session)
graphframes_api_call.filter_vertices.CopyFrom(
pb.FilterVertices(condition=col_or_expr)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
new_vertices = _dataframe_from_plan(
FilterVertices(self._vertices, self._edges, condition), self._spark
)
# Exactly like in the scala-core
new_edges = self._edges.join(
new_vertices.withColumn(self._SRC, F.col(self._ID)),
on=[self._SRC],
how="left_semi",
).join(
new_vertices.withColumn(self._DST, F.col(self._ID)),
on=[self._DST],
how="left_semi",
)
return GraphFrameConnect(new_vertices, new_edges)
def filterEdges(self, condition: str | Column) -> "GraphFrameConnect":
@final
class FilterEdges(LogicalPlan):
def __init__(self, v: DataFrame, e: DataFrame, condition: str | Column) -> None:
super().__init__(None)
self.v = v
self.e = e
self.c = condition
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
col_or_expr = make_column_or_expr(self.c, session)
graphframes_api_call.filter_edges.CopyFrom(pb.FilterEdges(condition=col_or_expr))
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
new_edges = _dataframe_from_plan(
FilterEdges(self._vertices, self._edges, condition), self._spark
)
return GraphFrameConnect(self._vertices, new_edges)
def detectingCycles(
self,
checkpoint_interval: int,
use_local_checkpoints: bool,
intermediate_storage_level: StorageLevel,
) -> DataFrame:
@final
class DetectingCycles(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
checkpoint_interval: int,
use_local_checkpoints: bool,
storage_level: StorageLevel,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.checkpoint_interval = checkpoint_interval
self.use_local_checkpoints = use_local_checkpoints
self.storage_level = storage_level
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.detecting_cycles.CopyFrom(
pb.DetectingCycles(
use_local_checkpoints=self.use_local_checkpoints,
checkpoint_interval=self.checkpoint_interval,
storage_level=storage_level_to_proto(self.storage_level),
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
return _dataframe_from_plan(
DetectingCycles(
self._vertices,
self._edges,
checkpoint_interval,
use_local_checkpoints,
intermediate_storage_level,
),
self._spark,
)
def dropIsolatedVertices(self) -> "GraphFrameConnect":
@final
class DropIsolatedVertices(LogicalPlan):
def __init__(self, v: DataFrame, e: DataFrame) -> None:
super().__init__(None)
self.v = v
self.e = e
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.drop_isolated_vertices.CopyFrom(pb.DropIsolatedVertices())
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
new_vertices = _dataframe_from_plan(
DropIsolatedVertices(self._vertices, self._edges), self._spark
)
return GraphFrameConnect(new_vertices, self._edges)
def bfs(
self,
fromExpr: Column | str,
toExpr: Column | str,
edgeFilter: Column | str | None = None,
maxPathLength: int = 10,
) -> DataFrame:
@final
class BFS(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
from_expr: Column | str,
to_expr: Column | str,
edge_filter: Column | str,
max_path_len: int,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.from_expr = from_expr
self.to_expr = to_expr
self.edge_filter = edge_filter
self.max_path_len = max_path_len
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.bfs.CopyFrom(
pb.BFS(
from_expr=make_column_or_expr(self.from_expr, session),
to_expr=make_column_or_expr(self.to_expr, session),
edge_filter=make_column_or_expr(self.edge_filter, session),
max_path_length=self.max_path_len,
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
if edgeFilter is None:
edgeFilter: Column = F.lit(True)
return _dataframe_from_plan(
BFS(
v=self._vertices,
e=self._edges,
from_expr=fromExpr,
to_expr=toExpr,
edge_filter=edgeFilter,
max_path_len=maxPathLength,
),
self._spark,
)
def aggregateMessages(
self,
aggCol: list[Column | str],
sendToSrc: list[Column | str],
sendToDst: list[Column | str],
intermediate_storage_level: StorageLevel,
) -> DataFrame:
@final
class AggregateMessages(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
agg_col: list[Column | str],
send2src: list[Column | str],
send2dst: list[Column | str],
storage_level: StorageLevel,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.agg_col = agg_col
self.send2src = send2src
self.send2dst = send2dst
self.storage_level = storage_level
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.aggregate_messages.CopyFrom(
pb.AggregateMessages(
agg_col=[make_column_or_expr(x, session) for x in self.agg_col],
send_to_src=[make_column_or_expr(x, session) for x in self.send2src],
send_to_dst=[make_column_or_expr(x, session) for x in self.send2dst],
storage_level=storage_level_to_proto(self.storage_level),
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
if (len(sendToSrc) == 0) and (len(sendToDst) == 0):
raise ValueError("Either `sendToSrc`, `sendToDst`, or both have to be provided")
return _dataframe_from_plan(
AggregateMessages(
self._vertices,
self._edges,
aggCol,
sendToSrc,
sendToDst,
intermediate_storage_level,
),
self._spark,
)
def connectedComponents(
self,
algorithm: str,
checkpointInterval: int,
broadcastThreshold: int,
useLabelsAsComponents: bool,
use_local_checkpoints: bool,
max_iter: int,
storage_level: StorageLevel,
) -> DataFrame:
@final
class ConnectedComponents(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
algorithm: str,
checkpoint_interval: int,
broadcast_threshold: int,
use_labels_as_components: bool,
use_local_checkpoints: bool,
max_iter: int,
storage_level: StorageLevel,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.algorithm = algorithm
self.checkpoint_interval = checkpoint_interval
self.broadcast_threshold = broadcast_threshold
self.use_labels_as_components = use_labels_as_components
self.use_local_checkpoints = use_local_checkpoints
self.max_iter = max_iter
self.storage_level = storage_level
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.connected_components.CopyFrom(
pb.ConnectedComponents(
algorithm=self.algorithm,
checkpoint_interval=self.checkpoint_interval,
broadcast_threshold=self.broadcast_threshold,
use_labels_as_components=self.use_labels_as_components,
use_local_checkpoints=self.use_local_checkpoints,
max_iter=self.max_iter,
storage_level=storage_level_to_proto(self.storage_level),
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
return _dataframe_from_plan(
ConnectedComponents(
self._vertices,
self._edges,
algorithm,
checkpointInterval,
broadcastThreshold,
useLabelsAsComponents,
use_local_checkpoints,
max_iter,
storage_level,
),
self._spark,
)
def labelPropagation(
self,
maxIter: int,
algorithm: str,
use_local_checkpoints: bool,
checkpoint_interval: int,
storage_level: StorageLevel,
) -> DataFrame:
@final
class LabelPropagation(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
max_iter: int,
algorithm: str,
use_local_checkpoints: bool,
checkpoint_interval: int,
storage_level: StorageLevel,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.max_iter = max_iter
self.algorithm = algorithm
self.use_local_checkpoints = use_local_checkpoints
self.checkpoint_interval = checkpoint_interval
self.storage_level = storage_level
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.label_propagation.CopyFrom(
pb.LabelPropagation(
algorithm=self.algorithm,
max_iter=self.max_iter,
use_local_checkpoints=self.use_local_checkpoints,
checkpoint_interval=self.checkpoint_interval,
storage_level=storage_level_to_proto(self.storage_level),
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
return _dataframe_from_plan(
LabelPropagation(
self._vertices,
self._edges,
maxIter,
algorithm,
use_local_checkpoints,
checkpoint_interval,
storage_level,
),
self._spark,
)
def _update_page_rank_edge_weights(self, new_vertices: DataFrame) -> "GraphFrameConnect":
cols2select = self.edges.columns + ["weight"]
new_edges = (
self._edges.join(
new_vertices.withColumn(self._SRC, F.col(self._ID)),
on=[self._SRC],
how="inner",
)
.join(
self.outDegrees.withColumn(self._SRC, F.col(self._ID)),
on=[self._SRC],
how="inner",
)
.withColumn("weight", F.col("pagerank") / F.col("outDegree"))
.select(*cols2select)
)
return GraphFrameConnect(new_vertices, new_edges)
def pageRank(
self,
resetProbability: float = 0.15,
sourceId: str | int | None = None,
maxIter: int | None = None,
tol: float | None = None,
) -> "GraphFrameConnect":
@final
class PageRank(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
reset_prob: float,
source_id: str | int | None,
max_iter: int | None,
tol: float | None,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.reset_prob = reset_prob
self.source_id = source_id
self.max_iter = max_iter
self.tol = tol
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.page_rank.CopyFrom(
pb.PageRank(
reset_probability=self.reset_prob,
source_id=(
None if self.source_id is None else make_str_or_long_id(self.source_id)
),
max_iter=self.max_iter,
tol=self.tol,
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
if (maxIter is None) == (tol is None):
# TODO: in classic it is not an axception but assert;
# at the same time I think it should be an exception.
raise ValueError("Exactly one of maxIter or tol should be set.")
new_vertices = _dataframe_from_plan(
PageRank(
self._vertices,
self._edges,
reset_prob=resetProbability,
source_id=sourceId,
max_iter=maxIter,
tol=tol,
),
self._spark,
)
# TODO: should this part to be optional? Like 'compute_edge_weights'?
return self._update_page_rank_edge_weights(new_vertices)
def parallelPersonalizedPageRank(
self,
resetProbability: float = 0.15,
sourceIds: list[str | int] | None = None,
maxIter: int | None = None,
) -> "GraphFrameConnect":
@final
class ParallelPersonalizedPageRank(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
reset_prob: float,
source_ids: list[str | int],
max_iter: int,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.reset_prob = reset_prob
self.source_ids = source_ids
self.max_iter = max_iter
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.parallel_personalized_page_rank.CopyFrom(
pb.ParallelPersonalizedPageRank(
reset_probability=self.reset_prob,
source_ids=[make_str_or_long_id(raw_id) for raw_id in self.source_ids],
max_iter=self.max_iter,
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
assert (
sourceIds is not None and len(sourceIds) > 0
), "Source vertices Ids sourceIds must be provided"
assert maxIter is not None, "Max number of iterations maxIter must be provided"
new_vertices = _dataframe_from_plan(
ParallelPersonalizedPageRank(
self._vertices,
self._edges,
reset_prob=resetProbability,
source_ids=sourceIds,
max_iter=maxIter,
),
self._spark,
)
return self._update_page_rank_edge_weights(new_vertices)
def powerIterationClustering(
self, k: int, maxIter: int, weightCol: str | None = None
) -> DataFrame:
@final
class PowerIterationClustering(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
k: int,
max_iter: int,
weight_col: str | None,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.k = k
self.max_iter = max_iter
self.weight_col = weight_col
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.power_iteration_clustering.CopyFrom(
pb.PowerIterationClustering(
k=self.k,
max_iter=self.max_iter,
weight_col=self.weight_col,
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
return _dataframe_from_plan(
PowerIterationClustering(self._vertices, self._edges, k, maxIter, weightCol),
self._spark,
)
def shortestPaths(
self,
landmarks: list[str | int],
algorithm: str,
use_local_checkpoints: bool,
checkpoint_interval: int,
storage_level: StorageLevel,
is_directed: bool,
) -> DataFrame:
@final
class ShortestPaths(LogicalPlan):
def __init__(
self,
v: DataFrame,
e: DataFrame,
landmarks: list[str | int],
algorithm: str,
use_local_checkpoints: bool,
checkpoint_interval: int,
storage_level: StorageLevel,
is_directed: bool,
) -> None:
super().__init__(None)
self.v = v
self.e = e
self.landmarks = landmarks
self.algorithm = algorithm
self.use_local_checkpoints = use_local_checkpoints
self.checkpoint_interval = checkpoint_interval
self.storage_level = storage_level
self.is_directed = is_directed
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.shortest_paths.CopyFrom(
pb.ShortestPaths(
landmarks=[make_str_or_long_id(raw_id) for raw_id in self.landmarks],
algorithm=self.algorithm,
use_local_checkpoints=self.use_local_checkpoints,
checkpoint_interval=self.checkpoint_interval,
storage_level=storage_level_to_proto(self.storage_level),
is_directed=self.is_directed,
)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan
return _dataframe_from_plan(
ShortestPaths(
self._vertices,
self._edges,
landmarks,
algorithm,
use_local_checkpoints,
checkpoint_interval,
storage_level,
is_directed,
),
self._spark,
)
def stronglyConnectedComponents(self, maxIter: int) -> DataFrame:
@final
class StronglyConnectedComponents(LogicalPlan):
def __init__(self, v: DataFrame, e: DataFrame, max_iter: int) -> None:
super().__init__(None)
self.v = v
self.e = e
self.max_iter = max_iter
@override
def plan(self, session: SparkConnectClient) -> proto.Relation:
graphframes_api_call = GraphFrameConnect._get_pb_api_message(
self.v, self.e, session
)
graphframes_api_call.strongly_connected_components.CopyFrom(
pb.StronglyConnectedComponents(max_iter=self.max_iter)
)
plan = self._create_proto_relation()
plan.extension.Pack(graphframes_api_call)
return plan