You are viewing a plain text version of this content. The canonical link for it is here.
Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2019/10/05 22:37:59 UTC
[GitHub] [spark] dongjoon-hyun commented on a change in pull request #24851:
[SPARK-27303][GRAPH] Add Spark Graph API
dongjoon-hyun commented on a change in pull request #24851: [SPARK-27303][GRAPH] Add Spark Graph API
URL: https://github.com/apache/spark/pull/24851#discussion_r331764027
##########
File path: graph/api/src/main/scala/org/apache/spark/graph/api/CypherSession.scala
##########
@@ -0,0 +1,186 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graph.api
+
+import scala.collection.JavaConverters._
+
+import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}
+
+object CypherSession {
+ val ID_COLUMN = "$ID"
+ val SOURCE_ID_COLUMN = "$SOURCE_ID"
+ val TARGET_ID_COLUMN = "$TARGET_ID"
+ val LABEL_COLUMN_PREFIX = ":"
+}
+
+/**
+ * The entry point for using property graphs in Spark.
+ *
+ * Provides factory methods for creating [[PropertyGraph]] instances.
+ *
+ * Wraps a [[org.apache.spark.sql.SparkSession]].
+ *
+ * @since 3.0.0
+ */
+trait CypherSession {
+
+ def sparkSession: SparkSession
+
+ /**
+ * Executes a Cypher query on the given input graph.
+ *
+ * @param graph [[PropertyGraph]] on which the query is executed
+ * @param query Cypher query to execute
+ * @since 3.0.0
+ */
+ def cypher(graph: PropertyGraph, query: String): CypherResult
+
+ /**
+ * Executes a Cypher query on the given input graph.
+ *
+ * @param graph [[PropertyGraph]] on which the query is executed
+ * @param query Cypher query to execute
+ * @param parameters parameters used by the Cypher query
+ * @since 3.0.0
+ */
+ def cypher(graph: PropertyGraph, query: String, parameters: Map[String, Any]): CypherResult
+
+ /**
+ * Executes a Cypher query on the given input graph.
+ *
+ * @param graph [[PropertyGraph]] on which the query is executed
+ * @param query Cypher query to execute
+ * @param parameters parameters used by the Cypher query
+ * @since 3.0.0
+ */
+ def cypher(graph: PropertyGraph,
+ query: String,
+ parameters: java.util.Map[String, Object]): CypherResult = {
+ cypher(graph, query, parameters.asScala.toMap)
+ }
+
+ /**
+ * Creates a [[PropertyGraph]] from a sequence of [[NodeFrame]]s and [[RelationshipFrame]]s.
+ * At least one [[NodeFrame]] has to be provided.
+ *
+ * For each label set and relationship type there can be at most one [[NodeFrame]] and at most one
+ * [[RelationshipFrame]], respectively.
+ *
+ * @param nodes NodeFrames that define the nodes in the graph
+ * @param relationships RelationshipFrames that define the relationships in the graph
+ * @since 3.0.0
+ */
+ def createGraph(nodes: Seq[NodeFrame], relationships: Seq[RelationshipFrame]): PropertyGraph
+
+ /**
+ * Creates a [[PropertyGraph]] from a sequence of [[NodeFrame]]s and [[RelationshipFrame]]s.
+ * At least one [[NodeFrame]] has to be provided.
+ *
+ * For each label set and relationship type there can be at most one [[NodeFrame]] and at most one
+ * [[RelationshipFrame]], respectively.
+ *
+ * @param nodes NodeFrames that define the nodes in the graph
+ * @param relationships RelationshipFrames that define the relationships in the graph
+ * @since 3.0.0
+ */
+ def createGraph(
+ nodes: java.util.List[NodeFrame],
+ relationships: java.util.List[RelationshipFrame]): PropertyGraph = {
+ createGraph(nodes.asScala, relationships.asScala)
+ }
+
+ /**
+ * Creates a [[PropertyGraph]] from nodes and relationships.
+ *
+ * The given DataFrames need to adhere to the following column naming conventions:
+ *
+ * {{{
+ * Id column: `$ID` (nodes and relationships)
+ * SourceId column: `$SOURCE_ID` (relationships)
+ * TargetId column: `$TARGET_ID` (relationships)
+ *
+ * Label columns: `:{LABEL_NAME}` (nodes)
+ * RelType columns: `:{REL_TYPE}` (relationships)
+ *
+ * Property columns: `{Property_Key}` (nodes and relationships)
+ * }}}
+ *
+ * @see [[CypherSession]]
+ * @param nodes node DataFrame
+ * @param relationships relationship DataFrame
+ * @since 3.0.0
+ */
+ def createGraph(nodes: DataFrame, relationships: DataFrame): PropertyGraph = {
+ val idColumn = CypherSession.ID_COLUMN
+ val sourceIdColumn = CypherSession.SOURCE_ID_COLUMN
+ val targetIdColumn = CypherSession.TARGET_ID_COLUMN
+
+ val labelColumns = nodes.columns.filter(_.startsWith(CypherSession.LABEL_COLUMN_PREFIX)).toSet
+ val nodeProperties = (nodes.columns.toSet - idColumn -- labelColumns)
+ .map(col => col -> col)
+ .toMap
+
+ val labelSets = labelColumns.subsets().toSet + Set.empty
+ val nodeFrames = labelSets.map { labelSet =>
+ val predicate = labelColumns
+ .map {
+ case labelColumn if labelSet.contains(labelColumn) => nodes.col(labelColumn)
+ case labelColumn => !nodes.col(labelColumn)
+ }
+ .reduce(_ && _)
+
+ NodeFrame(nodes.filter(predicate), idColumn, labelSet.map(_.substring(1)), nodeProperties)
+ }
+
+ val relColumns = relationships.columns.toSet
+ val relTypeColumns = relColumns.filter(_.startsWith(CypherSession.LABEL_COLUMN_PREFIX))
+ val propertyColumns = relColumns - idColumn - sourceIdColumn - targetIdColumn -- relTypeColumns
+ val relProperties = propertyColumns.map(col => col -> col).toMap
+ val relFrames = relTypeColumns.map { relTypeColumn =>
+ val predicate = relationships.col(relTypeColumn)
+
+ RelationshipFrame(
+ relationships.filter(predicate),
+ idColumn,
+ sourceIdColumn,
+ targetIdColumn,
+ relTypeColumn.substring(1),
+ relProperties)
+ }
+
+ createGraph(nodeFrames.toSeq, relFrames.toSeq)
+ }
+
+ /**
+ * Loads a [[PropertyGraph]] from the given location.
+ *
+ * @param path directory in which the graph is stored
+ * @since 3.0.0
+ */
+ def load(path: String): PropertyGraph
+
+ /**
+ * Saves a [[PropertyGraph]] to the given location.
+ *
+ * @param graph [[PropertyGraph]] to be stored
+ * @param path directory in which the graph should be stored
+ * @param saveMode specifies what happens when the destination already exists
+ * @since 3.0.0
+ */
+ def save(graph: PropertyGraph, path: String, saveMode: SaveMode): Unit
Review comment:
Yep. For this one, @mengxr 's advice is the recent common development direction inside Apache Spark. Please follow @mengxr 's advice. This is orthogonal to `DSv2`.
> Do we want to adapt to that pattern? Is this changing with DSv2 in Spark 3.0?
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org