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_r333814713
########## File path: graph/api/src/main/scala/org/apache/spark/graph/api/CypherSession.scala ########## @@ -0,0 +1,258 @@ +/* + * 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.internal.Logging +import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession} +import org.apache.spark.sql.types.{BooleanType, StructType} + +/** + * Contains constants used for convention based column naming. + */ +object CypherSession { + + /** + * Naming convention for identifier columns, both node and relationship identifiers. + */ + val ID_COLUMN = "$ID" + + /** + * Naming convention for relationship source identifier. + */ + val SOURCE_ID_COLUMN = "$SOURCE_ID" + + /** + * Naming convention for relationship target identifier. + */ + val TARGET_ID_COLUMN = "$TARGET_ID" + + /** + * Naming convention both for node label and relationship type prefixes. + */ + val LABEL_COLUMN_PREFIX = ":" +} + +/** + * A CypherSession allows for creating, storing and loading [[PropertyGraph]] instances as well as + * executing Cypher queries on them. + * + * Wraps a [[org.apache.spark.sql.SparkSession]]. + * + * @since 3.0.0 + */ +trait CypherSession extends Logging { + + 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. + * + * Note that queries can take optional parameters: + * + * {{{ + * Parameters: + * + * { + * "name" : "Alice" + * } + * + * Query: + * + * MATCH (n:Person) + * WHERE n.name = $name + * RETURN n + * }}} + * + * @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. + * + * Note that queries can take optional parameters: + * + * {{{ + * Parameters: + * + * { + * "name" : "Alice" + * } + * + * Query: + * + * MATCH (n:Person) + * WHERE n.name = $name + * RETURN n + * }}} + * + * @param graph [[PropertyGraph]] on which the query is executed + * @param query Cypher query to execute + * @param parameters parameters used by the Cypher query + * @since 3.0.0 + */ + def cypher( + graph: PropertyGraph, + query: String, + parameters: java.util.Map[String, Object]): CypherResult = { + cypher(graph, query, parameters.asScala.toMap) + } + + /** + * Creates a [[PropertyGraph]] from a sequence of [[NodeFrame]]s and [[RelationshipFrame]]s. + * At least one [[NodeFrame]] has to be provided. + * + * For each label set and relationship type there can be at most one [[NodeFrame]] and at most one + * [[RelationshipFrame]], respectively. + * + * @param nodes NodeFrames that define the nodes in the graph + * @param relationships RelationshipFrames that define the relationships in the graph + * @since 3.0.0 + */ + def createGraph(nodes: Array[NodeFrame], relationships: Array[RelationshipFrame]): PropertyGraph + + /** + * Creates a [[PropertyGraph]] from nodes and relationships. + * + * The given DataFrames need to adhere to the following column naming conventions: + * + * {{{ + * Id column: `$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) + * }}} + * + * @note It is recommended to cache the input DataFrames if they represent multiple label sets and + * relationship types. + * + * @see [[CypherSession]] + * @param nodes node DataFrame + * @param relationships relationship DataFrame + * @since 3.0.0 + */ + def createGraph(nodes: DataFrame, relationships: DataFrame): PropertyGraph = { + def validateLabelColumns(schema: StructType, columns: Set[String]): Unit = { + schema.fields.filter(f => columns.contains(f.name)).foreach(field => { + if (field.dataType != BooleanType) { + throw new IllegalArgumentException(s"Column ${field.name} must be of type BooleanType.") + } + }) + } + + val idColumn = CypherSession.ID_COLUMN + val sourceIdColumn = CypherSession.SOURCE_ID_COLUMN + val targetIdColumn = CypherSession.TARGET_ID_COLUMN + + val labelColumns = nodes.columns.filter(_.startsWith(CypherSession.LABEL_COLUMN_PREFIX)).toSet + validateLabelColumns(nodes.schema, labelColumns) + + val nodeProperties = (nodes.columns.toSet - idColumn -- labelColumns) + .map(col => col -> col) + .toMap + + val labelCount = labelColumns.size + if (labelCount > 5) { + log.warn( + s"$labelCount label columns will result in ${Math.pow(labelCount, 2)} node frames.") + if (labelCount > 10) { + throw new IllegalArgumentException( + s"Expected number of label columns to be less than or equal to 10, was $labelCount.") + } + } + + val labelSets = labelColumns.subsets().toSet + + val nodeFrames = labelSets.map { labelSet => + val predicate = labelColumns + .map { labelColumn => + if (labelSet.contains(labelColumn)) { + nodes.col(labelColumn) + } else { + !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)) + validateLabelColumns(relationships.schema, relTypeColumns) + 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.toArray, relFrames.toArray) + } + + /** + * Returns a [[PropertyGraphReader]] that can be used to read data in as a `PropertyGraph`. + * + * @since 3.0.0 + */ + def read: PropertyGraphReader + + /** + * Returns a [[NodeFrameBuilder]] that can be used to construct a [[NodeFrame]]. + * + * @param df DataFrame containing a single node in each row + * @since 3.0.0 + */ + def buildNodeFrame(df: DataFrame): NodeFrameBuilder = Review comment: ```scala - def buildNodeFrame(df: DataFrame): NodeFrameBuilder = + def buildNodeFrame(df: Dataset[Row]): NodeFrameBuilder = ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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