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From GitBox <...@apache.org>
Subject [GitHub] [spark] mengxr commented on a change in pull request #24851: [SPARK-27303][GRAPH] Add Spark Graph API
Date Mon, 14 Oct 2019 18:43:20 GMT
mengxr commented on a change in pull request #24851: [SPARK-27303][GRAPH] Add Spark Graph API
URL: https://github.com/apache/spark/pull/24851#discussion_r334602161
 
 

 ##########
 File path: graph/api/src/main/scala/org/apache/spark/graph/api/CypherSession.scala
 ##########
 @@ -0,0 +1,285 @@
+/*
+ * 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.slf4j.LoggerFactory
+
+import org.apache.spark.annotation.Evolving
+import org.apache.spark.sql.{Dataset, Row, SparkSession}
+import org.apache.spark.sql.types.{BooleanType, StructType}
+
+/**
+ * Contains constants used for convention based column naming.
+ */
+@Evolving
+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 = "$TARGET_ID"
+
+  /**
+   * Naming convention both for node label and relationship type prefixes.
+   */
+  val LABEL_COLUMN_PREFIX = ":"
+
+  /**
+   * Extracts [[NodeDataset]]s from a [[Dataset]] using column name conventions.
+   *
+   * For information about naming conventions, see [[CypherSession.createGraph]].
+   *
+   * @param nodes node dataset
+   * @since 3.0.0
+   */
+  def extractNodeDatasets(nodes: Dataset[Row]): Set[NodeDataset] = {
+    val labelColumns = nodes.columns.filter(_.startsWith(LABEL_COLUMN_PREFIX)).toSet
+    validateLabelColumns(nodes.schema, labelColumns)
+
+    val nodeProperties = (nodes.columns.toSet - ID_COLUMN -- labelColumns)
+      .map(col => col -> col)
+      .toMap
+
+    val labelCount = labelColumns.size
+    if (labelCount > 5) {
+      LoggerFactory.getLogger(CypherSession.getClass).warn(
+        s"$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
+
+    labelSets.map { labelSet =>
+      val predicate = labelColumns
+        .map { labelColumn =>
+          if (labelSet.contains(labelColumn)) {
+            nodes.col(labelColumn)
+          } else {
+            !nodes.col(labelColumn)
+          }
+        }
+        .reduce(_ && _)
+
+      NodeDataset(nodes.filter(predicate), ID_COLUMN, labelSet.map(_.substring(1)), nodeProperties)
+    }
+  }
+
+  /**
+   * Extracts [[RelationshipDataset]]s from a [[Dataset]] using column name conventions.
+   *
+   * For information about naming conventions, see [[CypherSession.createGraph]].
+   *
+   * @param relationships relationship dataset
+   * @since 3.0.0
+   */
+  def extractRelationshipDatasets(relationships: Dataset[Row]): Set[RelationshipDataset]
= {
+    val relColumns = relationships.columns.toSet
+    val relTypeColumns = relColumns.filter(_.startsWith(LABEL_COLUMN_PREFIX))
+    validateLabelColumns(relationships.schema, relTypeColumns)
+    val idColumns = Set(ID_COLUMN, SOURCE_ID_COLUMN, TARGET_ID_COLUMN)
+    val propertyColumns = relColumns -- idColumns -- relTypeColumns
+    val relProperties = propertyColumns.map(col => col -> col).toMap
+    relTypeColumns.map { relTypeColumn =>
+      val predicate = relationships.col(relTypeColumn)
+
+      RelationshipDataset(
+        relationships.filter(predicate),
+        ID_COLUMN,
+        SOURCE_ID_COLUMN,
+        TARGET_ID_COLUMN,
+        relTypeColumn.substring(1),
+        relProperties)
+    }
+  }
+
+  private 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.")
+      }
+    })
+  }
+
+}
+
+/**
+ * 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
+ */
+@Evolving
+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.
+   *
+   * 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 = $name
+   *     RETURN n
+   * }}}
+   *
+   * @param graph      [[PropertyGraph]] on which the query is executed
+   * @param query      Cypher query to execute
+   * @param parameters parameters used by the Cypher query
+   * @since 3.0.0
+   */
+  def cypher(
+      graph: PropertyGraph,
+      query: String,
+      parameters: java.util.Map[String, Object]): CypherResult = {
+    cypher(graph, query, parameters.asScala.toMap)
+  }
+
+  /**
+   * Creates a [[PropertyGraph]] from a sequence of [[NodeDataset]]s and [[RelationshipDataset]]s.
+   * At least one [[NodeDataset]] has to be provided.
+   *
+   * For each label set and relationship type there can be at most one [[NodeDataset]] and
at most
+   * one [[RelationshipDataset]], respectively.
+   *
+   * @param nodes         NodeDataset that define the nodes in the graph
+   * @param relationships RelationshipDataset that define the relationships in the graph
+   * @since 3.0.0
+   */
+  def createGraph(
+      nodes: Array[NodeDataset],
+      relationships: Array[RelationshipDataset]): PropertyGraph
+
+  /**
+   * Creates a [[PropertyGraph]] from nodes and relationships.
+   *
+   * The given dataset needs to adhere to the following column naming conventions:
+   *
+   * {{{
+   *     Id column:        `$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 datasets if they represent multiple label
sets and
+   *       relationship types.
+   *
+   * @see [[CypherSession]]
+   * @param nodes         node dataset
+   * @param relationships relationship dataset
+   * @since 3.0.0
+   */
+  def createGraph(nodes: Dataset[Row], relationships: Dataset[Row]): PropertyGraph = {
+    val nodeFrames = CypherSession.extractNodeDatasets(nodes)
+    val relationshipFrames = CypherSession.extractRelationshipDatasets(relationships)
+    createGraph(nodeFrames.toArray, relationshipFrames.toArray)
+  }
+
+  /**
+   * Returns a [[PropertyGraphReader]] that can be used to read data in as a `PropertyGraph`.
+   *
+   * @since 3.0.0
+   */
+  def read: PropertyGraphReader
+
+  /**
+   * Returns a [[NodeDatasetBuilder]] that can be used to construct a [[NodeDataset]].
+   *
+   * @param ds Dataset containing a single node in each row
+   * @since 3.0.0
+   */
+  def buildNodeDataset(ds: Dataset[Row]): NodeDatasetBuilder =
+    new NodeDatasetBuilder(ds)
+
+  /**
+   * Returns a [[RelationshipDatasetBuilder]] that can be used to construct
+   * a [[RelationshipDataset]].
+   *
+   * @param ds Dataset containing a single relationship in each row
+   * @since 3.0.0
+   */
+  def buildRelationshipDataset(ds: Dataset[Row]): RelationshipDatasetBuilder =
 
 Review comment:
   Ditto.

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