I am pretty new to GraphFrames and Scala. I am writing some sort of label propagation algorithm (very different from the library one). Essentially each vertex has an array "memVector" and the edge has a float value "floatWeights". I want to update each vertex's memVector to the be the sum of (floatWeights * memVector) from all of its neighbors. This is the code I have written for the same:
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.SQLContext
import org.apache.spark.SparkContext
import org.graphframes._
import org.graphframes.lib.AggregateMessages
import org.apache.spark.sql.functions.udf
val sqlContext = new SQLContext(sc)
val edges = spark.read.parquet("code/SampleGraphEdge")
val vertices = spark.read.parquet("code/SampleGraphVer")
val toInteger: String => Int = _.toInt
val toIntegerUDF = udf(toInteger)
val newEdges = edges.withColumn("floatWeights", toIntegerUDF('weights)).drop("weights")
val graph = GraphFrame(vertices, newEdges)
val AM = AggregateMessages
val msgToSrc = AM.dst("memVector")
val msgToDst = AM.src("memVector")
val msgFromEdge = AM.edge("floatWeights")
def aggfunc(msg: org.apache.spark.sql.Column) = sum(msg.getField("weights") * AM.msg.getField("memVector"))
val agg = graph.aggregateMessages.sendToSrc(msgToSrc).sendToDst(msgToDst).sendToSrc(sendFromEdge).sendToDst(sendFromEdge).agg(aggfunc(AM.msg).as("UpdatedVector"))
Now the aggfunc I wrote is not right as I cannot multiply an array and a float directly. I am running the above in spark-shell and I am getting the following error at the last line:
org.apache.spark.sql.AnalysisException: Can't extract value from MSG#750;
at org.apache.spark.sql.catalyst.expressions.ExtractValue$.apply(complexTypeExtractors.scala:73)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9$$anonfun$applyOrElse$5.applyOrElse(Analyzer.scala:613)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9$$anonfun$applyOrElse$5.applyOrElse(Analyzer.scala:605)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:312)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:311)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:269)
at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:279)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:283)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:283)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$8.apply(QueryPlan.scala:288)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:288)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9.applyOrElse(Analyzer.scala:605)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9.applyOrElse(Analyzer.scala:547)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:547)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:484)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:65)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:63)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:51)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
at org.apache.spark.sql.RelationalGroupedDataset.toDF(RelationalGroupedDataset.scala:62)
at org.apache.spark.sql.RelationalGroupedDataset.agg(RelationalGroupedDataset.scala:222)
at org.graphframes.lib.AggregateMessages.agg(AggregateMessages.scala:127)
... 50 elided
Am I approaching it right? Any workarounds/solutions will be greatly appreciated.
