Question

我正在尝试使用火花结构化流媒体从Kafka读取数据并预测传入数据。我正在使用我使用Spark ML训练过的模型。

val spark = SparkSession
  .builder()
  .appName("Spark SQL basic example")
  .master("local")
  .getOrCreate()
import spark.implicits._

val toString = udf((payload: Array[Byte]) => new String(payload))
val sentenceDataFrame = spark.readStream.format("kafka").option("kafka.bootstrap.servers","localhost:9092").option("subscribe", "topicname1")
  .load().selectExpr("CAST(value AS STRING)").as[(String)]
sentenceDataFrame.printSchema()
val regexTokenizer = new RegexTokenizer()
  .setInputCol("value")
  .setOutputCol("words")
  .setPattern("\\W")
val tokencsv = regexTokenizer.transform(sentenceDataFrame)
val remover = new StopWordsRemover()
  .setInputCol("words")
  .setOutputCol("filtered")

val removestopdf = remover.transform(tokencsv)
// Learn a mapping from words to Vectors.
val word2Vec = new Word2Vec()
  .setInputCol("filtered")
  .setOutputCol("result")
  .setVectorSize(300)
  .setMinCount(0)

val model = word2Vec.fit(removestopdf)

val result = model.transform(removestopdf)


val featureIndexer = new VectorIndexer()
  .setInputCol("result")
  .setOutputCol("indexedFeatures")
  .setMaxCategories(2)
  .fit(result)

val some = featureIndexer.transform(result)

val model1 = RandomForestClassificationModel.load("/home/akhil/Documents/traindata/stages/2_rfc_80e12c5d1259")

  val predict = model1.transform(result)

val query = predict.writeStream
  .outputMode("append")
  .format("console")
  .start()
query.awaitTermination()

当我对流数据进行预测时，它会出现以下错误：

 Exception in thread "main" org.apache.spark.sql.AnalysisException: 
 Queries with streaming sources must be executed with 
writeStream.start();;
kafka
at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$.org$apache$spark$sql$catalyst$analysis$UnsupportedOperationChecker$$throwError(UnsupportedOperationChecker.scala:196)
at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$$anonfun$checkForBatch$1.apply(UnsupportedOperationChecker.scala:35)
at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$$anonfun$checkForBatch$1.apply(UnsupportedOperationChecker.scala:33)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:128)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:127)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.analysis.UnsupportedOperationChecker$.checkForBatch(UnsupportedOperationChecker.scala:33)
at org.apache.spark.sql.execution.QueryExecution.assertSupported(QueryExecution.scala:58)
at org.apache.spark.sql.execution.QueryExecution.withCachedData$lzycompute(QueryExecution.scala:69)
at org.apache.spark.sql.execution.QueryExecution.withCachedData(QueryExecution.scala:67)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:73)
at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:73)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:79)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:75)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:84)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:87)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:87)
at org.apache.spark.sql.Dataset.rdd$lzycompute(Dataset.scala:2547)
at org.apache.spark.sql.Dataset.rdd(Dataset.scala:2544)
at org.apache.spark.ml.feature.Word2Vec.fit(Word2Vec.scala:175)
at predict1model$.main(predict1model.scala:53)
at predict1model.main(predict1model.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)

错误是指word2vec.fit（removestopdf）行。任何帮助将非常感激。

Answer 1

一般情况下，结构化流媒体不能（从Spark 2.2开始）用于训练Spark ML模型。结构化流中不支持某些操作。其中之一是将Dataset转换为rdd表示。特别是word2Vec，it needs to go to the rdd level to implement fit。

的情况

然而，可以在静态数据集上训练模型并将预测应用于流数据。 transform操作可在流媒体Dataset上使用，如上所述：val result = model.transform(removestopdf)

简而言之，我们需要在静态数据集上拟合 model。生成的transformer可以应用到流Dataset。

Answer 2

您可以在此Github project "Spark Structured Streaming ML"

上找到概念验证

您还可以关注SPARK-16424

必须使用writeStream.start（）;;执行带有流源的查询

2 个答案: