结合 Spark Streaming + MLlib

我尝试使用随机森林模型来预测示例流，但似乎我无法使用该模型对示例进行分类。 这是pyspark中使用的代码：

sc = SparkContext(appName="App")

model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, impurity='gini', numTrees=150)


ssc = StreamingContext(sc, 1)
lines = ssc.socketTextStream(hostname, int(port))

parsedLines = lines.map(parse)
parsedLines.pprint()

predictions = parsedLines.map(lambda event: model.predict(event.features))

在集群中编译时返回错误：

  Error : "It appears that you are attempting to reference SparkContext from a broadcast "
    Exception: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.

有没有办法使用从静态数据生成的模型来预测流示例？

谢谢你们，我真的很感激！

是的，您可以使用从静态数据生成的模型。您遇到的问题与流根本无关。您根本无法在操作或转换中使用基于 JVM 的模型（请参阅如何从操作或转换中使用 Java/Scala 函数？ https://stackoverflow.com/q/31684842/1560062解释原因）。相反，你应该申请predict方法来完成RDD例如使用transform on DStream:

from pyspark.mllib.tree import RandomForest
from pyspark.mllib.util import MLUtils
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from operator import attrgetter


sc = SparkContext("local[2]", "foo")
ssc = StreamingContext(sc, 1)

data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
trainingData, testData = data.randomSplit([0.7, 0.3])

model = RandomForest.trainClassifier(
    trainingData, numClasses=2, nmTrees=3
)

(ssc
    .queueStream([testData])
    # Extract features
    .map(attrgetter("features"))
    # Predict 
    .transform(lambda _, rdd: model.predict(rdd))
    .pprint())

ssc.start()
ssc.awaitTerminationOrTimeout(10)