结合 Spark Streaming + MLlib

Question

我尝试使用随机森林模型来预测示例流，但我似乎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))

以及在集群中编译时返回的错误：

  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？

谢谢大家，我真的很感激！！！！

Answer 1

是的，您可以使用从静态数据生成的模型。您遇到的问题根本与流媒体无关。您根本不能在动作或转换中使用基于 JVM 的模型（请参阅 How to use Java/Scala function from an action or a transformation? 了解原因）。相反，您应该将predict 方法应用于完整的RDD，例如在DStream 上使用transform：

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)