将现有函数用作 UDF 修改 Spark 数据帧列时出错

Question

我有一个数据帧，其中包含一列包含纯文本的字符串类型列，我想使用 pyspark.sql.functions.udf（或pyspark.sql.functions.UserDefinedFunction？）修改此列。

我在OSX 10.11.4上使用Python 2.7，Pyspark 1.6.1和Flask 0.10.1

。

当我使用 lambda 表达式时，它似乎工作正常：

@spark.route('/')
def run():
    df = ... # my dataframe
    myUDF = udf(lambda r: len(r),  IntegerType())
    df = df.withColumn('new_'+column, myUDF(df[column]))
    return render_template('index.html', data=df.take(1000))

一旦我尝试将 lambda 表达式移动到命名函数中：

def my_function(x):
    return len(x)
@spark.route('/')
def run():
    df = ... # my dataframe
    myUDF = udf(my_function,  IntegerType())
    df = df.withColumn('new_'+column, myUDF(df[column]))
    return render_template('index.html', data=df.take(1000))

我收到以下错误：

Py4JJavaError: An error occurred while calling z:org.apache.spark.sql.execution.EvaluatePython.takeAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/opt/spark/python/lib/pyspark.zip/pyspark/worker.py", line 98, in main
    command = pickleSer._read_with_length(infile)
  File "/opt/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 164, in _read_with_length
    return self.loads(obj)
  File "/opt/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 422, in loads
    return pickle.loads(obj)
  File "app/__init__.py", line 19, in <module>
    from app.controllers.main import main
  File "app/controllers/main/__init__.py", line 5, in <module>
    import default, source
  File "app/controllers/main/default.py", line 3, in <module>
    from app.controllers.main.source import file
  File "app/controllers/main/source/__init__.py", line 2, in <module>
    import file, online, database
  File "app/controllers/main/source/database.py", line 1, in <module>
    from app.controllers.spark import sqlContext
  File "app/controllers/spark/__init__.py", line 18, in <module>
    import default, grid #, pivot
  File "app/controllers/spark/default.py", line 2, in <module>
    from app.controllers.spark import spark, sc, sqlContext, grid as gridController
  File "app/controllers/spark/grid.py", line 14, in <module>
    from pyspark.ml import Pipeline
  File "/opt/spark/python/lib/pyspark.zip/pyspark/ml/__init__.py", line 18, in <module>
  File "/opt/spark/python/lib/pyspark.zip/pyspark/ml/pipeline.py", line 23, in <module>
  File "/opt/spark/python/lib/pyspark.zip/pyspark/mllib/__init__.py", line 25, in <module>
ImportError: No module named numpy
    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:166)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:207)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:125)
    at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:398)
    at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1.apply(python.scala:363)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

已安装 Numpy。删除 mllib 导入并不能解决问题。

Answer 1

如果你在"run"函数的主体内声明所有'my_function'的主体，它通常有效。否则，我还没有找到如何像您的情况一样调用外部函数。