Any examples? On 20 Apr. 2017 3:44 pm, "颜发才(Yan Facai)" <facai....@gmail.com> wrote:
> How about using `withColumn` and UDF? > > example: > + https://gist.github.com/zoltanctoth/2deccd69e3d1cde1dd78 > <https://gist.github.com/zoltanctoth/2deccd69e3d1cde1dd78> > + https://ragrawal.wordpress.com/2015/10/02/spark-custom-udf-example/ > > > > On Mon, Apr 17, 2017 at 8:25 PM, Zeming Yu <zemin...@gmail.com> wrote: > >> I've got a dataframe with a column looking like this: >> >> display(flight.select("duration").show()) >> >> +--------+ >> |duration| >> +--------+ >> | 15h10m| >> | 17h0m| >> | 21h25m| >> | 14h30m| >> | 24h50m| >> | 26h10m| >> | 14h30m| >> | 23h5m| >> | 21h30m| >> | 11h50m| >> | 16h10m| >> | 15h15m| >> | 21h25m| >> | 14h25m| >> | 14h40m| >> | 16h0m| >> | 24h20m| >> | 14h30m| >> | 14h25m| >> | 14h30m| >> +--------+ >> only showing top 20 rows >> >> >> >> I need to extract the hour as a number and store it as an additional >> column within the same dataframe. What's the best way to do that? >> >> >> I tried the following, but it failed: >> >> import re >> def getHours(x): >> return re.match('([0-9]+(?=h))', x) >> temp = flight.select("duration").rdd.map(lambda x:getHours(x[0])).toDF() >> temp.select("duration").show() >> >> >> error message: >> >> >> ---------------------------------------------------------------------------Py4JJavaError >> Traceback (most recent call >> last)<ipython-input-89-1d5bec255302> in <module>() 2 def getHours(x): >> 3 return re.match('([0-9]+(?=h))', x)----> 4 temp = >> flight.select("duration").rdd.map(lambda x:getHours(x[0])).toDF() 5 >> temp.select("duration").show() >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py >> in toDF(self, schema, sampleRatio) 55 [Row(name=u'Alice', >> age=1)] 56 """---> 57 return >> sparkSession.createDataFrame(self, schema, sampleRatio) 58 59 >> RDD.toDF = toDF >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py >> in createDataFrame(self, data, schema, samplingRatio, verifySchema) 518 >> 519 if isinstance(data, RDD):--> 520 rdd, schema = >> self._createFromRDD(data.map(prepare), schema, samplingRatio) 521 >> else: 522 rdd, schema = self._createFromLocal(map(prepare, >> data), schema) >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py >> in _createFromRDD(self, rdd, schema, samplingRatio) 358 """ >> 359 if schema is None or isinstance(schema, (list, tuple)):--> 360 >> struct = self._inferSchema(rdd, samplingRatio) 361 >> converter = _create_converter(struct) 362 rdd = >> rdd.map(converter) >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\session.py >> in _inferSchema(self, rdd, samplingRatio) 329 :return: >> :class:`pyspark.sql.types.StructType` 330 """--> 331 >> first = rdd.first() 332 if not first: 333 raise >> ValueError("The first row in RDD is empty, " >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\rdd.py >> in first(self) 1359 ValueError: RDD is empty 1360 """-> >> 1361 rs = self.take(1) 1362 if rs: 1363 >> return rs[0] >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\rdd.py >> in take(self, num) 1341 1342 p = range(partsScanned, >> min(partsScanned + numPartsToTry, totalParts))-> 1343 res = >> self.context.runJob(self, takeUpToNumLeft, p) 1344 1345 >> items += res >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\context.py >> in runJob(self, rdd, partitionFunc, partitions, allowLocal) 963 >> # SparkContext#runJob. 964 mappedRDD = >> rdd.mapPartitions(partitionFunc)--> 965 port = >> self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) >> 966 return list(_load_from_socket(port, >> mappedRDD._jrdd_deserializer)) 967 >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\java_gateway.py >> in __call__(self, *args) 1131 answer = >> self.gateway_client.send_command(command) 1132 return_value = >> get_return_value(-> 1133 answer, self.gateway_client, >> self.target_id, self.name) 1134 1135 for temp_arg in temp_args: >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\pyspark\sql\utils.py >> in deco(*a, **kw) 61 def deco(*a, **kw): 62 try:---> 63 >> return f(*a, **kw) 64 except >> py4j.protocol.Py4JJavaError as e: 65 s = >> e.java_exception.toString() >> C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\protocol.py >> in get_return_value(answer, gateway_client, target_id, name) 317 >> raise Py4JJavaError( 318 "An error occurred >> while calling {0}{1}{2}.\n".--> 319 format(target_id, >> ".", name), value) 320 else: 321 raise >> Py4JError( >> Py4JJavaError: An error occurred while calling >> z:org.apache.spark.api.python.PythonRDD.runJob. >> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 >> in stage 75.0 failed 1 times, most recent failure: Lost task 0.0 in stage >> 75.0 (TID 1035, localhost, executor driver): >> org.apache.spark.api.python.PythonException: Traceback (most recent call >> last): >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", >> line 174, in main >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", >> line 169, in process >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py", >> line 272, in dump_stream >> bytes = self.serializer.dumps(vs) >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py", >> line 427, in dumps >> return pickle.dumps(obj, protocol) >> _pickle.PicklingError: Can't pickle <class '_sre.SRE_Match'>: attribute >> lookup SRE_Match on _sre failed >> >> at >> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193) >> at >> org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234) >> at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152) >> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63) >> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) >> at org.apache.spark.scheduler.Task.run(Task.scala:99) >> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282) >> at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) >> at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) >> at java.lang.Thread.run(Unknown Source) >> >> Driver stacktrace: >> at >> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435) >> at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423) >> at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422) >> at >> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) >> at >> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422) >> at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802) >> at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802) >> at scala.Option.foreach(Option.scala:257) >> at >> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802) >> at >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650) >> at >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605) >> at >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594) >> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) >> at >> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628) >> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918) >> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931) >> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944) >> at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:441) >> at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala) >> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >> at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source) >> at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source) >> at java.lang.reflect.Method.invoke(Unknown Source) >> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) >> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) >> at py4j.Gateway.invoke(Gateway.java:280) >> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) >> at py4j.commands.CallCommand.execute(CallCommand.java:79) >> at py4j.GatewayConnection.run(GatewayConnection.java:214) >> at java.lang.Thread.run(Unknown Source) >> Caused by: org.apache.spark.api.python.PythonException: Traceback (most >> recent call last): >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", >> line 174, in main >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", >> line 169, in process >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py", >> line 272, in dump_stream >> bytes = self.serializer.dumps(vs) >> File >> "C:\spark-2.1.0-bin-hadoop2.7\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py", >> line 427, in dumps >> return pickle.dumps(obj, protocol) >> _pickle.PicklingError: Can't pickle <class '_sre.SRE_Match'>: attribute >> lookup SRE_Match on _sre failed >> >> at >> org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193) >> at >> org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234) >> at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152) >> at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63) >> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) >> at org.apache.spark.scheduler.Task.run(Task.scala:99) >> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282) >> at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source) >> at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source) >> ... 1 more >> >> >> >> >> >> >