我是 PySpark（Spark 2.1.0 和 python 3.5）的新手，我遇到了一个无法通过的问题。
我尝试在 UDF 中使用GeoText，这是我的代码：
def countries(x):
count = GeoText(x).countries
w = ''
if not count:
return ''
else:
for country in count:
w += country
return w
我创建了一个 UDF：
udfCountry=udf(countries, StringType())
然后我尝试使用：
df2 = df.withColumn('country',udfCountry(df2.Location))
但运行任何 sql 条件，例如：
df2.where(df2.country == 'a').show()
导致此堆栈跟踪：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-194-84c9636e0170> in <module>()
1 #df3.select(df3.country,df3.cities,df3._Location).where(df3.country!='').take(10)
----> 2 df4.where(df4.country == 'a').show()
/opt/spark-2.1.0/python/pyspark/sql/dataframe.py in show(self, n, truncate)
316 """
317 if isinstance(truncate, bool) and truncate:
--> 318 print(self._jdf.showString(n, 20))
319 else:
320 print(self._jdf.showString(n, int(truncate)))
/opt/spark-2.1.0/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:
/opt/spark-2.1.0/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()
/opt/spark-2.1.0/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 o3116.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 79.0 failed 1 times, most recent failure: Lost task 0.0 in stage 79.0 (TID 79, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 174, in main
process()
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 169, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/serializers.py", line 220, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/serializers.py", line 138, in dump_stream
for obj in iterator:
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/serializers.py", line 209, in _batched
for item in iterator:
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 92, in <lambda>
mapper = lambda a: udf(*a)
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 70, in <lambda>
return lambda *a: f(*a)
File "<ipython-input-191-27c9af37cc7f>", line 2, in countries
File "/opt/anaconda3/lib/python3.5/site-packages/geotext/geotext.py", line 107, in __init__
candidates = re.findall(city_regex, text)
File "/opt/anaconda3/lib/python3.5/re.py", line 213, in findall
return _compile(pattern, flags).findall(string)
TypeError: expected string or bytes-like object
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.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1.apply(BatchEvalPythonExec.scala:144)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1.apply(BatchEvalPythonExec.scala:87)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$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.sql.execution.SparkPlan.executeTake(SparkPlan.scala:333)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2371)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2765)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2370)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2377)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2113)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2112)
at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2795)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2112)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2327)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:248)
at sun.reflect.GeneratedMethodAccessor65.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
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(Thread.java:745)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 174, in main
process()
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 169, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/serializers.py", line 220, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/serializers.py", line 138, in dump_stream
for obj in iterator:
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/serializers.py", line 209, in _batched
for item in iterator:
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 92, in <lambda>
mapper = lambda a: udf(*a)
File "/opt/spark-2.1.0/python/lib/pyspark.zip/pyspark/worker.py", line 70, in <lambda>
return lambda *a: f(*a)
File "<ipython-input-191-27c9af37cc7f>", line 2, in countries
File "/opt/anaconda3/lib/python3.5/site-packages/geotext/geotext.py", line 107, in __init__
candidates = re.findall(city_regex, text)
File "/opt/anaconda3/lib/python3.5/re.py", line 213, in findall
return _compile(pattern, flags).findall(string)
TypeError: expected string or bytes-like object
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.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1.apply(BatchEvalPythonExec.scala:144)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1.apply(BatchEvalPythonExec.scala:87)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
我透露的是，像这样改变我的 UDF 函数：
def countries(x):
#count = GeoText(x).countries
count = 'a'
w = ''
if not count:
return ''
else:
for country in count:
w += country
return w
因为它正在工作。
谁能解释我为什么会这样？我该怎么做才能让它发挥作用？
编辑
有趣的是 - 当我将我的数据框保存到镶木地板然后再次阅读时，一切正常......