我目前正在尝试使用PySpark为集合[(1, "winter is coming"), (2, "ours is the fury"), (3, "the old the true the brave")]创建布尔索引，其中输出是键值对，其中键是唯一的单词，而值是包含单词的集合的原始键的列表
首先，我使用以下代码对我的收藏进行了并行处理：
collection=sc.parallelize([(1, "winter is coming"), (2, "ours is the fury"), (3, "the old the true the brave")])
然后，我使用以下代码创建索引：
collection.map(lambda x:(x[0],x[1].split(" "))).flatMapValues(lambda x:x).map(lambda x:(x[1],[x[0]])).distinct().reduceByKey(lambda x,y:x+y).collect()
然而，在运行线路后，我得到了一个错误：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-184-d6bc1884fb69> in <module>()
1 collection=sc.parallelize([(1, "winter is coming"), (2, "ours is the fury"), (3, "the old the true the brave")])
----> 2 collection.map(lambda x:(x[0],x[1].split(" "))).flatMapValues(lambda x:x).map(lambda x:(x[1],[x[0]])).distinct().reduceByKey(lambda x,y:x+y).collect()
3 frames
/content/spark-2.4.5-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 150.0 failed 1 times, most recent failure: Lost task 1.0 in stage 150.0 (TID 260, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/content/spark-2.4.5-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/content/spark-2.4.5-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 2499, in pipeline_func
return func(split, prev_func(split, iterator))
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 2499, in pipeline_func
return func(split, prev_func(split, iterator))
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 352, in func
return f(iterator)
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 1861, in combineLocally
merger.mergeValues(iterator)
File "/content/spark-2.4.5-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/shuffle.py", line 240, in mergeValues
d[k] = comb(d[k], v) if k in d else creator(v)
TypeError: unhashable type: 'list'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:592)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1124)
at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1130)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1879)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:990)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:385)
at org.apache.spark.rdd.RDD.collect(RDD.scala:989)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at sun.reflect.GeneratedMethodAccessor45.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:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/content/spark-2.4.5-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/content/spark-2.4.5-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 2499, in pipeline_func
return func(split, prev_func(split, iterator))
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 2499, in pipeline_func
return func(split, prev_func(split, iterator))
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 352, in func
return f(iterator)
File "/content/spark-2.4.5-bin-hadoop2.7/python/pyspark/rdd.py", line 1861, in combineLocally
merger.mergeValues(iterator)
File "/content/spark-2.4.5-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/shuffle.py", line 240, in mergeValues
d[k] = comb(d[k], v) if k in d else creator(v)
TypeError: unhashable type: 'list'
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:592)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1124)
at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1130)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
我删除了.dispinct((属性，即
collection.map(lambda x:(x[0],x[1].split(" "))).flatMapValues(lambda x:x).map(lambda x:(x[1],[x[0]])).reduceByKey(lambda x,y:x+y).collect()
除了关键字"the"包含它所在的原始元组的关键字的重复值之外，一切都很好。即3，如下所示：
[('is', [1, 2]),
('true', [3]),
('brave', [3]),
('winter', [1]),
('coming', [1]),
('ours', [2]),
('the', [2, 3, 3, 3]),
('fury', [2]),
('old', [3])]
因此，我的问题是，在将密钥'the'输入到reduceByKey()函数之前，如何删除它的重复值？提前感谢！！
您可以对split((创建的列表进行重复数据消除，以获得所需的结果。
collection.map(lambda x:(x[0],list(set(x[1].split(" "))))).flatMapValues(lambda x:x).map(lambda x:(x[1],[x[0]])).reduceByKey(lambda x,y:x+y).collect()
输出：[('fury', [2]), ('true', [3]), ('is', [1, 2]), ('old', [3]), ('the', [2, 3]), ('ours', [2]), ('brave', [3]), ('winter', [1]), ('coming', [1])]