我一直在尝试在PySpark中构建一个相似性检查器。我有两个数据帧，如下所示：
person_df
DataFrame[Person_EntityID: bigint, Person_Phrase: string]
post_df
DataFrame[Post_EntityID: bigint, Post_Phrase: string]
目标是在Person_Phrase和Post_Phrase的相似度超过80％时执行内部联接。
我使用了以下方法。我有一个UDF，如下所示：
def regex_filter(x, y):
if fuzz.ratio(x,y) > 80:
return True
return False
该功能已注册为filter_udf = udf(regex_filter, BooleanType())。我执行的连接查询是：
results = person_df.join(post_df, filter_udf(person_df.Person_Phrase, post_df.Post_Phrase), how='inner')
但这似乎不起作用。我收到以下错误：
Traceback (most recent call last):
File "/home/nikhil_baby/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-5-ca56973db95a>", line 1, in <module>
results.count()
File "/home/nikhil_baby/Programs/spark-2.2.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/sql/dataframe.py", line 427, in count
return int(self._jdf.count())
File "/home/nikhil_baby/Programs/spark-2.2.0-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/home/nikhil_baby/Programs/spark-2.2.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/home/nikhil_baby/Programs/spark-2.2.0-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o237.count.
: java.lang.RuntimeException: Invalid PythonUDF regex_filter(Person_Phrase#173, Post_Phrase#186), requires attributes from more than one child.
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:151)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:150)
at scala.collection.immutable.Stream.foreach(Stream.scala:594)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:150)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:113)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:93)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:99)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:99)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:99)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2832)
at org.apache.spark.sql.Dataset.count(Dataset.scala:2429)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
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:748)
我是一个全新的人。如果这是我的一个愚蠢的错误，请原谅我。提前谢谢。