我有一个spark数据框,如下所示,有7k列
+---+----+----+----+----+----+----+
| id| 1| 2| 3|sf_1|sf_2|sf_3|
+---+----+----+----+----+----+----+
| 2|null|null|null| 102| 202| 302|
| 4|null|null|null| 104| 204| 304|
| 1|null|null|null| 101| 201| 301|
| 3|null|null|null| 103| 203| 303|
| 1| 11| 21| 31|null|null|null|
| 2| 12| 22| 32|null|null|null|
| 4| 14| 24| 34|null|null|null|
| 3| 13| 23| 33|null|null|null|
+---+----+----+----+----+----+----+
我想通过合并空行来转换数据帧,如下所示。通过执行groupBy操作,我可以将其合并为一行,但是这种聚合的性能非常差,因为我的表中有7k列
import pyspark.sql.functions as F
(df.groupBy('id').agg(*[F.first(x,ignorenulls=True) for x in df.columns if x!='id'])
.show())
+---+----+----+----+----+----+----+
| id| 1| 2| 3|sf_1|sf_2|sf_3|
+---+----+----+----+----+----+----+
| 1| 11| 21| 31| 101| 201| 301|
| 2| 12| 22| 32| 102| 202| 302|
| 4| 14| 24| 34| 104| 204| 304|
| 3| 13| 23| 33| 103| 203| 303|
+---+----+----+----+----+----+----+
任何其他建议/优化/有效方法。谢谢
更新1:尝试使用自连接后
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-17-b7de100341cc> in <module>
15 """.format(table_name, query, join_key)
16
---> 17 spark.sql(final_query).dropDuplicates().filter(filters).count()
~/quartic/spark-3.0.0-bin-hadoop2.7/python/pyspark/sql/dataframe.py in count(self)
583 2
584 """
--> 585 return int(self._jdf.count())
586
587 @ignore_unicode_prefix
~/quartic/spark-3.0.0-bin-hadoop2.7/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
1303 answer = self.gateway_client.send_command(command)
1304 return_value = get_return_value(
-> 1305 answer, self.gateway_client, self.target_id, self.name)
1306
1307 for temp_arg in temp_args:
~/quartic/spark-3.0.0-bin-hadoop2.7/python/pyspark/sql/utils.py in deco(*a, **kw)
129 def deco(*a, **kw):
130 try:
--> 131 return f(*a, **kw)
132 except py4j.protocol.Py4JJavaError as e:
133 converted = convert_exception(e.java_exception)
~/quartic/spark-3.0.0-bin-hadoop2.7/python/lib/py4j-0.10.9-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 o148.count.
: java.lang.StackOverflowError
at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:35)
at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:38)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:184)
at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:47)
at scala.collection.generic.GenericCompanion.apply(GenericCompanion.scala:53)
at org.apache.spark.sql.catalyst.expressions.BinaryExpression.children(Expression.scala:533)
at org.apache.spark.sql.catalyst.trees.TreeNode.containsChild$lzycompute(TreeNode.scala:115)
at org.apache.spark.sql.catalyst.trees.TreeNode.containsChild(TreeNode.scala:115)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:349)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:330)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUp$1(TreeNode.scala:330)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:399)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:237)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:397)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:350)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:330)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUp$1(TreeNode.scala:330)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:399)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:237)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:397)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:350)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:330)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUp$1(TreeNode.scala:330)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:399)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:237)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:397)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:350)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:330)
您可以使用如下所示的自连接
你可以试试这个解决方案。如果速度快,请告诉我
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