As in What does "Correlated scalar subqueries must be Aggregated" mean?, SparkSQL complains for scalar sub-query.
So when catalyst can't make 100% sure just by looking at the SQL statement (without looking at your data) that the sub-query only returns a single row, this exception is thrown. If you are sure that your subquery only gives a single row you can use one of the following aggregation standard functions, so Spark Analyzer is happy:
- first
- avg
- max
- min
How can I make the SQL query below work?
SELECT
prerequisite AS prerequisite,
(SELECT e.description FROM course e WHERE e.course_no = c.prerequisite) as course_name,
COUNT(*) as cnt
FROM
course c
WHERE
c.prerequisite IS NOT NULL
GROUP BY
c.prerequisite
ORDER BY
prerequisite;
It works in Oracle and returns the result.
PREREQUISITE COURSE_NAME CNT
------------ -------------------------------------------------- ----------
10 Technology Concepts 1
20 Intro to Information Systems 5
25 Intro to Programming 2
80 Programming Techniques 2
120 Intro to Java Programming 1
122 Intermediate Java Programming 2
125 Java Developer I 1
130 Intro to Unix 2
132 Basics of Unix Admin 1
134 Advanced Unix Admin 1
140 Systems Analysis 1
204 Intro to SQL 1
220 PL/SQL Programming 1
310 Operating Systems 2
350 Java Developer II 2
420 Database System Principles 1
However it fails in SparkSQL with the known error:
AnalysisException: Correlated scalar subqueries must be aggregated
If I put FIRST or MAX, it throws another exception Couldn't find first(description) which seem to be because the scalar query returns only 1 row hence cannot find the first one from it.
SELECT
prerequisite AS prerequisite,
(SELECT FIRST(e.description) FROM course e WHERE e.course_no = c.prerequisite) as course_name,
COUNT(*) as cnt
FROM
course c
WHERE
c.prerequisite IS NOT NULL
GROUP BY
c.prerequisite
ORDER BY
prerequisite
----------
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
Input In [214], in <module>
1 query="""
2 SELECT
3 prerequisite AS prerequisite,
(...)
13 prerequisite
14 """
---> 15 spark.sql(query).show(truncate=False)
File /opt/spark/spark-3.1.2/python/lib/pyspark.zip/pyspark/sql/dataframe.py:486, in DataFrame.show(self, n, truncate, vertical)
484 print(self._jdf.showString(n, 20, vertical))
485 else:
--> 486 print(self._jdf.showString(n, int(truncate), vertical))
File /opt/spark/spark-3.1.2/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py:1304, in JavaMember.__call__(self, *args)
1298 command = proto.CALL_COMMAND_NAME \
1299 self.command_header \
1300 args_command \
1301 proto.END_COMMAND_PART
1303 answer = self.gateway_client.send_command(command)
-> 1304 return_value = get_return_value(
1305 answer, self.gateway_client, self.target_id, self.name)
1307 for temp_arg in temp_args:
1308 temp_arg._detach()
File /opt/spark/spark-3.1.2/python/lib/pyspark.zip/pyspark/sql/utils.py:111, in capture_sql_exception.<locals>.deco(*a, **kw)
109 def deco(*a, **kw):
110 try:
--> 111 return f(*a, **kw)
112 except py4j.protocol.Py4JJavaError as e:
113 converted = convert_exception(e.java_exception)
File /opt/spark/spark-3.1.2/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py:326, in get_return_value(answer, gateway_client, target_id, name)
324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
325 if answer[1] == REFERENCE_TYPE:
--> 326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
331 "An error occurred while calling {0}{1}{2}. Trace:\n{3}\n".
332 format(target_id, ".", name, value))
Py4JJavaError: An error occurred while calling o1058.showString.
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: first(description)#13046
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:75)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:318)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:318)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:323)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:408)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:244)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:406)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:359)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:323)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:307)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:74)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.$anonfun$bindReferences$1(BoundAttribute.scala:96)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at scala.collection.immutable.List.map(List.scala:298)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReferences(BoundAttribute.scala:96)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec.generateResultFunction(HashAggregateExec.scala:554)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduceWithKeys(HashAggregateExec.scala:741)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduce(HashAggregateExec.scala:148)
at org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:95)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:90)
at org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:90)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec.produce(HashAggregateExec.scala:47)
at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:655)
at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:718)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176)
at org.apache.spark.sql.execution.TakeOrderedAndProjectExec.executeCollect(limit.scala:187)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2722)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2929)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:338)
at sun.reflect.GeneratedMethodAccessor62.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: java.lang.RuntimeException: Couldn't find first(description)#13046 in [prerequisite#5422,count(1)#13045L]
at scala.sys.package$.error(package.scala:30)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.$anonfun$applyOrElse$1(BoundAttribute.scala:81)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
... 61 more
CodePudding user response：
Have you ever tried the writing method below?
`SELECT
prerequisite AS prerequisite,
FIRST((SELECT e.description FROM course e WHERE e.course_no = c.prerequisite)) as course_name,
COUNT(*) as cnt
FROM
course c
WHERE
c.prerequisite IS NOT NULL
GROUP BY
c.prerequisite
ORDER BY
prerequisite`
I think it is okay...
CodePudding user response：
I think you can simply use join instead of correlated subquery:
SELECT
c.prerequisite,
FIRST(e.description) AS course_name,
COUNT(*) AS cnt
FROM
course c
LEFT JOIN
course e
ON
e.course_no = c.prerequisite
WHERE
c.prerequisite IS NOT NULL
GROUP BY
c.prerequisite
ORDER BY
1