zhengruifeng commented on PR #40063:
URL: https://github.com/apache/spark/pull/40063#issuecomment-1434578211
It seems more complicated than I thought, I think we can simplify it in this
way
In client:
```
def sql(self, sqlQuery: str, args: Optional[Dict[str, str]] = None) ->
"DataFrame":
df = DataFrame.withPlan(SQL(sqlQuery, args), self)
print(df.schema) <- eagerly analyze the plan
return df
```
In connect planner:
```
private def transformSql(sql: proto.SQL): LogicalPlan = {
// scalastyle:off println
println(s"invoke transformSql $sql")
session
.sql(sql.getQuery, sql.getArgsMap.asScala.toMap) <- directly invoke
the spark session api
.logicalPlan
}
```
bin/pyspark --remote "local[*]"
```
>>> spark.sql("set spark.sql.adaptive.enabled=false")
invoke transformSql query: "set spark.sql.adaptive.enabled=false"
StructType([StructField('key', StringType(), False), StructField('value',
StringType(), False)])
DataFrame[key: string, value: string]
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
