LantaoJin opened a new pull request #26754: [SPARK-30115][SQL] Improve limit only query on datasource table URL: https://github.com/apache/spark/pull/26754 ### What changes were proposed in this pull request? We use Spark as ad-hoc query engine. Most of users' SELECT queries with LIMIT operation like 1) SELECT * FROM TABLE_A LIMIT N 2) SELECT colA FROM TABLE_A LIMIT N 3) CREATE TAB_B as SELECT * FROM TABLE_A LIMIT N If the TABLE_A is a large table (a RDD with thousands and thousands of partitions), the execution time would be very big since it has to list all files to build a RDD before execution. But almost time, the N is just like 10, 100, 1000, not very big. We don't need to scan all files. This optimization will create a **SinglePartitionReadRDD** to address it. In our production result, this optimization benefits a lot. The duration time of simple query with LIMIT could reduce 5~10 times. For example, before this optimization, a query on a table which has about one hundred thousands files would run over 30 seconds, after applying this optimization, the time decreased to 5 seconds. This PR only addresses Spark datasource table. Hive table and view will be filed after this merged. ### How to implement? 1. Add two configurations, `PARTIAL_LISTING_ENABLED` and `PARTIAL_LISTING_MAX_FILES` 2. In `FindDataSourceTable.apply()`, we resolve `GlobalLimit` to add a flag `partialListing` to `FileIndex` 3. In `DataSourceScanExec.inputRDD`, by checking the flag `partialListing` in `relation.location`, we create a `SinglePartitionReadRDD`. This RDD will assign less than `PARTIAL_LISTING_MAX_FILES` files to a single partition. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Add LimitOnlyQuerySuite
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