[SPARK-53523][SQL] Named parameters respect `spark.sql.caseSensitive`

### What changes were proposed in this pull request?

As the title.

### Why are the changes needed?

The issue was originally found during

- https://github.com/apache/iceberg/pull/13106

I don't see any special reason that named parameters should always be case sensitive. (correct me if I'm wrong)

I tested PostgreSQL, and the named parameters are case-insensitive by default.

```
psql (17.6 (Debian 17.6-1.pgdg13+1))
Type "help" for help.

postgres=# CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false)
RETURNS text
AS
$$
 SELECT CASE
        WHEN $3 THEN UPPER($1 || ' ' || $2)
        ELSE LOWER($1 || ' ' || $2)
        END;
$$
LANGUAGE SQL IMMUTABLE STRICT;
CREATE FUNCTION
postgres=# SELECT concat_lower_or_upper('Hello', 'World', true);
 concat_lower_or_upper
-----------------------
 HELLO WORLD
(1 row)

postgres=# SELECT concat_lower_or_upper(a => 'Hello', b => 'World');
 concat_lower_or_upper
-----------------------
 hello world
(1 row)

postgres=# SELECT concat_lower_or_upper(A => 'Hello', b => 'World');
 concat_lower_or_upper
-----------------------
 hello world
(1 row)

postgres=#
```

### Does this PR introduce _any_ user-facing change?

Yes, named parameters used by functions, procedures now respect `spark.sql.caseSensitive`, instead of always performing case sensitive.

### How was this patch tested?

Added UT.

### Was this patch authored or co-authored using generative AI tooling?

No.

Closes #52269 from pan3793/SPARK-53523.

Authored-by: Cheng Pan <chengpan@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
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tree: e2bd13ce683994b79ded23e25ac24ca3d3371185
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  6. build/
  7. common/
  8. conf/
  9. connector/
  10. core/
  11. data/
  12. dev/
  13. docs/
  14. examples/
  15. graphx/
  16. hadoop-cloud/
  17. launcher/
  18. licenses/
  19. licenses-binary/
  20. mllib/
  21. mllib-local/
  22. project/
  23. python/
  24. R/
  25. repl/
  26. resource-managers/
  27. sbin/
  28. sql/
  29. streaming/
  30. tools/
  31. ui-test/
  32. .asf.yaml
  33. .gitattributes
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  36. CONTRIBUTING.md
  37. LICENSE
  38. LICENSE-binary
  39. NOTICE
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  41. pom.xml
  42. README.md
  43. scalastyle-config.xml
README.md

Apache Spark

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R (Deprecated), and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

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Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

./build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

More detailed documentation is available from the project site, at “Building Spark”.

For general development tips, including info on developing Spark using an IDE, see “Useful Developer Tools”.

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1,000,000,000:

scala> spark.range(1000 * 1000 * 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1,000,000,000:

>>> spark.range(1000 * 1000 * 1000).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be spark:// URL, “yarn” to run on YARN, and “local” to run locally with one thread, or “local[N]” to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

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Please see the guidance on how to run tests for a module, or individual tests.

There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at “Specifying the Hadoop Version and Enabling YARN” for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

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