[SPARK-45610][BUILD][CORE][SQL][SS][CONNECT][GRAPHX][DSTREAM][ML][MLLIB][K8S][YARN][SHELL][PYTHON][R][AVRO][UI][EXAMPLES] Fix the compilation warning "Auto-application to `()` is deprecated" and turn it into a compilation error

### What changes were proposed in this pull request?
This PR mainly does two things:
1. Clean up all compilation warnings related to "Auto-application to () is deprecated".
2. Change the compilation options to convert this compilation warning into a compilation error.

Additionally, due to an issue with scalatest(https://github.com/scalatest/scalatest/issues/2297), there are some false positives. Therefore, this PR has added the corresponding rules to suppress them, and left the corresponding TODO(SPARK-45615). We can clean up these rules after scalatest fixes this issue(https://github.com/scalatest/scalatest/pull/2298).

### Why are the changes needed?
1. Clean up the deprecated usage methods.
2. As this compilation warning will become a compilation error in Scala 3, to ensure it does not occur again, this PR also converts it into a compilation error in Scala 2.13.

For example, for the following code:

```scala
class Foo {
  def isEmpty(): Boolean = true
}
val foo = new Foo
val ret = foo.isEmpty
```

In Scala 2.13:

```
Welcome to Scala 2.13.12 (OpenJDK 64-Bit Server VM, Java 17.0.8).
Type in expressions for evaluation. Or try :help.

scala> class Foo {
     |   def isEmpty(): Boolean = true
     | }
class Foo

scala> val foo = new Foo
     |
val foo: Foo = Foo7e15f4d4

scala> val ret = foo.isEmpty
                     ^
       warning: Auto-application to `()` is deprecated. Supply the empty argument list `()` explicitly to invoke method isEmpty,
       or remove the empty argument list from its definition (Java-defined methods are exempt).
       In Scala 3, an unapplied method like this will be eta-expanded into a function. [quickfixable]
val ret: Boolean = true
```

In Scala 3:

```
Welcome to Scala 3.3.1 (17.0.8, Java OpenJDK 64-Bit Server VM).
Type in expressions for evaluation. Or try :help.

scala> class Foo {
     |   def isEmpty(): Boolean = true
     | }
// defined class Foo

scala> val foo = new Foo
val foo: Foo = Foo150d6eaf

scala> val ret = foo.isEmpty
-- [E100] Syntax Error: --------------------------------------------------------
1 |val ret = foo.isEmpty
  |          ^^^^^^^^^^^
  |          method isEmpty in class Foo must be called with () argument
  |-----------------------------------------------------------------------------
  | Explanation (enabled by `-explain`)
  |- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  | Previously an empty argument list () was implicitly inserted when calling a nullary method without arguments. E.g.
  |
  | def next(): T = ...
  |         |next     // is expanded to next()
  |
  | In Dotty, this idiom is an error. The application syntax has to follow exactly the parameter syntax.
  | Excluded from this rule are methods that are defined in Java or that override methods defined in Java.
   -----------------------------------------------------------------------------
1 error found

```

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Pass GitHub Actions

### Was this patch authored or co-authored using generative AI tooling?
No

Closes #43472 from LuciferYang/SPARK-45610.

Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
594 files changed
tree: 42e215ff80a2d3577f4c8fe9842e9ae429271ca8
  1. .github/
  2. assembly/
  3. bin/
  4. binder/
  5. build/
  6. common/
  7. conf/
  8. connector/
  9. core/
  10. data/
  11. dev/
  12. docs/
  13. examples/
  14. graphx/
  15. hadoop-cloud/
  16. launcher/
  17. licenses/
  18. licenses-binary/
  19. mllib/
  20. mllib-local/
  21. project/
  22. python/
  23. R/
  24. repl/
  25. resource-managers/
  26. sbin/
  27. sql/
  28. streaming/
  29. tools/
  30. .asf.yaml
  31. .gitattributes
  32. .gitignore
  33. appveyor.yml
  34. CONTRIBUTING.md
  35. LICENSE
  36. LICENSE-binary
  37. NOTICE
  38. NOTICE-binary
  39. pom.xml
  40. README.md
  41. 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, 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.

https://spark.apache.org/

GitHub Actions Build AppVeyor Build PySpark Coverage PyPI Downloads

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

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

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

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.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.