[SPARK-55837][UI] Render Environment page tables client-side via REST API

What changes were proposed in this pull request?

Switches the Environment page from server-side Scala XML table rendering to client-side DataTables populated via the existing REST API endpoint (/api/v1/applications/{appId}/environment).

EnvironmentPage.scala:

Remove all UIUtils.listingTable calls and server-side data preparation
Remove badge counts from tabs (JS adds them dynamically after data loads)
Add loading spinner placeholders in each tab pane
Include environmentpage.js and utils.js as ES modules
Enable DataTables via useDataTables = true in headerSparkPage

environmentpage.js:

Rewrite as ES module importing getStandAloneAppId and setDataTableDefaults from utils.js
Fetch environment data via REST API on page load
Create DataTables with sorting, searching, and pagination for each section
Use deferRender: true for performance with 1000+ row tables (e.g., Hadoop Properties)
Format Resource Profiles with structured executor/task resource text
Dynamically update badge counts on tabs after data loads
Persist active tab state in localStorage
Adjust DataTable columns on tab switch for correct rendering in hidden panes

Why are the changes needed?

This continues the modernization of the Spark UI Environment page started in SPARK-55834. Moving to client-side rendering:

Reduces server-side HTML payload significantly (especially for 1000+ Hadoop properties)
Adds built-in search, sort, and pagination via DataTables
Follows the same pattern used by the Executors page
Improves user experience for environments with many properties

Does this PR introduce any user-facing change?

Yes. The Environment page tables now render client-side with DataTables, providing:

Built-in search/filter across all columns
Column sorting
Pagination (important for large property sets)
Loading spinners while data fetches

How was this patch tested?

./dev/lint-js — passed
./dev/scalastyle — passed
./build/sbt core/compile — passed

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

Yes, GitHub Copilot.

Closes #54632 from yaooqinn/SPARK-55837.

Authored-by: Kent Yao kentyao@microsoft.com Signed-off-by: Kent Yao kentyao@microsoft.com

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.

Official version: https://spark.apache.org/
Development version: https://apache.github.io/spark/

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.

Build Pipeline Status

Branch	Status
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branch-3.5

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.