| commit | ca8c269a15037ce716449b5bba581e46aa8d7fea | [log] [tgz] |
|---|---|---|
| author | Gengliang Wang <gengliang@apache.org> | Sat May 04 11:48:08 2024 -0700 |
| committer | Dongjoon Hyun <dhyun@apple.com> | Sat May 04 11:48:08 2024 -0700 |
| tree | c3a47faaefe557e0d4e5a0aa7e37b29b8857e695 | |
| parent | 3b8c0049a5b58f26eb16c2d42070aea31e37a6c3 [diff] |
[SPARK-48124][CORE] Disable structured logging for Interpreters by default
### What changes were proposed in this pull request?
For interpreters, structured logging should be disabled by default to avoid generating mixed plain text and structured logs on the same console.
spark-shell output with mixed plain text and structured logs:
```
Using Scala version 2.13.13 (OpenJDK 64-Bit Server VM, Java 17.0.9)
Type in expressions to have them evaluated.
Type :help for more information.
{"ts":"2024-05-04T01:11:03.797Z","level":"WARN","msg":"Unable to load native-hadoop library for your platform... using builtin-java classes where applicable","logger":"NativeCodeLoader"} {"ts":"2024-05-04T01:11:04.104Z","level":"WARN","msg":"Service 'SparkUI' could not bind on port 4040. Attempting port 4041.","logger":"Utils"}
Spark context Web UI available at http://10.10.114.155:4041/
Spark context available as 'sc' (master = local[*], app id = local-1714785064155).
Spark session available as 'spark'.
```
After changes, all the output are plain text:
```
Type :help for more information.
24/05/03 18:11:35 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
24/05/03 18:11:35 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
Spark context Web UI available at http://10.10.114.155:4041/
Spark context available as 'sc' (master = local[*], app id = local-1714785095892).
Spark session available as 'spark'.
```
Note that submitting a spark application using `spark-submit` will still generates structured logs.
### Why are the changes needed?
To avoid tgenerating mixed plain text and structured logs on the same console when using the Interpreters.
### Does this PR introduce _any_ user-facing change?
No, this reverts to the behavior of Spark 3.5
### How was this patch tested?
Manual test
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #46383 from gengliangwang/disableStructuredLogInRepl.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
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.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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”.
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()
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()
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.
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
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.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
