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Revision 61ebc8118665531b4c11a31f2b3d459dd201b097 authored by Kousuke Saruta on 13 December 2019, 22:30:11 UTC, committed by Marcelo Vanzin on 13 December 2019, 22:30:11 UTC 
[SPARK-30167][REPL] Log4j configuration for REPL can't override the root logger properly
 
### What changes were proposed in this pull request?

In the current implementation of `SparkShellLoggingFilter`, if the log level of the root logger and the log level of a message are different, whether a message should logged is decided based on log4j's configuration but whether the message should be output to the REPL's console is not cared.
So, if the log level of the root logger is `DEBUG`, the log level of REPL's logger is `WARN` and the log level of a message is `INFO`, the message will output to the REPL's console even though `INFO < WARN`.
https://github.com/apache/spark/pull/26798/files#diff-bfd5810d8aa78ad90150e806d830bb78L237

The ideal behavior should be like as follows and implemented them in this change.

1. If the log level of a message is greater than or equal to the log level of the root logger, the message should be logged but whether the message is output to the REPL's console should be decided based on whether the log level of the message is greater than or equal to the log level of the REPL's logger.

2. If a log level or custom appenders are explicitly defined for a category, whether a log message via the logger corresponding to the category is logged and output to the REPL's console should be decided baed on the log level of the category.
We can confirm whether a log level or appenders are explicitly set to a logger for a category by `Logger#getLevel` and `Logger#getAllAppenders.hasMoreElements`.

### Why are the changes needed?

This is a bug breaking a compatibility.

#9816 enabled REPL's log4j configuration to override root logger but #23675 seems to have broken the feature.
You can see one example when you modifies the default log4j configuration like as follows.
```
# Change the log level for rootCategory to DEBUG
log4j.rootCategory=DEBUG, console

...
# The log level for repl.Main remains WARN
log4j.logger.org.apache.spark.repl.Main=WARN
```
If you launch REPL with the configuration, INFO level logs appear even though the log level for REPL is WARN.
```
・・・

19/12/08 23:31:38 INFO Utils: Successfully started service 'sparkDriver' on port 33083.
19/12/08 23:31:38 INFO SparkEnv: Registering MapOutputTracker
19/12/08 23:31:38 INFO SparkEnv: Registering BlockManagerMaster
19/12/08 23:31:38 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
19/12/08 23:31:38 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
19/12/08 23:31:38 INFO SparkEnv: Registering BlockManagerMasterHeartbeat

・・・
```
Before #23675 was applied, those INFO level logs are not shown with the same log4j.properties.

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

Yes. The logging behavior for REPL is fixed.

### How was this patch tested?

Manual test and newly added unit test.

Closes #26798 from sarutak/fix-spark-shell-loglevel.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
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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,
MLlib for machine learning, GraphX for graph processing,
and Structured Streaming for stream processing.

<https://spark.apache.org/>

[![Jenkins Build](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/badge/icon)](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7)
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## Online Documentation

You can find the latest Spark documentation, including a programming
guide, on the [project web page](https://spark.apache.org/documentation.html).
This README file only contains basic setup instructions.

## Building Spark

Spark is built using [Apache Maven](https://maven.apache.org/).
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.)

You can build Spark using more than one thread by using the -T option with Maven, see ["Parallel builds in Maven 3"](https://cwiki.apache.org/confluence/display/MAVEN/Parallel+builds+in+Maven+3).
More detailed documentation is available from the project site, at
["Building Spark"](https://spark.apache.org/docs/latest/building-spark.html).

For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](https://spark.apache.org/developer-tools.html).

## 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 a mesos:// or 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](#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](https://spark.apache.org/developer-tools.html#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"](https://spark.apache.org/docs/latest/building-spark.html#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](https://spark.apache.org/docs/latest/configuration.html)
in the online documentation for an overview on how to configure Spark.

## Contributing

Please review the [Contribution to Spark guide](https://spark.apache.org/contributing.html)
for information on how to get started contributing to the project.
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