09fa7ecae1
### What changes were proposed in this pull request? The changes in [SPARK-32501 Inconsistent NULL conversions to strings](https://issues.apache.org/jira/browse/SPARK-32501) introduced some behavior that I'd like to clean up a bit. Here's sample code to illustrate the behavior I'd like to clean up: ```scala val rows = Seq[String](null) .toDF("value") .withColumn("struct1", struct('value as "value1")) .withColumn("struct2", struct('value as "value1", 'value as "value2")) .withColumn("array1", array('value)) .withColumn("array2", array('value, 'value)) // Show the DataFrame using the "first" codepath. rows.show(truncate=false) +-----+-------+-------------+------+--------+ |value|struct1|struct2 |array1|array2 | +-----+-------+-------------+------+--------+ |null |{ null}|{ null, null}|[] |[, null]| +-----+-------+-------------+------+--------+ // Write the DataFrame to disk, then read it back and show it to trigger the "codegen" code path: rows.write.parquet("rows") spark.read.parquet("rows").show(truncate=false) +-----+-------+-------------+-------+-------------+ |value|struct1|struct2 |array1 |array2 | +-----+-------+-------------+-------+-------------+ |null |{ null}|{ null, null}|[ null]|[ null, null]| +-----+-------+-------------+-------+-------------+ ``` Notice: 1. If the first element of a struct is null, it is printed with a leading space (e.g. "\{ null\}"). I think it's preferable to print it without the leading space (e.g. "\{null\}"). This is consistent with how non-null values are printed inside a struct. 2. If the first element of an array is null, it is not printed at all in the first code path, and the "codegen" code path prints it with a leading space. I think both code paths should be consistent and print it without a leading space (e.g. "[null]"). The desired result of this PR is to product the following output via both code paths: ``` +-----+-------+------------+------+------------+ |value|struct1|struct2 |array1|array2 | +-----+-------+------------+------+------------+ |null |{null} |{null, null}|[null]|[null, null]| +-----+-------+------------+------+------------+ ``` This contribution is my original work and I license the work to the project under the project’s open source license. ### Why are the changes needed? To correct errors and inconsistencies in how DataFrame.show() displays nulls inside arrays and structs. ### Does this PR introduce _any_ user-facing change? Yes. This PR changes what is printed out by DataFrame.show(). ### How was this patch tested? I added new test cases in CastSuite.scala to cover the cases addressed by this PR. Closes #30189 from stwhit/show_nulls. Authored-by: Stuart White <stuart.white1@gmail.com> Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com> |
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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.
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 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. 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.