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### What changes were proposed in this pull request? The proposition of this pull request is described in this JIRA ticket: [https://issues.apache.org/jira/browse/SPARK-33769](url) It proposes to improve the next-day function of the sql component to deal with Column type for the parameter dayOfWeek. ### Why are the changes needed? It makes this functionality easier to use. Actually the signature of this function is: > def next_day(date: Column, dayOfWeek: String): Column. It accepts the dayOfWeek parameter as a String. However in some cases, the dayOfWeek is in a Column, so a different value for each row of the dataframe. A current workaround is to use the NextDay function like this: > NextDay(dateCol.expr, dayOfWeekCol.expr). The proposition is to add another signature for this function: > def next_day(date: Column, dayOfWeek: Column): Column In fact it is already the case for some other functions in this scala object, exemple: > def date_sub(start: Column, days: Int): Column = date_sub(start, lit(days)) > def date_sub(start: Column, days: Column): Column = withExpr \{ DateSub(start.expr, days.expr) } or > def add_months(startDate: Column, numMonths: Int): Column = add_months(startDate, lit(numMonths)) > def add_months(startDate: Column, numMonths: Column): Column = withExpr { > AddMonths(startDate.expr, numMonths.expr) > } This pull request is the same idea for the function next_day. ### Does this PR introduce _any_ user-facing change? Yes With this pull request, users of spark will have a new signature of the function: > def next_day(date: Column, dayOfWeek: Column): Column But the existing function signature should still work: > def next_day(date: Column, dayOfWeek: String): Column So this change should be retrocompatible. ### How was this patch tested? The unit tests of the next_day function has been enhanced. It tests the dayOfWeek parameter both as String and Column. I also added a test case for the existing signature where the dayOfWeek is a non valid String. This should return null. Closes #30761 from chongguang/SPARK-33769. Authored-by: Chongguang LIU <chongguang.liu@laposte.fr> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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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.