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### What changes were proposed in this pull request? accuracyExpression can accept Long which may cause overflow error. accuracyExpression can accept fractions which are implicitly floored. accuracyExpression can accept null which is implicitly changed to 0. percentageExpression can accept null but cause MatchError. percentageExpression can accept ArrayType(_, nullable=true) in which the nulls are implicitly changed to zeros. ##### cases ```sql select percentile_approx(10.0, 0.5, 2147483648); -- overflow and fail select percentile_approx(10.0, 0.5, 4294967297); -- overflow but success select percentile_approx(10.0, 0.5, null); -- null cast to 0 select percentile_approx(10.0, 0.5, 1.2); -- 1.2 cast to 1 select percentile_approx(10.0, null, 1); -- scala.MatchError select percentile_approx(10.0, array(0.2, 0.4, null), 1); -- null cast to zero. ``` ##### behavior before ```sql +select percentile_approx(10.0, 0.5, 2147483648) +org.apache.spark.sql.AnalysisException +cannot resolve 'percentile_approx(10.0BD, CAST(0.5BD AS DOUBLE), CAST(2147483648L AS INT))' due to data type mismatch: The accuracy provided must be a positive integer literal (current value = -2147483648); line 1 pos 7 + +select percentile_approx(10.0, 0.5, 4294967297) +10.0 + +select percentile_approx(10.0, 0.5, null) +org.apache.spark.sql.AnalysisException +cannot resolve 'percentile_approx(10.0BD, CAST(0.5BD AS DOUBLE), CAST(NULL AS INT))' due to data type mismatch: The accuracy provided must be a positive integer literal (current value = 0); line 1 pos 7 + +select percentile_approx(10.0, 0.5, 1.2) +10.0 + +select percentile_approx(10.0, null, 1) +scala.MatchError +null + + +select percentile_approx(10.0, array(0.2, 0.4, null), 1) +[10.0,10.0,10.0] ``` ##### behavior after ```sql +select percentile_approx(10.0, 0.5, 2147483648) +10.0 + +select percentile_approx(10.0, 0.5, 4294967297) +10.0 + +select percentile_approx(10.0, 0.5, null) +org.apache.spark.sql.AnalysisException +cannot resolve 'percentile_approx(10.0BD, 0.5BD, NULL)' due to data type mismatch: argument 3 requires integral type, however, 'NULL' is of null type.; line 1 pos 7 + +select percentile_approx(10.0, 0.5, 1.2) +org.apache.spark.sql.AnalysisException +cannot resolve 'percentile_approx(10.0BD, 0.5BD, 1.2BD)' due to data type mismatch: argument 3 requires integral type, however, '1.2BD' is of decimal(2,1) type.; line 1 pos 7 + +select percentile_approx(10.0, null, 1) +java.lang.IllegalArgumentException +The value of percentage must be be between 0.0 and 1.0, but got null + +select percentile_approx(10.0, array(0.2, 0.4, null), 1) +java.lang.IllegalArgumentException +Each value of the percentage array must be be between 0.0 and 1.0, but got [0.2,0.4,null] ``` ### Why are the changes needed? bug fix ### Does this PR introduce any user-facing change? yes, fix some improper usages of percentile_approx as cases list above ### How was this patch tested? add ut Closes #26905 from yaooqinn/SPARK-30266. Authored-by: Kent Yao <yaooqinn@hotmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.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.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". 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.