8c5bee599d
### What changes were proposed in this pull request? This is a followup of https://github.com/apache/spark/pull/27627 to fix the remaining issues. There are 2 issues fixed in this PR: 1. `UnsafeRow.setDecimal` can set an overflowed decimal and causes an error when reading it. The expected behavior is to return null. 2. The update/merge expression for decimal type in `Sum` is wrong. We shouldn't turn the `sum` value back to 0 after it becomes null due to overflow. This issue was hidden because: 2.1 for hash aggregate, the buffer is unsafe row. Due to the first bug, we fail when overflow happens, so there is no chance to mistakenly turn null back to 0. 2.2 for sort-based aggregate, the buffer is generic row. The decimal can overflow (the Decimal class has unlimited precision) and we don't have the null problem. If we only fix the first bug, then the second bug is exposed and test fails. If we only fix the second bug, there is no way to test it. This PR fixes these 2 bugs together. ### Why are the changes needed? Fix issues during decimal sum when overflow happens ### Does this PR introduce _any_ user-facing change? Yes. Now decimal sum can return null correctly for overflow under non-ansi mode. ### How was this patch tested? new test and updated test Closes #29026 from cloud-fan/decimal. Authored-by: Wenchen Fan <wenchen@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
||
---|---|---|
.github | ||
assembly | ||
bin | ||
build | ||
common | ||
conf | ||
core | ||
data | ||
dev | ||
docs | ||
examples | ||
external | ||
graphx | ||
hadoop-cloud | ||
launcher | ||
licenses | ||
licenses-binary | ||
mllib | ||
mllib-local | ||
project | ||
python | ||
R | ||
repl | ||
resource-managers | ||
sbin | ||
sql | ||
streaming | ||
tools | ||
.asf.yaml | ||
.gitattributes | ||
.gitignore | ||
appveyor.yml | ||
CONTRIBUTING.md | ||
LICENSE | ||
LICENSE-binary | ||
NOTICE | ||
NOTICE-binary | ||
pom.xml | ||
README.md | ||
scalastyle-config.xml |
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.