| commit | 1817e676f1b75d68d22a825e413196ab5878506e | [log] [tgz] |
|---|---|---|
| author | Yihong He <heyihong.cn@gmail.com> | Thu Sep 11 22:08:49 2025 +0800 |
| committer | Wenchen Fan <wenchen@databricks.com> | Thu Sep 11 22:08:49 2025 +0800 |
| tree | 31fc8a2e8f032ecec2ed032524f134ac6f570501 | |
| parent | d0177795bbbe79d2c4e9ad66189425b2ecaec721 [diff] |
[SPARK-53524][CONNECT][SQL] Fix temporal value conversion in LiteralValueProtoConverter
### What changes were proposed in this pull request?
This PR fixes temporal value conversion issues in the `LiteralValueProtoConverter` for Spark Connect. The main changes include:
1. **Fixed temporal value conversion in `getConverter` method**: Updated the conversion logic for temporal data types (DATE, TIMESTAMP, TIMESTAMP_NTZ, DAY_TIME_INTERVAL, YEAR_MONTH_INTERVAL, TIME) to use proper utility methods from `SparkDateTimeUtils` and `SparkIntervalUtils` instead of directly returning raw protobuf values.
2. **Added comprehensive test coverage**: Extended the `PlanGenerationTestSuite` with a new test case that includes a tuple containing all temporal types to ensure proper conversion and serialization.
3. **Updated test expectations**: Modified the expected explain output and query test files to reflect the corrected temporal value handling.
### Why are the changes needed?
The struct type in typedlit doesn't work well with temporal values due to bugs in type conversions. For example, the code below fails:
```scala
import org.apache.spark.sql.functions.typedlit
spark.sql("select 1").select(typedlit((1, java.time.LocalDate.of(2020, 10, 10)))).collect()
"""
org.apache.spark.SparkIllegalArgumentException: The value (18545) of the type (java.lang.Integer) cannot be converted to the DATE type.
org.apache.spark.sql.catalyst.CatalystTypeConverters$DateConverter$.toCatalystImpl(CatalystTypeConverters.scala:356)
org.apache.spark.sql.catalyst.CatalystTypeConverters$DateConverter$.toCatalystImpl(CatalystTypeConverters.scala:347)
org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:110)
org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:271)
org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:251)
org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:110)
org.apache.spark.sql.catalyst.CatalystTypeConverters$.$anonfun$createToCatalystConverter$2(CatalystTypeConverters.scala:532)
org.apache.spark.sql.connect.planner.LiteralExpressionProtoConverter$.toCatalystExpression(LiteralExpressionProtoConverter.scala:116)
"""
```
### Does this PR introduce _any_ user-facing change?
**Yes.** This PR fixes temporal value conversion in LiteralValueProtoConverter, allowing the struct type in typedlit to work with temporal values.
### How was this patch tested?
`build/sbt "connect-client-jvm/testOnly org.apache.spark.sql.PlanGenerationTestSuite"`
`build/sbt "connect/testOnly org.apache.spark.sql.connect.ProtoToParsedPlanTestSuite"`
### Was this patch authored or co-authored using generative AI tooling?
Generated-by: Cursor 1.5.11
Closes #52270 from heyihong/SPARK-53524.
Authored-by: Yihong He <heyihong.cn@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R (Deprecated), 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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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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”.
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()
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()
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 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.
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
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.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
