| commit | 2fff424dba267c0d215a3b3c0c4945fb62c31a36 | [log] [tgz] |
|---|---|---|
| author | Shubhambhusate <bhusates6@gmail.com> | Thu Jan 08 16:51:32 2026 +0800 |
| committer | Wenchen Fan <wenchen@databricks.com> | Thu Jan 08 16:51:32 2026 +0800 |
| tree | eccd3613521baf9540c1ac945ec87539c050d0a9 | |
| parent | 9c675099c9bbe1076fed6564738486bb7742ebe6 [diff] |
[SPARK-54776][SQL] Improved the logs message regarding lambda function with SQL UDF
### What changes were proposed in this pull request?
**Changes made:**
Added new error condition in error-conditions.json:
UNSUPPORTED_FEATURE.LAMBDA_FUNCTION_WITH_SQL_UDF - A clear error message for SQL UDFs used in lambda functions.
### Why are the changes needed?
Currently, when a SQL UDF is used inside a higher-order function like transform, the error message is confusing:
```
CREATE FUNCTION lower_udf(s STRING) RETURNS STRING RETURN lower(s);
SELECT transform(array('A', 'B'), x -> lower_udf(x));
```
**Before (confusing error):**
[MISSING_ATTRIBUTES.RESOLVED_ATTRIBUTE_MISSING_FROM_INPUT]
Resolved attribute(s) "x" missing from in operator !Project [cast(lambda x#20395 as string) AS s#20397].
SQLSTATE: XX000
<img width="1728" height="427" alt="Screenshot 2025-12-18 at 6 13 29 PM" src="https://github.com/user-attachments/assets/8d7e79dd-bd86-4199-8b16-fae0b9313d46" />
This error doesn't explain why the attribute is missing or what the user should do.
**After (clear error):**
[UNSUPPORTED_FEATURE.LAMBDA_FUNCTION_WITH_SQL_UDF] The feature is not supported: Lambda function with SQL UDF "spark_catalog.default.lower_udf(lambda x)" in a higher order function. SQLSTATE: 0A000
<img width="1728" height="314" alt="Screenshot 2025-12-18 at 6 14 11 PM" src="https://github.com/user-attachments/assets/76b30d2d-1c3a-4a8d-8feb-65a5295d6d35" />
This is consistent with the existing error message for Python UDFs in the same scenario (UNSUPPORTED_FEATURE.LAMBDA_FUNCTION_WITH_PYTHON_UDF).
### Does this PR introduce _any_ user-facing change?
Yes. Users will now see a clearer, more actionable error message when attempting to use a SQL UDF inside a higher-order function's lambda expression.
### How was this patch tested?
**Test 1:**
Added a new test case "SQL UDF in higher-order function should fail with clear error message" in SQLFunctionSuite.scala that:
Creates a SQL UDF
Attempts to use it in a transform higher-order function
Verifies the error condition is UNSUPPORTED_FEATURE.LAMBDA_FUNCTION_WITH_SQL_UDF
Verifies the error message contains the function name and lambda x
**Test 2:**
Manual testing
spark.sql("CREATE OR REPLACE FUNCTION test_lower_udf(s STRING) RETURNS STRING RETURN lower(s)") spark.sql("SELECT transform(array('A', 'B'), x -> test_lower_udf(x))").show()
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #53542 from Shubhambhusate/fix/LAMBDA_FUNCTION_WITH_SQL_UDF.
Authored-by: Shubhambhusate <bhusates6@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.
