commit	2f305b6817bca87c975a470f5801c3cd3742c04b	[log] [tgz]
author	Szehon Ho <szehon.apache@gmail.com>	Tue Sep 09 11:26:16 2025 +0800
committer	Wenchen Fan <wenchen@databricks.com>	Tue Sep 09 11:26:16 2025 +0800
tree	8123b7e2b80d5fc5319e35569b1982605a5a9072
parent	adef6f30fe34634f2e7f0bdd2a51ca6243a50cca [diff]

[SPARK-53527][SQL] Improve fallback of analyzeExistenceDefaultValue

### What changes were proposed in this pull request?

https://github.com/apache/spark/pull/49962 added a fallback in case there were already broken (ie, non-resolved) persisted default values in catalogs. A broken one is something like 'current_database, current_user, current_timestamp' , these are non-deterministic and will bring wrong results in EXISTS_DEFAULT, where user expects the value resolved when they set the default.

Add yet another fallback for broken default default value, in this case one where there are nested function calls.

### Why are the changes needed?

Take the case where the EXISTS_DEFAULT is :
```CONCAT(YEAR(CURRENT_DATE), LPAD(WEEKOFYEAR(CURRENT_DATE), 2, '0'))```

the current code `Literal.fromSQL(defaultSQL)` will throw the exception before getting to the fallback:
```
Caused by: java.lang.AssertionError: assertion failed: function arguments must be resolved.
	at scala.Predef$.assert(Predef.scala:279)
	at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$.$anonfun$expressionBuilder$1(FunctionRegistry.scala:1278)
	at org.apache.spark.sql.catalyst.analysis.SimpleFunctionRegistryBase.lookupFunction(FunctionRegistry.scala:251)
	at org.apache.spark.sql.catalyst.analysis.SimpleFunctionRegistryBase.lookupFunction$(FunctionRegistry.scala:245)
	at org.apache.spark.sql.catalyst.analysis.SimpleFunctionRegistry.lookupFunction(FunctionRegistry.scala:317)
	at org.apache.spark.sql.catalyst.expressions.Literal$$anonfun$fromSQL$1.applyOrElse(literals.scala:325)
	at org.apache.spark.sql.catalyst.expressions.Literal$$anonfun$fromSQL$1.applyOrElse(literals.scala:317)
	at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUpWithPruning$4(TreeNode.scala:586)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:121)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUpWithPruning(TreeNode.scala:586)
	at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformUpWithPruning$1(TreeNode.scala:579)
	at scala.collection.immutable.List.map(List.scala:251)
	at scala.collection.immutable.List.map(List.scala:79)
	at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:768)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUpWithPruning(TreeNode.scala:579)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:556)
	at org.apache.spark.sql.catalyst.expressions.Literal$.fromSQL(literals.scala:317)
	at org.apache.spark.sql.catalyst.util.ResolveDefaultColumns$.analyzeExistenceDefaultValue(ResolveDefaultColumnsUtil.scala:393)
	at org.apache.spark.sql.catalyst.util.ResolveDefaultColumns$.getExistenceDefaultValue(ResolveDefaultColumnsUtil.scala:529)
	at org.apache.spark.sql.catalyst.util.ResolveDefaultColumns$.$anonfun$getExistenceDefaultValues$1(ResolveDefaultColumnsUtil.scala:524)
	at scala.collection.ArrayOps$.map$extension(ArrayOps.scala:936)
	at org.apache.spark.sql.catalyst.util.ResolveDefaultColumns$.getExistenceDefaultValues(ResolveDefaultColumnsUtil.scala:524)
	at org.apache.spark.sql.catalyst.util.ResolveDefaultColumns$.$anonfun$existenceDefaultValues$2(ResolveDefaultColumnsUtil.scala:594)
	at scala.Option.getOrElse(Option.scala:201)
	at org.apache.spark.sql.catalyst.util.ResolveDefaultColumns$.existenceDefaultValues(ResolveDefaultColumnsUtil.scala:592)
```

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Add unit test in StructTypeSuite

### Was this patch authored or co-authored using generative AI tooling?
No

Closes #52274 from szehon-ho/more_default_value_fallback.

Authored-by: Szehon Ho <szehon.apache@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

2 files changed

tree: 8123b7e2b80d5fc5319e35569b1982605a5a9072

README.md

Apache Spark

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.

Official version: https://spark.apache.org/
Development version: https://apache.github.io/spark/

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.

Build Pipeline Status

Branch	Status
master
























branch-4.0






branch-3.5

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 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.