[AURON #1891] Implement randn() function#1938
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Pull request overview
This PR implements the randn() function to improve Spark function coverage in Auron. The function generates random values from a standard normal distribution with optional seed support.
Changes:
- Added Rust implementation of
spark_randnfunction with seed handling - Registered the new function in the Scala converter and Rust function registry
- Added
rand_distrdependency for normal distribution sampling
Reviewed changes
Copilot reviewed 5 out of 6 changed files in this pull request and generated 3 comments.
Show a summary per file
| File | Description |
|---|---|
| spark-extension/src/main/scala/org/apache/spark/sql/auron/NativeConverters.scala | Added case handler for Randn expression to route to native implementation |
| native-engine/datafusion-ext-functions/src/spark_randn.rs | New implementation of randn function with seed handling and unit tests |
| native-engine/datafusion-ext-functions/src/lib.rs | Registered Spark_Randn function in the extension function factory |
| native-engine/datafusion-ext-functions/Cargo.toml | Added rand and rand_distr dependencies |
| Cargo.toml | Added rand_distr workspace dependency |
| Cargo.lock | Updated lock file with rand_distr package metadata |
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Resolve conflicts between randn and spark_partition_id features: - Proto: spark_partition_id_expr at 20101, randn_expr at 20102 - Planner: include both expression handlers Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@robreeves Nice work! LGTM. |
Resolved conflicts by assigning separate IDs to randn and monotonically_increasing_id: - MonotonicIncreasingIdExprNode: ID 20102 - RandnExprNode: ID 20103 Both expressions are now supported in the proto definitions and planner. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add test to AuronFunctionSuite to verify randn functionality with seeds. The test validates that Auron's native randn implementation produces the same reproducible results as Spark's baseline when using explicit seeds. Test covers: - randn with seed 42 - randn with seed 100 - Validates against Spark baseline using checkSparkAnswerAndOperator Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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I added a |
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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@richox can you run the PR checks again? |
Author
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@cxzl25 can you run the PR checks? Thanks |
ShreyeshArangath
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Changes LGTM, just one comment about the naming of this rust function.
…ntion Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
…nces Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ShreyeshArangath
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Feb 16, 2026
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hey, @cxzl25, gentle ping on this |
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Pull request overview
Copilot reviewed 9 out of 10 changed files in this pull request and generated 5 comments.
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@robreeves, could you resolve conflicts? |
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@SteNicholas yes I'll handle the updates this weekend. Apologies for the delay on this one. |
# Conflicts: # Cargo.lock # Cargo.toml # spark-extension-shims-spark/src/test/scala/org/apache/auron/AuronFunctionSuite.scala
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| use parking_lot::Mutex; | ||
| use rand::{SeedableRng, rngs::StdRng}; | ||
| use rand_distr::{Distribution, StandardNormal}; |
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The test does pass, but it is because there is a preexisting bug in the test suite. The Spark vanilla case is actually running with auron enabled so the cases always match in the assert.
I added this log:
@@ -55,6 +58,12 @@ abstract class AuronQueryTest
var expected: Seq[Row] = null
withSQLConf("spark.auron.enable" -> "false") {
+ // scalastyle:off println
+ println("[AURON_CONF_DEBUG] after setting spark.auron.enable=false -> " +
+ s"raw spark.auron.enable=${SQLConf.get.getConfString("spark.auron.enable", "<unset>")}, " +
+ s"raw spark.auron.enabled=${SQLConf.get.getConfString("spark.auron.enabled", "<unset>")}, " +
+ s"effective AURON_ENABLED.get()=${SparkAuronConfiguration.AURON_ENABLED.get()}")
+ // scalastyle:on println
val dfSpark = dataframe()
expected = dfSpark.collect()
}
and it shows this:
[AURON_CONF_DEBUG] after setting spark.auron.enable=false -> raw spark.auron.enable=false, raw spark.auron.enabled=<unset>, effective AURON_ENABLED.get()=true
I also reproduced it with this test.
test("alt config key spark.auron.enable is silently ignored") {
// Primary key is "spark.auron.enabled"; "spark.auron.enable" is only an alt key.
// Setting the ALT key has no effect -> effective value stays at the default (true).
withSQLConf("spark.auron.enable" -> "false") {
assert(SparkAuronConfiguration.AURON_ENABLED.get() === true)
}
// Setting the PRIMARY key works -> effective value becomes false.
withSQLConf("spark.auron.enabled" -> "false") {
assert(SparkAuronConfiguration.AURON_ENABLED.get() === false)
}
}Auron is enabled because spark.auron.enabled defaults to true here. The reason it is not set is because the alt keys are never registered with spark so ConfigEntry.findEntry("spark.auron.enable") does not return anything.
When I use the correct config spark.auron.enabled 3 tests failed. Since one test is unrelated I will open a separate PR to fix this first.
- acosh null propagation *** FAILED **
- randn function with seed *** FAILED ***
- randn function with foldable seed expression *** FAILED ***
- ```
Auron's ConfigOption alt keys (declared via addAltKey) were silently ignored: getFromSpark only consulted alt keys via ConfigEntry.findEntry (always null for Auron's unregistered options) and then synthesized a ConfigEntryWithDefaultFunction with an empty alternatives list, so only the primary key was ever read from SQLConf. As a result, e.g. setting spark.auron.enable (alt of spark.auron.enabled) had no effect. Pass the spark-prefixed alt keys as the synthesized entry's alternatives so ConfigEntry#readString reads primary +: alternatives, with the primary key taking precedence. Also add a test asserting alt keys are honored. Fixing this makes the test harness's spark.auron.enable=false baseline actually fall back to vanilla Spark, which exposed that acosh(0.0) yields NaN with a different (implementation-defined) bit pattern in each engine; QueryTest compares doubles via Double.doubleToRawLongBits, so update the acosh test to assert NaN-ness for the out-of-domain input rather than exact equality. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
QueryTest compares doubles via Double.doubleToRawLongBits, which is bit-exact. Vanilla Spark and the native engine can produce semantically equal NaNs with different (implementation-defined) bit patterns, so the comparison would spuriously fail. Canonicalize NaN on both sides before comparing. This lets the acosh null propagation test keep its original single-query form covering the out-of-domain (NaN) input. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
…cker Revert checkSparkAnswerAndOperator to plain checkAnswer and instead handle the NaN encoding difference locally in the acosh test. acosh of an out-of-domain input yields NaN, which vanilla Spark and the native engine may encode with different bits; checkAnswer/QueryTest compares doubles by raw bits. Split the test so in-domain/null values are compared numerically, and out-of-domain inputs are compared via the natively-supported isnan (a boolean) so no raw NaN bits are compared. This keeps the shared checker unchanged and avoids relaxing NaN comparison for all callers. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
With the config alt-key fix in place, the test harness's spark.auron.enable=false baseline now actually runs vanilla Spark, so checkSparkAnswerAndOperator compares Auron's randn against Spark's randn. These differ by design: the native engine uses StdRng/StandardNormal while Spark uses XORShiftRandom + nextGaussian, and randn is non-deterministic and not intended to be bit-compatible with Spark. Rewrite the randn tests to verify the expression is executed natively, produces a non-null value per row, and is reproducible for a fixed seed (and that different seeds produce different values), instead of asserting exact equality with vanilla Spark. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Which issue does this PR close?
Closes #1891
Rationale for this change
This improves function coverage in Auron by creating a native randn implementation.
What changes are included in this PR?
Adds a native randn implementation.
Are there any user-facing changes?
Yes, it adds the randn function.
How was this patch tested?
Added unit tests and manually tested in spark-shell.
Output: