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Posted to github@datafusion.apache.org by "nat-openai (via GitHub)" <gi...@apache.org> on 2026/10/02 18:14:19 UTC
[PR] improve Spark from_utc_timestamp compatibility [datafusion]
nat-openai opened a new pull request, #25979:
URL: https://github.com/apache/datafusion/pull/25979
## Which issue does this PR close?
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We generally require a GitHub issue to be filed for all bug fixes and enhancements and this helps us generate change logs for our releases. You can link an issue to this PR using the GitHub syntax. For example `Closes #123` indicates that this PR will close issue #123.
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This addresses the major incompatibility described in https://github.com/apache/datafusion-comet/issues/4654 for `CometFromUTCTimestamp`. It does not close that issue.
It's a follow-up to #19879.
## Rationale for this change
<!--
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Explaining clearly why changes are proposed helps reviewers understand your changes and offer better suggestions for fixes.
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For example, "The code in `foo.rs` doesn't handle nulls" is a symptom of the
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The timezone spellings accepted by Spark are different than those accepted by Arrow's parser. As a result, `from_utc_timestamp` in comet isn't safe to use, since it would fail on things like Java short IDs, prefixed offsets with second precision, and SystemV-style IDs. Additionally, Arrow will accept offsets beyond the Spark limit of 18 hours.
## What changes are included in this PR?
<!--
There is no need to duplicate the description in the issue here, but it is sometimes worth providing a summary of the individual changes in this PR.
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This change improves compatibility by parsing timezone IDs with the rules implemented by Spark. As part of doing this, I added SparkFromUtcTimestampExpr so that computed timezones evaluated for rows with non-null timestamps, avoiding failing when a timezone is invalid but wouldn't be required anyway. (Full compatibility with spark behaviour seems quite complex and I haven't attempted it here.)
It also adds more documentation recording remaining differences:
- timezone validation and evaluation order can differ from Spark
- chrono accepts a narrower calendar range than Spark implemented range
- chrono-tz doesn't currently perform DST transitions past 2099
This does not achieve full compatibility with Spark due to those issues, but it makes the region of incompatibility a lot smaller.
If this approach isn't the one that folks would prefer, please let me know if there are different avenues you'd rather I explore!
Finally, it's worth mentioning that this change was LLM-assisted.
## What is the testing strategy for this PR?
<!--
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Added unit tests in `datafusion/spark/src/function/datetime/timezone.rs` covering accepted spelling, the 18 hour boundary, and SystemV custom behaviour around DST transitions and exception.
Added SQL logic tests in `datafusion/sqllogictest/test_files/spark/datetime/from_utc_timestamp.slt` covering the accepted spellings.
## Are there any user-facing changes?
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If there are user-facing changes then we may require documentation to be updated before approving the PR.
If there are any breaking changes to public APIs, please add the `api change` label.
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Yes. The existing Spark `from_utc_timestamp` UDF now accepts additional Spark timezone spellings. It also rejects offsets outside Spark's 18-hour limit.
This won't affect most users since they'd need to have opted into the existing incompatible implementation.
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Re: [PR] improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "nat-openai (via GitHub)" <gi...@apache.org>.
nat-openai commented on PR #25979:
URL: https://github.com/apache/datafusion/pull/25979#issuecomment-6020092066
@sunchao, if this seems reasonable, can you enable the CI workflows?
Also cc @andygrove, since this is tangentially relevant to https://github.com/apache/datafusion-comet/issues/4654.
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Re: [PR] feat: improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "codecov-commenter (via GitHub)" <gi...@apache.org>.
codecov-commenter commented on PR #25979:
URL: https://github.com/apache/datafusion/pull/25979#issuecomment-6106916143
## [Codecov](https://app.codecov.io/gh/apache/datafusion/pull/25979?dropdown=coverage&src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) Report
:x: Patch coverage is `90.75391%` with `65 lines` in your changes missing coverage. Please review.
:white_check_mark: Project coverage is 82.79%. Comparing base ([`3310fd5`](https://app.codecov.io/gh/apache/datafusion/commit/3310fd59a743dad59329779451278518ac32d90a?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache)) to head ([`7594f11`](https://app.codecov.io/gh/apache/datafusion/commit/7594f111a214f619c148b98df5b0bc367f75c845?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache)).
:warning: Report is 7 commits behind head on main.
| [Files with missing lines](https://app.codecov.io/gh/apache/datafusion/pull/25979?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) | Patch % | Lines |
|---|---|---|
| [.../spark/src/function/datetime/from\_utc\_timestamp.rs](https://app.codecov.io/gh/apache/datafusion/pull/25979?src=pr&el=tree&filepath=datafusion%2Fspark%2Fsrc%2Ffunction%2Fdatetime%2Ffrom_utc_timestamp.rs&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache#diff-ZGF0YWZ1c2lvbi9zcGFyay9zcmMvZnVuY3Rpb24vZGF0ZXRpbWUvZnJvbV91dGNfdGltZXN0YW1wLnJz) | 88.94% | [32 Missing and 9 partials :warning: ](https://app.codecov.io/gh/apache/datafusion/pull/25979?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) |
| [datafusion/spark/src/function/datetime/timezone.rs](https://app.codecov.io/gh/apache/datafusion/pull/25979?src=pr&el=tree&filepath=datafusion%2Fspark%2Fsrc%2Ffunction%2Fdatetime%2Ftimezone.rs&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache#diff-ZGF0YWZ1c2lvbi9zcGFyay9zcmMvZnVuY3Rpb24vZGF0ZXRpbWUvdGltZXpvbmUucnM=) | 92.83% | [9 Missing and 14 partials :warning: ](https://app.codecov.io/gh/apache/datafusion/pull/25979?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) |
| [datafusion/functions/src/datetime/to\_local\_time.rs](https://app.codecov.io/gh/apache/datafusion/pull/25979?src=pr&el=tree&filepath=datafusion%2Ffunctions%2Fsrc%2Fdatetime%2Fto_local_time.rs&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache#diff-ZGF0YWZ1c2lvbi9mdW5jdGlvbnMvc3JjL2RhdGV0aW1lL3RvX2xvY2FsX3RpbWUucnM=) | 90.90% | [0 Missing and 1 partial :warning: ](https://app.codecov.io/gh/apache/datafusion/pull/25979?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) |
<details><summary>Additional details and impacted files</summary>
```diff
@@ Coverage Diff @@
## main #25979 +/- ##
==========================================
+ Coverage 82.77% 82.79% +0.01%
==========================================
Files 1147 1148 +1
Lines 450926 451621 +695
Branches 450926 451621 +695
==========================================
+ Hits 373275 373915 +640
- Misses 54927 54956 +29
- Partials 22724 22750 +26
```
</details>
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Re: [PR] feat: improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb merged PR #25979:
URL: https://github.com/apache/datafusion/pull/25979
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Re: [PR] feat: improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "sunchao (via GitHub)" <gi...@apache.org>.
sunchao commented on code in PR #25979:
URL: https://github.com/apache/datafusion/pull/25979#discussion_r4240758616
##########
datafusion/spark/src/function/datetime/from_utc_timestamp.rs:
##########
@@ -188,3 +214,529 @@ where
builder = builder.with_timezone_opt(return_tz_opt);
Ok(Arc::new(builder.finish()))
}
+
+fn parse_timezone(timezone: &str) -> Result<SparkTimeZone> {
+ SparkTimeZone::parse(timezone).map_err(|e| {
+ exec_datafusion_err!("`from_utc_timestamp`: invalid timezone '{timezone}': {e}")
+ })
+}
+
+/// Physical `from_utc_timestamp` expression with conditional timezone evaluation.
+///
+/// The timestamp child is evaluated once for each non-empty batch. If every timestamp is null,
+/// the timezone child is skipped. Otherwise, literal and column timezones are read directly, and
+/// their values are ignored on null timestamp rows. Computed timezone expressions are evaluated
+/// only for rows with non-null timestamps. Empty batches evaluate neither child.
+///
+/// This intentionally differs from Spark's generated evaluation of foldable timezones, which
+/// evaluates the timezone and validates a non-null value before processing timestamp rows. This
+/// expression can therefore skip an invalid timezone or an error in the timezone expression when
+/// no timestamp is non-null. It also evaluates the timestamp first when the timezone is null, so a
+/// timestamp error can be raised even though Spark's generated path would return null. Planners
+/// using this expression should expose these differences as an incompatibility.
+#[derive(Debug, Clone)]
+pub struct SparkFromUtcTimestampExpr {
+ timestamp: Arc<dyn PhysicalExpr>,
+ timezone: Arc<dyn PhysicalExpr>,
+}
+
+impl SparkFromUtcTimestampExpr {
+ /// Construct an expression that skips computed timezone evaluation for null timestamps.
+ pub fn new(
+ timestamp: Arc<dyn PhysicalExpr>,
+ timezone: Arc<dyn PhysicalExpr>,
+ ) -> Self {
+ Self {
+ timestamp,
+ timezone,
+ }
+ }
+}
+
+impl Display for SparkFromUtcTimestampExpr {
+ fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
+ write!(
+ f,
+ "from_utc_timestamp({}, {})",
+ self.timestamp, self.timezone
+ )
+ }
+}
+
+impl PartialEq for SparkFromUtcTimestampExpr {
+ fn eq(&self, other: &Self) -> bool {
+ self.timestamp.eq(&other.timestamp) && self.timezone.eq(&other.timezone)
+ }
+}
+
+impl Eq for SparkFromUtcTimestampExpr {}
+
+impl Hash for SparkFromUtcTimestampExpr {
+ fn hash<H: Hasher>(&self, state: &mut H) {
+ self.timestamp.hash(state);
+ self.timezone.hash(state);
+ }
+}
+
+impl PhysicalExpr for SparkFromUtcTimestampExpr {
+ fn fmt_sql(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
+ Display::fmt(self, f)
Review Comment:
Nit: could `fmt_sql` format each child through its `fmt_sql` implementation rather than delegate to `Display`? For example, column `Display` produces `timestamp@0`, whereas its SQL formatter produces `timestamp`. This would keep SQL-formatted output consistent with other physical expressions.
##########
datafusion/spark/src/function/datetime/from_utc_timestamp.rs:
##########
@@ -188,3 +214,529 @@ where
builder = builder.with_timezone_opt(return_tz_opt);
Ok(Arc::new(builder.finish()))
}
+
+fn parse_timezone(timezone: &str) -> Result<SparkTimeZone> {
+ SparkTimeZone::parse(timezone).map_err(|e| {
+ exec_datafusion_err!("`from_utc_timestamp`: invalid timezone '{timezone}': {e}")
+ })
+}
+
+/// Physical `from_utc_timestamp` expression with conditional timezone evaluation.
+///
+/// The timestamp child is evaluated once for each non-empty batch. If every timestamp is null,
+/// the timezone child is skipped. Otherwise, literal and column timezones are read directly, and
+/// their values are ignored on null timestamp rows. Computed timezone expressions are evaluated
+/// only for rows with non-null timestamps. Empty batches evaluate neither child.
+///
+/// This intentionally differs from Spark's generated evaluation of foldable timezones, which
+/// evaluates the timezone and validates a non-null value before processing timestamp rows. This
+/// expression can therefore skip an invalid timezone or an error in the timezone expression when
+/// no timestamp is non-null. It also evaluates the timestamp first when the timezone is null, so a
+/// timestamp error can be raised even though Spark's generated path would return null. Planners
+/// using this expression should expose these differences as an incompatibility.
+#[derive(Debug, Clone)]
+pub struct SparkFromUtcTimestampExpr {
+ timestamp: Arc<dyn PhysicalExpr>,
+ timezone: Arc<dyn PhysicalExpr>,
+}
+
+impl SparkFromUtcTimestampExpr {
+ /// Construct an expression that skips computed timezone evaluation for null timestamps.
Review Comment:
Nonblocking documentation suggestion: a short planner/consumer example would help show where to construct this expression instead of the registered UDF, and that its children need to be coerced before construction. Ordinary SQL still uses the eager UDF, while this constructor simply stores the supplied physical children. An integration example would make the boundary of the conditional-evaluation support clearer.
##########
datafusion/spark/benches/from_utc_timestamp.rs:
##########
@@ -0,0 +1,94 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+//! Measure the physical evaluator with a timezone column. The narrow and wide batches use
+//! identical timestamp and timezone columns; only the number of unused Int64 columns differs.
+//! Mixed-null timestamps expose any cost from filtering those unrelated columns.
+
+use std::hint::black_box;
+use std::sync::Arc;
+
+use arrow::array::{ArrayRef, Int64Array, StringArray, TimestampMicrosecondArray};
+use arrow::datatypes::{DataType, Field, Schema, TimeUnit};
+use arrow::record_batch::RecordBatch;
+use criterion::{BenchmarkId, Criterion, Throughput, criterion_group, criterion_main};
+use datafusion::physical_expr::expressions::Column;
+use datafusion_physical_expr_common::physical_expr::PhysicalExpr;
+use datafusion_spark::function::datetime::from_utc_timestamp::SparkFromUtcTimestampExpr;
+
+fn batch(rows: usize, columns: usize, half_null: bool) -> RecordBatch {
+ let mut fields = vec![
+ Field::new(
+ "timestamp",
+ DataType::Timestamp(TimeUnit::Microsecond, None),
+ true,
+ ),
+ Field::new("timezone", DataType::Utf8, false),
+ ];
+ let mut arrays: Vec<ArrayRef> = vec![
+ Arc::new(TimestampMicrosecondArray::from_iter((0..rows).map(|row| {
+ (!half_null || row % 2 != 0).then_some(row as i64 * 86_400_000_000)
+ }))),
+ Arc::new(StringArray::from_iter_values(
+ (0..rows).map(|row| if (row / 2) % 2 == 0 { "PST" } else { "EST" }),
+ )),
+ ];
+
+ for column in 2..columns {
+ fields.push(Field::new(
+ format!("unused_{column}"),
+ DataType::Int64,
+ false,
+ ));
+ arrays.push(Arc::new(Int64Array::from_iter_values(
+ (0..rows).map(|row| (column * rows + row) as i64),
+ )));
+ }
+
+ RecordBatch::try_new(Arc::new(Schema::new(fields)), arrays).unwrap()
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+ let rows = 8192;
+ let expr = SparkFromUtcTimestampExpr::new(
+ Arc::new(Column::new("timestamp", 0)),
+ Arc::new(Column::new("timezone", 1)),
Review Comment:
Nonblocking: could we also benchmark a computed timezone expression, with sparse and mixed null timestamps, across these batch widths? This `Column` takes the fast path and bypasses `evaluate_selection`, so it does not exercise the branch that filters every input column. A computed-expression case would measure the remaining allocation/width tradeoff; a literal timezone case would also cover scalar broadcasting.
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Re: [PR] feat: improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on PR #25979:
URL: https://github.com/apache/datafusion/pull/25979#issuecomment-6384217803
I merged up to resolve a conflict as I am trying to clear our PR merge queue
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Re: [PR] feat: improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "ajsquared (via GitHub)" <gi...@apache.org>.
ajsquared commented on code in PR #25979:
URL: https://github.com/apache/datafusion/pull/25979#discussion_r4367126662
##########
datafusion/functions/src/datetime/to_local_time.rs:
##########
@@ -311,7 +310,10 @@ pub(crate) fn to_local_time(time_value: &ColumnarValue) -> Result<ColumnarValue>
/// ```
///
/// See `test_adjust_to_local_time()` for example
-pub fn adjust_to_local_time<T: ArrowTimestampType>(ts: i64, tz: Tz) -> Result<i64> {
+pub fn adjust_to_local_time<T: ArrowTimestampType>(
+ ts: i64,
+ tz: impl TimeZone + Copy,
Review Comment:
[P2] Changing the public timezone parameter from `Tz` to `impl TimeZone + Copy` breaks downstream calls that rely on type inference. For example, `adjust_to_local_time::<TimestampMicrosecondType>(0, "UTC".parse().unwrap())` previously inferred `Tz`. The new signature gives `parse()` no concrete target type, so the same call no longer compiles. Keep the public `Tz` signature and delegate to a separately named generic helper that the Spark fixed-offset path can also call.
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Re: [PR] feat: improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "sunchao (via GitHub)" <gi...@apache.org>.
sunchao commented on PR #25979:
URL: https://github.com/apache/datafusion/pull/25979#issuecomment-6106721787
@nat-openai just enabled the CI run
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Re: [PR] feat: improve Spark from_utc_timestamp compatibility [datafusion]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #25979:
URL: https://github.com/apache/datafusion/pull/25979#issuecomment-6328560135
<!-- semver-check-comment -->
Thank you for opening this pull request!
Reviewer note: [cargo-semver-checks](https://github.com/obi1kenobi/cargo-semver-checks) reported the current version number is not SemVer-compatible with the changes in this pull request (compared against the base branch).
<details>
<summary>Details</summary>
```
Cloning apache/main
Building datafusion-functions v55.1.0 (current)
Built [ 39.976s] (current)
Parsing datafusion-functions v55.1.0 (current)
Parsed [ 0.107s] (current)
Building datafusion-functions v55.1.0 (baseline)
Built [ 38.888s] (baseline)
Parsing datafusion-functions v55.1.0 (baseline)
Parsed [ 0.109s] (baseline)
Checking datafusion-functions v55.1.0 -> v55.1.0 (no change; assume patch)
Checked [ 0.425s] 229 checks: 226 pass, 3 fail, 0 warn, 31 skip
--- failure function_missing: pub fn removed or renamed ---
Description:
A publicly-visible function cannot be imported by its prior path. A `pub use` may have been removed, or the function itself may have been renamed or removed entirely.
ref: https://doc.rust-lang.org/cargo/reference/semver.html#item-remove
impl: https://github.com/obi1kenobi/cargo-semver-checks/tree/v0.51.0/src/lints/function_missing.ron
Failed in:
function datafusion_functions::datetime::timezone, previously in file /home/runner/work/datafusion/datafusion/target/semver-checks/git-apache_main/48c1df5ecb5bf547ee86cf270c64eb5c559919d0/datafusion/functions/src/datetime/mod.rs:51
--- failure module_missing: pub module removed or renamed ---
Description:
A publicly-visible module cannot be imported by its prior path. A `pub use` may have been removed, or the module may have been renamed, removed, or made non-public.
ref: https://doc.rust-lang.org/cargo/reference/semver.html#item-remove
impl: https://github.com/obi1kenobi/cargo-semver-checks/tree/v0.51.0/src/lints/module_missing.ron
Failed in:
mod datafusion_functions::datetime::timezone, previously in file /home/runner/work/datafusion/datafusion/target/semver-checks/git-apache_main/48c1df5ecb5bf547ee86cf270c64eb5c559919d0/datafusion/functions/src/datetime/timezone.rs:18
--- failure struct_missing: pub struct removed or renamed ---
Description:
A publicly-visible struct cannot be imported by its prior path. A `pub use` may have been removed, or the struct itself may have been renamed or removed entirely.
ref: https://doc.rust-lang.org/cargo/reference/semver.html#item-remove
impl: https://github.com/obi1kenobi/cargo-semver-checks/tree/v0.51.0/src/lints/struct_missing.ron
Failed in:
struct datafusion_functions::datetime::timezone::TimezoneFunc, previously in file /home/runner/work/datafusion/datafusion/target/semver-checks/git-apache_main/48c1df5ecb5bf547ee86cf270c64eb5c559919d0/datafusion/functions/src/datetime/timezone.rs:87
Summary semver requires new major version: 3 major and 0 minor checks failed
Finished [ 81.092s] datafusion-functions
Building datafusion-spark v55.1.0 (current)
Built [ 51.559s] (current)
Parsing datafusion-spark v55.1.0 (current)
Parsed [ 0.075s] (current)
Building datafusion-spark v55.1.0 (baseline)
Built [ 51.291s] (baseline)
Parsing datafusion-spark v55.1.0 (baseline)
Parsed [ 0.076s] (baseline)
Checking datafusion-spark v55.1.0 -> v55.1.0 (no change; assume patch)
Checked [ 0.390s] 229 checks: 229 pass, 31 skip
Summary no semver update required
Finished [ 104.608s] datafusion-spark
Building datafusion-sqllogictest v55.1.0 (current)
Built [ 106.211s] (current)
Parsing datafusion-sqllogictest v55.1.0 (current)
Parsed [ 0.016s] (current)
Building datafusion-sqllogictest v55.1.0 (baseline)
Built [ 108.538s] (baseline)
Parsing datafusion-sqllogictest v55.1.0 (baseline)
Parsed [ 0.017s] (baseline)
Checking datafusion-sqllogictest v55.1.0 -> v55.1.0 (no change; assume patch)
Checked [ 0.109s] 229 checks: 229 pass, 31 skip
Summary no semver update required
Finished [ 217.781s] datafusion-sqllogictest
```
</details>
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