andygrove opened a new issue, #3114:
URL: https://github.com/apache/datafusion-comet/issues/3114
## What is the problem the feature request solves?
> **Note:** This issue was generated with AI assistance. The specification
details have been extracted from Spark documentation and may need verification.
Comet does not currently support the Spark `timestamp_add_interval`
function, causing queries using this function to fall back to Spark's JVM
execution instead of running natively on DataFusion.
The `TimestampAddInterval` expression adds a time interval to a timestamp
value. It supports both day-time intervals (containing days, hours, minutes,
seconds) and calendar intervals (containing months, days, and microseconds),
with proper timezone handling for accurate temporal arithmetic.
Supporting this expression would allow more Spark workloads to benefit from
Comet's native acceleration.
## Describe the potential solution
### Spark Specification
**Syntax:**
```sql
timestamp_column + INTERVAL '1' DAY
timestamp_column + INTERVAL '1-2' YEAR TO MONTH
```
```scala
// DataFrame API usage
df.select(col("timestamp_col") + expr("INTERVAL '1' DAY"))
```
**Arguments:**
| Argument | Type | Description |
|----------|------|-------------|
| start | Expression | The base timestamp value (TimestampType or
TimestampNTZType) |
| interval | Expression | The interval to add (CalendarIntervalType or
DayTimeIntervalType) |
| timeZoneId | Option[String] | Optional timezone identifier for
timezone-aware operations |
**Return Type:** Returns the same data type as the input `start` expression
(either `TimestampType` or `TimestampNTZType`).
**Supported Data Types:**
- **Input timestamp**: `AnyTimestampType` (both `TimestampType` and
`TimestampNTZType`)
- **Input interval**: `CalendarIntervalType` or `DayTimeIntervalType`
**Edge Cases:**
- **Null handling**: Returns null if either the timestamp or interval input
is null (`nullIntolerant = true`)
- **Timezone transitions**: Properly handles daylight saving time
transitions and timezone offset changes
- **Calendar arithmetic**: Month additions handle variable month lengths
(e.g., adding 1 month to Jan 31 may result in Feb 28/29)
- **Overflow behavior**: May produce invalid results for extremely large
interval values that exceed timestamp boundaries
**Examples:**
```sql
-- Add 1 day to a timestamp
SELECT timestamp_col + INTERVAL '1' DAY FROM events;
-- Add 2 months and 15 days
SELECT timestamp_col + INTERVAL '2-0' YEAR TO MONTH + INTERVAL '15' DAY FROM
events;
-- Add precise time intervals
SELECT timestamp_col + INTERVAL '1 2:30:45.123' DAY TO SECOND FROM events;
```
```scala
// DataFrame API usage
import org.apache.spark.sql.functions._
// Add interval using SQL expression
df.select(col("timestamp_col") + expr("INTERVAL '1' DAY"))
// Using interval functions
df.select(col("timestamp_col") + expr("make_interval(0, 1, 0, 0, 0, 0)"))
```
### Implementation Approach
See the [Comet guide on adding new
expressions](https://datafusion.apache.org/comet/contributor-guide/adding_a_new_expression.html)
for detailed instructions.
1. **Scala Serde**: Add expression handler in
`spark/src/main/scala/org/apache/comet/serde/`
2. **Register**: Add to appropriate map in `QueryPlanSerde.scala`
3. **Protobuf**: Add message type in `native/proto/src/proto/expr.proto` if
needed
4. **Rust**: Implement in `native/spark-expr/src/` (check if DataFusion has
built-in support first)
## Additional context
**Difficulty:** Medium
**Spark Expression Class:**
`org.apache.spark.sql.catalyst.expressions.TimestampAddInterval`
**Related:**
- `TimestampDiff` - Calculate difference between timestamps
- `DateAdd` - Add days to date values
- `AddMonths` - Add months to date/timestamp values
- `IntervalExpression` - Create interval literals
---
*This issue was auto-generated from Spark reference documentation.*
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