vinodkc opened a new pull request, #53334:
URL: https://github.com/apache/spark/pull/53334
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### What changes were proposed in this pull request?
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This PR modifies the MS SQL Server JDBC dialect to map Spark's
`TimestampType` and `TimestampNTZType` to SQL Server's DATETIME2 type instead
of the legacy DATETIME type, preventing microsecond precision loss.
### Why are the changes needed?
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**Bug** : Spark's microsecond-precision timestamps are silently truncated to
milliseconds when writing to MS SQL Server, causing permanent data loss.
**Root Cause**:
Spark's `TimestampType` stores timestamps with microsecond precision (6
decimal places)
Current code maps timestamps to SQL Server's `DATETIME` type (3 decimal
places - milliseconds only)
The last 3 decimal places are truncated.
eg:
```scala
// Spark stores timestamps with microsecond precision
val df = Seq(
Timestamp.valueOf("2024-12-03 14:25:37.789123"), // 789123 microseconds
Timestamp.valueOf("2024-12-03 14:25:37.789456") // 789456 microseconds
).toDF("timestamp")
// Write to SQL Server with current code
df.write.jdbc(url, "table", props)
// Read back - microseconds are LOST
val result = spark.read.jdbc(url, "table", props)
// Both timestamps now show: 789000 microseconds
// Lost: 123 and 456 microseconds (permanently!)
// Events now appear simultaneous - ordering corrupted
```
**Solution**:
Use SQL Server's `DATETIME2` type which provides 7 decimal places
(100-nanosecond precision):
- Fully preserves Spark's 6-decimal microsecond precision
- Available since SQL Server 2008 (16+ years old, 99%+ adoption)
- Microsoft-recommended for all new development since 2008
- Used by other ORMs (Entity Framework, Hibernate) as the default
### Does this PR introduce _any_ user-facing change?
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Yes. This PR changes the default SQL Server type mapping for timestamps.
- New Behavior (Default):
`TimestampType` → `DATETIME2` (7 decimal places)
`TimestampNTZType` → `DATETIME2` (7 decimal places)
- Full microsecond precision preserved
**Configuration**:
```scala
// Default: Use DATETIME2 (prevents data loss)
spark.conf.set("spark.sql.mssqlserver.useDatetime2ForTimestamp.enabled",
"true")
// Legacy: Use DATETIME for SQL Server 2005 compatibility (if needed)
spark.conf.set("spark.sql.mssqlserver.useDatetime2ForTimestamp.enabled",
"false")
```
**Why Default to true**:
1. Prevents silent data corruption
2. SQL Server 2008+ is nearly universal (released 2008)
3. Aligns with Microsoft best practices
4. Consistent with PostgreSQL, MySQL, Oracle behavior
5. Other frameworks (Entity Framework, Hibernate) default to DATETIME2
### How was this patch tested?
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Integration tests add in `MsSqlServerIntegrationSuite`
### Was this patch authored or co-authored using generative AI tooling?
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No.
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