vinodkc opened a new pull request, #53189:
URL: https://github.com/apache/spark/pull/53189
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### What changes were proposed in this pull request?
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This PR adds Avro serialization and deserialization support for Spark's TIME
type.
### Why are the changes needed?
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1. If you propose a new API, clarify the use case for a new API.
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TIME type currently lacks Avro support, preventing users from:
- Reading/writing Avro files with TIME columns
- Using TIME with data exchange pipelines (Kafka, streaming)
- Integrating TIME data with Avro-based systems
- Preserving TIME precision in schema evolution
### Does this PR introduce _any_ user-facing change?
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Yes. Users can now:
1. Read Avro with TIME columns
```scala
spark.read.format("avro").load("data.avro")
// Returns DataFrame with TIME columns preserved
```
2. Write DataFrames with TIME to Avro
```scala
val df = spark.sql("SELECT TIME'14:30:45.123456' as shift_start")
df.write.format("avro").save("output.avro")
```
3. Use to_avro/from_avro functions with TIME
```scala
import org.apache.spark.sql.avro.functions.{to_avro, from_avro}
// Serialize TIME to Avro binary
val avroDF = df.select(to_avro($"shift_start").as("avro"))
// Deserialize Avro binary back to TIME (with precision metadata)
val schema = """
{
"type": "long",
"logicalType": "time-micros",
"spark.sql.catalyst.type": "time(3)"
}
"""
val timeDF = avroDF.select(from_avro($"avro", schema).as("shift_start"))
```
4. Use TIME in Avro-based streaming
```scala
// Kafka with Avro serialization
df.selectExpr("to_avro(struct(shift_start)) as value")
.write
.format("kafka")
.save()
```
### How was this patch tested?
Added tests in `AvroSuite` and `AvroFunctionsSuite.scala`
Also manually tested using
`spark-shell --packages org.apache.spark:spark-avro_2.13:4.0.0`
```scala
val df = spark.sql("SELECT TIME'14:30:45.123456' as shift_start")
import org.apache.spark.sql.avro.functions.{to_avro, from_avro}
val avroDF = df.select(to_avro($"shift_start").as("avro"))
// Deserialize Avro binary back to TIME (with precision metadata)
val schema = """
{
"type": "long",
"logicalType": "time-micros",
"spark.sql.catalyst.type": "time(3)"
}
"""
val timeDF = avroDF.select(from_avro($"avro", schema).as("shift_start"))
timeDF.show
```
```
+---------------+
| shift_start|
+---------------+
|14:30:45.123456|
+---------------+
```
```scala
timeDF.printSchema
```
```
root
|-- shift_start: time(3) (nullable = true)
```
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### Was this patch authored or co-authored using generative AI tooling?
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Yes.
Generated-by: Claude 3.5 Sonnet
AI assistance was used for:
- Code pattern analysis and design discussions
- Implementation guidance following Spark conventions
- Test case generation and organization
- Documentation and examples
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