pvary commented on code in PR #15328:
URL: https://github.com/apache/iceberg/pull/15328#discussion_r2816719209
##########
spark/v4.1/spark/src/jmh/java/org/apache/iceberg/spark/data/parquet/SparkParquetWritersNestedDataBenchmark.java:
##########
@@ -121,10 +126,28 @@ public void writeUsingSparkWriter() throws IOException {
.set("spark.sql.parquet.outputTimestampType", "TIMESTAMP_MICROS")
.set("spark.sql.caseSensitive", "false")
.set("spark.sql.parquet.fieldId.write.enabled", "false")
+ .set("spark.sql.parquet.variant.annotateLogicalType.enabled",
"false")
.schema(SCHEMA)
.build()) {
writer.addAll(rows);
}
}
+
+ @Benchmark
+ @Threads(1)
+ public void writeUsingRegistryWriter() throws IOException {
+ try (DataWriter<InternalRow> writer =
+ FormatModelRegistry.dataWriteBuilder(
+ FileFormat.PARQUET,
+ InternalRow.class,
+
EncryptedFiles.plainAsEncryptedOutput(Files.localOutput(dataFile)))
+ .schema(SCHEMA)
+ .engineSchema(SparkSchemaUtil.convert(SCHEMA))
Review Comment:
Probably we could create a new builderWriter method to avoid the need to set
the engine schema for Spark when it is not needed, like this:
```
public static <T> ParquetValueWriter<T> buildWriter(
Schema icebergSchema, StructType dfSchema, MessageType type) {
return (ParquetValueWriter<T>)
ParquetWithSparkSchemaVisitor.visit(
dfSchema != null ? dfSchema :
SparkSchemaUtil.convert(icebergSchema),
type,
new WriteBuilder(type));
}
```
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