parthchandra commented on code in PR #1103:
URL: https://github.com/apache/datafusion-comet/pull/1103#discussion_r1849161016
##########
spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:
##########
@@ -2507,23 +2508,22 @@ object QueryPlanSerde extends Logging with
ShimQueryPlanSerde with CometExprShim
partitions.foreach(p => {
val inputPartitions =
p.asInstanceOf[DataSourceRDDPartition].inputPartitions
inputPartitions.foreach(partition => {
- partition2Proto(partition.asInstanceOf[FilePartition],
nativeScanBuilder)
+ partition2Proto(partition.asInstanceOf[FilePartition],
nativeScanBuilder, scan)
})
})
case rdd: FileScanRDD =>
rdd.filePartitions.foreach(partition => {
- partition2Proto(partition, nativeScanBuilder)
+ partition2Proto(partition, nativeScanBuilder, scan)
})
case _ =>
+ assert(false)
}
- val requiredSchemaParquet =
- new
SparkToParquetSchemaConverter(conf).convert(scan.requiredSchema)
- val dataSchemaParquet =
- new
SparkToParquetSchemaConverter(conf).convert(scan.relation.dataSchema)
+ val projection_vector: Array[java.lang.Long] =
scan.requiredSchema.fields.map(field => {
Review Comment:
This change essentially means that any schema 'adaptation' made in
`SparkToParquetSchemaConverter.convert` to support legacy timestamps and
decimals will not be supported. But we will probably fail tests with incorrect
results.
Also, Comet's Parquet file reader uses
`CometParquetReadSupport.clipParquetSchema` to do similar conversion and it
includes support for Parquet
[field_id](https://github.com/apache/parquet-format/blob/c70281359087dfaee8bd43bed9748675f4aabe11/src/main/thrift/parquet.thrift#L473)
which is desirable for delta sources like Iceberg.
Basically a field_id, if present, identifies a field more precisely (in the
event of field name changes) in a schema.
##########
spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala:
##########
@@ -3191,9 +3191,25 @@ object QueryPlanSerde extends Logging with
ShimQueryPlanSerde with CometExprShim
private def partition2Proto(
partition: FilePartition,
- nativeScanBuilder: OperatorOuterClass.NativeScan.Builder): Unit = {
+ nativeScanBuilder: OperatorOuterClass.NativeScan.Builder,
+ scan: CometScanExec): Unit = {
val partitionBuilder = OperatorOuterClass.SparkFilePartition.newBuilder()
+ val sparkContext = scan.session.sparkContext
+ var schema_saved: Boolean = false;
partition.files.foreach(file => {
+ if (!schema_saved) {
+ // TODO: This code shouldn't be here, but for POC it's fine.
+ // Extract the schema and stash it.
+ val hadoopConf =
+
scan.relation.sparkSession.sessionState.newHadoopConfWithOptions(scan.relation.options)
+ val broadcastedHadoopConf =
+ sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
+ val sharedConf = broadcastedHadoopConf.value.value
+ val footer = FooterReader.readFooter(sharedConf, file)
Review Comment:
You're right. This can never be in production code. For one, this is
expensive.
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