Fixed Decimal 128 bug in ParquetCachedBatchSerializer

integration_tests/src/main/python/cache_test.py

@@ -24,8 +24,7 @@
 enable_vectorized_confs = [{"spark.sql.inMemoryColumnarStorage.enableVectorizedReader": "true"},
                            {"spark.sql.inMemoryColumnarStorage.enableVectorizedReader": "false"}]
 
-# cache does not work with 128-bit decimals, see https://github.com/NVIDIA/spark-rapids/issues/4826
-_cache_decimal_gens = [decimal_gen_32bit, decimal_gen_64bit]
+_cache_decimal_gens = [decimal_gen_32bit, decimal_gen_64bit, decimal_gen_128bit]
 _cache_single_array_gens_no_null = [ArrayGen(gen) for gen in all_basic_gens_no_null + _cache_decimal_gens]
 
 decimal_struct_gen= StructGen([['child0', sub_gen] for ind, sub_gen in enumerate(_cache_decimal_gens)])

sql-plugin/src/main/311+-all/scala/com/nvidia/spark/rapids/shims/v2/ParquetCachedBatchSerializer.scala

@@ -53,7 +53,7 @@ import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference,
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
 import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, ArrayData, GenericArrayData, MapData}
 import org.apache.spark.sql.columnar.CachedBatch
-import org.apache.spark.sql.execution.datasources.parquet.{ParquetReadSupport, ParquetToSparkSchemaConverter, ParquetWriteSupport, SparkToParquetSchemaConverter, VectorizedColumnReader}
+import org.apache.spark.sql.execution.datasources.parquet.{ParquetReadSupport, ParquetToSparkSchemaConverter, ParquetWriteSupport, VectorizedColumnReader}
 import org.apache.spark.sql.execution.datasources.parquet.rapids.shims.v2.{ParquetRecordMaterializer, ShimVectorizedColumnReader}
 import org.apache.spark.sql.execution.vectorized.{OffHeapColumnVector, WritableColumnVector}
 import org.apache.spark.sql.internal.SQLConf
@@ -889,13 +889,9 @@ protected class ParquetCachedBatchSerializer extends GpuCachedBatchSerializer wi
         val inMemCacheSparkSchema = parquetToSparkSchemaConverter.convert(inMemCacheParquetSchema)
 
         val totalRowCount = parquetFileReader.getRowGroups.asScala.map(_.getRowCount).sum
-        val sparkToParquetSchemaConverter = new SparkToParquetSchemaConverter(hadoopConf)
         val inMemReqSparkSchema = StructType(selectedAttributes.toStructType.map { field =>
           inMemCacheSparkSchema.fields(inMemCacheSparkSchema.fieldIndex(field.name))
         })
-        val inMemReqParquetSchema = sparkToParquetSchemaConverter.convert(inMemReqSparkSchema)
-        val columnsRequested: util.List[ColumnDescriptor] = inMemReqParquetSchema.getColumns
-
         val reqSparkSchemaInCacheOrder = StructType(inMemCacheSparkSchema.filter(f =>
           inMemReqSparkSchema.fields.exists(f0 => f0.name.equals(f.name))))
 
@@ -907,23 +903,26 @@ protected class ParquetCachedBatchSerializer extends GpuCachedBatchSerializer wi
           index -> inMemReqSparkSchema.fields.indexOf(reqSparkSchemaInCacheOrder.fields(index))
         }.toMap
 
-        val reqParquetSchemaInCacheOrder =
-          sparkToParquetSchemaConverter.convert(reqSparkSchemaInCacheOrder)
+        val reqParquetSchemaInCacheOrder = new org.apache.parquet.schema.MessageType(
+          inMemCacheParquetSchema.getName(), reqSparkSchemaInCacheOrder.fields.map { f =>
+            inMemCacheParquetSchema.getFields().get(inMemCacheParquetSchema.getFieldIndex(f.name))
+          }:_*)
 
+        val columnsRequested: util.List[ColumnDescriptor] = reqParquetSchemaInCacheOrder.getColumns
         // reset spark schema calculated from parquet schema
         hadoopConf.set(ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA, inMemReqSparkSchema.json)
         hadoopConf.set(ParquetWriteSupport.SPARK_ROW_SCHEMA, inMemReqSparkSchema.json)
 
         val columnsInCache: util.List[ColumnDescriptor] = reqParquetSchemaInCacheOrder.getColumns
         val typesInCache: util.List[Type] = reqParquetSchemaInCacheOrder.asGroupType.getFields
-        val missingColumns = new Array[Boolean](inMemReqParquetSchema.getFieldCount)
+        val missingColumns = new Array[Boolean](reqParquetSchemaInCacheOrder.getFieldCount)
 
         // initialize missingColumns to cover the case where requested column isn't present in the
         // cache, which should never happen but just in case it does
-        val paths: util.List[Array[String]] = inMemReqParquetSchema.getPaths
+        val paths: util.List[Array[String]] = reqParquetSchemaInCacheOrder.getPaths
 
-        for (i <- 0 until inMemReqParquetSchema.getFieldCount) {
-          val t = inMemReqParquetSchema.getFields.get(i)
+        for (i <- 0 until reqParquetSchemaInCacheOrder.getFieldCount) {
+          val t = reqParquetSchemaInCacheOrder.getFields.get(i)
           if (!t.isPrimitive || t.isRepetition(Type.Repetition.REPEATED)) {
             throw new UnsupportedOperationException("Complex types not supported.")
           }

sql-plugin/src/main/311until320-nondb/scala/com/nvidia/spark/rapids/shims/v2/Spark31XShims.scala

@@ -430,7 +430,7 @@ abstract class Spark31XShims extends Spark301until320Shims with Logging {
         }),
       GpuOverrides.exec[InMemoryTableScanExec](
         "Implementation of InMemoryTableScanExec to use GPU accelerated Caching",
-        ExecChecks((TypeSig.commonCudfTypes + TypeSig.DECIMAL_64 + TypeSig.STRUCT
+        ExecChecks((TypeSig.commonCudfTypes + TypeSig.DECIMAL_128 + TypeSig.STRUCT
             + TypeSig.ARRAY + TypeSig.MAP).nested(), TypeSig.all),
         (scan, conf, p, r) => new InMemoryTableScanMeta(scan, conf, p, r)),
       GpuOverrides.exec[ArrowEvalPythonExec](