yihua commented on code in PR #13882:
URL: https://github.com/apache/hudi/pull/13882#discussion_r2345444460
##########
hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/SparkAdapterSupport.scala:
##########
@@ -43,4 +43,6 @@ object SparkAdapterSupport {
getClass.getClassLoader.loadClass(adapterClass)
.newInstance().asInstanceOf[SparkAdapter]
}
+
+ def getSparkAdapter: SparkAdapter = sparkAdapter
Review Comment:
In Java code, this is used to get the spark adapter
`SparkAdapterSupport$.MODULE$.sparkAdapter()` in other classes
##########
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/io/storage/row/HoodieRowParquetWriteSupport.java:
##########
@@ -18,47 +18,149 @@
package org.apache.hudi.io.storage.row;
+import org.apache.hudi.HoodieSparkUtils;
+import org.apache.hudi.SparkAdapterSupport;
+import org.apache.hudi.avro.HoodieAvroUtils;
import org.apache.hudi.avro.HoodieBloomFilterWriteSupport;
import org.apache.hudi.common.bloom.BloomFilter;
import org.apache.hudi.common.config.HoodieConfig;
import org.apache.hudi.common.config.HoodieStorageConfig;
import org.apache.hudi.common.util.Option;
import org.apache.hudi.common.util.ReflectionUtils;
+import org.apache.hudi.config.HoodieWriteConfig;
+import org.apache.hudi.internal.schema.convert.AvroInternalSchemaConverter;
+import org.apache.hudi.internal.schema.utils.SerDeHelper;
+import org.apache.avro.LogicalType;
+import org.apache.avro.LogicalTypes;
+import org.apache.avro.Schema;
import org.apache.hadoop.conf.Configuration;
+import org.apache.parquet.avro.AvroSchemaConverter;
+import org.apache.parquet.avro.AvroWriteSupport;
import org.apache.parquet.hadoop.api.WriteSupport;
+import org.apache.parquet.io.api.Binary;
+import org.apache.parquet.io.api.RecordConsumer;
+import org.apache.parquet.schema.MessageType;
+import org.apache.spark.sql.HoodieInternalRowUtils;
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.catalyst.expressions.SpecializedGetters;
+import org.apache.spark.sql.catalyst.util.ArrayData;
+import org.apache.spark.sql.catalyst.util.DateTimeUtils;
+import org.apache.spark.sql.catalyst.util.MapData;
+import org.apache.spark.sql.execution.datasources.DataSourceUtils;
import org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport;
+import org.apache.spark.sql.internal.SQLConf;
+import org.apache.spark.sql.types.ArrayType;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Decimal;
+import org.apache.spark.sql.types.DecimalType;
+import org.apache.spark.sql.types.MapType;
import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.types.TimestampNTZType;
import org.apache.spark.unsafe.types.UTF8String;
+import org.apache.spark.util.VersionUtils;
import java.util.Collections;
+import java.util.HashMap;
import java.util.Map;
+import java.util.stream.IntStream;
+import scala.Enumeration;
+import scala.Function1;
+
+import static org.apache.hudi.avro.AvroSchemaUtils.resolveNullableSchema;
import static
org.apache.hudi.common.config.HoodieStorageConfig.PARQUET_FIELD_ID_WRITE_ENABLED;
/**
- * Hoodie Write Support for directly writing Row to Parquet.
+ * Hoodie Write Support for directly writing Row to Parquet and adding the
Hudi bloom index to the file metadata.
+ * The implementation is adapted from Spark's {@link
org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport} but has
the following major differences:
+ * <ul>
+ * <li>Decimals are always written with the legacy format to ensure
compatibility with parquet-avro and other readers</li>
+ * <li>Writing 2-level or 3-level lists is configurable with either the Hudi
option, hoodie.parquet.writelegacyformat.enabled, or the parquet-avro option,
parquet.avro.write-old-list-structure
+ * to ensure consistency across writer paths.</li>
+ * <li>The scale of the timestamps is determined by the Hudi writer schema
instead of relying on Spark configuration</li>
+ * </ul>
*/
-public class HoodieRowParquetWriteSupport extends ParquetWriteSupport {
+public class HoodieRowParquetWriteSupport extends WriteSupport<InternalRow> {
+ private static final Schema MAP_KEY_SCHEMA =
Schema.create(Schema.Type.STRING);
+ private static final String MAP_REPEATED_NAME = "key_value";
+ private static final String MAP_KEY_NAME = "key";
+ private static final String MAP_VALUE_NAME = "value";
private final Configuration hadoopConf;
private final Option<HoodieBloomFilterWriteSupport<UTF8String>>
bloomFilterWriteSupportOpt;
+ private final byte[] decimalBuffer = new
byte[Decimal.minBytesForPrecision()[DecimalType.MAX_PRECISION()]];
+ private final Enumeration.Value datetimeRebaseMode = (Enumeration.Value)
SparkAdapterSupport.getSparkAdapter().getDateTimeRebaseMode();
Review Comment:
nit: use `SparkAdapterSupport$.MODULE$.sparkAdapter()` so there is no need
to add a new API in `SparkAdapterSupport`, to be consistent with the usage in
other Java classes
##########
hudi-spark-datasource/hudi-spark3.5.x/src/main/scala/org/apache/spark/sql/adapter/Spark3_5Adapter.scala:
##########
@@ -38,7 +38,7 @@ import
org.apache.spark.sql.execution.datasources.orc.Spark35OrcReader
import org.apache.spark.sql.execution.datasources.parquet.{ParquetFileFormat,
Spark35LegacyHoodieParquetFileFormat, Spark35ParquetReader}
import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation
import org.apache.spark.sql.hudi.analysis.TableValuedFunctions
-import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.internal.{LegacyBehaviorPolicy, SQLConf}
Review Comment:
So the class is moved in Spark thus the adapter pattern.
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