yihua commented on a change in pull request #4789:
URL: https://github.com/apache/hudi/pull/4789#discussion_r813265854
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/keygen/BuiltinKeyGenerator.java
##########
@@ -69,7 +67,7 @@ protected BuiltinKeyGenerator(TypedProperties config) {
@PublicAPIMethod(maturity = ApiMaturityLevel.EVOLVING)
public String getRecordKey(Row row) {
if (null == converterFn) {
- converterFn = AvroConversionHelper.createConverterToAvro(row.schema(),
STRUCT_NAME, NAMESPACE);
+ converterFn = AvroConversionUtils.createConverterToAvro(row.schema(),
STRUCT_NAME, NAMESPACE);
}
GenericRecord genericRecord = (GenericRecord) converterFn.apply(row);
return getKey(genericRecord).getRecordKey();
Review comment:
nit: no need for type cast and directly do `return
getKey(converterFn.apply(row)).getRecordKey();`?
##########
File path:
hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/keygen/BuiltinKeyGenerator.java
##########
@@ -84,7 +82,7 @@ public String getRecordKey(Row row) {
@PublicAPIMethod(maturity = ApiMaturityLevel.EVOLVING)
public String getPartitionPath(Row row) {
if (null == converterFn) {
- converterFn = AvroConversionHelper.createConverterToAvro(row.schema(),
STRUCT_NAME, NAMESPACE);
+ converterFn = AvroConversionUtils.createConverterToAvro(row.schema(),
STRUCT_NAME, NAMESPACE);
}
GenericRecord genericRecord = (GenericRecord) converterFn.apply(row);
return getKey(genericRecord).getPartitionPath();
Review comment:
same here
##########
File path:
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieMergeOnReadRDD.scala
##########
@@ -323,32 +322,60 @@ private object HoodieMergeOnReadRDD {
def scanLog(split: HoodieMergeOnReadFileSplit, logSchema: Schema, config:
Configuration): HoodieMergedLogRecordScanner = {
val fs = FSUtils.getFs(split.tablePath, config)
- val partitionPath: String = if (split.logPaths.isEmpty ||
split.logPaths.get.asJava.isEmpty) {
- null
+ val logFiles = split.logFiles.get
+
+ if (HoodieTableMetadata.isMetadataTable(split.tablePath)) {
Review comment:
I need some clarification here. So why is metadata table treated
differently here? It's basically another Hudi table.
##########
File path:
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala
##########
@@ -41,15 +54,105 @@ abstract class HoodieBaseRelation(
protected val sparkSession: SparkSession = sqlContext.sparkSession
- protected val tableAvroSchema: Schema = {
+ protected lazy val tableAvroSchema: Schema = {
val schemaUtil = new TableSchemaResolver(metaClient)
- Try
(schemaUtil.getTableAvroSchema).getOrElse(SchemaConverters.toAvroType(userSchema.get))
+ Try(schemaUtil.getTableAvroSchema).getOrElse(
+ // If there is no commit in the table, we can't get the schema
+ // t/h [[TableSchemaResolver]], fallback to provided the [[userSchema]]
instead.
Review comment:
nit: typo "fallback to provided the" -> "fallback to the provided"
##########
File path:
hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/AvroConversionUtils.scala
##########
@@ -41,8 +107,8 @@ object AvroConversionUtils {
else {
val schema = new Schema.Parser().parse(schemaStr)
val dataType = convertAvroSchemaToStructType(schema)
- val convertor = AvroConversionHelper.createConverterToRow(schema,
dataType)
- records.map { x => convertor(x).asInstanceOf[Row] }
+ val converter = createConverterToRow(schema, dataType)
Review comment:
@nsivabalan @alexeykudinkin @xushiyan This is more like an internal
conversion. And looks like it is only used by read path? So does this expose
`InternalRow` in some way to public APIs and data written, i.e., if
`InternalRow` changes in Spark, could it break Hudi read/write flows?
End-to-end wise for write side, I think eventually we want to avoid Row/Avro
conversion for Spark Row Writer path. On the write side, I don't think it's a
good idea to depend on `InternalRow` which is Spark SQL's internal abstraction,
i.e., on the write side `Row`s should be passed along e2e when row abstraction
is required.
cc @vinothchandar @bvaradar
##########
File path:
hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/AvroConversionUtils.scala
##########
@@ -18,20 +18,105 @@
package org.apache.hudi
-import org.apache.avro.Schema
-import org.apache.avro.JsonProperties
+import org.apache.avro.Schema.Type
import org.apache.avro.generic.{GenericRecord, GenericRecordBuilder,
IndexedRecord}
+import org.apache.avro.{AvroRuntimeException, JsonProperties, Schema}
+import org.apache.hudi.HoodieSparkUtils.sparkAdapter
import org.apache.hudi.avro.HoodieAvroUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.avro.SchemaConverters
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.types.{DataType, StructType}
import org.apache.spark.sql.{Dataset, Row, SparkSession}
-import scala.collection.JavaConverters._
import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
object AvroConversionUtils {
+ /**
+ * Check the nullability of the input Avro type and resolve it when it is
nullable. The first
+ * return value is a [[Boolean]] indicating if the input Avro type is
nullable. The second
+ * return value is either provided Avro type if it's not nullable, or its
resolved non-nullable part
+ * in case it is
+ */
+ def resolveAvroTypeNullability(avroType: Schema): (Boolean, Schema) = {
+ if (avroType.getType == Type.UNION) {
+ val fields = avroType.getTypes.asScala
+ val actualType = fields.filter(_.getType != Type.NULL)
+ if (fields.length != 2 || actualType.length != 1) {
+ throw new AvroRuntimeException(
+ s"Unsupported Avro UNION type $avroType: Only UNION of a null type
and a non-null " +
+ "type is supported")
+ }
+ (true, actualType.head)
+ } else {
+ (false, avroType)
+ }
+ }
+
+ /**
+ * Creates converter to transform Avro payload into Spark's Catalyst one
+ *
+ * @param rootAvroType Avro [[Schema]] to be transformed from
+ * @param rootCatalystType Catalyst [[StructType]] to be transformed into
+ * @return converter accepting Avro payload and transforming it into a
Catalyst one (in the form of [[InternalRow]])
+ */
+ def createAvroToInternalRowConverter(rootAvroType: Schema, rootCatalystType:
StructType): GenericRecord => Option[InternalRow] =
+ record => sparkAdapter.createAvroDeserializer(rootAvroType,
rootCatalystType)
+ .deserialize(record)
+ .map(_.asInstanceOf[InternalRow])
+
+ /**
+ * Creates converter to transform Catalyst payload into Avro one
+ *
+ * @param rootCatalystType Catalyst [[StructType]] to be transformed from
+ * @param rootAvroType Avro [[Schema]] to be transformed into
+ * @param nullable whether Avro record is nullable
+ * @return converter accepting Catalyst payload (in the form of
[[InternalRow]]) and transforming it into an Avro one
+ */
+ def createInternalRowToAvroConverter(rootCatalystType: StructType,
rootAvroType: Schema, nullable: Boolean): InternalRow => GenericRecord = {
+ row => sparkAdapter.createAvroSerializer(rootCatalystType, rootAvroType,
nullable)
+ .serialize(row)
+ .asInstanceOf[GenericRecord]
+ }
+
+ /**
+ * @deprecated please use
[[AvroConversionUtils.createInternalRowToAvroConverter]]
Review comment:
wrong ref to the new API?
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