[GitHub] [hudi] yihua commented on a change in pull request #4789: [HUDI-1296] Support Metadata Table in Spark Datasource

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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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