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[HUDI-3271] Code optimization and clean up unused code in HoodieSparkSqlWriter (#4631)

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董可伦
2022-01-21 07:49:04 +08:00
committed by GitHub
parent 79bf6ab00b
commit 8547f11752
1 changed files with 18 additions and 19 deletions
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13
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieSparkSqlWriter.scala
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@@ -69,8 +69,8 @@ object HoodieSparkSqlWriter {
df: DataFrame,
hoodieTableConfigOpt: Option[HoodieTableConfig] = Option.empty,
hoodieWriteClient: Option[SparkRDDWriteClient[HoodieRecordPayload[Nothing]]] = Option.empty,
asyncCompactionTriggerFn: Option[Function1[SparkRDDWriteClient[HoodieRecordPayload[Nothing]], Unit]] = Option.empty,
asyncClusteringTriggerFn: Option[Function1[SparkRDDWriteClient[HoodieRecordPayload[Nothing]], Unit]] = Option.empty
asyncCompactionTriggerFn: Option[SparkRDDWriteClient[HoodieRecordPayload[Nothing]] => Unit] = Option.empty,
asyncClusteringTriggerFn: Option[SparkRDDWriteClient[HoodieRecordPayload[Nothing]] => Unit] = Option.empty
)
: (Boolean, common.util.Option[String], common.util.Option[String], common.util.Option[String],
SparkRDDWriteClient[HoodieRecordPayload[Nothing]], HoodieTableConfig) = {
@@ -199,7 +199,7 @@ object HoodieSparkSqlWriter {
// Get list of partitions to delete
val partitionsToDelete = if (parameters.containsKey(DataSourceWriteOptions.PARTITIONS_TO_DELETE.key())) {
val partitionColsToDelete = parameters.get(DataSourceWriteOptions.PARTITIONS_TO_DELETE.key()).get.split(",")
val partitionColsToDelete = parameters(DataSourceWriteOptions.PARTITIONS_TO_DELETE.key()).split(",")
java.util.Arrays.asList(partitionColsToDelete: _*)
} else {
genericRecords.map(gr => keyGenerator.getKey(gr).getPartitionPath).toJavaRDD().distinct().collect()
@@ -317,7 +317,7 @@ object HoodieSparkSqlWriter {
if (FSUtils.isTableExists(basePath.toString, fs)) {
val tableMetaClient = HoodieTableMetaClient.builder.setConf(sparkContext.hadoopConfiguration).setBasePath(basePath.toString).build()
val tableSchemaResolver = new TableSchemaResolver(tableMetaClient)
latestSchema = tableSchemaResolver.getLatestSchema(schema, false, null);
latestSchema = tableSchemaResolver.getLatestSchema(schema, false, null)
}
latestSchema
}
@@ -345,7 +345,7 @@ object HoodieSparkSqlWriter {
val sparkContext = sqlContext.sparkContext
val fs = basePath.getFileSystem(sparkContext.hadoopConfiguration)
tableExists = fs.exists(new Path(basePath, HoodieTableMetaClient.METAFOLDER_NAME))
var tableConfig = getHoodieTableConfig(sparkContext, path, hoodieTableConfigOpt)
val tableConfig = getHoodieTableConfig(sparkContext, path, hoodieTableConfigOpt)
validateTableConfig(sqlContext.sparkSession, optParams, tableConfig)
val (parameters, hoodieConfig) = mergeParamsAndGetHoodieConfig(optParams, tableConfig)
@@ -364,7 +364,6 @@ object HoodieSparkSqlWriter {
schema = HoodieAvroUtils.getNullSchema.toString
}
// Handle various save modes
if (mode == SaveMode.Ignore && tableExists) {
log.warn(s"hoodie table at $basePath already exists. Ignoring & not performing actual writes.")
@@ -453,7 +452,7 @@ object HoodieSparkSqlWriter {
// Sort modes are not yet supported when meta fields are disabled
new NonSortPartitionerWithRows()
}
val arePartitionRecordsSorted = bulkInsertPartitionerRows.arePartitionRecordsSorted();
val arePartitionRecordsSorted = bulkInsertPartitionerRows.arePartitionRecordsSorted()
parameters.updated(HoodieInternalConfig.BULKINSERT_ARE_PARTITIONER_RECORDS_SORTED, arePartitionRecordsSorted.toString)
val isGlobalIndex = if (populateMetaFields) {
SparkHoodieIndexFactory.isGlobalIndex(writeConfig)