[HUDI-1526] Translate the api partitionBy to hoodie.datasource.write.partitionpath.field

hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/DataSourceOptions.scala

@@ -23,9 +23,11 @@ import org.apache.hudi.common.model.WriteOperationType
 import org.apache.hudi.config.HoodieWriteConfig
 import org.apache.hudi.hive.HiveSyncTool
 import org.apache.hudi.hive.SlashEncodedDayPartitionValueExtractor
-import org.apache.hudi.keygen.SimpleKeyGenerator
+import org.apache.hudi.keygen.TimestampBasedAvroKeyGenerator.Config
+import org.apache.hudi.keygen.{CustomKeyGenerator, SimpleKeyGenerator}
 import org.apache.hudi.keygen.constant.KeyGeneratorOptions
 import org.apache.log4j.LogManager
+import org.apache.spark.sql.execution.datasources.{DataSourceUtils => SparkDataSourceUtils}
 
 /**
   * List of options that can be passed to the Hoodie datasource,
@@ -192,6 +194,42 @@ object DataSourceWriteOptions {
     }
   }
 
+  /**
+    * Translate spark parameters to hudi parameters
+    *
+    * @param optParams Parameters to be translated
+    * @return Parameters after translation
+    */
+  def translateSqlOptions(optParams: Map[String, String]): Map[String, String] = {
+    var translatedOptParams = optParams
+    // translate the api partitionBy of spark DataFrameWriter to PARTITIONPATH_FIELD_OPT_KEY
+    if (optParams.contains(SparkDataSourceUtils.PARTITIONING_COLUMNS_KEY)) {
+      val partitionColumns = optParams.get(SparkDataSourceUtils.PARTITIONING_COLUMNS_KEY)
+        .map(SparkDataSourceUtils.decodePartitioningColumns)
+        .getOrElse(Nil)
+      val keyGeneratorClass = optParams.getOrElse(DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY,
+        DataSourceWriteOptions.DEFAULT_KEYGENERATOR_CLASS_OPT_VAL)
+
+      val partitionPathField =
+        keyGeneratorClass match {
+          // Only CustomKeyGenerator needs special treatment, because it needs to be specified in a way
+          // such as "field1:PartitionKeyType1,field2:PartitionKeyType2".
+          // partitionBy can specify the partition like this: partitionBy("p1", "p2:SIMPLE", "p3:TIMESTAMP")
+          case c if c == classOf[CustomKeyGenerator].getName =>
+            partitionColumns.map(e => {
+              if (e.contains(":")) {
+                e
+              } else {
+                s"$e:SIMPLE"
+              }
+            }).mkString(",")
+          case _ =>
+            partitionColumns.mkString(",")
+        }
+      translatedOptParams = optParams ++ Map(PARTITIONPATH_FIELD_OPT_KEY -> partitionPathField)
+    }
+    translatedOptParams
+  }
 
   /**
     * Hive table name, to register the table into.

hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/hudi/DefaultSource.scala

@@ -31,7 +31,7 @@ import org.apache.spark.sql.execution.streaming.Sink
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.streaming.OutputMode
 import org.apache.spark.sql.types.StructType
-import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}
+import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode, SparkSession}
 
 import scala.collection.JavaConverters._
 
@@ -46,6 +46,14 @@ class DefaultSource extends RelationProvider
   with StreamSinkProvider
   with Serializable {
 
+  SparkSession.getActiveSession.foreach { spark =>
+    val sparkVersion = spark.version
+    if (sparkVersion.startsWith("0.") || sparkVersion.startsWith("1.") || sparkVersion.startsWith("2.")) {
+      // Enable "passPartitionByAsOptions" to support "write.partitionBy(...)"
+      spark.conf.set("spark.sql.legacy.sources.write.passPartitionByAsOptions", "true")
+    }
+  }
+
   private val log = LogManager.getLogger(classOf[DefaultSource])
 
   override def createRelation(sqlContext: SQLContext,
@@ -126,12 +134,13 @@ class DefaultSource extends RelationProvider
                               optParams: Map[String, String],
                               df: DataFrame): BaseRelation = {
     val parameters = HoodieWriterUtils.parametersWithWriteDefaults(optParams)
+    val translatedOptions = DataSourceWriteOptions.translateSqlOptions(parameters)
     val dfWithoutMetaCols = df.drop(HoodieRecord.HOODIE_META_COLUMNS.asScala:_*)
 
-    if (parameters(OPERATION_OPT_KEY).equals(BOOTSTRAP_OPERATION_OPT_VAL)) {
-      HoodieSparkSqlWriter.bootstrap(sqlContext, mode, parameters, dfWithoutMetaCols)
+    if (translatedOptions(OPERATION_OPT_KEY).equals(BOOTSTRAP_OPERATION_OPT_VAL)) {
+      HoodieSparkSqlWriter.bootstrap(sqlContext, mode, translatedOptions, dfWithoutMetaCols)
     } else {
-      HoodieSparkSqlWriter.write(sqlContext, mode, parameters, dfWithoutMetaCols)
+      HoodieSparkSqlWriter.write(sqlContext, mode, translatedOptions, dfWithoutMetaCols)
     }
     new HoodieEmptyRelation(sqlContext, dfWithoutMetaCols.schema)
   }
@@ -141,9 +150,10 @@ class DefaultSource extends RelationProvider
                           partitionColumns: Seq[String],
                           outputMode: OutputMode): Sink = {
     val parameters = HoodieWriterUtils.parametersWithWriteDefaults(optParams)
+    val translatedOptions = DataSourceWriteOptions.translateSqlOptions(parameters)
     new HoodieStreamingSink(
       sqlContext,
-      parameters,
+      translatedOptions,
       partitionColumns,
       outputMode)
   }

hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestCOWDataSource.scala

@@ -25,12 +25,16 @@ import org.apache.hudi.common.table.timeline.HoodieInstant
 import org.apache.hudi.common.testutils.HoodieTestDataGenerator
 import org.apache.hudi.common.testutils.RawTripTestPayload.recordsToStrings
 import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.hudi.keygen._
+import org.apache.hudi.keygen.TimestampBasedAvroKeyGenerator.Config
 import org.apache.hudi.testutils.HoodieClientTestBase
 import org.apache.hudi.{DataSourceReadOptions, DataSourceWriteOptions, HoodieDataSourceHelpers}
 import org.apache.spark.sql._
-import org.apache.spark.sql.functions.{col, lit}
-import org.apache.spark.sql.types.{DataTypes, DateType, IntegerType, StringType, StructField, StructType, TimestampType}
-import org.junit.jupiter.api.Assertions.{assertEquals, assertTrue}
+import org.apache.spark.sql.functions.{col, concat, lit, udf}
+import org.apache.spark.sql.types._
+import org.joda.time.DateTime
+import org.joda.time.format.DateTimeFormat
+import org.junit.jupiter.api.Assertions.{assertEquals, assertTrue, fail}
 import org.junit.jupiter.api.{AfterEach, BeforeEach, Test}
 import org.junit.jupiter.params.ParameterizedTest
 import org.junit.jupiter.params.provider.ValueSource
@@ -348,4 +352,151 @@ class TestCOWDataSource extends HoodieClientTestBase {
 
     assertTrue(HoodieDataSourceHelpers.hasNewCommits(fs, basePath, "000"))
   }
+
+  private def getDataFrameWriter(keyGenerator: String): DataFrameWriter[Row] = {
+    val records = recordsToStrings(dataGen.generateInserts("000", 100)).toList
+    val inputDF = spark.read.json(spark.sparkContext.parallelize(records, 2))
+
+    inputDF.write.format("hudi")
+      .options(commonOpts)
+      .option(DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY, keyGenerator)
+      .mode(SaveMode.Overwrite)
+  }
+
+  @Test def testSparkPartitonByWithCustomKeyGenerator(): Unit = {
+    // Without fieldType, the default is SIMPLE
+    var writer = getDataFrameWriter(classOf[CustomKeyGenerator].getName)
+    writer.partitionBy("current_ts")
+      .save(basePath)
+
+    var recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*")
+
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= col("current_ts").cast("string")).count() == 0)
+
+    // Specify fieldType as TIMESTAMP
+    writer = getDataFrameWriter(classOf[CustomKeyGenerator].getName)
+    writer.partitionBy("current_ts:TIMESTAMP")
+      .option(Config.TIMESTAMP_TYPE_FIELD_PROP, "EPOCHMILLISECONDS")
+      .option(Config.TIMESTAMP_OUTPUT_DATE_FORMAT_PROP, "yyyyMMdd")
+      .save(basePath)
+
+    recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*")
+
+    val udf_date_format = udf((data: Long) => new DateTime(data).toString(DateTimeFormat.forPattern("yyyyMMdd")))
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= udf_date_format(col("current_ts"))).count() == 0)
+
+    // Mixed fieldType
+    writer = getDataFrameWriter(classOf[CustomKeyGenerator].getName)
+    writer.partitionBy("driver", "rider:SIMPLE", "current_ts:TIMESTAMP")
+      .option(Config.TIMESTAMP_TYPE_FIELD_PROP, "EPOCHMILLISECONDS")
+      .option(Config.TIMESTAMP_OUTPUT_DATE_FORMAT_PROP, "yyyyMMdd")
+      .save(basePath)
+
+    recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*/*")
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!=
+      concat(col("driver"), lit("/"), col("rider"), lit("/"), udf_date_format(col("current_ts")))).count() == 0)
+
+    // Test invalid partitionKeyType
+    writer = getDataFrameWriter(classOf[CustomKeyGenerator].getName)
+    writer = writer.partitionBy("current_ts:DUMMY")
+      .option(Config.TIMESTAMP_TYPE_FIELD_PROP, "EPOCHMILLISECONDS")
+      .option(Config.TIMESTAMP_OUTPUT_DATE_FORMAT_PROP, "yyyyMMdd")
+    try {
+      writer.save(basePath)
+      fail("should fail when invalid PartitionKeyType is provided!")
+    } catch {
+      case e: Exception =>
+        assertTrue(e.getMessage.contains("No enum constant org.apache.hudi.keygen.CustomAvroKeyGenerator.PartitionKeyType.DUMMY"))
+    }
+  }
+
+  @Test def testSparkPartitonByWithSimpleKeyGenerator() {
+    // Use the `driver` field as the partition key
+    var writer = getDataFrameWriter(classOf[SimpleKeyGenerator].getName)
+    writer.partitionBy("driver")
+      .save(basePath)
+
+    var recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*")
+
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= col("driver")).count() == 0)
+
+    // Use the `driver,rider` field as the partition key, If no such field exists, the default value `default` is used
+    writer = getDataFrameWriter(classOf[SimpleKeyGenerator].getName)
+    writer.partitionBy("driver", "rider")
+      .save(basePath)
+
+    recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*")
+
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= lit("default")).count() == 0)
+  }
+
+  @Test def testSparkPartitonByWithComplexKeyGenerator() {
+    // Use the `driver` field as the partition key
+    var writer = getDataFrameWriter(classOf[ComplexKeyGenerator].getName)
+    writer.partitionBy("driver")
+      .save(basePath)
+
+    var recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*")
+
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= col("driver")).count() == 0)
+
+    // Use the `driver`,`rider` field as the partition key
+    writer = getDataFrameWriter(classOf[ComplexKeyGenerator].getName)
+    writer.partitionBy("driver", "rider")
+      .save(basePath)
+
+    recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*")
+
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= concat(col("driver"), lit("/"), col("rider"))).count() == 0)
+  }
+
+  @Test def testSparkPartitonByWithTimestampBasedKeyGenerator() {
+    val writer = getDataFrameWriter(classOf[TimestampBasedKeyGenerator].getName)
+    writer.partitionBy("current_ts")
+      .option(Config.TIMESTAMP_TYPE_FIELD_PROP, "EPOCHMILLISECONDS")
+      .option(Config.TIMESTAMP_OUTPUT_DATE_FORMAT_PROP, "yyyyMMdd")
+      .save(basePath)
+
+    val recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*/*")
+    val udf_date_format = udf((data: Long) => new DateTime(data).toString(DateTimeFormat.forPattern("yyyyMMdd")))
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= udf_date_format(col("current_ts"))).count() == 0)
+  }
+
+  @Test def testSparkPartitonByWithGlobalDeleteKeyGenerator() {
+    val writer = getDataFrameWriter(classOf[GlobalDeleteKeyGenerator].getName)
+    writer.partitionBy("driver")
+      .save(basePath)
+
+    val recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*")
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= lit("")).count() == 0)
+  }
+
+  @Test def testSparkPartitonByWithNonpartitionedKeyGenerator() {
+    // Empty string column
+    var writer = getDataFrameWriter(classOf[NonpartitionedKeyGenerator].getName)
+    writer.partitionBy("")
+      .save(basePath)
+
+    var recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*")
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= lit("")).count() == 0)
+
+    // Non-existent column
+    writer = getDataFrameWriter(classOf[NonpartitionedKeyGenerator].getName)
+    writer.partitionBy("abc")
+      .save(basePath)
+
+    recordsReadDF = spark.read.format("org.apache.hudi")
+      .load(basePath + "/*")
+    assertTrue(recordsReadDF.filter(col("_hoodie_partition_path") =!= lit("")).count() == 0)
+  }
 }