[SPARK-25336][SS]Revert SPARK-24863 and SPARK-24748

## What changes were proposed in this pull request?

Revert SPARK-24863 (apache#21819) and SPARK-24748 (apache#21721) as per discussion in apache#21721. We will revisit them when the data source v2 APIs are out.

## How was this patch tested?

Jenkins

Closes apache#22334 from zsxwing/revert-SPARK-24863-SPARK-24748.

Authored-by: Shixiong Zhu <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>

Author: zsxwing

external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/JsonUtils.scala

@@ -29,11 +29,6 @@ import org.json4s.jackson.Serialization
  */
 private object JsonUtils {
   private implicit val formats = Serialization.formats(NoTypeHints)
-  implicit val ordering = new Ordering[TopicPartition] {
-    override def compare(x: TopicPartition, y: TopicPartition): Int = {
-      Ordering.Tuple2[String, Int].compare((x.topic, x.partition), (y.topic, y.partition))
-    }
-  }
 
   /**
    * Read TopicPartitions from json string
@@ -56,7 +51,7 @@ private object JsonUtils {
    * Write TopicPartitions as json string
    */
   def partitions(partitions: Iterable[TopicPartition]): String = {
-    val result = HashMap.empty[String, List[Int]]
+    val result = new HashMap[String, List[Int]]
     partitions.foreach { tp =>
       val parts: List[Int] = result.getOrElse(tp.topic, Nil)
       result += tp.topic -> (tp.partition::parts)
@@ -85,31 +80,19 @@ private object JsonUtils {
    * Write per-TopicPartition offsets as json string
    */
   def partitionOffsets(partitionOffsets: Map[TopicPartition, Long]): String = {
-    val result = HashMap.empty[String, HashMap[Int, Long]]
+    val result = new HashMap[String, HashMap[Int, Long]]()
+    implicit val ordering = new Ordering[TopicPartition] {
+      override def compare(x: TopicPartition, y: TopicPartition): Int = {
+        Ordering.Tuple2[String, Int].compare((x.topic, x.partition), (y.topic, y.partition))
+      }
+    }
     val partitions = partitionOffsets.keySet.toSeq.sorted  // sort for more determinism
     partitions.foreach { tp =>
         val off = partitionOffsets(tp)
-        val parts = result.getOrElse(tp.topic, HashMap.empty[Int, Long])
+        val parts = result.getOrElse(tp.topic, new HashMap[Int, Long])
         parts += tp.partition -> off
         result += tp.topic -> parts
     }
     Serialization.write(result)
   }
-
-  /**
-   * Write per-topic partition lag as json string
-   */
-  def partitionLags(
-      latestOffsets: Map[TopicPartition, Long],
-      processedOffsets: Map[TopicPartition, Long]): String = {
-    val result = HashMap.empty[String, HashMap[Int, Long]]
-    val partitions = latestOffsets.keySet.toSeq.sorted
-    partitions.foreach { tp =>
-      val lag = latestOffsets(tp) - processedOffsets.getOrElse(tp, 0L)
-      val parts = result.getOrElse(tp.topic, HashMap.empty[Int, Long])
-      parts += tp.partition -> lag
-      result += tp.topic -> parts
-    }
-    Serialization.write(Map("lag" -> result))
-  }
 }

external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaMicroBatchReadSupport.scala

@@ -22,7 +22,6 @@ import java.io._
 import java.nio.charset.StandardCharsets
 
 import org.apache.commons.io.IOUtils
-import org.apache.kafka.common.TopicPartition
 
 import org.apache.spark.SparkEnv
 import org.apache.spark.internal.Logging
@@ -33,9 +32,9 @@ import org.apache.spark.sql.catalyst.expressions.UnsafeRow
 import org.apache.spark.sql.execution.streaming.{HDFSMetadataLog, SerializedOffset, SimpleStreamingScanConfig, SimpleStreamingScanConfigBuilder}
 import org.apache.spark.sql.execution.streaming.sources.RateControlMicroBatchReadSupport
 import org.apache.spark.sql.kafka010.KafkaSourceProvider.{INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_FALSE, INSTRUCTION_FOR_FAIL_ON_DATA_LOSS_TRUE}
-import org.apache.spark.sql.sources.v2.{CustomMetrics, DataSourceOptions}
+import org.apache.spark.sql.sources.v2.DataSourceOptions
 import org.apache.spark.sql.sources.v2.reader._
-import org.apache.spark.sql.sources.v2.reader.streaming.{MicroBatchReadSupport, Offset, SupportsCustomReaderMetrics}
+import org.apache.spark.sql.sources.v2.reader.streaming.{MicroBatchReadSupport, Offset}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.UninterruptibleThread
 
@@ -61,8 +60,7 @@ private[kafka010] class KafkaMicroBatchReadSupport(
     options: DataSourceOptions,
     metadataPath: String,
     startingOffsets: KafkaOffsetRangeLimit,
-    failOnDataLoss: Boolean)
-  extends RateControlMicroBatchReadSupport with SupportsCustomReaderMetrics with Logging {
+    failOnDataLoss: Boolean) extends RateControlMicroBatchReadSupport with Logging {
 
   private val pollTimeoutMs = options.getLong(
     "kafkaConsumer.pollTimeoutMs",
@@ -156,13 +154,6 @@ private[kafka010] class KafkaMicroBatchReadSupport(
     KafkaMicroBatchReaderFactory
   }
 
-  // TODO: figure out the life cycle of custom metrics, and make this method take `ScanConfig` as
-  // a parameter.
-  override def getCustomMetrics(): CustomMetrics = {
-    KafkaCustomMetrics(
-      kafkaOffsetReader.fetchLatestOffsets(), endPartitionOffsets.partitionToOffsets)
-  }
-
   override def deserializeOffset(json: String): Offset = {
     KafkaSourceOffset(JsonUtils.partitionOffsets(json))
   }
@@ -384,18 +375,3 @@ private[kafka010] case class KafkaMicroBatchPartitionReader(
     }
   }
 }
-
-/**
- * Currently reports per topic-partition lag.
- * This is the difference between the offset of the latest available data
- * in a topic-partition and the latest offset that has been processed.
- */
-private[kafka010] case class KafkaCustomMetrics(
-    latestOffsets: Map[TopicPartition, Long],
-    processedOffsets: Map[TopicPartition, Long]) extends CustomMetrics {
-  override def json(): String = {
-    JsonUtils.partitionLags(latestOffsets, processedOffsets)
-  }
-
-  override def toString: String = json()
-}

external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaMicroBatchSourceSuite.scala

@@ -30,8 +30,6 @@ import scala.util.Random
 
 import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord, RecordMetadata}
 import org.apache.kafka.common.TopicPartition
-import org.json4s.DefaultFormats
-import org.json4s.jackson.JsonMethods._
 import org.scalatest.concurrent.PatienceConfiguration.Timeout
 import org.scalatest.time.SpanSugar._
 
@@ -958,41 +956,6 @@ class KafkaMicroBatchV2SourceSuite extends KafkaMicroBatchSourceSuiteBase {
     intercept[IllegalArgumentException] { test(minPartitions = "-1", 1, true) }
   }
 
-  test("custom lag metrics") {
-    import testImplicits._
-    val topic = newTopic()
-    testUtils.createTopic(topic, partitions = 2)
-    testUtils.sendMessages(topic, (1 to 100).map(_.toString).toArray)
-    require(testUtils.getLatestOffsets(Set(topic)).size === 2)
-
-    val kafka = spark
-      .readStream
-      .format("kafka")
-      .option("subscribe", topic)
-      .option("startingOffsets", s"earliest")
-      .option("maxOffsetsPerTrigger", 10)
-      .option("kafka.bootstrap.servers", testUtils.brokerAddress)
-      .load()
-      .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
-      .as[(String, String)]
-
-    implicit val formats = DefaultFormats
-
-    val mapped = kafka.map(kv => kv._2.toInt + 1)
-    testStream(mapped)(
-      StartStream(trigger = OneTimeTrigger),
-      AssertOnQuery { query =>
-        query.awaitTermination()
-        val source = query.lastProgress.sources(0)
-        // masOffsetsPerTrigger is 10, and there are two partitions containing 50 events each
-        // so 5 events should be processed from each partition and a lag of 45 events
-        val custom = parse(source.customMetrics)
-          .extract[Map[String, Map[String, Map[String, Long]]]]
-        custom("lag")(topic)("0") == 45 && custom("lag")(topic)("1") == 45
-      }
-    )
-  }
-
 }
 
 abstract class KafkaSourceSuiteBase extends KafkaSourceTest {

sql/core/src/main/java/org/apache/spark/sql/sources/v2/CustomMetrics.java

@@ -22,20 +22,14 @@ import java.util.{Date, UUID}
 
 import scala.collection.JavaConverters._
 import scala.collection.mutable
-import scala.util.control.NonFatal
-
-import org.json4s.jackson.JsonMethods.parse
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.catalyst.plans.logical.{EventTimeWatermark, LogicalPlan}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.execution.QueryExecution
-import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2ScanExec, WriteToDataSourceV2Exec}
-import org.apache.spark.sql.execution.streaming.sources.MicroBatchWritSupport
-import org.apache.spark.sql.sources.v2.CustomMetrics
-import org.apache.spark.sql.sources.v2.reader.streaming.{MicroBatchReadSupport, SupportsCustomReaderMetrics}
-import org.apache.spark.sql.sources.v2.writer.streaming.{StreamingWriteSupport, SupportsCustomWriterMetrics}
+import org.apache.spark.sql.execution.datasources.v2.DataSourceV2ScanExec
+import org.apache.spark.sql.sources.v2.reader.streaming.MicroBatchReadSupport
 import org.apache.spark.sql.streaming._
 import org.apache.spark.sql.streaming.StreamingQueryListener.QueryProgressEvent
 import org.apache.spark.util.Clock
@@ -162,51 +156,19 @@ trait ProgressReporter extends Logging {
     }
     logDebug(s"Execution stats: $executionStats")
 
-    // extracts and validates custom metrics from readers and writers
-    def extractMetrics(
-        getMetrics: () => Option[CustomMetrics],
-        onInvalidMetrics: (Exception) => Unit): Option[String] = {
-      try {
-        getMetrics().map(m => {
-          val json = m.json()
-          parse(json)
-          json
-        })
-      } catch {
-        case ex: Exception if NonFatal(ex) =>
-          onInvalidMetrics(ex)
-          None
-      }
-    }
-
     val sourceProgress = sources.distinct.map { source =>
-      val customReaderMetrics = source match {
-        case s: SupportsCustomReaderMetrics =>
-          extractMetrics(() => Option(s.getCustomMetrics), s.onInvalidMetrics)
-
-        case _ => None
-      }
-
       val numRecords = executionStats.inputRows.getOrElse(source, 0L)
       new SourceProgress(
         description = source.toString,
         startOffset = currentTriggerStartOffsets.get(source).orNull,
         endOffset = currentTriggerEndOffsets.get(source).orNull,
         numInputRows = numRecords,
         inputRowsPerSecond = numRecords / inputTimeSec,
-        processedRowsPerSecond = numRecords / processingTimeSec,
-        customReaderMetrics.orNull
+        processedRowsPerSecond = numRecords / processingTimeSec
       )
     }
 
-    val customWriterMetrics = extractWriteSupport() match {
-      case Some(s: SupportsCustomWriterMetrics) =>
-        extractMetrics(() => Option(s.getCustomMetrics), s.onInvalidMetrics)
-
-      case _ => None
-    }
-
-    val sinkProgress = new SinkProgress(sink.toString, customWriterMetrics.orNull)
+    val sinkProgress = new SinkProgress(sink.toString)
 
     val newProgress = new StreamingQueryProgress(
       id = id,
@@ -235,18 +197,6 @@ trait ProgressReporter extends Logging {
     currentStatus = currentStatus.copy(isTriggerActive = false)
   }
 
-  /** Extract writer from the executed query plan. */
-  private def extractWriteSupport(): Option[StreamingWriteSupport] = {
-    if (lastExecution == null) return None
-    lastExecution.executedPlan.collect {
-      case p if p.isInstanceOf[WriteToDataSourceV2Exec] =>
-        p.asInstanceOf[WriteToDataSourceV2Exec].writeSupport
-    }.headOption match {
-      case Some(w: MicroBatchWritSupport) => Some(w.writeSupport)
-      case _ => None
-    }
-  }
-
   /** Extract statistics about stateful operators from the executed query plan. */
   private def extractStateOperatorMetrics(hasNewData: Boolean): Seq[StateOperatorProgress] = {
     if (lastExecution == null) return Nil