[SPARK-20979][SS] Add RateSource to generate values for tests and benchmark

## What changes were proposed in this pull request?

This PR adds RateSource for Structured Streaming so that the user can use it to generate data for tests and benchmark easily.

This source generates increment long values with timestamps. Each generated row has two columns: a timestamp column for the generated time and an auto increment long column starting with 0L.

It supports the following options:
- `rowsPerSecond` (e.g. 100, default: 1): How many rows should be generated per second.
- `rampUpTime` (e.g. 5s, default: 0s): How long to ramp up before the generating speed becomes `rowsPerSecond`. Using finer granularities than seconds will be truncated to integer seconds.
- `numPartitions` (e.g. 10, default: Spark's default parallelism): The partition number for the generated rows. The source will try its best to reach `rowsPerSecond`, but the query may be resource constrained, and `numPartitions` can be tweaked to help reach the desired speed.

Here is a simple example that prints 10 rows per seconds:
```
    spark.readStream
      .format("rate")
      .option("rowsPerSecond", "10")
      .load()
      .writeStream
      .format("console")
      .start()
```

The idea came from marmbrus and he did the initial work.

## How was this patch tested?

The added tests.

Author: Shixiong Zhu <shixiong@databricks.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #18199 from zsxwing/rate.

sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister

@@ -5,3 +5,4 @@ org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat
 org.apache.spark.sql.execution.datasources.text.TextFileFormat
 org.apache.spark.sql.execution.streaming.ConsoleSinkProvider
 org.apache.spark.sql.execution.streaming.TextSocketSourceProvider
+org.apache.spark.sql.execution.streaming.RateSourceProvider

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala

+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.streaming
+
+import java.io._
+import java.nio.charset.StandardCharsets
+import java.util.concurrent.TimeUnit
+
+import org.apache.commons.io.IOUtils
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.network.util.JavaUtils
+import org.apache.spark.sql.{DataFrame, SQLContext}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils}
+import org.apache.spark.sql.sources.{DataSourceRegister, StreamSourceProvider}
+import org.apache.spark.sql.types._
+import org.apache.spark.util.{ManualClock, SystemClock}
+
+/**
+ *  A source that generates increment long values with timestamps. Each generated row has two
+ *  columns: a timestamp column for the generated time and an auto increment long column starting
+ *  with 0L.
+ *
+ *  This source supports the following options:
+ *  - `rowsPerSecond` (e.g. 100, default: 1): How many rows should be generated per second.
+ *  - `rampUpTime` (e.g. 5s, default: 0s): How long to ramp up before the generating speed
+ *    becomes `rowsPerSecond`. Using finer granularities than seconds will be truncated to integer
+ *    seconds.
+ *  - `numPartitions` (e.g. 10, default: Spark's default parallelism): The partition number for the
+ *    generated rows. The source will try its best to reach `rowsPerSecond`, but the query may
+ *    be resource constrained, and `numPartitions` can be tweaked to help reach the desired speed.
+ */
+class RateSourceProvider extends StreamSourceProvider with DataSourceRegister {
+
+  override def sourceSchema(
+      sqlContext: SQLContext,
+      schema: Option[StructType],
+      providerName: String,
+      parameters: Map[String, String]): (String, StructType) =
+    (shortName(), RateSourceProvider.SCHEMA)
+
+  override def createSource(
+      sqlContext: SQLContext,
+      metadataPath: String,
+      schema: Option[StructType],
+      providerName: String,
+      parameters: Map[String, String]): Source = {
+    val params = CaseInsensitiveMap(parameters)
+
+    val rowsPerSecond = params.get("rowsPerSecond").map(_.toLong).getOrElse(1L)
+    if (rowsPerSecond <= 0) {
+      throw new IllegalArgumentException(
+        s"Invalid value '${params("rowsPerSecond")}'. The option 'rowsPerSecond' " +
+          "must be positive")
+    }
+
+    val rampUpTimeSeconds =
+      params.get("rampUpTime").map(JavaUtils.timeStringAsSec(_)).getOrElse(0L)
+    if (rampUpTimeSeconds < 0) {
+      throw new IllegalArgumentException(
+        s"Invalid value '${params("rampUpTime")}'. The option 'rampUpTime' " +
+          "must not be negative")
+    }
+
+    val numPartitions = params.get("numPartitions").map(_.toInt).getOrElse(
+      sqlContext.sparkContext.defaultParallelism)
+    if (numPartitions <= 0) {
+      throw new IllegalArgumentException(
+        s"Invalid value '${params("numPartitions")}'. The option 'numPartitions' " +
+          "must be positive")
+    }
+
+    new RateStreamSource(
+      sqlContext,
+      metadataPath,
+      rowsPerSecond,
+      rampUpTimeSeconds,
+      numPartitions,
+      params.get("useManualClock").map(_.toBoolean).getOrElse(false) // Only for testing
+    )
+  }
+  override def shortName(): String = "rate"
+}
+
+object RateSourceProvider {
+  val SCHEMA =
+    StructType(StructField("timestamp", TimestampType) :: StructField("value", LongType) :: Nil)
+
+  val VERSION = 1
+}
+
+class RateStreamSource(
+    sqlContext: SQLContext,
+    metadataPath: String,
+    rowsPerSecond: Long,
+    rampUpTimeSeconds: Long,
+    numPartitions: Int,
+    useManualClock: Boolean) extends Source with Logging {
+
+  import RateSourceProvider._
+  import RateStreamSource._
+
+  val clock = if (useManualClock) new ManualClock else new SystemClock
+
+  private val maxSeconds = Long.MaxValue / rowsPerSecond
+
+  if (rampUpTimeSeconds > maxSeconds) {
+    throw new ArithmeticException(
+      s"Integer overflow. Max offset with $rowsPerSecond rowsPerSecond" +
+        s" is $maxSeconds, but 'rampUpTimeSeconds' is $rampUpTimeSeconds.")
+  }
+
+  private val startTimeMs = {
+    val metadataLog =
+      new HDFSMetadataLog[LongOffset](sqlContext.sparkSession, metadataPath) {
+        override def serialize(metadata: LongOffset, out: OutputStream): Unit = {
+          val writer = new BufferedWriter(new OutputStreamWriter(out, StandardCharsets.UTF_8))
+          writer.write("v" + VERSION + "\n")
+          writer.write(metadata.json)
+          writer.flush
+        }
+
+        override def deserialize(in: InputStream): LongOffset = {
+          val content = IOUtils.toString(new InputStreamReader(in, StandardCharsets.UTF_8))
+          // HDFSMetadataLog guarantees that it never creates a partial file.
+          assert(content.length != 0)
+          if (content(0) == 'v') {
+            val indexOfNewLine = content.indexOf("\n")
+            if (indexOfNewLine > 0) {
+              val version = parseVersion(content.substring(0, indexOfNewLine), VERSION)
+              LongOffset(SerializedOffset(content.substring(indexOfNewLine + 1)))
+            } else {
+              throw new IllegalStateException(
+                s"Log file was malformed: failed to detect the log file version line.")
+            }
+          } else {
+            throw new IllegalStateException(
+              s"Log file was malformed: failed to detect the log file version line.")
+          }
+        }
+      }
+
+    metadataLog.get(0).getOrElse {
+      val offset = LongOffset(clock.getTimeMillis())
+      metadataLog.add(0, offset)
+      logInfo(s"Start time: $offset")
+      offset
+    }.offset
+  }
+
+  /** When the system time runs backward, "lastTimeMs" will make sure we are still monotonic. */
+  @volatile private var lastTimeMs = startTimeMs
+
+  override def schema: StructType = RateSourceProvider.SCHEMA
+
+  override def getOffset: Option[Offset] = {
+    val now = clock.getTimeMillis()
+    if (lastTimeMs < now) {
+      lastTimeMs = now
+    }
+    Some(LongOffset(TimeUnit.MILLISECONDS.toSeconds(lastTimeMs - startTimeMs)))
+  }
+
+  override def getBatch(start: Option[Offset], end: Offset): DataFrame = {
+    val startSeconds = start.flatMap(LongOffset.convert(_).map(_.offset)).getOrElse(0L)
+    val endSeconds = LongOffset.convert(end).map(_.offset).getOrElse(0L)
+    assert(startSeconds <= endSeconds, s"startSeconds($startSeconds) > endSeconds($endSeconds)")
+    if (endSeconds > maxSeconds) {
+      throw new ArithmeticException("Integer overflow. Max offset with " +
+        s"$rowsPerSecond rowsPerSecond is $maxSeconds, but it's $endSeconds now.")
+    }
+    // Fix "lastTimeMs" for recovery
+    if (lastTimeMs < TimeUnit.SECONDS.toMillis(endSeconds) + startTimeMs) {
+      lastTimeMs = TimeUnit.SECONDS.toMillis(endSeconds) + startTimeMs
+    }
+    val rangeStart = valueAtSecond(startSeconds, rowsPerSecond, rampUpTimeSeconds)
+    val rangeEnd = valueAtSecond(endSeconds, rowsPerSecond, rampUpTimeSeconds)
+    logDebug(s"startSeconds: $startSeconds, endSeconds: $endSeconds, " +
+      s"rangeStart: $rangeStart, rangeEnd: $rangeEnd")
+
+    if (rangeStart == rangeEnd) {
+      return sqlContext.internalCreateDataFrame(sqlContext.sparkContext.emptyRDD, schema)
+    }
+
+    val localStartTimeMs = startTimeMs + TimeUnit.SECONDS.toMillis(startSeconds)
+    val relativeMsPerValue =
+      TimeUnit.SECONDS.toMillis(endSeconds - startSeconds).toDouble / (rangeEnd - rangeStart)
+
+    val rdd = sqlContext.sparkContext.range(rangeStart, rangeEnd, 1, numPartitions).map { v =>
+      val relative = math.round((v - rangeStart) * relativeMsPerValue)
+      InternalRow(DateTimeUtils.fromMillis(relative + localStartTimeMs), v)
+    }
+    sqlContext.internalCreateDataFrame(rdd, schema)
+  }
+
+  override def stop(): Unit = {}
+
+  override def toString: String = s"RateSource[rowsPerSecond=$rowsPerSecond, " +
+    s"rampUpTimeSeconds=$rampUpTimeSeconds, numPartitions=$numPartitions]"
+}
+
+object RateStreamSource {
+
+  /** Calculate the end value we will emit at the time `seconds`. */
+  def valueAtSecond(seconds: Long, rowsPerSecond: Long, rampUpTimeSeconds: Long): Long = {
+    // E.g., rampUpTimeSeconds = 4, rowsPerSecond = 10
+    // Then speedDeltaPerSecond = 2
+    //
+    // seconds   = 0 1 2  3  4  5  6
+    // speed     = 0 2 4  6  8 10 10 (speedDeltaPerSecond * seconds)
+    // end value = 0 2 6 12 20 30 40 (0 + speedDeltaPerSecond * seconds) * (seconds + 1) / 2
+    val speedDeltaPerSecond = rowsPerSecond / (rampUpTimeSeconds + 1)
+    if (seconds <= rampUpTimeSeconds) {
+      // Calculate "(0 + speedDeltaPerSecond * seconds) * (seconds + 1) / 2" in a special way to
+      // avoid overflow
+      if (seconds % 2 == 1) {
+        (seconds + 1) / 2 * speedDeltaPerSecond * seconds
+      } else {
+        seconds / 2 * speedDeltaPerSecond * (seconds + 1)
+      }
+    } else {
+      // rampUpPart is just a special case of the above formula: rampUpTimeSeconds == seconds
+      val rampUpPart = valueAtSecond(rampUpTimeSeconds, rowsPerSecond, rampUpTimeSeconds)
+      rampUpPart + (seconds - rampUpTimeSeconds) * rowsPerSecond
+    }
+  }
+}

sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/RateSourceSuite.scala

+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.streaming
+
+import java.util.concurrent.TimeUnit
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.streaming.{StreamingQueryException, StreamTest}
+import org.apache.spark.util.ManualClock
+
+class RateSourceSuite extends StreamTest {
+
+  import testImplicits._
+
+  case class AdvanceRateManualClock(seconds: Long) extends AddData {
+    override def addData(query: Option[StreamExecution]): (Source, Offset) = {
+      assert(query.nonEmpty)
+      val rateSource = query.get.logicalPlan.collect {
+        case StreamingExecutionRelation(source, _) if source.isInstanceOf[RateStreamSource] =>
+          source.asInstanceOf[RateStreamSource]
+      }.head
+      rateSource.clock.asInstanceOf[ManualClock].advance(TimeUnit.SECONDS.toMillis(seconds))
+      (rateSource, rateSource.getOffset.get)
+    }
+  }
+
+  test("basic") {
+    val input = spark.readStream
+      .format("rate")
+      .option("rowsPerSecond", "10")
+      .option("useManualClock", "true")
+      .load()
+    testStream(input)(
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch((0 until 10).map(v => new java.sql.Timestamp(v * 100L) -> v): _*),
+      StopStream,
+      StartStream(),
+      // Advance 2 seconds because creating a new RateSource will also create a new ManualClock
+      AdvanceRateManualClock(seconds = 2),
+      CheckLastBatch((10 until 20).map(v => new java.sql.Timestamp(v * 100L) -> v): _*)
+    )
+  }
+
+  test("uniform distribution of event timestamps") {
+    val input = spark.readStream
+      .format("rate")
+      .option("rowsPerSecond", "1500")
+      .option("useManualClock", "true")
+      .load()
+      .as[(java.sql.Timestamp, Long)]
+      .map(v => (v._1.getTime, v._2))
+    val expectedAnswer = (0 until 1500).map { v =>
+      (math.round(v * (1000.0 / 1500)), v)
+    }
+    testStream(input)(
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch(expectedAnswer: _*)
+    )
+  }
+
+  test("valueAtSecond") {
+    import RateStreamSource._
+
+    assert(valueAtSecond(seconds = 0, rowsPerSecond = 5, rampUpTimeSeconds = 0) === 0)
+    assert(valueAtSecond(seconds = 1, rowsPerSecond = 5, rampUpTimeSeconds = 0) === 5)
+
+    assert(valueAtSecond(seconds = 0, rowsPerSecond = 5, rampUpTimeSeconds = 2) === 0)
+    assert(valueAtSecond(seconds = 1, rowsPerSecond = 5, rampUpTimeSeconds = 2) === 1)
+    assert(valueAtSecond(seconds = 2, rowsPerSecond = 5, rampUpTimeSeconds = 2) === 3)
+    assert(valueAtSecond(seconds = 3, rowsPerSecond = 5, rampUpTimeSeconds = 2) === 8)
+
+    assert(valueAtSecond(seconds = 0, rowsPerSecond = 10, rampUpTimeSeconds = 4) === 0)
+    assert(valueAtSecond(seconds = 1, rowsPerSecond = 10, rampUpTimeSeconds = 4) === 2)
+    assert(valueAtSecond(seconds = 2, rowsPerSecond = 10, rampUpTimeSeconds = 4) === 6)
+    assert(valueAtSecond(seconds = 3, rowsPerSecond = 10, rampUpTimeSeconds = 4) === 12)
+    assert(valueAtSecond(seconds = 4, rowsPerSecond = 10, rampUpTimeSeconds = 4) === 20)
+    assert(valueAtSecond(seconds = 5, rowsPerSecond = 10, rampUpTimeSeconds = 4) === 30)
+  }
+
+  test("rampUpTime") {
+    val input = spark.readStream
+      .format("rate")
+      .option("rowsPerSecond", "10")
+      .option("rampUpTime", "4s")
+      .option("useManualClock", "true")
+      .load()
+      .as[(java.sql.Timestamp, Long)]
+      .map(v => (v._1.getTime, v._2))
+    testStream(input)(
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch((0 until 2).map(v => v * 500 -> v): _*), // speed = 2
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch((2 until 6).map(v => 1000 + (v - 2) * 250 -> v): _*), // speed = 4
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch({
+        Seq(2000 -> 6, 2167 -> 7, 2333 -> 8, 2500 -> 9, 2667 -> 10, 2833 -> 11)
+      }: _*), // speed = 6
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch((12 until 20).map(v => 3000 + (v - 12) * 125 -> v): _*), // speed = 8
+      AdvanceRateManualClock(seconds = 1),
+      // Now we should reach full speed
+      CheckLastBatch((20 until 30).map(v => 4000 + (v - 20) * 100 -> v): _*), // speed = 10
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch((30 until 40).map(v => 5000 + (v - 30) * 100 -> v): _*), // speed = 10
+      AdvanceRateManualClock(seconds = 1),
+      CheckLastBatch((40 until 50).map(v => 6000 + (v - 40) * 100 -> v): _*) // speed = 10
+    )
+  }
+
+  test("numPartitions") {
+    val input = spark.readStream
+      .format("rate")
+      .option("rowsPerSecond", "10")
+      .option("numPartitions", "6")
+      .option("useManualClock", "true")
+      .load()
+      .select(spark_partition_id())
+      .distinct()
+    testStream(input)(
+      AdvanceRateManualClock(1),
+      CheckLastBatch((0 until 6): _*)
+    )
+  }
+
+  testQuietly("overflow") {
+    val input = spark.readStream
+      .format("rate")
+      .option("rowsPerSecond", Long.MaxValue.toString)
+      .option("useManualClock", "true")
+      .load()
+      .select(spark_partition_id())
+      .distinct()
+    testStream(input)(
+      AdvanceRateManualClock(2),
+      ExpectFailure[ArithmeticException](t => {
+        Seq("overflow", "rowsPerSecond").foreach { msg =>
+          assert(t.getMessage.contains(msg))
+        }
+      })
+    )
+  }
+
+  testQuietly("illegal option values") {
+    def testIllegalOptionValue(
+        option: String,
+        value: String,
+        expectedMessages: Seq[String]): Unit = {
+      val e = intercept[StreamingQueryException] {
+        spark.readStream
+          .format("rate")
+          .option(option, value)
+          .load()
+          .writeStream
+          .format("console")
+          .start()
+          .awaitTermination()
+      }
+      assert(e.getCause.isInstanceOf[IllegalArgumentException])
+      for (msg <- expectedMessages) {
+        assert(e.getCause.getMessage.contains(msg))
+      }
+    }
+
+    testIllegalOptionValue("rowsPerSecond", "-1", Seq("-1", "rowsPerSecond", "positive"))
+    testIllegalOptionValue("numPartitions", "-1", Seq("-1", "numPartitions", "positive"))
+  }
+}

sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala

@@ -172,8 +172,10 @@ trait StreamTest extends QueryTest with SharedSQLContext with Timeouts {
    *
    * @param isFatalError if this is a fatal error. If so, the error should also be caught by
    *                     UncaughtExceptionHandler.
+   * @param assertFailure a function to verify the error.
    */
   case class ExpectFailure[T <: Throwable : ClassTag](
+      assertFailure: Throwable => Unit = _ => {},
       isFatalError: Boolean = false) extends StreamAction {
     val causeClass: Class[T] = implicitly[ClassTag[T]].runtimeClass.asInstanceOf[Class[T]]
     override def toString(): String =
@@ -455,6 +457,7 @@ trait StreamTest extends QueryTest with SharedSQLContext with Timeouts {
                     s"\tExpected: ${ef.causeClass}\n\tReturned: $streamThreadDeathCause")
                 streamThreadDeathCause = null
               }
+              ef.assertFailure(exception.getCause)
             } catch {
               case _: InterruptedException =>
               case e: org.scalatest.exceptions.TestFailedDueToTimeoutException =>