diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/RateStreamProviderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/RateStreamProviderSuite.scala
new file mode 100644
index 000000000000..ff14ec38e66a
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/RateStreamProviderSuite.scala
@@ -0,0 +1,334 @@
+/*
+ * 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.sources
+
+import java.nio.file.Files
+import java.util.Optional
+import java.util.concurrent.TimeUnit
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.sql.{AnalysisException, Row, SparkSession}
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.execution.datasources.DataSource
+import org.apache.spark.sql.execution.streaming._
+import org.apache.spark.sql.execution.streaming.continuous._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions, MicroBatchReadSupport}
+import org.apache.spark.sql.sources.v2.reader.streaming.Offset
+import org.apache.spark.sql.streaming.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]): (BaseStreamingSource, Offset) = {
+      assert(query.nonEmpty)
+      val rateSource = query.get.logicalPlan.collect {
+        case StreamingExecutionRelation(source: RateStreamMicroBatchReader, _) => source
+      }.head
+
+      rateSource.clock.asInstanceOf[ManualClock].advance(TimeUnit.SECONDS.toMillis(seconds))
+      val offset = LongOffset(TimeUnit.MILLISECONDS.toSeconds(
+        rateSource.clock.getTimeMillis() - rateSource.creationTimeMs))
+      (rateSource, offset)
+    }
+  }
+
+  test("microbatch in registry") {
+    DataSource.lookupDataSource("rate", spark.sqlContext.conf).newInstance() match {
+      case ds: MicroBatchReadSupport =>
+        val reader = ds.createMicroBatchReader(Optional.empty(), "dummy", DataSourceOptions.empty())
+        assert(reader.isInstanceOf[RateStreamMicroBatchReader])
+      case _ =>
+        throw new IllegalStateException("Could not find read support for rate")
+    }
+  }
+
+  test("compatible with old path in registry") {
+    DataSource.lookupDataSource("org.apache.spark.sql.execution.streaming.RateSourceProvider",
+      spark.sqlContext.conf).newInstance() match {
+      case ds: MicroBatchReadSupport =>
+        assert(ds.isInstanceOf[RateStreamProvider])
+      case _ =>
+        throw new IllegalStateException("Could not find read support for rate")
+    }
+  }
+
+  test("microbatch - 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("microbatch - 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("microbatch - set offset") {
+    val temp = Files.createTempDirectory("dummy").toString
+    val reader = new RateStreamMicroBatchReader(DataSourceOptions.empty(), temp)
+    val startOffset = LongOffset(0L)
+    val endOffset = LongOffset(1L)
+    reader.setOffsetRange(Optional.of(startOffset), Optional.of(endOffset))
+    assert(reader.getStartOffset() == startOffset)
+    assert(reader.getEndOffset() == endOffset)
+  }
+
+  test("microbatch - infer offsets") {
+    val tempFolder = Files.createTempDirectory("dummy").toString
+    val reader = new RateStreamMicroBatchReader(
+      new DataSourceOptions(
+        Map("numPartitions" -> "1", "rowsPerSecond" -> "100", "useManualClock" -> "true").asJava),
+      tempFolder)
+    reader.clock.asInstanceOf[ManualClock].advance(100000)
+    reader.setOffsetRange(Optional.empty(), Optional.empty())
+    reader.getStartOffset() match {
+      case r: LongOffset => assert(r.offset === 0L)
+      case _ => throw new IllegalStateException("unexpected offset type")
+    }
+    reader.getEndOffset() match {
+      case r: LongOffset => assert(r.offset >= 100)
+      case _ => throw new IllegalStateException("unexpected offset type")
+    }
+  }
+
+  test("microbatch - predetermined batch size") {
+    val temp = Files.createTempDirectory("dummy").toString
+    val reader = new RateStreamMicroBatchReader(
+      new DataSourceOptions(Map("numPartitions" -> "1", "rowsPerSecond" -> "20").asJava), temp)
+    val startOffset = LongOffset(0L)
+    val endOffset = LongOffset(1L)
+    reader.setOffsetRange(Optional.of(startOffset), Optional.of(endOffset))
+    val tasks = reader.createDataReaderFactories()
+    assert(tasks.size == 1)
+    val dataReader = tasks.get(0).createDataReader()
+    val data = ArrayBuffer[Row]()
+    while (dataReader.next()) {
+      data.append(dataReader.get())
+    }
+    assert(data.size === 20)
+  }
+
+  test("microbatch - data read") {
+    val temp = Files.createTempDirectory("dummy").toString
+    val reader = new RateStreamMicroBatchReader(
+      new DataSourceOptions(Map("numPartitions" -> "11", "rowsPerSecond" -> "33").asJava), temp)
+    val startOffset = LongOffset(0L)
+    val endOffset = LongOffset(1L)
+    reader.setOffsetRange(Optional.of(startOffset), Optional.of(endOffset))
+    val tasks = reader.createDataReaderFactories()
+    assert(tasks.size == 11)
+
+    val readData = tasks.asScala
+      .map(_.createDataReader())
+      .flatMap { reader =>
+        val buf = scala.collection.mutable.ListBuffer[Row]()
+        while (reader.next()) buf.append(reader.get())
+        buf
+      }
+
+    assert(readData.map(_.getLong(1)).sorted == Range(0, 33))
+  }
+
+  test("valueAtSecond") {
+    import RateStreamProvider._
+
+    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[IllegalArgumentException] {
+        spark.readStream
+          .format("rate")
+          .option(option, value)
+          .load()
+          .writeStream
+          .format("console")
+          .start()
+          .awaitTermination()
+      }
+      for (msg <- expectedMessages) {
+        assert(e.getMessage.contains(msg))
+      }
+    }
+
+    testIllegalOptionValue("rowsPerSecond", "-1", Seq("-1", "rowsPerSecond", "positive"))
+    testIllegalOptionValue("numPartitions", "-1", Seq("-1", "numPartitions", "positive"))
+  }
+
+  test("user-specified schema given") {
+    val exception = intercept[AnalysisException] {
+      spark.readStream
+        .format("rate")
+        .schema(spark.range(1).schema)
+        .load()
+    }
+    assert(exception.getMessage.contains(
+      "rate source does not support a user-specified schema"))
+  }
+
+  test("continuous in registry") {
+    DataSource.lookupDataSource("rate", spark.sqlContext.conf).newInstance() match {
+      case ds: ContinuousReadSupport =>
+        val reader = ds.createContinuousReader(Optional.empty(), "", DataSourceOptions.empty())
+        assert(reader.isInstanceOf[RateStreamContinuousReader])
+      case _ =>
+        throw new IllegalStateException("Could not find read support for continuous rate")
+    }
+  }
+
+  test("continuous data") {
+    val reader = new RateStreamContinuousReader(
+      new DataSourceOptions(Map("numPartitions" -> "2", "rowsPerSecond" -> "20").asJava))
+    reader.setStartOffset(Optional.empty())
+    val tasks = reader.createDataReaderFactories()
+    assert(tasks.size == 2)
+
+    val data = scala.collection.mutable.ListBuffer[Row]()
+    tasks.asScala.foreach {
+      case t: RateStreamContinuousDataReaderFactory =>
+        val startTimeMs = reader.getStartOffset()
+          .asInstanceOf[RateStreamOffset]
+          .partitionToValueAndRunTimeMs(t.partitionIndex)
+          .runTimeMs
+        val r = t.createDataReader().asInstanceOf[RateStreamContinuousDataReader]
+        for (rowIndex <- 0 to 9) {
+          r.next()
+          data.append(r.get())
+          assert(r.getOffset() ==
+            RateStreamPartitionOffset(
+              t.partitionIndex,
+              t.partitionIndex + rowIndex * 2,
+              startTimeMs + (rowIndex + 1) * 100))
+        }
+        assert(System.currentTimeMillis() >= startTimeMs + 1000)
+
+      case _ => throw new IllegalStateException("Unexpected task type")
+    }
+
+    assert(data.map(_.getLong(1)).toSeq.sorted == Range(0, 20))
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index bc4a120f7042..548036ae585a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -61,6 +61,7 @@ private[sql] trait SQLTestUtils extends SparkFunSuite with SQLTestUtilsBase with
 
   protected override def beforeAll(): Unit = {
     super.beforeAll()
+    SparkSession.setActiveSession(spark)
     if (loadTestDataBeforeTests) {
       loadTestData()
     }