Github user brkyvz commented on a diff in the pull request:
https://github.com/apache/spark/pull/18199#discussion_r121191735
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala
---
@@ -0,0 +1,279 @@
+/*
+ * 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:
+ * - `tuplesPerSecond` (e.g. 100, default: 1): How many tuples should be
generated per second.
+ * - `rampUpTime` (e.g. 5s, default: 0s): How long to ramp up before the
generating speed
+ * becomes `tuplesPerSecond`. 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 tuples. The source will try its best to reach
`tuplesPerSecond`, 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 tuplesPerSecond =
params.get("tuplesPerSecond").map(_.toLong).getOrElse(1L)
+ if (tuplesPerSecond <= 0) {
+ throw new IllegalArgumentException(
+ s"Invalid value '${params("tuplesPerSecond")}'. The option
'tuplesPerSecond' " +
+ "must be positive")
+ }
+
+ val rampUpTimeSeconds =
+
params.get("rampUpTime").map(JavaUtils.timeStringAsSec(_)).getOrElse(0L)
+ if (rampUpTimeSeconds < 0) {
+ throw new IllegalArgumentException(
+ s"Invalid value '${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,
+ tuplesPerSecond,
+ 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,
+ tuplesPerSecond: 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 / tuplesPerSecond
+
+ if (rampUpTimeSeconds > maxSeconds) {
+ throw new ArithmeticException(
+ s"Integer overflow. Max offset with $tuplesPerSecond
tuplesPerSecond" +
+ 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($endSeconds)")
+ if (endSeconds > maxSeconds) {
+ throw new ArithmeticException("Integer overflow. Max offset with " +
+ s"$tuplesPerSecond tuplesPerSecond 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, tuplesPerSecond,
rampUpTimeSeconds)
+ val rangeEnd = valueAtSecond(endSeconds, tuplesPerSecond,
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 timeIntervalSizeMs = TimeUnit.SECONDS.toMillis(endSeconds -
startSeconds)
+
+ val func =
+ if (timeIntervalSizeMs < rangeEnd - rangeStart) {
+ // Different rows may have the same timestamp
+ val valueSizePerMs = (rangeEnd - rangeStart) / timeIntervalSizeMs
+ val remainderValue = (rangeEnd - rangeStart) % timeIntervalSizeMs
+
+ (v: Long) => {
+ val relativeValue = v - rangeStart
+ val relativeMs = {
+ // Increase the timestamp per "valueSizePerMs + 1" values
before
+ // "(valueSizePerMs + 1) * remainderValue", and increase the
timestamp per
+ // "valueSizePerMs" values for remaining values.
+
+ // The following condition is the same as
+ // "relativeValue < (valueSizePerMs + 1) * remainderValue",
just rewrite it to avoid
+ // overflow.
+ if (relativeValue - remainderValue < valueSizePerMs *
remainderValue) {
--- End diff --
also around `valueSizePerMs * remainderValue`
=> `(relativeValue - remainderValue) < (valueSizePerMs * remainderValue)`
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