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From brkyvz <...@git.apache.org>
Subject [GitHub] spark pull request #18199: [SPARK-20979][SS]Add RateSource to generate value...
Date Mon, 05 Jun 2017 21:53:48 GMT
Github user brkyvz commented on a diff in the pull request:

    https://github.com/apache/spark/pull/18199#discussion_r120219455
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala
---
    @@ -0,0 +1,240 @@
    +/*
    + * 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 relativeMsPerValue =
    +      TimeUnit.SECONDS.toMillis(endSeconds - startSeconds) / (rangeEnd - rangeStart)
    --- End diff --
    
    integer division bug! This can easily return 0 right?


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