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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 20:20:02 GMT
Github user brkyvz commented on a diff in the pull request:

    https://github.com/apache/spark/pull/18199#discussion_r120197129
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/RateSourceProvider.scala
---
    @@ -0,0 +1,208 @@
    +/*
    + * 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.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` (default: 1): How many tuples should be generated per second.
    + *  - `rampUpTimeSeconds` (default: 0): How many seconds to ramp up before the generating
speed
    + *    becomes `tuplesPerSecond`.
    + *  - `numPartitions` (default: Spark's default parallelism): The partition number for
the generated
    + *    tuples.
    + */
    +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 '${params("tuplesPerSecond")}' for option 'tuplesPerSecond',
" +
    +          "must be positive")
    +    }
    +
    +    val rampUpTimeSeconds = params.get("rampUpTimeSeconds").map(_.toLong).getOrElse(0L)
    +    if (rampUpTimeSeconds < 0) {
    +      throw new IllegalArgumentException(
    +        s"Invalid value '${params("rampUpTimeSeconds")}' for option 'rampUpTimeSeconds',
" +
    +          "must not be negative")
    +    }
    +
    +    val numPartitions = params.get("numPartitions").map(_.toInt).getOrElse(
    +      sqlContext.sparkContext.defaultParallelism)
    +    if (numPartitions <= 0) {
    +      throw new IllegalArgumentException(
    +        s"Invalid value '${params("numPartitions")}' for option 'numPartitions', " +
    +          "must be positive")
    +    }
    +
    +    new RateStreamSource(
    +      sqlContext,
    +      metadataPath,
    +      tuplesPerSecond,
    +      rampUpTimeSeconds,
    +      numPartitions,
    +      params.get("useManualClock").map(_.toBoolean).getOrElse(false) // Only for testing
    +    )
    +  }
    +  override def shortName(): String = "rate"
    +}
    +
    +object RateSourceProvider {
    +  val SCHEMA =
    +    StructType(StructField("timestamp", TimestampType) :: StructField("value", LongType)
:: Nil)
    +
    +  val VERSION = 1
    +}
    +
    +class RateStreamSource(
    +    sqlContext: SQLContext,
    +    metadataPath: String,
    +    tuplesPerSecond: Long,
    +    rampUpTimeSeconds: Long,
    +    numPartitions: Int,
    +    useManualClock: Boolean) extends Source with Logging {
    +
    +  import RateSourceProvider._
    +
    +  val clock = if (useManualClock) new ManualClock else new SystemClock
    +
    +  private val maxSeconds = Long.MaxValue / tuplesPerSecond
    +
    +  if (rampUpTimeSeconds > maxSeconds) {
    +    throw new ArithmeticException("integer overflow. Max offset with tuplesPerSecond
" +
    +      s"$tuplesPerSecond is $maxSeconds, but 'rampUpTimeSeconds' is $rampUpTimeSeconds.")
    +  }
    +
    +  private val startTimeMs = {
    --- End diff --
    
    do we need to go to this complexity for this source??


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