Github user chenliang613 commented on a diff in the pull request:

    https://github.com/apache/carbondata/pull/1886#discussion_r165807899
  
    --- Diff: 
examples/spark2/src/main/scala/org/apache/carbondata/examples/TimeSeriesPreAggregateTableExample.scala
 ---
    @@ -0,0 +1,105 @@
    +/*
    + * 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.carbondata.examples
    +
    +import java.io.File
    +
    +import org.apache.spark.sql.SaveMode
    +
    +import org.apache.carbondata.core.constants.CarbonCommonConstants
    +import org.apache.carbondata.core.util.CarbonProperties
    +
    +/**
    + * This example is for time series pre-aggregate tables.
    + */
    +
    +object TimeSeriesPreAggregateTableExample {
    +
    +  def main(args: Array[String]) {
    +
    +    val rootPath = new File(this.getClass.getResource("/").getPath
    +                            + "../../../..").getCanonicalPath
    +    val testData = 
s"$rootPath/integration/spark-common-test/src/test/resources/timeseriestest.csv"
    +    val spark = 
ExampleUtils.createCarbonSession("TimeSeriesPreAggregateTableExample")
    +
    +    spark.sparkContext.setLogLevel("ERROR")
    +
    +    import spark.implicits._
    +
    +    import scala.util.Random
    +    val r = new Random()
    +    val df = spark.sparkContext.parallelize(1 to 10 * 1000 * 1000 )
    --- End diff --
    
    please reduce the data size


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