Github user chenliang613 commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/1886#discussion_r165807902
--- 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"$rootPath/integration/spark-common-test/src/test/resources/timeseriestest.csv"
+ val spark =
ExampleUtils.createCarbonSession("TimeSeriesPreAggregateTableExample")
+
+ spark.sparkContext.setLogLevel("ERROR")
+
+ import spark.implicits._
+
+ import scala.util.Random
+ val r = new Random()
+ val df = spark.sparkContext.parallelize(1 to 10 * 1000 * 1000 )
+ .map(x => ("" + 20 + "%02d".format(r.nextInt(20)) + "-" +
"%02d".format(r.nextInt(11) + 1) +
+ "-" + "%02d".format(r.nextInt(27) + 1) + " " +
"%02d".format(r.nextInt(12)) + ":" +
+ "%02d".format(r.nextInt(59)) + ":" + "%02d".format(r.nextInt(59)),
"name" + x % 8,
+ r.nextInt(60))).toDF("mytime", "name", "age")
+
+ // 1. simple usage for time series Pre-aggregate tables creation and
query
+ spark.sql("drop table if exists timeSeriesTable")
+ spark.sql("CREATE TABLE timeSeriesTable(mytime timestamp," +
+ " name string, age int) STORED BY 'org.apache.carbondata.format'")
+ spark.sql(
+ s"""
+ | CREATE DATAMAP agg0_hour ON TABLE timeSeriesTable
+ | USING 'timeSeries'
+ | DMPROPERTIES (
+ | 'EVENT_TIME'='mytime',
+ | 'HOUR_GRANULARITY'='1')
+ | AS SELECT mytime, SUM(age) FROM timeSeriesTable
+ | GROUP BY mytime
+ """.stripMargin)
+ spark.sql(
+ s"""
+ | CREATE DATAMAP agg0_day ON TABLE timeSeriesTable
+ | USING 'timeSeries'
+ | DMPROPERTIES (
+ | 'EVENT_TIME'='mytime',
+ | 'DAY_GRANULARITY'='1')
+ | AS SELECT mytime, SUM(age) FROM timeSeriesTable
+ | GROUP BY mytime
+ """.stripMargin)
+
+
+ CarbonProperties.getInstance()
+ .addProperty(CarbonCommonConstants.CARBON_TIMESTAMP_FORMAT,
"yyyy-MM-dd HH:mm:ss")
+
+// spark.sql(s"LOAD DATA LOCAL INPATH '$testData' into table
timeSeriesTable")
--- End diff --
remove it
---
