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From jackylk <...@git.apache.org>
Subject [GitHub] incubator-carbondata pull request #624: [CARBONDATA-747] Add simple performa...
Date Thu, 16 Mar 2017 15:29:06 GMT
Github user jackylk commented on a diff in the pull request:

    https://github.com/apache/incubator-carbondata/pull/624#discussion_r106448892
  
    --- Diff: examples/spark2/src/main/scala/org/apache/carbondata/examples/CompareTest.scala
---
    @@ -0,0 +1,372 @@
    +/*
    + * 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 java.text.SimpleDateFormat
    +import java.util.Date
    +
    +import scala.util.Random
    +
    +import org.apache.spark.sql.{DataFrame, Row, SaveMode, SparkSession}
    +import org.apache.spark.sql.types._
    +
    +import org.apache.carbondata.core.constants.CarbonCommonConstants
    +import org.apache.carbondata.core.util.CarbonProperties
    +
    +/**
    + * A query test case
    + * @param sqlText SQL statement
    + * @param queryType type of query: scan, filter, aggregate, topN
    + * @param desc description of the goal of this test case
    + */
    +case class Query(sqlText: String, queryType: String, desc: String)
    +
    +// scalastyle:off println
    +object CompareTest {
    +
    +  def parquetTableName: String = "comparetest_parquet"
    +  def carbonTableName(version: String): String = s"comparetest_carbonV$version"
    +
    +  // Table schema:
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | Column name | Data type | Cardinality | Column type | Dictionary |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | city        | string    | 8           | dimension   | yes        |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | country     | string    | 1103        | dimension   | yes        |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | planet      | string    | 100,007     | dimension   | yes        |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | id          | string    | 10,000,000  | dimension   | no         |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | m1          | short     | NA          | measure     | no         |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | m2          | int       | NA          | measure     | no         |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | m3          | big int   | NA          | measure     | no         |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | m4          | double    | NA          | measure     | no         |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  // | m5          | double    | NA          | measure     | no         |
    +  // +-------------+-----------+-------------+-------------+------------+
    +  private def generateDataFrame(spark: SparkSession): DataFrame = {
    +    val r = new Random()
    +    val rdd = spark.sparkContext
    +        .parallelize(1 to 1 * 1000 * 1000, 4)
    +        .map { x =>
    +          ("city" + x % 8, "country" + x % 1103, "planet" + x % 10007, x.toString,
    +          (x % 16).toShort, x / 2, (x << 1).toLong, x.toDouble / 13, x.toDouble
/ 11)
    +        }.map { x =>
    +          Row(x._1, x._2, x._3, x._4, x._5, x._6, x._7, x._8, x._9)
    +        }
    +
    +    val schema = StructType(
    +      Seq(
    +        StructField("city", StringType, nullable = false),
    +        StructField("country", StringType, nullable = false),
    +        StructField("planet", StringType, nullable = false),
    +        StructField("id", StringType, nullable = false),
    +        StructField("m1", ShortType, nullable = false),
    +        StructField("m2", IntegerType, nullable = false),
    +        StructField("m3", LongType, nullable = false),
    +        StructField("m4", DoubleType, nullable = false),
    +        StructField("m5", DoubleType, nullable = false)
    +      )
    +    )
    +
    +    spark.createDataFrame(rdd, schema)
    +  }
    +
    +  // performance test queries, they are designed to test various data access type
    +  val queries: Array[Query] = Array(
    +    // ===========================================================================
    +    // ==                     FULL SCAN AGGREGATION                             ==
    +    // ===========================================================================
    +    Query(
    +      "select sum(m1) from $table",
    +      "full scan",
    +      "full scan query, 1 aggregate"
    +    ),
    +    Query(
    +      "select sum(m1), sum(m2) from $table",
    +      "full scan",
    +      "full scan query, 2 aggregate"
    +    ),
    +    Query(
    +      "select sum(m1), sum(m2), sum(m3) from $table",
    +      "full scan",
    +      "full scan query, 3 aggregate"
    +    ),
    +    Query(
    +      "select sum(m1), sum(m2), sum(m3), sum(m4) from $table",
    +      "full scan",
    +      "full scan query, 4 aggregate"
    +    ),
    +    Query(
    +      "select sum(m1), sum(m2), sum(m3), sum(m4), avg(m5) from $table",
    +      "full scan",
    +      "full scan query, 5 aggregate"
    +    ),
    +    Query(
    +      "select * from $table",
    +      "full scan",
    +      "full scan query, big result set"
    +    ),
    +    Query(
    +      "select count(distinct id) from $table",
    +      "full scan",
    +      "full scan and count distinct of high card column"
    +    ),
    +    Query(
    +      "select count(distinct country) from $table",
    +      "full scan",
    +      "full scan and count distinct of medium card column"
    +    ),
    +    Query(
    +      "select count(distinct city) from $table",
    +      "full scan",
    +      "full scan and count distinct of low card column"
    +    ),
    +    // ===========================================================================
    +    // ==                      FULL SCAN GROUP BY AGGREGATE                     ==
    +    // ===========================================================================
    +    Query(
    +      "select id, sum(m1) from $table group by id",
    +      "aggregate",
    +      "group by on big data, on high card column, big result set"
    +    ),
    +    Query(
    +      "select country, sum(m1) from $table group by country",
    +      "aggregate",
    +      "group by on big data, on medium card column, medium result set,"
    +    ),
    +    Query(
    +      "select city, sum(m1) from $table group by city",
    +      "aggregate",
    +      "group by on big data, on low card column, small result set,"
    +    ),
    +    Query(
    +      "select id, sum(m1) as metric from $table group by id order by metric desc limit
100",
    +      "topN",
    +      "top N on high card column"
    +    ),
    +    Query(
    +      "select country,sum(m1) as metric from $table group by country order by metric
desc limit 10",
    +      "topN",
    +      "top N on medium card column"
    +    ),
    +    Query(
    +      "select city,sum(m1) as metric from $table group by city order by metric desc limit
10",
    +      "topN",
    +      "top N on low card column"
    +    ),
    +    // ===========================================================================
    +    // ==                  FILTER SCAN GROUP BY AGGREGATION                     ==
    +    // ===========================================================================
    +    Query(
    +      "select country, sum(m1) from $table where city='city8' group by country ",
    +      "filter scan and aggregate",
    +      "group by on large data, small result set"
    +    ),
    +    Query(
    +      "select id, sum(m1) from $table where planet='planet10' group by id",
    +      "filter scan and aggregate",
    +      "group by on medium data, large result set"
    +    ),
    +    Query(
    +      "select city, sum(m1) from $table where country='country12' group by city ",
    +      "filter scan and aggregate",
    +      "group by on medium data, small result set"
    +    ),
    +    // ===========================================================================
    +    // ==                             FILTER SCAN                               ==
    +    // ===========================================================================
    +    Query(
    +      "select * from $table where city = 'city3' limit 10000",
    +      "filter scan",
    +      "filter on low card dimension, limit, medium result set, fetch all columns"
    +    ),
    +    Query(
    +      "select * from $table where country = 'country9' ",
    +      "filter scan",
    +      "filter on low card dimension, medium result set, fetch all columns"
    +    ),
    +    Query(
    +      "select * from $table where planet = 'planet101' ",
    +      "filter scan",
    +      "filter on medium card dimension, small result set, fetch all columns"
    +    ),
    +    Query(
    +      "select * from $table where id = '408938' ",
    +      "filter scan",
    +      "filter on high card dimension"
    +    ),
    +    Query(
    +      "select * from $table where country='country10000'  ",
    +      "filter scan",
    +      "filter on low card dimension, not exist"
    +    ),
    +    Query(
    +      "select * from $table where country='country2' and city ='city8' ",
    +      "filter scan",
    +      "filter on 2 dimensions, small result set, fetch all columns"
    +    ),
    +    Query(
    +      "select * from $table where city='city1' and country='country2' and planet ='planet3'
",
    +      "filter scan",
    +      "filter on 3 dimensions, small result set, fetch all columns"
    +    ),
    +    Query(
    +      "select * from $table where m1 < 3",
    +      "filter scan",
    +      "filter on measure, small result set, fetch all columns"
    +    ),
    +    Query(
    +      "select * from $table where id like '1%' ",
    +      "fuzzy filter scan",
    +      "like filter, big result set"
    +    ),
    +    Query(
    +      "select * from $table where id like '%111'",
    +      "fuzzy filter scan",
    +      "like filter, medium result set"
    +    ),
    +    Query(
    +      "select * from $table where id like 'xyz%' ",
    +      "fuzzy filter scan",
    +      "like filter, full scan but not exist"
    +    )
    +  )
    +
    +  private def loadParquetTable(spark: SparkSession, input: DataFrame): Double = time
{
    +    // partitioned by last 1 digit of id column
    +    val dfWithPartition = input.withColumn("partitionCol", input.col("id").%(10))
    +    dfWithPartition.write
    +        .partitionBy("partitionCol")
    +        .mode(SaveMode.Overwrite)
    +        .parquet(parquetTableName)
    +  }
    +
    +  private def loadCarbonTable(spark: SparkSession, input: DataFrame, version: String):
Double = {
    +    CarbonProperties.getInstance().addProperty(
    +      CarbonCommonConstants.CARBON_DATA_FILE_VERSION,
    +      version
    +    )
    +    spark.sql(s"drop table if exists ${carbonTableName(version)}")
    +    time {
    +      input.write
    +          .format("carbondata")
    +          .option("tableName", carbonTableName(version))
    +          .option("tempCSV", "false")
    +          .option("single_pass", "true")
    +          .option("dictionary_exclude", "id") // id is high cardinality column
    +          .mode(SaveMode.Overwrite)
    +          .save()
    +    }
    +  }
    +
    +  // load data into parquet, carbonV2, carbonV3
    +  private def prepareTable(spark: SparkSession): Unit = {
    +    val df = generateDataFrame(spark)
    +    println(s"loading ${df.count} records, schema: ${df.schema}")
    +    val loadParquetTime = loadParquetTable(spark, df)
    +    val loadCarbonV3Time = loadCarbonTable(spark, df, version = "3")
    +    println(s"load completed, time: $loadParquetTime, $loadCarbonV3Time")
    +    spark.read.parquet(parquetTableName).registerTempTable(parquetTableName)
    +  }
    +
    +  // Run all queries for the specified table
    +  private def runQueries(spark: SparkSession, tableName: String): Array[(Double, Array[Row])]
= {
    +    println(s"start running queries for $tableName...")
    +    var result: Array[Row] = null
    +    queries.zipWithIndex.map { case (query, index) =>
    +      val sqlText = query.sqlText.replace("$table", tableName)
    +      print(s"running query ${index + 1}: $sqlText ")
    +      val rt = time {
    +        result = spark.sql(sqlText).collect()
    +      }
    +      println(s"=> $rt sec")
    +      (rt, result)
    --- End diff --
    
    ok


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