ravipesala commented on a change in pull request #3275: [WIP]Added documentation for mv URL: https://github.com/apache/carbondata/pull/3275#discussion_r293699548
########## File path: docs/datamap/mv-datamap-guide.md ########## @@ -0,0 +1,263 @@ +<!-- + 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. +--> + +# CarbonData MV DataMap + +* [Quick Example](#quick-example) +* [MV DataMap](#mv-datamap-introduction) +* [Loading Data](#loading-data) +* [Querying Data](#querying-data) +* [Compaction](#compacting-mv-tables) +* [Data Management](#data-management-with-mv-tables) + +## Quick example +Download and unzip spark-2.2.0-bin-hadoop2.7.tgz, and export $SPARK_HOME + +Package carbon jar, and copy assembly/target/scala-2.11/carbondata_2.11-x.x.x-SNAPSHOT-shade-hadoop2.7.2.jar to $SPARK_HOME/jars +```shell +mvn clean package -DskipTests -Pspark-2.2 -Pmv +``` + +Start spark-shell in new terminal, type :paste, then copy and run the following code. +```scala + import java.io.File + import org.apache.spark.sql.CarbonSession._ + import org.apache.spark.sql.SparkSession + + val warehouse = new File("./warehouse").getCanonicalPath + val metastore = new File("./metastore").getCanonicalPath + + val spark = SparkSession + .builder() + .master("local") + .appName("MVDatamapExample") + .config("spark.sql.warehouse.dir", warehouse) + .getOrCreateCarbonSession(warehouse, metastore) + + spark.sparkContext.setLogLevel("ERROR") + + // drop table if exists previously + spark.sql(s"DROP TABLE IF EXISTS sales") + + // Create main table + spark.sql( + s""" + | CREATE TABLE sales ( + | user_id string, + | country string, + | quantity int, + | price bigint) + | STORED AS carbondata + """.stripMargin) + + // Create mv datamap table on the main table + // If main table already have data, following command + // will trigger one immediate load to the mv table + spark.sql( + s""" + | CREATE DATAMAP agg_sales + | ON TABLE sales + | USING "mv" + | AS + | SELECT country, sum(quantity), avg(price) + | FROM sales + | GROUP BY country + """.stripMargin) + + import spark.implicits._ + import org.apache.spark.sql.SaveMode + import scala.util.Random + + // Load data to the main table, it will also + // trigger immediate load to mv table in case of non-lazy datamap. + val r = new Random() + spark.sparkContext.parallelize(1 to 10) + .map(x => ("ID." + r.nextInt(100000), "country" + x % 8, x % 50, x % 60)) + .toDF("user_id", "country", "quantity", "price") + .write + .format("carbondata") + .option("tableName", "sales") + .option("compress", "true") + .mode(SaveMode.Append) + .save() + + spark.sql( + s""" + |SELECT country, sum(quantity), avg(price) + | from sales GROUP BY country + """.stripMargin).show + + spark.stop +``` + +## MV DataMap Introduction + Pre-aggregate datamap supports only aggregation on single table where as MV datamap is implemented + to support projection, projection with filter, aggregation and join capabilities also. MV tables are + created as DataMaps and managed as tables internally by CarbonData. User can create as many MV + datamaps required to improve query performance, provided the storage requirements and loading + speeds are acceptable. + + MV datamap can be a lazy or a non-lazy datamap. Once MV datamaps are created, CarbonData's + CarbonAnalyzer helps to select the most efficient MV datamap based on the user query and rewrite + the SQL to select the data from MV datamap instead of main table. Since the data size of MV + datamap is smaller, user queries are much faster. + + For instance, main table called **sales** which is defined as + + ``` + CREATE TABLE sales ( + order_time timestamp, + user_id string, + sex string, + country string, + quantity int, + price bigint) + STORED AS carbondata + ``` + + User can create MV tables using the Create DataMap DDL + + ``` + CREATE DATAMAP agg_sales + ON TABLE sales + USING "MV" + AS + SELECT country, sex, sum(quantity), avg(price) + FROM sales + GROUP BY country, sex + ``` + **NOTE**: + * Group by/Filter columns has to be provided in projection list while creating mv datamap + * If only one parent table is involved in mv datamap creation, then TableProperties of Parent table + (if not present in a aggregate function like sum(col)) like SORT_COLUMNS, SORT_SCOPE, TABLE_BLOCKSIZE, + FLAT_FOLDER, LONG_STRING_COLUMNS, LOCAL_DICTIONARY_ENABLE, LOCAL_DICTIONARY_THRESHOLD, + LOCAL_DICTIONARY_INCLUDE, LOCAL_DICTIONARY_EXCLUDE, DICTIONARY_INCLUDE, DICTIONARY_EXCLUDE, + INVERTED_INDEX, NO_INVERTED_INDEX, COLUMN_COMPRESSOR will be inherited to datamap table + * All columns of main table at once cannot participate in mv datamap table creation + * TableProperties can be provided in DMProperties excluding LOCAL_DICTIONARY_INCLUDE, + LOCAL_DICTIONARY_EXCLUDE, DICTIONARY_INCLUDE, DICTIONARY_EXCLUDE, INVERTED_INDEX, + NO_INVERTED_INDEX, SORT_COLUMNS, LONG_STRING_COLUMNS, RANGE_COLUMN & COLUMN_META_CACHE(**NOTE**: Review comment: NOTE inside a note is not a good idea. 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