MarvinLitt commented on a change in pull request #3521: [doc_zh_cn] add a commercial inventory DB and carbon data query performance comparison doc chinese doc to carbondata
URL:
https://github.com/apache/carbondata/pull/3521#discussion_r360832602
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File path: docs/zh_cn/SybaseIQ和CarbonData查询性能对比.md
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+
+## Carbondata 替换Sybase IQ查询性能对比
+
+本文主要在于给用户呈现Carbondata在替换Syabse IQ过程中对于Sybase IQ的查询性能，Carbondata自身的优势和特点，本文的数据仅为基于某领域查询特点框架下SQL的查询结果，只代表该特定查询特点下的性能对比。
+
+
+
+
+
+## 1.集群状态对比
+
+| 集群 | 描述 |
+| ---------- | --------------------------------------------------------- |
+| IQ集群 | 1个加载节点，1个协调节点，1个查询节点，SSD硬盘，磁阵 |
+| Hadoop集群 | 2个namenode，6个datanode，STAT硬盘，查询队列分配1/6的资源 |
+
+## 2.查询SQL模型介绍
+
+IQ与Carbon查询SQL本身存在差异，在执行性能测试之前需要对SQL进行修改。
+
+```IQ的查询SQL模型：```
+
+SELECT TOP 5000 SUM(COALESCE(COLUMN_A, 0)) + SUM(COALESCE(COLUMN_B, 0)) AS COLUMN_C , SUM(COALESCE(COLUMN_A, 0)) AS COLUMN_A_A , SUM(COALESCE(COLUMN_B, 0)) AS COLUMN_B_B , SUM(COALESCE(COLUMN_D, 0)) + SUM(COALESCE(COLUMN_E, 0)) AS COLUMN_F , SUM(COALESCE(COLUMN_D, 0)) AS COLUMN_D_D , SUM(COALESCE(COLUMN_E, 0)) AS COLUMN_E_E , (SUM(COALESCE(COLUMN_A, 0)) + SUM(COALESCE(COLUMN_B, 0))) * 8 / 72000 / 1024 AS COLUMN_F , SUM(COALESCE(COLUMN_A, 0)) * 8 / 72000 / 1024 AS COLUMN_G , SUM(COALESCE(COLUMN_B, 0)) * 8 / 72000 / 1024 AS COLUMN_H , MT."202080101" AS "202080101", COUNT(1) OVER () AS countNum FROM ( SELECT COALESCE(SUM("COLUMN_1_A"), 0) AS COLUMN_A , COALESCE(SUM("COLUMN_1_B"), 0) AS COLUMN_B , COALESCE(SUM("COLUMN_1_E"), 0) AS COLUMN_E , COALESCE(SUM("COLUMN_1_D"), 0) AS COLUMN_D , TABLE_A."202080101" AS "202080101" FROM TABLE_B LEFT JOIN ( SELECT "COLUMN_CSI" AS "202050101" , CASE WHEN "TYPE_ID" = 2 THEN "COLUMN_CSI" END AS "202080101" , CASE WHEN "TYPE_ID" = 2 THEN "CLOUMN_NAME" END AS NAME_202080101 FROM DIMENSION_TABLE GROUP BY "COLUMN_CSI", CASE WHEN "TYPE_ID" = 2 THEN "COLUMN_CSI" END, CASE WHEN "TYPE_ID" = 2 THEN "CLOUMN_NAME" END ) TABLE_A ON "COLUMN_CSI" = TABLE_A."202050101" WHERE TABLE_A.NAME_202080101 IS NOT NULL AND "TIME" < 1576087200 AND "TIME" >= 1576015200 GROUP BY TABLE_A."202080101" ) MT GROUP BY MT."202080101" ORDER BY COLUMN_C DESC
+
+其中一个SUM后面称为一个counter
+
+```Spark的查询SQL模型：```
+
+SELECT COALESCE(SUM(COLUMN_A), 0) + COALESCE(SUM(COLUMN_B), 0) AS COLUMN_C , COALESCE(SUM(COLUMN_A), 0) AS COLUMN_A_A , COALESCE(SUM(COLUMN_B), 0) AS COLUMN_B_B , COALESCE(SUM(COLUMN_D), 0) + COALESCE(SUM(COLUMN_E), 0) AS COLUMN_F , COALESCE(SUM(COLUMN_D), 0) AS COLUMN_D_D , COALESCE(SUM(COLUMN_E), 0) AS COLUMN_E_E , (COALESCE(SUM(COLUMN_A), 0) + COALESCE(SUM(COLUMN_B), 0)) * 8 / 72000 / 1024 AS COLUMN_F , COALESCE(SUM(COLUMN_A), 0) * 8 / 72000 / 1024 AS COLUMN_G , COALESCE(SUM(COLUMN_B), 0) * 8 / 72000 / 1024 AS COLUMN_H , MT.`202080101` AS `202080101` FROM ( SELECT `COLUMN_1_A` AS COLUMN_A, `COLUMN_1_E` AS COLUMN_E, `COLUMN_1_B` AS COLUMN_B, `COLUMN_1_D` AS COLUMN_D, TABLE_A.`202080101` AS `202080101` FROM TABLE_B LEFT JOIN ( SELECT `COLUMN_CSI` AS `202050101` , CASE WHEN `TYPE_ID` = 2 THEN `COLUMN_CSI` END AS `202080101` , CASE WHEN `TYPE_ID` = 2 THEN `COLUMN_NAME` END AS NAME_202080101 FROM DIMENSION_TABLE GROUP BY `COLUMN_CSI`, CASE WHEN `TYPE_ID` = 2 THEN `COLUMN_CSI` END, CASE WHEN `TYPE_ID` = 2 THEN `COLUMN_NAME` END ) TABLE_A ON `COLUMN_CSI` = TABLE_A.`202050101` WHERE TABLE_A.NAME_202080101 IS NOT NULL AND `TIME` >= 1576015200 AND `TIME` < 1576087200 ) MT GROUP BY MT.`202080101` ORDER BY COLUMN_C DESC LIMIT 5000
+
+## 3.Carbon主要配置参数
+
+```主要配置```
+
+| Carbon主要配置 | 参数值 | 描述 |
+| ------------------------------------ | ------ | ------------------------------------------------------------ |
+| carbon.inmemory.record.size | 480000 | 查询每个表需要加载到内存的总行数。 |
+| carbon.number.of.cores | 4 | carbon查询过程中并行扫描的线程数。 |
+| carbon.number.of.cores.while.loading | 15 | carbon数据加载过程中并行扫描的线程数。 |
+| carbon.sort.file.buffer.size | 20 | 在合并排序(读/写)操作时存储每个临时过程文件的所使用的总缓存大小。单位为MB |
+| carbon.sort.size | 500000 | 在数据加载操作时，每次被排序的记录数。 |
+| Spark主要配置 | | |
+| spark.sql.shuffle.partitions | 70 | |
+| spark.executor.instances | 6 | |
+| spark.executor.cores | 13 | |
+| spark.locality.wait | 0 | |
+| spark.executor.memory | 5G | |
+| spark.driver.cores | 3 | |
+| spark.driver.memory | 50G | |
+| spark.sql.codegen.wholeStage | True | |
+| spark.sql.codegen.hugeMethodLimit | 8000 | |
+
+## 4.不同数量级查询性能对比结果：
+
+carbondata的查询取多次求平均值，并且排除了首次查询的较长耗时，目前首次查询的耗时正在优化中。
Review comment:
okay, done.
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