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[DOC] add a chinese document to compare query performance of CarbonDa…
…ta and columnar DB This closes apache#3521
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| <!-- | ||
| 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 | ||
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| 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. | ||
| --> | ||
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| ## CarbonData与商业列存DB性能对比 | ||
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| 本文描述了CarbonData与某商业列存DB的查询性能对比，通过此对比可以看出CarbonData的优势和特点。本文的测试场景是基于某聚合查询业务（分析报表），测试结果只代表该特定查询场景下的性能对比。 | ||
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| ## 1. 测试环境 | ||
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| 商业列存DB使用SSD硬盘，配置一台查询节点。CarbonData6个DataNode，配置STAT硬盘，但是查询队列设置1/6的资源，等同于1台商业DB服务器对比1台CarbonData服务器的查询性能。同时CarbonData使用的服务器的磁盘是STAT盘，成本比某商业列存DB服务器低。 | ||
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| | 集群 | CPU | vCore | Memory | 硬盘 | 描述 | | ||
| | ---------------- | -------------------- | ----- | ------ | ---- | ------------------------------------------------------------ | | ||
| | 某商业列存DB集群 | Gold 6132 CPU@2.60GZ | 56 | 256GB | SSD | 3节点，一个节点作为查询节点 | | ||
| | Hadoop集群 | Gold 6132 CPU@2.60GZ | 56 | 256GB | SATA | 2个namenode，6个datanode， 查询队列分配1/6的资源，等同于一个节点 | | ||
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| ## 2. SQL测试语句介绍 | ||
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| ```Spark SQL的查询语句：``` | ||
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| ```SQL | ||
| 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)) * delta AS COLUMN_F , | ||
| COALESCE(SUM(COLUMN_A), 0) * delta AS COLUMN_G , | ||
| COALESCE(SUM(COLUMN_B), 0) * delta AS COLUMN_H , | ||
| MT.`TEMP` AS `TEMP` | ||
| 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.`TEMP` AS `TEMP` | ||
| FROM TABLE_B LEFT JOIN ( | ||
| SELECT | ||
| `COLUMN_CSI` AS `TEMP2` , | ||
| CASE WHEN `TYPE_ID` = 2 THEN `COLUMN_CSI` END AS `TEMP` , | ||
| CASE WHEN `TYPE_ID` = 2 THEN `COLUMN_NAME` END AS NAME_TEMP | ||
| 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.`TEMP2` | ||
| WHERE | ||
| TABLE_A.NAME_TEMP IS NOT NULL AND | ||
| `TIME` >= A AND `TIME` < B | ||
| ) MT | ||
| GROUP BY MT.`TEMP` | ||
| ORDER BY COLUMN_C DESC | ||
| LIMIT 5000 | ||
| ``` | ||
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| 其中一个SUM后面称为一个counter | ||
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| ## 3. CarbonData主要配置参数 | ||
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| ```主要配置``` | ||
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| | CarbonData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 | 配置汇聚时shuffle的分区数 | | ||
| | spark.executor.instances | 6 | executor实例的个数，6台服务器每台一个实例 | | ||
| | spark.executor.cores | 13 | 每一个实例的核数，这里配置13核 | | ||
| | spark.locality.wait | 0 | 配置数据本地化的等待时间为不等待 | | ||
| | spark.executor.memory | 30G | executor的内存配置 | | ||
| | spark.driver.cores | 3 | driver程序的CPU内核数量，设置为3 | | ||
| | spark.driver.memory | 50G | driver进程使用的内存数 | | ||
| | spark.sql.codegen.wholeStage | True | 打开codegen开关，该开关默认也是开启的 | | ||
| | spark.sql.codegen.hugeMethodLimit | 8000 | codegen应用的方法的长度限制，这里应该配置的与JDK相同 | | ||
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| ## 4. 不同数量量的查询性能对比 | ||
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| 某商业列存DB与CarbonData的查询均为取多次求平均值。 | ||
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| | 表的分类：数据量+counter个数 | 表记录数（条） | counter 个数 | 某商业列存DB 5次 查询平均耗时（s） | CarbonData 5次查询平均耗时（s） | | ||
| | ---------------------------- | -------------- | ------------ | ---------------------------------- | ------------------------------- | | ||
| | 100K_9Counter | 100K | 9Counter | 0.91 | 3.53 | | ||
| | 100K_18Counter | 100K | 18Counter | 1.30 | 3.81 | | ||
| | 100K_36Counter | 100K | 36Counter | 1.87 | 4.29 | | ||
| | 100K_72Counter | 100K | 72Counter | 3.82 | 5.09 | | ||
| | 500K_9Counter | 500K | 9Counter | 1.47 | 4.04 | | ||
| | 500K_18Counter | 500K | 18Counter | 1.98 | 4.61 | | ||
| | 500K_36Counter | 500K | 36Counter | 2.99 | 5.63 | | ||
| | 500K_72Counter | 500K | 72Counter | 5.67 | 7.53 | | ||
| | 1M_9Counter | 1M | 9Counter | 4.72 | 4.24 | | ||
| | 1M_18Counter | 1M | 18Counter | 5.13 | 4.84 | | ||
| | 1M_36Counter | 1M | 36Counter | 6.55 | 5.83 | | ||
| | 1M_72Counter | 1M | 72Counter | 10.83 | 7.90 | | ||
| | 5M_9Counter | 5M | 9Counter | 5.82 | 4.59 | | ||
| | 5M_18Counter | 5M | 18Counter | 7.70 | 5.26 | | ||
| | 5M_36Counter | 5M | 36Counter | 11.32 | 6.73 | | ||
| | 5M_72Counter | 5M | 72Counter | 21.78 | 9.27 | | ||
| | 10M_9Counter | 10M | 9Counter | 7.98 | 5.32 | | ||
| | 10M_18Counter | 10M | 18Counter | 11.39 | 6.03 | | ||
| | 10M_36Counter | 10M | 36Counter | 17.40 | 7.43 | | ||
| | 10M_72Counter | 10M | 72Counter | 34.50 | 10.48 | | ||
| | 50M_9Counter | 50M | 9Counter | 16.89 | 8.95 | | ||
| | 50M_18Counter | 50M | 18Counter | 25.50 | 10.42 | | ||
| | 50M_36Counter | 50M | 36Counter | 268.10 | 12.78 | | ||
| | 50M_72Counter | 50M | 72Counter | 554.16 | 18.79 | | ||
| | 100M_9Counter | 100M | 9Counter | 25.13 | 13.19 | | ||
| | 100M_18Counter | 100M | 18Counter | 35.57 | 14.87 | | ||
| | 100M_36Counter | 100M | 36Counter | 299.43 | 18.96 | | ||
| | 100M_72Counter | 100M | 72Counter | 678.72 | 28.12 | | ||
| | 1B_9Counter | 1B | 9Counter | 167.50 | 47.95 | | ||
| | 1B_18Counter | 1B | 18Counter | 261.20 | 55.79 | | ||
| | 1B_36Counter | 1B | 36Counter | 654.99 | 73.14 | | ||
| | 1B_72Counter | 1B | 72Counter | 1575.81 | 116.63 | | ||
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| ## 5. 总结 | ||
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| 通过上面的测试结果可以看出： | ||
| 1. 在同等CPU内存资源及使用SATA盘劣势资源的情况下，CarbonData的查询性能要高于某商业列存DB。 | ||
| 2. 在百万级及以上数据量的查询中CarbonData的查询性能明显高于商业列存DB，整体查询性能有了较高的提升，平均查询性能提升1.5-10倍。 | ||
| 3. 在百万级数据以上，随着数据量的增大，CarbonData的查询优势越来越明显。 |