总览
Doris更优的方面
- 使用更简单，如建表更简单，SQL标准支持更好， Join性能更好，导数功能更强大，综合并发更高
- 运维更简单，如灵活的扩缩容能力，故障节点自动恢复，社区提供的支持更好
- 分布式更强，支持事务和幂等性导数，物化视图自动聚合，查询自动路由，全面元数据管理
- 对数据湖的支持好，代替了一部分trino的功能
- 点查能力更强，并发更高，多表查询性能好
ClickHouse更优的方面
- 单表性能更佳，导入性能和单表查询性能更好，同时可靠性更好
- 功能丰富，非常多的表引擎，更多类型和函数支持，更好的聚合函数以及庞大的优化参数选项
- 集群管理工具更多，更好多租户和配额管理，灵活的集群管理，方便的集群间迁移工具
- 导入数据速度非常快
架构
Benckmark
clickbench单表基准测试
clickbench是clickhouse设计的一个单表查询的benchmark，涵盖50余种数据引擎场景
地址：ClickBench — a Benchmark For Analytical DBMS
服务器规格：192线程 500GB内存
统计方法：每个查询中最快的引擎耗时为1，其他引擎的相对耗时=耗时/最快引擎耗时，取43个查询的平均相对系数
热查询的相对耗时排行
冷查询相对耗时排行
数据导入相对耗时排行
热查询明细
点击展开\折叠表格
| 查询SQL | 查询类型 | ClickHouse 23.11热查询 | StarRocks热查询 | SelectDB热查询 | SelectDB冷查询 | StarRocks冷查询 | ClickHouse23.11冷查询 |
|---|---|---|---|---|---|---|---|
| Load time: | 加载时间 | 132s (×1.00) | 369s (×2.80) | 433s (×3.29) | 369s (×2.80) | 433s (×3.29) | 132s (×1.00) |
| Data size: | 数据大小 | 13.47 GiB (×1.00) | 15.95 GiB (×1.18) | 16.49 GiB (×1.22) | 15.95 GiB (×1.18) | 16.49 GiB (×1.22) | 13.47 GiB (×1.00) |
| SELECT COUNT(*) FROM hits; | count | 0.00s (×1.00) | 0.03s (×3.08) | 0.00s (×0.77) | 0.01s (×1.00) | 0.04s (×2.50) | 0.06s (×3.55) |
| SELECT COUNT(*) FROM hits WHERE AdvEngineID <> 0; | count | 0.01s (×1.00) | 0.03s (×1.82) | 0.02s (×1.36) | 0.03s (×1.48) | 0.12s (×4.81) | 0.02s (×1.00) |
| SELECT SUM(AdvEngineID), COUNT(*), AVG(ResolutionWidth) FROM hits; | 汇总函数 | 0.02s (×1.00) | 0.06s (×2.41) | 0.04s (×1.72) | 0.04s (×1.00) | 0.66s (×13.40) | 0.05s (×1.16) |
| SELECT AVG(UserID) FROM hits; | 汇总函数 | 0.02s (×1.00) | 0.06s (×2.33) | 0.06s (×2.33) | 0.07s (×1.00) | 2.08s (×26.12) | 0.71s (×9.06) |
| SELECT COUNT(DISTINCT UserID) FROM hits; | 去重 | 0.20s (×2.32) | 0.08s (×1.00) | 0.15s (×1.78) | 0.17s (×1.64) | 0.10s (×1.00) | 0.88s (×8.10) |
| SELECT COUNT(DISTINCT SearchPhrase) FROM hits; | 去重 | 0.17s (×1.00) | 0.21s (×1.19) | 0.28s (×1.57) | 0.30s (×1.00) | 2.19s (×7.10) | 1.09s (×3.56) |
| SELECT MIN(EventDate), MAX(EventDate) FROM hits; | 汇总函数 | 0.02s (×1.35) | 0.02s (×1.50) | 0.01s (×1.00) | 0.01s (×1.00) | 0.04s (×2.50) | 0.02s (×1.50) |
| SELECT AdvEngineID, COUNT() FROM hits WHERE AdvEngineID <> 0 GROUP BY AdvEngineID ORDER BY COUNT() DESC; | 分桶 | 0.02s (×1.13) | 0.04s (×1.67) | 0.02s (×1.00) | 0.02s (×1.00) | 0.06s (×2.33) | 0.04s (×1.67) |
| SELECT RegionID, COUNT(DISTINCT UserID) AS u FROM hits GROUP BY RegionID ORDER BY u DESC LIMIT 10; | 分桶 | 0.26s (×2.10) | 0.12s (×1.00) | 0.33s (×2.62) | 0.32s (×1.00) | 0.99s (×3.03) | 0.87s (×2.67) |
| SELECT RegionID, SUM(AdvEngineID), COUNT(*) AS c, AVG(ResolutionWidth), COUNT(DISTINCT UserID) FROM hits GROUP BY RegionID ORDER BY c DESC LIMIT 10; | 分桶 | 0.27s (×1.00) | 0.70s (×2.55) | 0.34s (×1.26) | 0.36s (×1.00) | 0.73s (×2.00) | 1.14s (×3.10) |
| SELECT MobilePhoneModel, COUNT(DISTINCT UserID) AS u FROM hits WHERE MobilePhoneModel <> ‘’ GROUP BY MobilePhoneModel ORDER BY u DESC LIMIT 10; | 分桶 | 0.09s (×1.60) | 0.05s (×1.00) | 0.11s (×2.00) | 0.11s (×1.00) | 0.13s (×1.17) | 0.38s (×3.27) |
| SELECT MobilePhone, MobilePhoneModel, COUNT(DISTINCT UserID) AS u FROM hits WHERE MobilePhoneModel <> ‘’ GROUP BY MobilePhone, MobilePhoneModel ORDER BY u DESC LIMIT 10; | 分桶 | 0.09s (×1.49) | 0.06s (×1.00) | 0.10s (×1.57) | 0.11s (×1.00) | 1.04s (×8.75) | 0.74s (×6.23) |
| SELECT SearchPhrase, COUNT(*) AS c FROM hits WHERE SearchPhrase <> ‘’ GROUP BY SearchPhrase ORDER BY c DESC LIMIT 10; | 分桶 | 0.16s (×1.00) | 0.17s (×1.05) | 0.23s (×1.40) | 0.23s (×1.33) | 0.17s (×1.00) | 1.25s (×7.00) |
| SELECT SearchPhrase, COUNT(DISTINCT UserID) AS u FROM hits WHERE SearchPhrase <> ‘’ GROUP BY SearchPhrase ORDER BY u DESC LIMIT 10; | 分桶 | 0.19s (×1.00) | 0.23s (×1.22) | 0.75s (×3.88) | 0.55s (×2.33) | 0.23s (×1.00) | 1.91s (×7.99) |
| SELECT SearchEngineID, SearchPhrase, COUNT(*) AS c FROM hits WHERE SearchPhrase <> ‘’ GROUP BY SearchEngineID, SearchPhrase ORDER BY c DESC LIMIT 10; | 分桶 | 0.16s (×1.00) | 0.18s (×1.10) | 0.30s (×1.79) | 0.32s (×1.00) | 0.54s (×1.67) | 1.24s (×3.78) |
| SELECT UserID, COUNT() FROM hits GROUP BY UserID ORDER BY COUNT() DESC LIMIT 10; | 分桶 | 0.12s (×1.43) | 0.08s (×1.00) | 0.16s (×1.89) | 0.18s (×1.90) | 0.09s (×1.00) | 0.50s (×5.12) |
| SELECT UserID, SearchPhrase, COUNT() FROM hits GROUP BY UserID, SearchPhrase ORDER BY COUNT() DESC LIMIT 10; | 分桶 | 0.34s (×1.14) | 0.30s (×1.00) | 0.50s (×1.65) | 0.53s (×1.69) | 0.31s (×1.00) | 2.06s (×6.45) |
| SELECT UserID, SearchPhrase, COUNT(*) FROM hits GROUP BY UserID, SearchPhrase LIMIT 10; | 分桶 | 0.24s (×2.28) | 0.10s (×1.00) | 0.20s (×1.91) | 0.20s (×1.62) | 0.12s (×1.00) | 1.58s (×12.27) |
| SELECT UserID, extract(minute FROM EventTime) AS m, SearchPhrase, COUNT() FROM hits GROUP BY UserID, m, SearchPhrase ORDER BY COUNT() DESC LIMIT 10; | 分桶 | 0.61s (×1.38) | 0.44s (×1.00) | 1.06s (×2.38) | 1.04s (×1.00) | 1.27s (×1.22) | 3.67s (×3.50) |
| SELECT UserID FROM hits WHERE UserID = 435090932899640449; | 点查 | 0.01s (×1.05) | 0.00s (×0.50) | 0.00s (×0.50) | 0.01s (×1.00) | 0.01s (×1.00) | 0.09s (×5.00) |
| SELECT COUNT(*) FROM hits WHERE URL LIKE ‘%google%’; | 模糊搜索 | 0.13s (×1.00) | 0.15s (×1.19) | 0.60s (×4.52) | 0.59s (×1.00) | 12.09s (×20.17) | 9.27s (×15.46) |
| SELECT SearchPhrase, MIN(URL), COUNT(*) AS c FROM hits WHERE URL LIKE ‘%google%’ AND SearchPhrase <> ‘’ GROUP BY SearchPhrase ORDER BY c DESC LIMIT 10; | 模糊搜索 | 0.17s (×1.35) | 0.12s (×1.00) | 0.34s (×2.69) | 0.48s (×2.72) | 0.17s (×1.00) | 10.93s (×60.77) |
| SELECT SearchPhrase, MIN(URL), MIN(Title), COUNT(*) AS c, COUNT(DISTINCT UserID) FROM hits WHERE Title LIKE ‘%Google%’ AND URL NOT LIKE ‘%.google.%’ AND SearchPhrase <> ‘’ GROUP BY SearchPhrase ORDER BY c DESC LIMIT 10; | 模糊搜索 | 0.22s (×1.00) | 0.24s (×1.11) | 0.37s (×1.68) | 0.38s (×1.00) | 10.91s (×28.00) | 13.05s (×33.50) |
| SELECT * FROM hits WHERE URL LIKE ‘%google%’ ORDER BY EventTime LIMIT 10; | 模糊搜索 | 0.59s (×2.31) | 0.86s (×3.35) | 0.25s (×1.00) | 0.25s (×1.00) | 28.25s (×108.69) | 36.63s (×140.92) |
| SELECT SearchPhrase FROM hits WHERE SearchPhrase <> ‘’ ORDER BY EventTime LIMIT 10; | 排序 | 0.04s (×1.83) | 0.02s (×1.00) | 0.04s (×1.67) | 0.04s (×1.25) | 0.03s (×1.00) | 2.02s (×50.85) |
| SELECT SearchPhrase FROM hits WHERE SearchPhrase <> ‘’ ORDER BY SearchPhrase LIMIT 10; | 排序 | 0.04s (×1.02) | 0.04s (×1.00) | 0.14s (×3.00) | 0.15s (×2.29) | 0.06s (×1.00) | 1.33s (×19.09) |
| SELECT SearchPhrase FROM hits WHERE SearchPhrase <> ‘’ ORDER BY EventTime, SearchPhrase LIMIT 10; | 排序 | 0.05s (×2.00) | 0.02s (×1.00) | 0.04s (×1.67) | 0.04s (×1.67) | 0.02s (×1.00) | 2.71s (×90.63) |
| SELECT CounterID, AVG(length(URL)) AS l, COUNT() AS c FROM hits WHERE URL <> ‘’ GROUP BY CounterID HAVING COUNT() > 100000 ORDER BY l DESC LIMIT 25; | having | 0.25s (×1.18) | 0.21s (×1.00) | 0.54s (×2.50) | 0.55s (×1.00) | 0.63s (×1.14) | 9.63s (×17.22) |
| SELECT REGEXP_REPLACE(Referer, ‘^https?://(?:www.)?([^/]+)/.$’, ‘\1’) AS k, AVG(length(Referer)) AS l, COUNT() AS c, MIN(Referer) FROM hits WHERE Referer <> ‘’ GROUP BY k HAVING COUNT(*) > 100000 ORDER BY l DESC LIMIT 25; | having | 1.19s (×1.00) | 1.60s (×1.34) | 1.34s (×1.12) | 1.39s (×1.00) | 8.77s (×6.27) | 8.05s (×5.76) |
| SELECT SUM(ResolutionWidth), SUM(ResolutionWidth + 1), SUM(ResolutionWidth + 2), SUM(ResolutionWidth + 3), SUM(ResolutionWidth + 4), SUM(ResolutionWidth + 5), SUM(ResolutionWidth + 6), SUM(ResolutionWidth + 7), SUM(ResolutionWidth + 8), SUM(ResolutionWidth + 9), SUM(ResolutionWidth + 10), SUM(ResolutionWidth + 11), SUM(ResolutionWidth + 12), SUM(ResolutionWidth + 13), SUM(ResolutionWidth + 14), SUM(ResolutionWidth + 15), SUM(ResolutionWidth + 16), SUM(ResolutionWidth + 17), SUM(ResolutionWidth + 18), SUM(ResolutionWidth + 19), SUM(ResolutionWidth + 20), SUM(ResolutionWidth + 21), SUM(ResolutionWidth + 22), SUM(ResolutionWidth + 23), SUM(ResolutionWidth + 24), SUM(ResolutionWidth + 25), SUM(ResolutionWidth + 26), SUM(ResolutionWidth + 27), SUM(ResolutionWidth + 28), SUM(ResolutionWidth + 29), SUM(ResolutionWidth + 30), SUM(ResolutionWidth + 31), SUM(ResolutionWidth + 32), SUM(ResolutionWidth + 33), SUM(ResolutionWidth + 34), SUM(ResolutionWidth + 35), SUM(ResolutionWidth + 36), SUM(ResolutionWidth + 37), SUM(ResolutionWidth + 38), SUM(ResolutionWidth + 39), SUM(ResolutionWidth + 40), SUM(ResolutionWidth + 41), SUM(ResolutionWidth + 42), SUM(ResolutionWidth + 43), SUM(ResolutionWidth + 44), SUM(ResolutionWidth + 45), SUM(ResolutionWidth + 46), SUM(ResolutionWidth + 47), SUM(ResolutionWidth + 48), SUM(ResolutionWidth + 49), SUM(ResolutionWidth + 50), SUM(ResolutionWidth + 51), SUM(ResolutionWidth + 52), SUM(ResolutionWidth + 53), SUM(ResolutionWidth + 54), SUM(ResolutionWidth + 55), SUM(ResolutionWidth + 56), SUM(ResolutionWidth + 57), SUM(ResolutionWidth + 58), SUM(ResolutionWidth + 59), SUM(ResolutionWidth + 60), SUM(ResolutionWidth + 61), SUM(ResolutionWidth + 62), SUM(ResolutionWidth + 63), SUM(ResolutionWidth + 64), SUM(ResolutionWidth + 65), SUM(ResolutionWidth + 66), SUM(ResolutionWidth + 67), SUM(ResolutionWidth + 68), SUM(ResolutionWidth + 69), SUM(ResolutionWidth + 70), SUM(ResolutionWidth + 71), SUM(ResolutionWidth + 72), SUM(ResolutionWidth + 73), SUM(ResolutionWidth + 74), SUM(ResolutionWidth + 75), SUM(ResolutionWidth + 76), SUM(ResolutionWidth + 77), SUM(ResolutionWidth + 78), SUM(ResolutionWidth + 79), SUM(ResolutionWidth + 80), SUM(ResolutionWidth + 81), SUM(ResolutionWidth + 82), SUM(ResolutionWidth + 83), SUM(ResolutionWidth + 84), SUM(ResolutionWidth + 85), SUM(ResolutionWidth + 86), SUM(ResolutionWidth + 87), SUM(ResolutionWidth + 88), SUM(ResolutionWidth + 89) FROM hits; | 数值计算 | 0.41s (×3.81) | 0.10s (×1.00) | 0.69s (×6.36) | 0.68s (×5.31) | 0.12s (×1.00) | 0.41s (×3.26) |
| SELECT SearchEngineID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth) FROM hits WHERE SearchPhrase <> ‘’ GROUP BY SearchEngineID, ClientIP ORDER BY c DESC LIMIT 10; | 分桶 | 0.11s (×1.00) | 0.13s (×1.20) | 0.23s (×2.05) | 0.24s (×1.00) | 1.33s (×5.36) | 1.43s (×5.76) |
| SELECT WatchID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth) FROM hits WHERE SearchPhrase <> ‘’ GROUP BY WatchID, ClientIP ORDER BY c DESC LIMIT 10; | 分桶 | 0.14s (×1.00) | 0.18s (×1.30) | 0.20s (×1.44) | 0.21s (×1.00) | 3.46s (×15.77) | 5.36s (×24.43) |
| SELECT WatchID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth) FROM hits GROUP BY WatchID, ClientIP ORDER BY c DESC LIMIT 10; | 分桶 | 0.75s (×1.00) | 0.86s (×1.14) | 1.53s (×2.02) | 1.45s (×1.49) | 0.97s (×1.00) | 4.38s (×4.48) |
| SELECT URL, COUNT(*) AS c FROM hits GROUP BY URL ORDER BY c DESC LIMIT 10; | 分桶 | 0.67s (×1.00) | 0.96s (×1.44) | 1.67s (×2.49) | 1.84s (×1.93) | 0.95s (×1.00) | 9.13s (×9.52) |
| SELECT 1, URL, COUNT(*) AS c FROM hits GROUP BY 1, URL ORDER BY c DESC LIMIT 10; | 分桶 | 0.70s (×1.00) | 0.97s (×1.38) | 1.75s (×2.47) | 1.83s (×1.90) | 0.96s (×1.00) | 9.11s (×9.40) |
| SELECT ClientIP, ClientIP - 1, ClientIP - 2, ClientIP - 3, COUNT(*) AS c FROM hits GROUP BY ClientIP, ClientIP - 1, ClientIP - 2, ClientIP - 3 ORDER BY c DESC LIMIT 10; | 分桶 | 0.17s (×1.38) | 0.12s (×1.00) | 0.20s (×1.62) | 0.19s (×1.33) | 0.14s (×1.00) | 0.22s (×1.55) |
| SELECT URL, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= ‘2013-07-01’ AND EventDate <= ‘2013-07-31’ AND DontCountHits = 0 AND IsRefresh = 0 AND URL <> ‘’ GROUP BY URL ORDER BY PageViews DESC LIMIT 10; | 分桶 | 0.04s (×1.00) | 0.06s (×1.52) | 0.04s (×1.09) | 0.03s (×1.00) | 0.07s (×2.00) | 0.06s (×1.70) |
| SELECT Title, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= ‘2013-07-01’ AND EventDate <= ‘2013-07-31’ AND DontCountHits = 0 AND IsRefresh = 0 AND Title <> ‘’ GROUP BY Title ORDER BY PageViews DESC LIMIT 10; | 分桶 | 0.02s (×1.00) | 0.04s (×1.52) | 0.03s (×1.21) | 0.03s (×1.11) | 0.05s (×1.67) | 0.03s (×1.00) |
| SELECT URL, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= ‘2013-07-01’ AND EventDate <= ‘2013-07-31’ AND IsRefresh = 0 AND IsLink <> 0 AND IsDownload = 0 GROUP BY URL ORDER BY PageViews DESC LIMIT 10 OFFSET 1000; | 分桶 | 0.02s (×1.07) | 0.03s (×1.33) | 0.02s (×1.00) | 0.02s (×1.00) | 0.04s (×1.67) | 0.04s (×1.67) |
| SELECT TraficSourceID, SearchEngineID, AdvEngineID, CASE WHEN (SearchEngineID = 0 AND AdvEngineID = 0) THEN Referer ELSE ‘’ END AS Src, URL AS Dst, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= ‘2013-07-01’ AND EventDate <= ‘2013-07-31’ AND IsRefresh = 0 GROUP BY TraficSourceID, SearchEngineID, AdvEngineID, Src, Dst ORDER BY PageViews DESC LIMIT 10 OFFSET 1000; | 分桶 | 0.06s (×1.00) | 0.09s (×1.49) | 0.06s (×1.04) | 0.06s (×1.00) | 0.10s (×1.57) | 0.09s (×1.40) |
| SELECT URLHash, EventDate, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= ‘2013-07-01’ AND EventDate <= ‘2013-07-31’ AND IsRefresh = 0 AND TraficSourceID IN (-1, 6) AND RefererHash = 3594120000172545465 GROUP BY URLHash, EventDate ORDER BY PageViews DESC LIMIT 10 OFFSET 100; | 分桶 | 0.02s (×1.00) | 0.03s (×1.43) | 0.02s (×1.07) | 0.02s (×1.00) | 0.47s (×16.00) | 0.04s (×1.50) |
| SELECT WindowClientWidth, WindowClientHeight, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= ‘2013-07-01’ AND EventDate <= ‘2013-07-31’ AND IsRefresh = 0 AND DontCountHits = 0 AND URLHash = 2868770270353813622 GROUP BY WindowClientWidth, WindowClientHeight ORDER BY PageViews DESC LIMIT 10 OFFSET 10000; | 时间处理 | 0.02s (×1.00) | 0.03s (×1.48) | 0.02s (×1.11) | 0.02s (×1.00) | 0.25s (×8.67) | 0.02s (×1.13) |
| SELECT DATE_TRUNC(‘minute’, EventTime) AS M, COUNT(*) AS PageViews FROM hits WHERE CounterID = 62 AND EventDate >= ‘2013-07-14’ AND EventDate <= ‘2013-07-15’ AND IsRefresh = 0 AND DontCountHits = 0 GROUP BY DATE_TRUNC(‘minute’, EventTime) ORDER BY DATE_TRUNC(‘minute’, EventTime) LIMIT 10 OFFSET 1000; | 时间处理 | 0.01s (×1.00) | 0.03s (×1.60) | 0.02s (×1.20) | 0.02s (×1.07) | 0.04s (×1.79) | 0.02s (×1.00) |
SSB-flat 星型模型打平为宽表基准测试
没有公开的rank，下图为starrocks单方面提供的图,总体结论是强于clickhouse
决策支持系统基准测试
clickhouse没有参与TPC-H和TPC-DS的基准测试，只有starrocks单方面提供的和trino对比的结果
数据更新
StarRocks primary key 更新性能初探
StarRocks 版本：2.0.1
| 机器 | 3台 阿里云主机 |
|---|---|
| CPU | 16core |
| 内存 | 64GB |
| 网络带宽 | 5 Gbits/s |
| 磁盘 | 系统盘ESSD 40G 数据盘ESSD高效云盘 100GB |
StarRocks 的主键模型（primary key）能够每秒对1万条数据进行更新操作。
比较表格
| Clickhouse | Doris | 更好 |
|---|---|---|
| 部署复杂性与周边服务 | 在配置文件中手动管理集群，需要部署Zookeeper，CHProxy，需要自己管理导数工具 | 只需要FE和BE两个组件 |
| 数据导入 | 可以通过引擎连接其他存储系统，也可以使用外部工具进行导数。 | 内置导数工具，不支持复杂ETL，导入性能略差。部分load工具支持upsert，支持部分列写入 |
| 可以直接导入本地表文件，导入速度很快 | ||
| 多租户管理 | 全面 | 无查询内存限制、用户限制 |
| 集群迁移与扩容分布式与高可用 | remote/copier实现迁移，依靠zookeeper实现分布式 | 在线动态扩缩容，内置分布式协议和元数据同步 |
| 事务支持 | 有对单机MergeTree有实验性质的事务支持 | 提供了导入的事务支持，可以保证导数的幂等性 |
| 表引擎 | 支持字典，内存表，多表试图 | 支持智能路由，元数据一致性变更 |
| 数据类型 | 支持array，nested，tuple，enum | 支持array,struct,map,json |
| 外部数据源 | 相比doris支持消息队列、更多种的关系型\非关系型数据库 | 相比ck支持数据湖、ES，并且对外部数据源的使用强于Trino |
| 查询架构 | 支持Local join，Broadcast join | 支持Local join，Broadcast join，Shuffle join，Hash join |
| 函数支持 | 漏斗，留存，直方图，完整的聚合函数支持 | - |
| 单表查询性能 | 最快 | 接近clickhouse |
| join性能 | 1000行万数据以下比doris快，宽表join块 | 1000万行数据以上的大表与大表join快；多表join快 |
| 点查能力 | 较弱 | 相对较强，有特定的优化 |
| QPS | 单表查询clickhouse强于doris，join查询两者相近 | 多表查询doris高 |
| 社区活跃 | 非常活跃 | 较为活跃 |