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版本：3.1

SSB Flat Table 性能测试

测试结论​

Star Schema Benchmark（以下简称 SSB）是学术界和工业界广泛使用的一个星型模型测试集（来源论文），通过这个测试集合可以方便的对比各种 OLAP 产品的基础性能指标。ClickHouse 通过改写 SSB，将星型模型打平转化成宽表 (flat table)，改造成了一个单表测试 benchmark（参考链接 StarRocks、ClickHouse 和 Apache Druid 在 SSB 单表数据集上的性能对比结果，测试结论如下：

  • 在标准测试数据集的 13 个查询上，StarRocks 整体查询性能是 ClickHouse 的 2.1 倍，Apache Druid 的 8.7 倍。
  • StarRocks 启用 Bitmap Index 后整体查询性能是未启用的 1.3 倍，此时整体查询性能是 ClickHouse 的 2.8 倍，Apache Druid 的 11.4 倍。

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本文在 SSB 单表场景下对比了 StarRocks、ClickHouse 和 Apache Druid 的查询性能。采用 4x16core 64GB 内存的云主机，在 6 亿行的数据规模进行测试。

测试准备​

硬件环境​

机器4 台阿里云主机
CPU16core Intel(R) Xeon(R) Platinum 8269CY CPU @ 2.50GHz
Cache size: 36608 KB
内存64GB
网络带宽5 Gbits/s
磁盘ESSD 云盘

软件环境​

StarRocks，ClickHouse 和 Apache Druid 部署在相同配置的机器上分别进行测试。

  • StarRocks 部署 1 个 FE 和 3 个 BE。
  • ClickHouse 部署 3 个节点后建立分布式表。
  • Apache Druid 部署 3 个 Data Servers，同时选择 1 个节点混合部署 Master Servers，另 1 个节点混合部署 Query Servers。

内核版本：Linux 3.10.0-1160.59.1.el7.x86_64

操作系统版本：CentOS Linux release 7.9.2009

软件版本：StarRocks 社区版 3.0，ClickHouse 23.3，Apache Druid 25.0.0

测试数据与结果​

测试数据​

表名行数说明
lineorder6 亿SSB 商品订单表
customer300 万SSB 客户表
part140 万SSB 零部件表
supplier20 万SSB 供应商表
dates2556日期表
lineorder_flat6 亿SSB 打平后的宽表

测试结果​

查询时间的单位是 ms。 所有查询预热一次，执行三次取平均值作为结果。 StarRocks 与 ClickHouse、Druid 的查询性能对比，分别使用 ClickHouse、Druid 的查询时间除以 StarRocks 的查询时间，结果数字越大代表 StarRocks 性能越好。

StarRocks-3.0StarRocks-3.0-indexClickHouse-23.3ClickHouse vs StarRocksDruid-25.0.0Druid vs StarRocks
Q1.13330481.4543013.03
Q1.21010151.5027027.00
Q1.32330140.6182035.65
Q2.11861163011.627604.09
Q2.2156502731.759205.90
Q2.373362553.4991012.47
Q3.11732333982.3010806.24
Q3.2120803192.668507.08
Q3.3123302271.858907.24
Q3.41316181.3875057.69
Q4.12031964692.3112306.06
Q4.273761602.19102013.97
Q4.350361482.9682016.40
SUM123693926452.14107508.70

测试步骤​

ClickHouse 的建表导入参考 官方文档 的数据生成导入流程如下：

生成数据​

首先下载 ssb-poc 工具包并编译。

wget https://starrocks-public.oss-cn-zhangjiakou.aliyuncs.com/ssb-poc-1.0.zip
unzip ssb-poc-1.0.zip
cd ssb-poc-1.0/
make && make install
cd output/

编译完成后，所有相关工具都安装在 output 目录下，后续所有操作都在 output 目录下进行。

首先生成 SSB 标准测试集scale factor=100的数据。

sh bin/gen-ssb.sh 100 data_dir

创建表结构​

修改配置文件 conf/starrocks.conf，指定脚本操作的集群地址，重点关注 mysql_host和mysql_port，然后执行建表操作。

sh bin/create_db_table.sh ddl_100

导入数据​

使用 Stream Load 导入多表数据，然后使用 INSERT INTO 将多表打平成单表。

sh bin/flat_insert.sh data_dir

查询数据​

sh bin/benchmark.sh ssb-flat

启用 Bitmap Index​

StarRocks 在启用 Bitmap Index 的情况下，性能更胜一筹，尤其在 Q2.2 Q2.3 Q3.3 上有显著提升。如果您希望测试启用 Bitmap Index 下的性能，可以对所有字符串列创建 Bitmap Index，具体操作如下:

  1. lineorder_flat 重新建表，建表时创建所有 Bitmap Index。

    sh bin/create_db_table.sh ddl_100_bitmap_index

  2. 在所有 BE 节点的配置文件中新增如下参数，然后重启 BE。

    bitmap_max_filter_ratio=1000

  3. 重新执行导入命令。

    sh bin/flat_insert.sh data_dir

导入完成后需要等待 compaction 完成，再重新执行步骤 4.4，此时就是启用 Bitmap Index 后的查询结果。

可以通过 select CANDIDATES_NUM from information_schema.be_compactions 命令查看 compaction 进度。对于 3 个 BE 节点，如下结果说明 compaction 完成：

mysql> select CANDIDATES_NUM from information_schema.be_compactions;
+----------------+
| CANDIDATES_NUM |
+----------------+
|              0 |
|              0 |
|              0 |
+----------------+
3 rows in set (0.01 sec)

测试 SQL 与建表语句​

测试 SQL​

--Q1.1 
SELECT sum(lo_extendedprice * lo_discount) AS `revenue` 
FROM lineorder_flat 
WHERE lo_orderdate >= '1993-01-01' and lo_orderdate <= '1993-12-31'
AND lo_discount BETWEEN 1 AND 3 AND lo_quantity < 25; 
 
--Q1.2 
SELECT sum(lo_extendedprice * lo_discount) AS revenue FROM lineorder_flat  
WHERE lo_orderdate >= '1994-01-01' and lo_orderdate <= '1994-01-31'
AND lo_discount BETWEEN 4 AND 6 AND lo_quantity BETWEEN 26 AND 35; 
 
--Q1.3 
SELECT sum(lo_extendedprice * lo_discount) AS revenue 
FROM lineorder_flat 
WHERE weekofyear(lo_orderdate) = 6
AND lo_orderdate >= '1994-01-01' and lo_orderdate <= '1994-12-31' 
AND lo_discount BETWEEN 5 AND 7 AND lo_quantity BETWEEN 26 AND 35; 
 
--Q2.1 
SELECT sum(lo_revenue), year(lo_orderdate) AS year,  p_brand 
FROM lineorder_flat 
WHERE p_category = 'MFGR#12' AND s_region = 'AMERICA' 
GROUP BY year, p_brand 
ORDER BY year, p_brand; 
 
--Q2.2
SELECT 
sum(lo_revenue), year(lo_orderdate) AS year, p_brand 
FROM lineorder_flat 
WHERE p_brand >= 'MFGR#2221' AND p_brand <= 'MFGR#2228' AND s_region = 'ASIA' 
GROUP BY year, p_brand 
ORDER BY year, p_brand; 
  
--Q2.3
SELECT sum(lo_revenue), year(lo_orderdate) AS year, p_brand 
FROM lineorder_flat 
WHERE p_brand = 'MFGR#2239' AND s_region = 'EUROPE' 
GROUP BY year, p_brand 
ORDER BY year, p_brand; 
 
--Q3.1
SELECT
    c_nation,
    s_nation,
    year(lo_orderdate) AS year,
    sum(lo_revenue) AS revenue FROM lineorder_flat 
WHERE c_region = 'ASIA' AND s_region = 'ASIA' AND lo_orderdate >= '1992-01-01'
AND lo_orderdate <= '1997-12-31' 
GROUP BY c_nation, s_nation, year 
ORDER BY  year ASC, revenue DESC; 
 
--Q3.2 
SELECT c_city, s_city, year(lo_orderdate) AS year, sum(lo_revenue) AS revenue
FROM lineorder_flat 
WHERE c_nation = 'UNITED STATES' AND s_nation = 'UNITED STATES'
AND lo_orderdate  >= '1992-01-01' AND lo_orderdate <= '1997-12-31' 
GROUP BY c_city, s_city, year 
ORDER BY year ASC, revenue DESC; 
 
--Q3.3 
SELECT c_city, s_city, year(lo_orderdate) AS year, sum(lo_revenue) AS revenue 
FROM lineorder_flat 
WHERE c_city in ( 'UNITED KI1' ,'UNITED KI5') AND s_city in ('UNITED KI1', 'UNITED KI5')
AND lo_orderdate  >= '1992-01-01' AND lo_orderdate <= '1997-12-31' 
GROUP BY c_city, s_city, year 
ORDER BY year ASC, revenue DESC; 
 
--Q3.4 
SELECT c_city, s_city, year(lo_orderdate) AS year, sum(lo_revenue) AS revenue 
FROM lineorder_flat 
WHERE c_city in ('UNITED KI1', 'UNITED KI5') AND s_city in ('UNITED KI1', 'UNITED KI5')
AND lo_orderdate  >= '1997-12-01' AND lo_orderdate <= '1997-12-31' 
GROUP BY c_city, s_city, year 
ORDER BY year ASC, revenue DESC; 
 
--Q4.1 
SELECT year(lo_orderdate) AS year, c_nation, sum(lo_revenue - lo_supplycost) AS profit
FROM lineorder_flat 
WHERE c_region = 'AMERICA' AND s_region = 'AMERICA' AND p_mfgr in ('MFGR#1', 'MFGR#2') 
GROUP BY year, c_nation 
ORDER BY year ASC, c_nation ASC; 
 
--Q4.2 
SELECT year(lo_orderdate) AS year, 
    s_nation, p_category, sum(lo_revenue - lo_supplycost) AS profit 
FROM lineorder_flat 
WHERE c_region = 'AMERICA' AND s_region = 'AMERICA'
AND lo_orderdate >= '1997-01-01' and lo_orderdate <= '1998-12-31'
AND p_mfgr in ( 'MFGR#1' , 'MFGR#2') 
GROUP BY year, s_nation, p_category 
ORDER BY year ASC, s_nation ASC, p_category ASC; 
 
--Q4.3 
SELECT year(lo_orderdate) AS year, s_city, p_brand, 
    sum(lo_revenue - lo_supplycost) AS profit 
FROM lineorder_flat 
WHERE s_nation = 'UNITED STATES'
AND lo_orderdate >= '1997-01-01' and lo_orderdate <= '1998-12-31'
AND p_category = 'MFGR#14' 
GROUP BY year, s_city, p_brand 
ORDER BY year ASC, s_city ASC, p_brand ASC;

建表语句​

lineorder_flat 默认建表​

这个建表语句是为了匹配当前集群和数据规格（3 个 BE，scale factor = 100）。如果您的集群有更多的 BE 节点，或者更大的数据规格，可以调整分桶数，重新建表和导数据，可实现更好的测试效果。

CREATE TABLE `lineorder_flat` (
  `LO_ORDERDATE` date NOT NULL COMMENT "",
  `LO_ORDERKEY` int(11) NOT NULL COMMENT "",
  `LO_LINENUMBER` tinyint(4) NOT NULL COMMENT "",
  `LO_CUSTKEY` int(11) NOT NULL COMMENT "",
  `LO_PARTKEY` int(11) NOT NULL COMMENT "",
  `LO_SUPPKEY` int(11) NOT NULL COMMENT "",
  `LO_ORDERPRIORITY` varchar(100) NOT NULL COMMENT "",
  `LO_SHIPPRIORITY` tinyint(4) NOT NULL COMMENT "",
  `LO_QUANTITY` tinyint(4) NOT NULL COMMENT "",
  `LO_EXTENDEDPRICE` int(11) NOT NULL COMMENT "",
  `LO_ORDTOTALPRICE` int(11) NOT NULL COMMENT "",
  `LO_DISCOUNT` tinyint(4) NOT NULL COMMENT "",
  `LO_REVENUE` int(11) NOT NULL COMMENT "",
  `LO_SUPPLYCOST` int(11) NOT NULL COMMENT "",
  `LO_TAX` tinyint(4) NOT NULL COMMENT "",
  `LO_COMMITDATE` date NOT NULL COMMENT "",
  `LO_SHIPMODE` varchar(100) NOT NULL COMMENT "",
  `C_NAME` varchar(100) NOT NULL COMMENT "",
  `C_ADDRESS` varchar(100) NOT NULL COMMENT "",
  `C_CITY` varchar(100) NOT NULL COMMENT "",
  `C_NATION` varchar(100) NOT NULL COMMENT "",
  `C_REGION` varchar(100) NOT NULL COMMENT "",
  `C_PHONE` varchar(100) NOT NULL COMMENT "",
  `C_MKTSEGMENT` varchar(100) NOT NULL COMMENT "",
  `S_NAME` varchar(100) NOT NULL COMMENT "",
  `S_ADDRESS` varchar(100) NOT NULL COMMENT "",
  `S_CITY` varchar(100) NOT NULL COMMENT "",
  `S_NATION` varchar(100) NOT NULL COMMENT "",
  `S_REGION` varchar(100) NOT NULL COMMENT "",
  `S_PHONE` varchar(100) NOT NULL COMMENT "",
  `P_NAME` varchar(100) NOT NULL COMMENT "",
  `P_MFGR` varchar(100) NOT NULL COMMENT "",
  `P_CATEGORY` varchar(100) NOT NULL COMMENT "",
  `P_BRAND` varchar(100) NOT NULL COMMENT "",
  `P_COLOR` varchar(100) NOT NULL COMMENT "",
  `P_TYPE` varchar(100) NOT NULL COMMENT "",
  `P_SIZE` tinyint(4) NOT NULL COMMENT "",
  `P_CONTAINER` varchar(100) NOT NULL COMMENT ""
) ENGINE=OLAP
DUPLICATE KEY(`LO_ORDERDATE`, `LO_ORDERKEY`)
COMMENT "OLAP"
PARTITION BY date_trunc('year', `LO_ORDERDATE`)
DISTRIBUTED BY HASH(`LO_ORDERKEY`) BUCKETS 48
PROPERTIES ("replication_num" = "1");

lineorder_flat 创建 Bitmap Index 表​

CREATE TABLE `lineorder_flat` (
  `LO_ORDERDATE` date NOT NULL COMMENT "",
  `LO_ORDERKEY` int(11) NOT NULL COMMENT "",
  `LO_LINENUMBER` tinyint(4) NOT NULL COMMENT "",
  `LO_CUSTKEY` int(11) NOT NULL COMMENT "",
  `LO_PARTKEY` int(11) NOT NULL COMMENT "",
  `LO_SUPPKEY` int(11) NOT NULL COMMENT "",
  `LO_ORDERPRIORITY` varchar(100) NOT NULL COMMENT "",
  `LO_SHIPPRIORITY` tinyint(4) NOT NULL COMMENT "",
  `LO_QUANTITY` tinyint(4) NOT NULL COMMENT "",
  `LO_EXTENDEDPRICE` int(11) NOT NULL COMMENT "",
  `LO_ORDTOTALPRICE` int(11) NOT NULL COMMENT "",
  `LO_DISCOUNT` tinyint(4) NOT NULL COMMENT "",
  `LO_REVENUE` int(11) NOT NULL COMMENT "",
  `LO_SUPPLYCOST` int(11) NOT NULL COMMENT "",
  `LO_TAX` tinyint(4) NOT NULL COMMENT "",
  `LO_COMMITDATE` date NOT NULL COMMENT "",
  `LO_SHIPMODE` varchar(100) NOT NULL COMMENT "",
  `C_NAME` varchar(100) NOT NULL COMMENT "",
  `C_ADDRESS` varchar(100) NOT NULL COMMENT "",
  `C_CITY` varchar(100) NOT NULL COMMENT "",
  `C_NATION` varchar(100) NOT NULL COMMENT "",
  `C_REGION` varchar(100) NOT NULL COMMENT "",
  `C_PHONE` varchar(100) NOT NULL COMMENT "",
  `C_MKTSEGMENT` varchar(100) NOT NULL COMMENT "",
  `S_NAME` varchar(100) NOT NULL COMMENT "",
  `S_ADDRESS` varchar(100) NOT NULL COMMENT "",
  `S_CITY` varchar(100) NOT NULL COMMENT "",
  `S_NATION` varchar(100) NOT NULL COMMENT "",
  `S_REGION` varchar(100) NOT NULL COMMENT "",
  `S_PHONE` varchar(100) NOT NULL COMMENT "",
  `P_NAME` varchar(100) NOT NULL COMMENT "",
  `P_MFGR` varchar(100) NOT NULL COMMENT "",
  `P_CATEGORY` varchar(100) NOT NULL COMMENT "",
  `P_BRAND` varchar(100) NOT NULL COMMENT "",
  `P_COLOR` varchar(100) NOT NULL COMMENT "",
  `P_TYPE` varchar(100) NOT NULL COMMENT "",
  `P_SIZE` tinyint(4) NOT NULL COMMENT "",
  `P_CONTAINER` varchar(100) NOT NULL COMMENT "",
  index bitmap_lo_orderpriority (lo_orderpriority) USING BITMAP,
  index bitmap_lo_shipmode (lo_shipmode) USING BITMAP,
  index bitmap_c_name (c_name) USING BITMAP,
  index bitmap_c_address (c_address) USING BITMAP,
  index bitmap_c_city (c_city) USING BITMAP,
  index bitmap_c_nation (c_nation) USING BITMAP,
  index bitmap_c_region (c_region) USING BITMAP,
  index bitmap_c_phone (c_phone) USING BITMAP,
  index bitmap_c_mktsegment (c_mktsegment) USING BITMAP,
  index bitmap_s_region (s_region) USING BITMAP,
  index bitmap_s_nation (s_nation) USING BITMAP,
  index bitmap_s_city (s_city) USING BITMAP,
  index bitmap_s_name (s_name) USING BITMAP,
  index bitmap_s_address (s_address) USING BITMAP,
  index bitmap_s_phone (s_phone) USING BITMAP,
  index bitmap_p_name (p_name) USING BITMAP,
  index bitmap_p_mfgr (p_mfgr) USING BITMAP,
  index bitmap_p_category (p_category) USING BITMAP,
  index bitmap_p_brand (p_brand) USING BITMAP,
  index bitmap_p_color (p_color) USING BITMAP,
  index bitmap_p_type (p_type) USING BITMAP,
  index bitmap_p_container (p_container) USING BITMAP
) ENGINE=OLAP
DUPLICATE KEY(`LO_ORDERDATE`, `LO_ORDERKEY`)
COMMENT "OLAP"
PARTITION BY date_trunc('year', `LO_ORDERDATE`)
DISTRIBUTED BY HASH(`LO_ORDERKEY`) BUCKETS 48
PROPERTIES ("replication_num" = "1");
Rocky the happy otterStarRocks Assistant

AI generated answers are based on docs and other sources. Please test answers in non-production environments.