starRocks安装测试报告

下载安装部署

StarRocks的集群部署分为两种模式，第一种是使用命令部署，第二种是使用 StarRocksManager 自动化部署。自动部署的版本只需要在页面上简单进行配置、选择、输入后批量完成，并且包含Supervisor进程管理、滚动升级、备份、回滚等功能。因 StarRocksManager并未开源，因此我们只能使用命令部署。

生产环境使用官方推荐配置：BE推荐16核64GB以上(StarRocks的元数据都在内存中保存)，FE推荐8核16GB以上

下载地址：

https://www.starrocks.com/zh-CN/download/request-download/4

服务器配置

下载社区版最新版本：StarRocks-1.19.1，因资源限制（使用4台虚机），FE采取单实例部署，BE部署三个实例。

IP	host	配置	部署
192.168.130.178	fe1	4C16G	FE主节点
192.168.130.183	be1	4C8G	BE节点
192.168.130.36	be2	4C8G	BE节点
192.168.130.149	be3	4C8G	BE节点

FE单实例部署

# 修改fe配置
cd StarRocks-1.19.1/fe

# 配置文件conf/fe.conf
# 根据FE内存大小调整 -Xmx4096m，为了避免GC建议16G以上，StarRocks的元数据都在内存中保存。

# 创建元数据目录
mkdir -p meta
# 启动进程
bin/start_fe.sh --daemon

# 使用浏览器访问8030端口, 打开StarRocks的WebUI, 用户名为root, 密码为空

StarRocks UI

http://192.168.130.178:8030/ 打开StarRocks的WebUI,，用户名为root，密码为空。

使用MySQL客户端访问FE

第一步: 安装mysql客户端(如果已经安装，可忽略此步)：

Ubuntu：sudo apt-get install mysql-client

Centos：sudo yum install mysql-client

wget http://repo.mysql.com/mysql57-community-release-sles12.rpm

rpm -ivh mysql57-community-release-sles12.rpm

# 安装mysql
yum install mysql-server

# 启动mysql
service mysqld start

第二步: 使用mysql客户端连接：

mysql -h 127.0.0.1 -P9030 -uroot

注意：这里默认root用户密码为空，端口为fe/conf/fe.conf中的query_port配置项，默认为9030

第三步: 查看FE状态：

MySQL [(none)]> SHOW PROC '/frontends'\G
*************************** 1. row ***************************
             Name: 192.168.130.178_9010_1636811945380
               IP: 192.168.130.178
         HostName: fe1
      EditLogPort: 9010
         HttpPort: 8030
        QueryPort: 9030
          RpcPort: 9020
             Role: FOLLOWER
         IsMaster: true
        ClusterId: 985620692
             Join: true
            Alive: true
ReplayedJournalId: 43507
    LastHeartbeat: 2021-11-15 14:41:48
         IsHelper: true
           ErrMsg: 
1 row in set (0.05 sec)

Role为FOLLOWER说明这是一个能参与选主的FE；IsMaster为true，说明该FE当前为主节点。

如果MySQL客户端连接不成功，请查看log/fe.warn.log日志文件，确认问题。由于是初次启动，如果在操作过程中遇到任何意外问题，都可以删除并重新创建FE的元数据目录，再从头开始操作。

BE部署

BE的基本配置

BE的配置文件为StarRocks-1.19.1/be/conf/be.conf，默认配置已经足以启动集群，不建议初尝用户修改配置，有经验的用户可以查看手册的系统配置章节，为生产环境定制配置。为了让用户更好的理解集群的工作原理, 此处只列出基础配置。

192.168.130.183

# 修改fe配置
cd StarRocks-1.19.1/be

# 配置文件conf/fe.conf
# 调整BE参数，默认配置已经足以启动集群，暂不做调整。

# 创建元数据目录
mkdir -p storage

# 通过mysql客户端添加BE节点。
# 这里IP地址为和priority_networks设置匹配的IP，portheartbeat_service_port，默认为9050
mysql> ALTER SYSTEM ADD BACKEND "be1:9050";

# 启动be
bin/start_be.sh --daemon

添加BE节点如出现错误，需要删除BE节点，应用下列命令：

alter system decommission backend "be_host:be_heartbeat_service_port";
alter system dropp backend "be_host:be_heartbeat_service_port";

# 查看防火墙状态
[root@be1 be]# systemctl status firewalld
# 如已开启，需关闭防火墙，防止网络不通，导致BE和FE无法连接
[root@be1 be]# systemctl stop firewalld

第四步: 查看BE状态, 确认BE就绪。同样步骤添加另外两个BE节点，mysql客户端中执行

MySQL [(none)]> SHOW PROC '/backends' \G;
*************************** 1. row ***************************
            BackendId: 163038
              Cluster: default_cluster
                   IP: 192.168.130.183
             HostName: be1
        HeartbeatPort: 9050
               BePort: 9060
             HttpPort: 8040
             BrpcPort: 8060
        LastStartTime: 2021-11-21 13:56:47
        LastHeartbeat: 2021-11-21 14:58:35
                Alive: true
 SystemDecommissioned: false
ClusterDecommissioned: false
            TabletNum: 5
     DataUsedCapacity: .000 
        AvailCapacity: 33.451 GB
        TotalCapacity: 36.974 GB
              UsedPct: 9.53 %
       MaxDiskUsedPct: 9.53 %
               ErrMsg: 
              Version: 1.19.1-65e87c3
               Status: {"lastSuccessReportTabletsTime":"2021-11-21 14:57:48"}
    DataTotalCapacity: 33.451 GB
          DataUsedPct: 0.00 %
*************************** 2. row ***************************
            BackendId: 163066
              Cluster: default_cluster
                   IP: 192.168.130.36
             HostName: be2
        HeartbeatPort: 9050
               BePort: 9060
             HttpPort: 8040
             BrpcPort: 8060
        LastStartTime: 2021-11-21 14:56:34
        LastHeartbeat: 2021-11-21 14:58:35
                Alive: true
 SystemDecommissioned: false
ClusterDecommissioned: false
            TabletNum: 5
     DataUsedCapacity: .000 
        AvailCapacity: 33.452 GB
        TotalCapacity: 36.974 GB
              UsedPct: 9.53 %
       MaxDiskUsedPct: 9.53 %
               ErrMsg: 
              Version: 1.19.1-65e87c3
               Status: {"lastSuccessReportTabletsTime":"2021-11-21 14:58:35"}
    DataTotalCapacity: 33.452 GB
          DataUsedPct: 0.00 %
*************************** 3. row ***************************
            BackendId: 163072
              Cluster: default_cluster
                   IP: 192.168.130.149
             HostName: be3
        HeartbeatPort: 9050
               BePort: 9060
             HttpPort: 8040
             BrpcPort: 8060
        LastStartTime: 2021-11-21 14:58:15
        LastHeartbeat: 2021-11-21 14:58:35
                Alive: true
 SystemDecommissioned: false
ClusterDecommissioned: false
            TabletNum: 3
     DataUsedCapacity: .000 
        AvailCapacity: 33.521 GB
        TotalCapacity: 36.974 GB
              UsedPct: 9.34 %
       MaxDiskUsedPct: 9.34 %
               ErrMsg: 
              Version: 1.19.1-65e87c3
               Status: {"lastSuccessReportTabletsTime":"2021-11-21 14:58:16"}
    DataTotalCapacity: 33.521 GB
          DataUsedPct: 0.00 %

如果isAlive为true，则说明BE正常接入集群。如果BE没有正常接入集群，请查看log目录下的be.WARNING日志文件确定原因。

至此，安装完成。

参数设置

Swappiness

关闭交换区，消除交换内存到虚拟内存时对性能的扰动。

echo 0 | sudo tee /proc/sys/vm/swappiness

Compaction相关

当使用聚合表或更新模型，导入数据比较快的时候，可在配置文件 be.conf 中修改下列参数以加速compaction。

cumulative_compaction_num_threads_per_disk = 4
base_compaction_num_threads_per_disk = 2
cumulative_compaction_check_interval_seconds = 2

并行度

在客户端执行命令，修改StarRocks的并行度(类似clickhouse set max_threads= 8)。并行度可以设置为当前机器CPU核数的一半。

set global parallel_fragment_exec_instance_num =  8;

使用MySQL客户端访问StarRocks

Root用户登录

使用MySQL客户端连接某一个FE实例的query_port(9030), StarRocks内置root用户，密码默认为空：

mysql -h fe_host -P9030 -u root

清理环境：

mysql > drop database if exists example_db;

mysql > drop user test;

创建新用户

通过下面的命令创建一个普通用户：

mysql > create user 'test_xxx' identified by 'xxx123456';

创建数据库

StarRocks中root账户才有权建立数据库，使用root用户登录，建立example_db数据库:

mysql > create database test_xxx_db;

数据库创建完成之后，可以通过show databases查看数据库信息：

MySQL [(none)]> show databases;
+--------------------+
| Database           |
+--------------------+
| _statistics_       |
| information_schema |
| test_xxx_db        |
+--------------------+
3 rows in set (0.00 sec)

information_schema是为了兼容mysql协议而存在，实际中信息可能不是很准确，所以关于具体数据库的信息建议通过直接查询相应数据库而获得。

账户授权

example_db创建完成之后，可以通过root账户example_db读写权限授权给test账户，授权之后采用test账户登录就可以操作example_db数据库了：

mysql > grant all on test_xxx_db to test_xxx;

退出root账户，使用test登录StarRocks集群：

mysql > exit

mysql -h 127.0.0.1 -P9030 -utest_xxx -pxxx123456

建表

StarRocks支持支持单分区和复合分区两种建表方式。

在复合分区中：

第一级称为Partition，即分区。用户可以指定某一维度列作为分区列（当前只支持整型和时间类型的列），并指定每个分区的取值范围。
第二级称为Distribution，即分桶。用户可以指定某几个维度列（或不指定，即所有KEY列）以及桶数对数据进行HASH分布。

以下场景推荐使用复合分区：

有时间维度或类似带有有序值的维度：可以以这类维度列作为分区列。分区粒度可以根据导入频次、分区数据量等进行评估。
历史数据删除需求：如有删除历史数据的需求（比如仅保留最近N 天的数据）。使用复合分区，可以通过删除历史分区来达到目的。也可以通过在指定分区内发送DELETE语句进行数据删除。
解决数据倾斜问题：每个分区可以单独指定分桶数量。如按天分区，当每天的数据量差异很大时，可以通过指定分区的分桶数，合理划分不同分区的数据,分桶列建议选择区分度大的列。

用户也可以不使用复合分区，即使用单分区。则数据只做HASH分布。

下面分别演示两种分区的建表语句：

首先切换数据库：mysql > use test_xxx_db;
建立单分区表建立一个名字为table1的逻辑表。使用全hash分桶，分桶列为siteid，桶数为10。这个表的schema如下：

siteid：类型是INT（4字节）, 默认值为10
city_code：类型是SMALLINT（2字节）
username：类型是VARCHAR, 最大长度为32, 默认值为空字符串
pv：类型是BIGINT（8字节）, 默认值是0; 这是一个指标列, StarRocks内部会对指标列做聚合操作, 这个列的聚合方法是求和（SUM）。这里采用了聚合模型，除此之外StarRocks还支持明细模型和更新模型，具体参考数据模型介绍。

建表语句如下:

mysql >
CREATE TABLE table1
(
    siteid INT DEFAULT '10',
    citycode SMALLINT,
    username VARCHAR(32) DEFAULT '',
    pv BIGINT SUM DEFAULT '0'
)
AGGREGATE KEY(siteid, citycode, username)
DISTRIBUTED BY HASH(siteid) BUCKETS 10
PROPERTIES("replication_num" = "1");

建立复合分区表

建立一个名字为table2的逻辑表。这个表的 schema 如下：

event_day：类型是DATE，无默认值
siteid：类型是INT（4字节）, 默认值为10
city_code：类型是SMALLINT（2字节）
username：类型是VARCHAR, 最大长度为32, 默认值为空字符串
pv：类型是BIGINT（8字节）, 默认值是0; 这是一个指标列, StarRocks 内部会对指标列做聚合操作, 这个列的聚合方法是求和（SUM）

我们使用event_day列作为分区列，建立3个分区: p1, p2, p3

p1：范围为 [最小值, 2017-06-30)
p2：范围为 [2017-06-30, 2017-07-31)
p3：范围为 [2017-07-31, 2017-08-31)

每个分区使用siteid进行哈希分桶，桶数为10。

建表语句如下:

CREATE TABLE table2
(
event_day DATE,
siteid INT DEFAULT '10',
citycode SMALLINT,
username VARCHAR(32) DEFAULT '',
pv BIGINT SUM DEFAULT '0'
)
AGGREGATE KEY(event_day, siteid, citycode, username)
PARTITION BY RANGE(event_day)
(
PARTITION p1 VALUES LESS THAN ('2017-06-30'),
PARTITION p2 VALUES LESS THAN ('2017-07-31'),
PARTITION p3 VALUES LESS THAN ('2017-08-31')
)
DISTRIBUTED BY HASH(siteid) BUCKETS 10
PROPERTIES("replication_num" = "1");

表建完之后，可以查看example_db中表的信息:

mysql> show tables;

+-------------------------+
| Tables_in_example_db    |
+-------------------------+
| table1                  |
| table2                  |
+-------------------------+
2 rows in set (0.01 sec)

  <br/>

mysql> desc table1;

+----------+-------------+------+-------+---------+-------+
| Field    | Type        | Null | Key   | Default | Extra |
+----------+-------------+------+-------+---------+-------+
| siteid   | int(11)     | Yes  | true  | 10      |       |
| citycode | smallint(6) | Yes  | true  | N/A     |       |
| username | varchar(32) | Yes  | true  |         |       |
| pv       | bigint(20)  | Yes  | false | 0       | SUM   |
+----------+-------------+------+-------+---------+-------+
4 rows in set (0.00 sec)

  <br/>

mysql> desc table2;

+-----------+-------------+------+-------+---------+-------+
| Field     | Type        | Null | Key   | Default | Extra |
+-----------+-------------+------+-------+---------+-------+
| event_day | date        | Yes  | true  | N/A     |       |
| siteid    | int(11)     | Yes  | true  | 10      |       |
| citycode  | smallint(6) | Yes  | true  | N/A     |       |
| username  | varchar(32) | Yes  | true  |         |       |
| pv        | bigint(20)  | Yes  | false | 0       | SUM   |
+-----------+-------------+------+-------+---------+-------+
5 rows in set (0.00 sec)

MySQL [(none)]> use test_xxx_db;
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A

Database changed
MySQL [test_xxx_db]> insert into table1 values(1,3708,'zhaop',0);
Query OK, 1 row affected (0.20 sec)
{'label':'insert_d7aee4d4-52a4-11ec-be98-fa163e7a663b', 'status':'VISIBLE', 'txnId':'2005'}


MySQL [test_xxx_db]> select * from table1;
+--------+----------+----------+------+
| siteid | citycode | username | pv   |
+--------+----------+----------+------+
|      1 |     3708 | zhaop    |    0 |
+--------+----------+----------+------+
1 row in set (0.03 sec)

导入数据

curl --location-trusted -u test_xxx:xxx123456 -T table1_data -H "label: table1_20211121" \
    -H "column_separator:," \
    http://127.0.0.1:8030/api/test_xxx_db/table1/_stream_load
    
## 报错
curl: Can't open 'table1_data'!
curl: try 'curl --help' or 'curl --manual' for more information

问题

执行sql报错

MySQL [test_xxx_db]> select * from table1;
ERROR 1064 (HY000): Could not initialize class com.starrocks.rpc.BackendServiceProxy

后台日志：

2021-11-21 16:03:49,835 WARN (starrocks-mysql-nio-pool-31|379) [StmtExecutor.execute():456] execute Exception, sql select * from table1
java.lang.NoClassDefFoundError: Could not initialize class com.starrocks.rpc.BackendServiceProxy
	at com.starrocks.qe.Coordinator$BackendExecState.execRemoteFragmentAsync(Coordinator.java:1695) ~[starrocks-fe.jar:?]
	at com.starrocks.qe.Coordinator.exec(Coordinator.java:522) ~[starrocks-fe.jar:?]
	at com.starrocks.qe.StmtExecutor.handleQueryStmt(StmtExecutor.java:771) ~[starrocks-fe.jar:?]
	at com.starrocks.qe.StmtExecutor.execute(StmtExecutor.java:371) ~[starrocks-fe.jar:?]
	at com.starrocks.qe.ConnectProcessor.handleQuery(ConnectProcessor.java:248) ~[starrocks-fe.jar:?]
	at com.starrocks.qe.ConnectProcessor.dispatch(ConnectProcessor.java:397) ~[starrocks-fe.jar:?]
	at com.starrocks.qe.ConnectProcessor.processOnce(ConnectProcessor.java:633) ~[starrocks-fe.jar:?]
	at com.starrocks.mysql.nio.ReadListener.lambda$handleEvent$480(ReadListener.java:54) ~[starrocks-fe.jar:?]
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) [?:1.8.0_312]
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) [?:1.8.0_312]
	at java.lang.Thread.run(Thread.java:748) [?:1.8.0_312]

解决方案：openjdk改为

[root@fe1 jre-1.8.0-openjdk]# java -version
openjdk version "1.8.0_312"
OpenJDK Runtime Environment (build 1.8.0_312-b07)
OpenJDK 64-Bit Server VM (build 25.312-b07, mixed mode)

# 修改为
[root@fe1 java]# java -version
java version "1.8.0_192"
Java(TM) SE Runtime Environment (build 1.8.0_192-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.192-b12, mixed mod

BE启动失败，java.net.NoRouteToHostException: No route to host (Host unreachable)

# 报错信息
W1128 23:13:56.493264 24083 utils.cpp:90] Fail to get master client from cache. host= port=0 code=THRIFT_RPC_ERROR
# 查看防火墙状态
[root@be1 be]# systemctl status firewalld
# 如已开启，需关闭防火墙，防止网络不通，导致BE和FE无法连接
[root@be1 be]# systemctl stop firewalld

至此，starRocks可以进行正常的增删改查操作。

性能测试

共执行了4类场景，13条查询语句，分别为单表查询和多表查询，结果为毫秒，并发为1。

数据创建

下载ssb-poc工具包并编译

mkdir poc
wget https://starrocks-public.oss-cn-zhangjiakou.aliyuncs.com/ssb-poc-0.9.2.zip
unzip ssb-poc-0.9.2.zip
cd ssb-poc
make && make install  
# 编译成功之后，可以看到output目录
[root@fe1 ssb-poc]# cd output/
[root@fe1 output]# ll
total 0
drwxr-xr-x. 2 root root 197 Nov 28 21:01 bin
drwxr-xr-x. 2 root root  37 Nov 28 21:01 conf
drwxr-xr-x. 3 root root  22 Nov 28 21:01 lib
drwxr-xr-x. 4 root root  39 Nov 28 21:01 share

所有相关工具都会安装到output目录

生成数据

bin/gen-ssb.sh 30 data_dir

这里会在data_dir目录下生成30GB规模的数据

导入数据

安装python3

压测脚本需要使用python3，先安装python3，参考https://www.jianshu.com/p/a916a22de3eb。详细步骤不再赘述。

确定测试目录

[root@fe1 output]# pwd
/root/starrocks/poc/ssb-poc/output

修改配置文件conf/doris.conf

[doris]
# for mysql cmd
mysql_host: fe1
mysql_port: 9030
mysql_user: root
mysql_password:
doris_db: ssb

# cluster ports
http_port: 8030
be_heartbeat_port: 9050
broker_port: 8000
...

执行建表脚本

bin/create_db_table.sh ddl_100

完成后我们在ssb下创建了6张表：lineorder, supplier, dates, customer, part, lineorder_flat

MySQL [ssb]> show tables;
+----------------+
| Tables_in_ssb  |
+----------------+
| customer       |
| dates          |
| lineorder      |
| lineorder_flat |
| part           |
| supplier       |
+----------------+
6 rows in set (0.00 sec)

数据导入

通过stream load导入数据

bin/stream_load.sh data_dir

data_dir是之前生成的数据目录，最大表数据量1.8亿

表名	数据量	备注
lineorder	179998372	SSB商品订单表
supplier	60000	SSB客户表
dates	2556	SSB 零部件表
customer	900000	SSB 供应商表
part	1000000	日期表
lineorder_flat	54675488	宽表

测试单表查询

[root@fe1 output]# time bin/benchmark.sh -p -d ssb
------ dataset: ssb, concurrency: 1 ------
sql\time(ms)\parallel_num	1
q1	3466.0
q2	473.0
q3	464.0
q4	1703.0
q5	818.0
q6	621.0
q7	1807.0
q8	1039.0
q9	591.0
q10	506.0
q11	1865.0
q12	1077.0
q13	945.0
# 二次执行
------ dataset: ssb, concurrency: 1 ------
sql\time(ms)\parallel_num	1
q1	563.0
q2	466.0
q3	537.0
q4	936.0
q5	850.0
q6	590.0
q7	1597.0
q8	970.0
q9	542.0
q10	540.0
q11	1701.0
q12	1104.0
q13	1008.0

测试ssb宽表查询

# 生成宽表数据
[root@fe1 output]# bin/flat_insert.sh
sql: ssb_flat_insert start
sql: ssb_flat_insert. flat insert error, msg: (1064, 'index channel has intoleralbe failure')

# 测试宽表性能
time bin/benchmark.sh -p -d ssb-flat
[root@fe1 output]# bin/benchmark.sh -p -d ssb-flat
------ dataset: ssb-flat, concurrency: 1 ------
sql\time(ms)\parallel_num	1
q1	6435.0
q2	165.0
q3	74.0
q4	7925.0
q5	5307.0
q6	4514.0
q7	7621.0
q8	6821.0
q9	4740.0
q10	116.0
q11	6568.0
q12	301.0
q13	82.0
[root@fe1 output]# time bin/benchmark.sh -p -d ssb-flat
------ dataset: ssb-flat, concurrency: 1 ------
sql\time(ms)\parallel_num	1
q1	5693.0
q2	98.0
q3	77.0
q4	5811.0
q5	4549.0
q6	4111.0
q7	6819.0
q8	6389.0
q9	4298.0
q10	143.0
q11	6583.0
q12	231.0
q13	75.0

real	0m51.565s
user	0m0.192s
sys	0m0.152s

结论

在1.8亿条单表记录，5500万条宽表记录情况下，通过SSB测试，多表join查询性能较高，实测性能和官方提供的测试报告有所差距。主要原因可能有以下几点：

测试使用为虚机，配置低（官方使用16核64G ESSD高效云盘 10Gbits/s网络带宽）
未经过参数调优

测试SQL附录

单表测试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;

多表测试SQL

--Q1.1 
select sum(lo_revenue) as revenue
from lineorder join dates on lo_orderdate = d_datekey
where d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25;

--Q1.2
select sum(lo_revenue) as revenue
from lineorder
join dates on lo_orderdate = d_datekey
where d_yearmonthnum = 199401
and lo_discount between 4 and 6
and lo_quantity between 26 and 35;

--Q1.3
select sum(lo_revenue) as revenue
from lineorder
join dates on lo_orderdate = d_datekey
where d_weeknuminyear = 6 and d_year = 1994
and lo_discount between 5 and 7
and lo_quantity between 26 and 35;


--Q2.1
select sum(lo_revenue) as lo_revenue, d_year, p_brand
from lineorder
inner join dates on lo_orderdate = d_datekey
join part on lo_partkey = p_partkey
join supplier on lo_suppkey = s_suppkey
where p_category = 'MFGR#12' and s_region = 'AMERICA'
group by d_year, p_brand
order by d_year, p_brand;

--Q2.2
select sum(lo_revenue) as lo_revenue, d_year, p_brand
from lineorder
join dates on lo_orderdate = d_datekey
join part on lo_partkey = p_partkey
join supplier on lo_suppkey = s_suppkey
where p_brand between 'MFGR#2221' and 'MFGR#2228' and s_region = 'ASIA'
group by d_year, p_brand
order by d_year, p_brand;

--Q2.3
select sum(lo_revenue) as lo_revenue, d_year, p_brand
from lineorder
join dates on lo_orderdate = d_datekey
join part on lo_partkey = p_partkey
join supplier on lo_suppkey = s_suppkey
where p_brand = 'MFGR#2239' and s_region = 'EUROPE'
group by d_year, p_brand
order by d_year, p_brand;


--Q3.1
select c_nation, s_nation, d_year, sum(lo_revenue) as lo_revenue
from lineorder
join dates on lo_orderdate = d_datekey
join customer on lo_custkey = c_custkey
join supplier on lo_suppkey = s_suppkey
where c_region = 'ASIA' and s_region = 'ASIA'and d_year >= 1992 and d_year <= 1997
group by c_nation, s_nation, d_year
order by d_year asc, lo_revenue desc;

--Q3.2
select c_city, s_city, d_year, sum(lo_revenue) as lo_revenue
from lineorder
join dates on lo_orderdate = d_datekey
join customer on lo_custkey = c_custkey
join supplier on lo_suppkey = s_suppkey
where c_nation = 'UNITED STATES' and s_nation = 'UNITED STATES'
and d_year >= 1992 and d_year <= 1997
group by c_city, s_city, d_year
order by d_year asc, lo_revenue desc;

--Q3.3
select c_city, s_city, d_year, sum(lo_revenue) as lo_revenue
from lineorder
join dates on lo_orderdate = d_datekey
join customer on lo_custkey = c_custkey
join supplier on lo_suppkey = s_suppkey
where (c_city='UNITED KI1' or c_city='UNITED KI5')
and (s_city='UNITED KI1' or s_city='UNITED KI5')
and d_year >= 1992 and d_year <= 1997
group by c_city, s_city, d_year
order by d_year asc, lo_revenue desc;

--Q3.4
select c_city, s_city, d_year, sum(lo_revenue) as lo_revenue
from lineorder
join dates on lo_orderdate = d_datekey
join customer on lo_custkey = c_custkey
join supplier on lo_suppkey = s_suppkey
where (c_city='UNITED KI1' or c_city='UNITED KI5') and (s_city='UNITED KI1' or s_city='UNITED KI5') and d_yearmonth
 = 'Dec1997'
group by c_city, s_city, d_year
order by d_year asc, lo_revenue desc;


--Q4.1
select d_year, c_nation, sum(lo_revenue) - sum(lo_supplycost) as profit
from lineorder
join dates on lo_orderdate = d_datekey
join customer on lo_custkey = c_custkey
join supplier on lo_suppkey = s_suppkey
join part on lo_partkey = p_partkey
where c_region = 'AMERICA' and s_region = 'AMERICA' and (p_mfgr = 'MFGR#1' or p_mfgr = 'MFGR#2')
group by d_year, c_nation
order by d_year, c_nation;

--Q4.2
select d_year, s_nation, p_category, sum(lo_revenue) - sum(lo_supplycost) as profit
from lineorder
join dates on lo_orderdate = d_datekey
join customer on lo_custkey = c_custkey
join supplier on lo_suppkey = s_suppkey
join part on lo_partkey = p_partkey
where c_region = 'AMERICA'and s_region = 'AMERICA'
and (d_year = 1997 or d_year = 1998)
and (p_mfgr = 'MFGR#1' or p_mfgr = 'MFGR#2')
group by d_year, s_nation, p_category
order by d_year, s_nation, p_category;

--Q4.3
select d_year, s_city, p_brand, sum(lo_revenue) - sum(lo_supplycost) as profit
from lineorder
join dates on lo_orderdate = d_datekey
join customer on lo_custkey = c_custkey
join supplier on lo_suppkey = s_suppkey
join part on lo_partkey = p_partkey
where c_region = 'AMERICA'and s_nation = 'UNITED STATES'
and (d_year = 1997 or d_year = 1998)
and p_category = 'MFGR#14'
group by d_year, s_city, p_brand
order by d_year, s_city, p_brand;

posted @ 2022-03-11 15:17 huonan 阅读(2404) 评论(0) 收藏举报

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