MatrixOne从入门到实践08——SSB性能测试
MatrixOne从入门到实践——SSB性能测试
SSB 星型模式基准测试是 OLAP 数据库性能测试的常用场景,通过本篇教程,您可以了解到如何在 MatrixOne 中实现 SSB 测试。
测试环境
机器配置
机器数量 部署方式 CPU 内存 磁盘 1 单节点 6 36G 100G MO版本
0.5.1版本
编译dbgen
获取源码
git clone https://github.com/vadimtk/ssb-dbgen.git
如果因为网络问题导致clone失败,建议使用gitee将上述链接项目导入到自己的仓库,然后使用gitee的链接clone
编译
cd ssb-dbgen
make
生成数据
当使用 -s 1 时 dbgen 命令会生产近600万行数据(670MB),当使用-s 10时会生产近6000万行数据,会耗费大量时间。
./dbgen -s 1 -T c
./dbgen -s 1 -T l
./dbgen -s 1 -T p
./dbgen -s 1 -T s
./dbgen -s 1 -T d
生成完成后,会有以下数据文件,这里只生成了多表的数据。
[root@motest ssb]# ll -h |grep tbl
-r-sr-S--T. 1 root root 3.2M Oct 1 09:30 customer.tbl
-rw-r--r--. 1 root root 270K Oct 1 09:31 date.tbl
-rw-r--r--. 1 root root 641M Oct 1 09:31 lineorder.tbl
-rw-r--r--. 1 root root 20M Oct 1 09:31 part.tbl
-rw-r--r--. 1 root root 187K Oct 1 09:31 supplier.tbl
SSB的大宽表数据集请下载:
wget https://community-shared-data-1308875761.cos.ap-beijing.myqcloud.com/lineorder_flat.tar.bz2
解压数据集:
tar -jxvf lineorder_flat.tar.bz2
MatrixOne 准备工作
安装 、 启动MatrixOne
详情请参考MatrixOne部署一文
建表
create database if not exists ssb;
use ssb;
drop table if exists lineorder;
drop table if exists part;
drop table if exists supplier;
drop table if exists customer;
drop table if exists dates;
drop table if exists lineorder_flat; create table lineorder (
lo_orderkey bigint,
lo_linenumber int,
lo_custkey int,
lo_partkey int,
lo_suppkey int,
lo_orderdate date,
lo_orderpriority char (15),
lo_shippriority tinyint,
lo_quantity double,
lo_extendedprice double,
lo_ordtotalprice double,
lo_discount double,
lo_revenue double,
lo_supplycost double,
lo_tax double,
lo_commitdate date,
lo_shipmode char (10)
) ; create table part (
p_partkey int,
p_name varchar (22),
p_mfgr char (6),
p_category char (7),
p_brand char (9),
p_color varchar (11),
p_type varchar (25),
p_size int,
p_container char (10)
) ; create table supplier (
s_suppkey int,
s_name char (25),
s_address varchar (25),
s_city char (10),
s_nation char (15),
s_region char (12),
s_phone char (15)
) ; create table customer (
c_custkey int,
c_name varchar (25),
c_address varchar (25),
c_city char (10),
c_nation char (15),
c_region char (12),
c_phone char (15),
c_mktsegment char (10)
) ; create table dates (
d_datekey date,
d_date char (18),
d_dayofweek char (9),
d_month char (9),
d_yearmonthnum int,
d_yearmonth char (7),
d_daynuminweek varchar(12),
d_daynuminmonth int,
d_daynuminyear int,
d_monthnuminyear int,
d_weeknuminyear int,
d_sellingseason varchar (12),
d_lastdayinweekfl varchar (1),
d_lastdayinmonthfl varchar (1),
d_holidayfl varchar (1),
d_weekdayfl varchar (1)
) ; CREATE TABLE lineorder_flat(
LO_ORDERKEY bigint key,
LO_LINENUMBER int,
LO_CUSTKEY int,
LO_PARTKEY int,
LO_SUPPKEY int,
LO_ORDERDATE date,
LO_ORDERPRIORITY char(15),
LO_SHIPPRIORITY tinyint,
LO_QUANTITY double,
LO_EXTENDEDPRICE double,
LO_ORDTOTALPRICE double,
LO_DISCOUNT double,
LO_REVENUE int unsigned,
LO_SUPPLYCOST int unsigned,
LO_TAX double,
LO_COMMITDATE date,
LO_SHIPMODE char(10),
C_NAME varchar(25),
C_ADDRESS varchar(25),
C_CITY char(10),
C_NATION char(15),
C_REGION char(12),
C_PHONE char(15),
C_MKTSEGMENT char(10),
S_NAME char(25),
S_ADDRESS varchar(25),
S_CITY char(10),
S_NATION char(15),
S_REGION char(12),
S_PHONE char(15),
P_NAME varchar(22),
P_MFGR char(6),
P_CATEGORY char(7),
P_BRAND char(9),
P_COLOR varchar(11),
P_TYPE varchar(25),
P_SIZE int,
P_CONTAINER char(10)
);
导入数据
请根据自己生成的数据的路径,自行调整导入语句中的路径参数
如我生成数据的路径为:/home/ssb/ssb/***.tbl
则对应单表导入语句为:
load data infile '/home/ssb/ssb/supplier.tbl' into table supplier FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n'; load data infile '/home/ssb/ssb/customer.tbl' into table customer FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n'; load data infile '/home/ssb/ssb/date.tbl' into table dates FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n'; load data infile '/home/ssb/ssb/part.tbl' into table part FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n'; load data infile '/home/ssb/ssb/lineorder.tbl' into table lineorder FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n';
大宽表导入语句为:
load data infile '/home/ssb/ssb/lineorder_flat.tbl' into table lineorder_flat FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n';
运行SSB测试
大宽表查询
查询语句
--Q1.1
SELECT sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue FROM lineorder_flat WHERE year(LO_ORDERDATE)=1993 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 year(LO_ORDERDATE)=1994 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 year(LO_ORDERDATE)=1994 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(LO_ORDERDATE), 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 BETWEEN 'MFGR#2221' AND 'MFGR#2228' AND S_REGION = 'ASIA' GROUP BY year(LO_ORDERDATE), 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(LO_ORDERDATE), 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 year(LO_ORDERDATE) between 1992 AND 1997 GROUP BY C_NATION, S_NATION, year(LO_ORDERDATE) 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 = 'CHINA' AND S_NATION = 'CHINA' AND year(LO_ORDERDATE) between 1992 AND 1997 GROUP BY C_CITY, S_CITY, year(LO_ORDERDATE) 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 = 'UNITED KI0' OR C_CITY = 'UNITED KI7') AND (S_CITY = 'UNITED KI0' OR S_CITY = 'UNITED KI7') AND year(LO_ORDERDATE) between 1992 AND 1997 GROUP BY C_CITY, S_CITY, year(LO_ORDERDATE) 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 = 'UNITED KI0' OR C_CITY = 'UNITED KI7') AND (S_CITY = 'MOZAMBIQU1' OR S_CITY = 'KENYA 4') AND year(LO_ORDERDATE)= 1997 GROUP BY C_CITY, S_CITY, year(LO_ORDERDATE) 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 = 'MFGR#1' OR P_MFGR = 'MFGR#2') GROUP BY year(LO_ORDERDATE), C_NATION ORDER BY year, C_NATION; --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 (year(LO_ORDERDATE) = 1997 OR year(LO_ORDERDATE) = 1998) AND (P_MFGR = 'MFGR#1' OR P_MFGR = 'MFGR#2') GROUP BY year(LO_ORDERDATE), S_NATION, P_CATEGORY ORDER BY year, S_NATION, P_CATEGORY; --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 (year(LO_ORDERDATE) = 1997 OR year(LO_ORDERDATE) = 1998) AND P_CATEGORY = 'MFGR#14' GROUP BY year(LO_ORDERDATE), S_CITY, P_BRAND ORDER BY year, S_CITY, P_BRAND;
多表查询
查询语句
--Q1.1
select sum(lo_revenue) as revenue
from lineorder join dates on lo_orderdate = d_datekey
where year(d_datekey) = 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 year(d_datekey) = 1994
and lo_discount between 5 and 7
and lo_quantity between 26 and 35; --Q2.1
select sum(lo_revenue) as lo_revenue, year(d_datekey) as 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_category = 'MFGR#12' and s_region = 'AMERICA'
group by year(d_datekey), p_brand
order by year, p_brand; --Q2.2
select sum(lo_revenue) as lo_revenue, year(d_datekey) as 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 year(d_datekey), p_brand
order by year, p_brand; --Q2.3
select sum(lo_revenue) as lo_revenue, year(d_datekey) as 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 year(d_datekey), p_brand
order by year, p_brand; --Q3.1
select c_nation, s_nation, year(d_datekey) as 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 year(d_datekey) between 1992 and 1997
group by c_nation, s_nation, year(d_datekey)
order by year asc, lo_revenue desc; --Q3.2
select c_city, s_city, year(d_datekey) as 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 year(d_datekey) between 1992 and 1997
group by c_city, s_city, year(d_datekey)
order by year asc, lo_revenue desc; --Q3.3
select c_city, s_city, year(d_datekey) as 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 year(d_datekey) between 1992 and 1997
group by c_city, s_city, year(d_datekey)
order by year asc, lo_revenue desc; --Q3.4
select c_city, s_city, year(d_datekey) as 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 = '199712'
group by c_city, s_city, year(d_datekey)
order by year(d_datekey) asc, lo_revenue desc; --Q4.1
select year(d_datekey) as 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 year(d_datekey), c_nation
order by year, c_nation; --Q4.2
select year(d_datekey) as 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 (year(d_datekey) = 1997 or year(d_datekey) = 1998)
and (p_mfgr = 'MFGR#1' or p_mfgr = 'MFGR#2')
group by year(d_datekey), s_nation, p_category
order by year, s_nation, p_category; --Q4.3
select year(d_datekey) as year, s_city, p_brand, sum(lo_revenue) - sum(lo_supplycost) as profit, c_region, s_nation, p_category
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
(year(d_datekey) = 1997 or year(d_datekey) = 1998)
and s_nation='ALGERIA'
group by year(d_datekey), s_city, p_brand, c_region, s_nation, p_category
order by year, s_city, p_brand;
查询报告
大宽表
查询ID 第一次查询 第二次查询 第三次查询 第四次查询 第五次查询 平均查询速度 Q1.1 0.14 sec 0.13 sec 0.14 sec 0.13 sece 0.13 sec 0.134 sec Q1.2 0.17 sec 0.14 sec 0.14 sec 0.14 sece 0.15 sec 0.148 sec Q1.3 0.14 sec 0.17 sec 0.14 sec 0.16 sec 0.14 sec 0.15 sec Q2.1 0.64 sec 0.61 sec 0.59 sec 0.65 sec 0.62 sec 0.622 sec Q2.2 0.77 sec 0.77 sec 0.77 sec 0.74 sec 0.75 sec 0.76 sec Q2.3 0.58 sec 0.56 sec 0.55 sec 0.60 sec 0.54 sec 0.566 sec Q3.1 0.79 sec 0.74 sec 0.76 sec 0.73 sec 0.75 sec 0.754 sec Q3.2 0.71 sec 0.72 sec 0.70 sec 0.74 sec 0.70 sec 0.714 sec Q3.3 0.99 sec 0.97 sec 0.97 sec 1.10 sec 0.97 sec 1 sec Q3.4 1.07 sec 0.98 sec 0.98 sec 0.97 sec 0.97 sec 0.994 sec Q4.1 1.13 sec 1.10 sec 1.04 sec 1.04 sec 1.07 sec 1.076 sec Q4.2 1.19 sec 1.16 sec 1.18 sec 1.19 sec 1.19 sec 1.182 sec Q4.3 0.72 sec 0.74 sec 0.71 sec 0.76 sec 0.69 sec 0.724 sec 多表查询
查询ID 第一次查询 第二次查询 第三次查询 第四次查询 第五次查询 平均查询速度 Q1.1 0.08 sec 0.11 sec 0.08 sec 0.09 sec 0.06 sec 0.084 sec Q1.2 0.12 sec 0.07 sec 0.07 sec 0.09 sec 0.07 sec 0.084 sec Q1.3 0.09 sec 0.08 sec 0.09 sec 0.07 sec 0.10 sec 0.086 sec Q2.1 0.28 sec 0.26 sec 0.27 sec 0.26 sec 0.28 sec 0.27 sec Q2.2 0.29 sec 0.28 sec 0.28 sec 0.28 sec 0.28 sec 0.282 sec Q2.3 0.26 sec 0.26 sec 0.28 sec 0.26 sec 0.23 sec 0.258 sec Q3.1 0.40 sec 0.34 sec 0.43 sec 0.36 sec 0.40 sec 0.386 sec Q3.2 0.12 sec 0.15 sec 0.10 sec 0.09 sec 0.15 sec 0.122 sec Q3.3 0.13 sec 0.06 sec 0.11 sec 0.07 sec 0.10 sec 0.094 sec Q3.4 0.08 sec 0.12 sec 0.07 sec 0.11 sec 0.07 sec 0.09 sec Q4.1 0.45 sec 0.39 sec 0.46 sec 0.42 sec 0.44 sec 0.432 sec Q4.2 0.32 sec 0.40 sec 0.32 sec 0.39 sec 0.31 sec 0.348 sec Q4.3 0.36 sec 0.32 sec 0.36 sec 0.32 sec 0.37 sec 0.346 sec 查询对比报告
通过SSB测试发现,MatrixOne多表join查询性能异常强大,超过了传统的大宽表模型,这意味着在MatrixOne中我们可以使用丰富的数据模型来满足我们的业务需要。

MatrixOne从入门到实践08——SSB性能测试的更多相关文章
- MatrixOne从入门到实践03——部署MatrixOne
MatrixOne从入门到实践--部署MatrixOne 前两章节我们简单介绍了MatrixOne和源码编译了MatrixOne.本章节将使用不同的部署方式,来部署MatrixOne的服务. 注意:不 ...
- MatrixOne从入门到实践02——源码编译
MatrixOne从入门到实践--源码编译 在部署MatrixOne前,我们可能会比较纠结使用哪个版本合适,MatrixOne在github上有各个版本的Releases,包含源码包和适用于Lin ...
- MatrixOne从入门到实践01——初识MatrixOne
初识MatrixOne 简介 MatrixOrigin 矩阵起源 是一家数据智能领域的创新企业,其愿景是成为数字世界的核心技术提供者. 物理世界的数字化和智能化无处不在.我们致力于建设开放的技术开源社 ...
- MatrixOne从入门到实战04——MatrixOne的连接和建表
MatrixOne从入门到实战--MatrixOne的连接和建表 前景回顾 前几篇文章,为大家介绍了MatrixOne这个产品,以及编译.部署MatrixOne的服务. 直通车: MatrixOne从 ...
- Storm实时计算:流操作入门编程实践
转自:http://shiyanjun.cn/archives/977.html Storm实时计算:流操作入门编程实践 Storm是一个分布式是实时计算系统,它设计了一种对流和计算的抽象,概念比 ...
- Python+Selenium基础入门及实践
Python+Selenium基础入门及实践 32018.08.29 11:21:52字数 3220阅读 23422 一.Selenium+Python环境搭建及配置 1.1 selenium 介绍 ...
- 《Python编程:从入门到实践》第十八章笔记:Django最基本用法笔记
最近在看Python编程:从入门到实践,这是这本书"项目3 Web应用程序"第18章的笔记.记录了django最基本的一些日常用法,以便自己查阅. 可能是我的这本书版本比较老,书上 ...
- 《Github入门与实践》读书笔记 蟲咋先生的追求之旅(上)
<Github入门与实践>作者: [日] 大塚弘记 译者:支鹏浩/刘斌 简介 本书从Git的基本知识和操作方法入手,详细介绍了GitHub的各种功能,GitHub与其他工具或服务的协作 ...
- Python编程从入门到实践笔记——异常和存储数据
Python编程从入门到实践笔记——异常和存储数据 #coding=gbk #Python编程从入门到实践笔记——异常和存储数据 #10.3异常 #Python使用被称为异常的特殊对象来管理程序执行期 ...
随机推荐
- 四边形不等式优化 dp (doing)
目录 1. 四边形不等式与决策单调性 2. 决策单调性优化 dp - (i) 关于符号 1. 四边形不等式与决策单调性 定义(四边形不等式) 设 \(w(x,y)\) 是定义在整数集合上的二元函数,若 ...
- 《深入了解java虚拟机》高效并发读书笔记——Java内存模型,线程,线程安全 与锁优化
<深入了解java虚拟机>高效并发读书笔记--Java内存模型,线程,线程安全 与锁优化 本文主要参考<深入了解java虚拟机>高效并发章节 关于锁升级,偏向锁,轻量级锁参考& ...
- javascript 原生class操作
<script type="text/javascript"> function hasClass(elements, cName) { return elements ...
- Python3的单元测试模块Mock与性能测试模块CProfile
原文转载自「刘悦的技术博客」https://v3u.cn/a_id_92 我们知道写完了代码需要自己跑一跑进行测试,一个写好的程序如果连测试都没有就上到生产环境是不敢想象的,这么做的人不是太自信就是太 ...
- 基于阿里云直播实现视频推流(ffmpeg)/拉流(Django2.0)以及在线视频直播播放(支持http/https)功能
原文转载自「刘悦的技术博客」https://v3u.cn/a_id_146 由于5g网络的光速推广,视频业务又被推上了风口浪尖,在2019年初我们还在谈论照片,短视频等关键字,而进入2020年,我们津 ...
- 125. 验证回文串--LeetCode
来源:力扣(LeetCode) 链接:https://leetcode.cn/problems/valid-palindrome 著作权归领扣网络所有.商业转载请联系官方授权,非商业转载请注明出处. ...
- TypeScript 项目报错 Unknown file extension ".ts"
下图是该问题的详细报错截图,经过多次捣鼓,初步猜测是模块有问题,要用 ES Module 还真是曲折,最不容易出错的就是 CommonJS 模块: 在百度.Bing 上搜索了好久的帖子也都没有相关的解 ...
- 从零开始搭建react基础开发环境(基于webpack5)
前言 最近利用闲暇时间把webpack系统的学习了下,搭建出一个react环境的脚手架,写篇文章总结一下,帮助正在学习webpack小伙伴们,如有写的不对的地方或还有可以优化的地方,望大佬们指出,及时 ...
- Python入门系列(一)安装环境
python是什么 python是一门很受欢迎的语言,除了不能生孩子以外,其它都可以做. 它擅长的领域是脚本工具和科学数据这一块,比如大数据,数据分析什么的. python安装 为了演示和验证教程可用 ...
- 利用Hugging Face中的模型进行句子相似性实践
Hugging Face是什么?它作为一个GitHub史上增长最快的AI项目,创始人将它的成功归功于弥补了科学与生产之间的鸿沟.什么意思呢?因为现在很多AI研究者写了大量的论文和开源了大量的代码, ...