当前位置:网站首页>jeecgboot输出日志,@Slf4j的使用方法
jeecgboot输出日志,@Slf4j的使用方法
2022-07-01 03:17:00 【三个人工作室】
直接上代码:
基本格式
@Slf4j
public class xxxxx {
log.info("xxxxxxxxxxxxxxxxxxx");
}
动态输出
@Slf4j
class LogTest {
@Test
void testLog() {
String testInfo = "Free flying flowers are like dreams";
log.info("The test info is :{}", testInfo);
}
}
效果:
springboot部署@Slf4j的方式及引用:
https://blog.csdn.net/cslucifer/article/details/80953400
边栏推荐
- Nacos
- Pathmeasure implements loading animation
- 衡量两个向量相似度的方法:余弦相似度、pytorch 求余弦相似度:torch.nn.CosineSimilarity(dim=1, eps=1e-08)
- [nine day training] content III of the problem solution of leetcode question brushing Report
- Introduction to EtherCAT
- md5sum操作
- Finally in promise
- 8 pits of redis distributed lock
- Cookie&Session
- LeetCode 31下一个排列、LeetCode 64最小路径和、LeetCode 62不同路径、LeetCode 78子集、LeetCode 33搜索旋转排序数组(修改二分法)
猜你喜欢
![Pyramid scene parsing network [pspnet] thesis reading](/img/05/4645c8a595083479dee6835620335d.png)
Pyramid scene parsing network [pspnet] thesis reading

实现pow(x,n)函数

Bilinear upsampling and f.upsample in pytorch_ bilinear

Overview of EtherCAT principle

Pyramid Scene Parsing Network【PSPNet】论文阅读

FCN full Convolution Network Understanding and Code Implementation (from pytorch Official Implementation)

C#实现基于广度优先BFS求解无权图最短路径----完整程序展示

The 'mental (tiring) process' of building kubernetes/kubesphere environment with kubekey

pytorch训练深度学习网络设置cuda指定的GPU可见

The best learning method in the world: Feynman learning method
随机推荐
[小样本分割]论文解读Prior Guided Feature Enrichment Network for Few-Shot Segmentation
Kmeans
岭回归和lasso回归
排序链表(归并排序)
Feature Pyramid Networks for Object Detection论文理解
二叉树神级遍历:Morris遍历
Ctfshow blasting WP
Promise中finally的用法
FCN full Convolution Network Understanding and Code Implementation (from pytorch Official Implementation)
Basic concepts of database
BluePrism注册下载并安装-RPA第一章
Edge drawing: a combined real-time edge and segment detector
TEC: Knowledge Graph Embedding with Triple Context
Overview of EtherCAT principle
[深度学习]激活函数(Sigmoid等)、前向传播、反向传播和梯度优化;optimizer.zero_grad(), loss.backward(), optimizer.step()的作用及原理
Analyze datahub, a new generation metadata platform of 4.7K star
Gorilla/mux framework (RK boot): RPC error code design
后台系统页面左边菜单按钮和右边内容的处理,后台系统页面出现双滚动
Thread data sharing and security -threadlocal
MySQL knowledge points