LVM

pv ##物理卷
被lv命令处理过的物理分区
vg ##物理卷组
被组装到一起的物理卷
pe ##图里扩展
lvm设备的最小存储单元lvm时pe的整数倍
lvm ##逻辑卷
直接使用的设备,可以增大缩减并保持原有数据不变

#lvm建立
1.分区并设定分区标签为8e
pvcreate /dev/vdb6
pvcreate /dev/vdb7
vgcreate vg0 /dev/vdb6
vgextend vg0 /dev/vdb7
lvcreate -L 50M -n lv0 vg0
lvextend -L 100M /dev/vg0/lv0
mkfs.xfs /dev/vg0/lv0
mount /dev/vg0/lv0 /mnt
df -H /mnt/
在这里插入图片描述
在这里插入图片描述

#lvm扩展
1.先添加一个新的pv
[root@server_sshd ~]# pvcreate /dev/vdb8
2.再将新的片v添加到vg0中
[root@server_sshd ~]# vgextend vg0 /dev/vdb8
3.最后扩展文件系统
[root@server_sshd ~]# lvextend -L 500M /dev/vg0/lv0
在这里插入图片描述
在这里插入图片描述

#lvm的收缩
注意:之前操作都是在xfs文件格式下,该文件格式只支持扩展,因此我们要先重新格式话为ext4格式
1.首先卸载umount /mnt,格式化为ext4格式并扫描
[root@server_sshd ~]# umount /mnt
[root@server_sshd ~]# mkfs.ext4 /dev/vg0/lv0
2.先缩减文件系统(必须,否则整个系统崩溃)
resize2fs /dev/vg0/lv0 300M
3.最后缩减lv
[root@server_sshd ~]# lvreduce -L 300M /dev/vg0/lv0
4.清除多余pv
[root@server_sshd ~]# pvmove /dev/vdb7 /dev/vdb8
[root@server_sshd ~]# vgreduce vg0 /dev/vdb7
[root@server_sshd ~]# pvremove /dev/vdb7
在这里插入图片描述
在这里插入图片描述

#lvm快照
1.卸载umount /mnt
[root@server_sshd ~]# umount /mnt
2.制作快照
[root@server_sshd ~]# lvcreate -L 50M -n /dev/vg0/lv0_snap -s /dev/vg0/lv0
3.将快照挂载
[root@server_sshd ~]# mount /dev/vg0/lv0_snap /mnt
在这里插入图片描述

#删除快照
1.卸载umount /mnt
[root@server_sshd ~]# umount /mnt
2.移除lv,vg,pv
[root@server_sshd ~]lvremove /dev/vg0/lv0_snap
[root@server_sshd ~]lvremove /dev/vg0/lv0
[root@server_sshd ~]# vgremove vg0
[root@server_sshd ~]# pvremove /dev/vdb6
[root@server_sshd ~]# pvremove /dev/vdb8
3.将lvm分区删除

根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
06-19
### LVM in Linux: Configuration, Management, and Troubleshooting LVM (Logical Volume Manager) is a powerful tool in Linux that allows for flexible disk management. It provides a layer of abstraction over physical disks, enabling dynamic resizing and allocation of storage resources. Below is an overview of configuration, management, and troubleshooting aspects of LVM. #### Configuration of LVM To configure LVM, the process involves creating physical volumes, volume groups, and logical volumes. When using LVM with initramfs, specific hooks must be added to ensure proper initialization during boot. For systemd-based initramfs, the file `/etc/mkinitcpio.conf` should be edited to include the `sd-lvm2` hook[^1]. This ensures that the system can recognize and mount LVM volumes during the boot process. The updated line in the configuration file would look like this: ```bash HOOKS=(base systemd ... block sd-lvm2 filesystems) ``` For busy-box based initramfs, the `lvm2` hook should be added instead of `sd-lvm2`. #### Management of LVM Managing LVM involves several key operations such as creating, extending, and reducing logical volumes. These operations are performed using commands like `pvcreate`, `vgcreate`, `lvcreate`, `lvextend`, and `lvreduce`. Here is an example of creating a logical volume: ```bash # Create a physical volume pvcreate /dev/sdb # Create a volume group named 'myvg' vgcreate myvg /dev/sdb # Create a logical volume named 'mylv' with a size of 10GB lvcreate -L 10G -n mylv myvg ``` Once created, file systems can be formatted on the logical volumes and mounted as needed. #### Troubleshooting LVM Troubleshooting LVM often involves resolving issues related to volume recognition, mounting, or resizing. Common problems include missing devices, incorrect configurations, or insufficient space. Tools like `lvdisplay`, `vgdisplay`, and `pvdisplay` can help diagnose issues by providing detailed information about the current state of LVM components. For instance, if a logical volume fails to mount, checking the status of the volume group and logical volume can provide insights: ```bash # Display information about the volume group vgdisplay myvg # Display information about the logical volume lvdisplay /dev/myvg/mylv ``` If the issue persists, verifying the integrity of the file system using tools like `fsck` may be necessary. ```bash fsck /dev/myvg/mylv ``` #### Example Playbook for Automating LVM Tasks with Ansible Ansible can automate LVM tasks by defining playbooks. Below is an example playbook that creates a physical volume, volume group, and logical volume: ```yaml --- - name: Configure LVM hosts: all become: yes tasks: - name: Create physical volume community.general.lvol: vg: myvg lv: mylv size: 10G state: present ``` This playbook uses the `community.general.lvol` module to manage LVM volumes[^3]. ###
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