MySQL主从数据库Last_Error: Error ‘Row size too large. The maximum row size for the used table type, not counting BLOBs, is 65535. This includes storage overhead, check the manual. You have to change some columns to TEXT or BLOBs’ on query

MySQL 版本: MySQL 5.7.31
MySQL 架构: 主从数据库架构
告警节点: 从数据库
运行场景: 某大型制造企业

             Slave_IO_Running: Yes
            Slave_SQL_Running: No
              Replicate_Do_DB: 
          Replicate_Ignore_DB: 
           Replicate_Do_Table: 
       Replicate_Ignore_Table: 
      Replicate_Wild_Do_Table: 
  Replicate_Wild_Ignore_Table: 
                   Last_Errno: 1118
                   Last_Error: Error 'Row size too large. The maximum row size for the used table type, not counting BLOBs, is 65535. This includes storage overhead, check the manual. You have to change some columns to TEXT or BLOBs' on query. Default database: 'ekp'. Query: 'alter table ekp_ff4cf69105f28480891e add column fd_3ddc5be2f5270e varchar(4000)'

解决办法,修改MySQL配置参数,从新进行数据同步。

[mysqld]
port=3360
bind_address=0.0.0.0
server-id = 128
log_bin = mysql-bin
binlog_cache_size = 4M
binlog_format = mixed
expire_logs_days = 99
relay-log=mysqld-relay-bin
init-connect = 'SET NAMES utf8mb4'
character-set-server = utf8mb4
datadir=/home/mysql
socket=/home/mysql/mysql.sock
symbolic-links=0
log-error=/var/log/mysqld.log
pid-file=/var/run/mysqld/mysqld.pid
open_files_limit = 5000
key_buffer_size = 256M
query_cache_size = 64M
thread_cache_size = 64
lower_case_table_names = 1
default_storage_engine = InnoDB
innodb_file_format = Barracuda   # 启用 Barracuda,支持大行
innodb_file_per_table = 1             # 独立表空间,便于管理
innodb_default_row_format = DYNAMIC   # 默认 DYNAMIC 行格式,避免行大小限制
innodb_buffer_pool_size = 2048M       # 增加缓冲池(根据内存调整)
innodb_log_file_size = 512M                # 增加日志文件大小,支持大表变更
innodb_log_buffer_size = 16M             # 日志缓冲区,提升写入性能
innodb_large_prefix = 1                        # MySQL 5.7 需启用,支持大索引前缀
performance_schema = 0
explicit_defaults_for_timestamp
skip-external-locking
skip-name-resolve
max_allowed_packet=2560M
wait_timeout=60000
[client]
port = 3360
socket=/home/mysql/mysql.sock

Ubuntu 24.04 LTS如何安装Nvidia显卡驱动、CUDA、NVIDIA Container Toolkit套件

Ubuntu 24.04 LTS如何安装Nvidia显卡驱动、CUDA、NVIDIA Container Toolkit套件

1、安装Nvidia显卡驱动
若有Nvidia显卡,Ubuntu系统会安装开源的nouveau驱动,用指令sudo lshw -C display确认,driver区域会显示"nouveau"。

#卸载自带的驱动
sudo apt update
sudo apt upgrade
sudo apt purge *nvidia*

使用ubuntu-drivers list指令列出目前Nvidia显示卡可用的驱动版本

# 让Ubuntu自动挑选推荐的驱动版本

sudo ubuntu-drivers install

# 或者手动指定版本,填入要安装的Nvidia驱动版本号。
sudo ubuntu-drivers install nvidia:570
安装后nouveau应会自动加入黑名单禁止加载。接着重新启动,用sudo lshw -C display确认是否安装成功,driver区域应会显示"nvidia"。

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2、双GPU显卡笔记本电脑
像Intel+Nvidia这种的双GPU笔记本电脑,即使装了Nvidia驱动也可能继续用Intel的GPU渲染3D,导致3D性能低下。

此时可以使用prime-select指令,指定用Nvidia显示卡负责渲染。

sudo prime-select nvidia
重开机后再使用指令:vulkaninfo --summary查看主显示卡为何。

3、Ubuntu安装cuda,CUDA Toolkit Installer。

Installation Instructions:
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-8

用 nvcc --version 确认cuda的版本,如果显示Command nvcc not found,则编辑~/.bashrc

vim ~/.bashrc
export PATH=/usr/local/cuda-12.8/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.8/lib64:${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

#更新变量
source ~/.bashrc

# nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2025 NVIDIA Corporation
Built on Fri_Feb_21_20:23:50_PST_2025
Cuda compilation tools, release 12.8, V12.8.93
Build cuda_12.8.r12.8/compiler.35583870_0

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4、安装NVIDIA Container Toolkit,这是设计给Docker和Podman容器用的Nvidia工具,使容器可以使用CUDA计算。

即使宿主机没有安装CUDA,容器內照样可以使用CUDA计算,方便你在容器里面跑不同版本的CUDA,不会受到宿主机的CUDA版本影响。

必须先安装Nvidia专有驱动才可以安装NVIDIA Container Toolkit。

(1)在Ubuntu安装Docker
(2)加入NVIDIA Container Toolkit的套件库

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
    
安装NVIDIA Container Toolkit
sudo apt update
sudo apt install nvidia-container-toolkit

向Docker注册Nvidia
sudo nvidia-ctk runtime configure --runtime=docker

重新启动Docker
sudo systemctl restart docker

执行Ubuntu容器,测试能否出现Nvidia显卡的信息
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

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5、安装TensorRT,TensorRT是Nvidia推出的深度学习推理平台。

必须先安装CUDA才能安装TensorRT。 https://developer.nvidia.com/nvidia-tensorrt-download
安装TensorRT的deb档,加入套件库

# 指定系统版本
os="ubuntu2204"

# 指定TensorRT版本
tag="10.5.0.x-1+cuda12.6"

sudo dpkg -i nv-tensorrt-local-repo-${os}-${tag}_1.0-1_amd64.deb
sudo cp /var/nv-tensorrt-local-repo-${os}-${tag}/*-keyring.gpg /usr/share/keyrings/

sudo apt update
安装TensorRT
sudo apt install tensorrt