GreatSQL从单机到MGR扩展纪实

GreatSQL vs MySQL性能测试来了,速围观~ 无论ibp(innodb_buffer_pool_size)是否充足,MySQL的性能都远不如GreatSQL。MySQL的性能平均约为GreatSQL的70%(最高84.5%,最低61.7%)。在ibp充分的情况下,随着并发数的增加,MySQL并没有表现出该有的性能提升,反倒掉头向下,可见还是不够稳定。在ibp不够的情况下,GreatSQL开启thread pool性能有所提升;当ibp充足的情况下,区别就不大了。MySQL vs GreatSQL性能数据对比各数据库版本分别为。 阅读详情

一、前言

原有的业务系统跑在MySQL主从架构中,高可用通过脚本完成,但存在切换数据丢失和切换不及时风险,调研了高可用更稳定的MGR后,准备入手一试。本篇文章主要记录GreatSQL从单机扩展到MGR的详细过程,遇到的问题及解决方法。

二、基础环境

服务器角色如下

IP端口主机名作用
172.17.140.253310gdb1原单机
172.17.140.243310gdb2MGR扩展节点1
172.17.139.1643310gdb3MGR扩展节点2

本次验证使用的是GreatSQL8.0.32-24版本,相比MySQL官网的8.0.32,GreatSQL在性能、可靠性、易用性和安全方面,都有不错的提升,可以在后续环境中验证。具体提升点可以参考:GreatSQL简介[https://greatsql.cn/docs/8032/user-manual/2-about-greatsql/2-1-greatsql-brief-intro.html]

通过下面的命令下载GreatSQL8.0.32-24进行测试。

$ wget https://product.greatdb.com/GreatSQL-8.0.32-24/GreatSQL-8.0.32-24-Linux-glibc2.17-x86_64-minimal.tar.xz

三、单机搭建

3.1 单机搭建

将软件包,上传至172.17.140.25的/opt/software目录,解压安装

# cd /opt/software
# xz -d GreatSQL-8.0.32-24-Linux-glibc2.17-x86_64-minimal.tar.xz
# tar xf GreatSQL-8.0.32-24-Linux-glibc2.17-x86_64-minimal.tar
# ln -s GreatSQL-8.0.32-24-Linux-glibc2.17-x86_64-minimal mysql

结果如下所示

file 创建必要的目录,并修改属主

[#25#root@root /opt/software 09:55:30]25 mkdir -p /data/dbdata/data3310/{data,log,tmp}
[#26#root@root /opt/software 09:55:41]26 chown -R mysql. /data/dbdata/data3310
[#27#root@root /opt/software 09:55:53]27 ll /data/dbdata/data3310
total 12
drwxr-xr-x 2 mysql mysql 4096 Apr 26 09:55 data
drwxr-xr-x 2 mysql mysql 4096 Apr 26 09:55 log
drwxr-xr-x 2 mysql mysql 4096 Apr 26 09:55 tmp
[#28#root@root /opt/software 09:56:02]28 

创建配置文件/data/dbdata/data3310/my3310.cnf,配置文件中并没有与MGR相关的参数配置,内容如下

[client]
socket = /data/dbdata/data3310/mysql3310.sock

[mysqld]
report_host=172.17.139.164
report_port=3310
user=mysql
basedir = /data/mysql
datadir = /data/dbdata/data3310/data
log_error=/data/dbdata/data3310/log/error3310.log
port = 3310
socket = /data/dbdata/data3310/mysql3310.sock
server-id = 253310
default_authentication_plugin=mysql_native_password
back_log = 5000
binlog_format = row
character_set_server = utf8
enforce_gtid_consistency = 1
expire_logs_days = 7
federated = 1
gtid_mode = on
innodb_buffer_pool_instances = 8
innodb_buffer_pool_size = 2G
innodb_change_buffering = all
innodb_doublewrite = true
innodb_file_per_table = 1
innodb_flush_log_at_trx_commit = 1
innodb_flush_method = O_DIRECT
innodb_io_capacity = 8000
#innodb_locks_unsafe_for_binlog = 1
innodb_log_buffer_size = 256M
innodb_log_file_size = 1G
innodb_log_files_in_group = 4
innodb_print_all_deadlocks = on
innodb_read_io_threads = 16
innodb_temp_data_file_path = ibtmp1:12M:autoextend:max:8192M
innodb_thread_concurrency = 64
innodb_write_io_threads = 16
interactive_timeout = 3600
lock_wait_timeout = 600
log_bin = mysql-bin
log_bin_trust_function_creators = 1
log_slave_updates = 1
log_timestamps = SYSTEM
long_query_time = 1
lower_case_table_names = 1
master_info_repository = TABLE
max_allowed_packet = 16M
max_connections = 20480
max_prepared_stmt_count = 1048576
net_read_timeout = 10000
net_write_timeout = 10000
open_files_limit = 1000000
relay_log = mysql-relay
relay_log_info_repository = TABLE
skip_external_locking = 1
skip_name_resolve = 1
skip_slave_start = 1
slave_parallel_type = LOGICAL_CLOCK
slave_parallel_workers = 16
slave_pending_jobs_size_max = 2147483648
slave_preserve_commit_order = on
slave_rows_search_algorithms = INDEX_SCAN,HASH_SCAN
slow_query_log = on
sort_buffer_size = 2M
ssl = OFF
sync_binlog = 1
table_definition_cache = 5000
table_open_cache = 5000
thread_cache_size = 3000
tmpdir = /data/dbdata/data3310/tmp
transaction_isolation = READ-COMMITTED
wait_timeout = 31536000
plugin_dir = /opt/software/mysql/lib/plugin
transaction_write_set_extraction=XXHASH64
binlog_transaction_dependency_tracking=WRITESET

初始化实例,并启动

[#34#root@root /opt/software 10:01:55]34 /opt/software/mysql/bin/mysqld --defaults-file=/data/dbdata/data3310/my3310.cnf --initialize-insecure
[#35#root@root /opt/software 10:02:05]35 
[#35#root@root /opt/software 10:02:25]35 /opt/software/mysql/bin/mysqld_safe --defaults-file=/data/dbdata/data3310/my3310.cnf &
[1] 4038
[#36#root@root /opt/software 10:02:36]36 2023-04-26T02:02:37.359772Z mysqld_safe Logging to '/data/dbdata/data3310/log/error3310.log'.
2023-04-26T02:02:37.423502Z mysqld_safe Starting mysqld daemon with databases from /data/dbdata/data3310/data

[#36#root@root /opt/software 10:02:39]36 ps -ef| grep 3310
root      4038 21630  1 10:02 pts/1    00:00:00 /bin/sh /opt/software/mysql/bin/mysqld_safe --defaults-file=/data/dbdata/data3310/my3310.cnf
mysql     5167  4038 72 10:02 pts/1    00:00:05 /opt/software/mysql/bin/mysqld --defaults-file=/data/dbdata/data3310/my3310.cnf --basedir=/data/mysql --datadir=/data/dbdata/data3310/data --plugin-dir=/data/mysql/lib/plugin --user=mysql 
--log-error=/data/dbdata/data3310/log/error3310.log --open-files-limit=1000000 --pid-file=root.pid --socket=/data/dbdata/data3310/mysql3310.sock --port=3310root      5294 21630  0 10:02 pts/1    00:00:00 grep --color=auto 3310
[#37#root@root /opt/software 10:02:44]37 

通过进程信息可以确认,数据库启动完成。

3.2 测试数据构造

连接数据库,修改默认密码,并且创建部分测试数据

[#37#root@root /opt/software 10:06:12]37 /opt/software/mysql/bin/mysql --defaults-file=/data/dbdata/data3310/my3310.cnf
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 8
Server version: 8.0.32-24 GreatSQL, Release 24, Revision 3714067bc8c

Copyright (c) 2021-2021 GreatDB Software Co., Ltd
Copyright (c) 2009-2021 Percona LLC and/or its affiliates
Copyright (c) 2000, 2021, Oracle and/or its affiliates.

Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql> select user();
+----------------+
| user()         |
+----------------+
| root@localhost |
+----------------+
1 row in set (0.00 sec)

mysql> alter user user() identified by 'a123456';
Query OK, 0 rows affected (0.01 sec)

mysql> create user m1 identified by 'a123456';
Query OK, 0 rows affected (0.01 sec)

mysql> grant all on test.* to m1;
Query OK, 0 rows affected (0.01 sec)

mysql> create database test;
Query OK, 1 row affected (0.01 sec)

mysql> create table test.t1(id int primary key, cname varchar(10));
Query OK, 0 rows affected (0.03 sec)

mysql> insert into test.t1 values(1,'a'),(2,'b'),(3,'c');
Query OK, 3 rows affected (0.02 sec)
Records: 3  Duplicates: 0  Warnings: 0

mysql> select * from test.t1;
+----+-------+
| id | cname |
+----+-------+
|  1 | a     |
|  2 | b     |
|  3 | c     |
+----+-------+
3 rows in set (0.00 sec)

四、扩展节点搭建,MGR构建

4.1 扩展节点搭建

按照3.1章节步骤,分别在服务器172.17.140.24、172.17.139.164上初始化单机实例

4.2 在gdb1、gdb2、gdb3实例中,创建用于MGR同步数据的用户

[#41#root@root /opt/software 10:23:42]41 /opt/software/mysql/bin/mysql -S /data/dbdata/data3310/mysql3310.sock -pa123456
mysql: [Warning] Using a password on the command line interface can be insecure.
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 9
Server version: 8.0.32-24 GreatSQL, Release 24, Revision 3714067bc8c

Copyright (c) 2021-2021 GreatDB Software Co., Ltd
Copyright (c) 2009-2021 Percona LLC and/or its affiliates
Copyright (c) 2000, 2021, Oracle and/or its affiliates.

Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql> create user mgr_sync identified by '!QAZ2wsx';
Query OK, 0 rows affected (0.01 sec)

mysql> GRANT REPLICATION SLAVE,BACKUP_ADMIN ON *.* to mgr_sync;
Query OK, 0 rows affected (0.01 sec)

4.3 安装必要插件

在gdb1、gdb2、gdb3服务器实例中,安装下面插件

[#46#root@root /opt/software 10:37:08]46 /opt/software/mysql/bin/mysql -S /data/dbdata/data3310/mysql3310.sock -pa123456
mysql: [Warning] Using a password on the command line interface can be insecure.
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 8
Server version: 8.0.32-24 GreatSQL, Release 24, Revision 3714067bc8c

Copyright (c) 2021-2021 GreatDB Software Co., Ltd
Copyright (c) 2009-2021 Percona LLC and/or its affiliates
Copyright (c) 2000, 2021, Oracle and/or its affiliates.

Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql> install plugin group_replication soname 'group_replication.so';
Query OK, 0 rows affected (0.23 sec)

mysql> install plugin clone soname 'mysql_clone.so';
Query OK, 0 rows affected (0.09 sec)

4.4 配置并启动MGR

在三个节点中,进行如下参数配置

mysql> set persist group_replication_group_name='0762c38f-e3dd-11ed-9fb3-00163ece3e28';
mysql> set persist group_replication_group_seeds='172.17.140.25:13310,172.17.140.24:13310,172.17.139.164:13310';
mysql> CHANGE MASTER TO MASTER_USER='mgr_sync', MASTER_PASSWORD='!QAZ2wsx' FOR CHANNEL 'group_replication_recovery';

在三个节点中,对group_replication_local_address进行配置,配置规则见章节5.2 错误2

mysql> set persist group_replication_local_address='172.17.140.25:13310';

在已有数据节点gdb1设置引导参数,并且启动mgr

mysql> set global group_replication_bootstrap_group=on;
Query OK, 0 rows affected (0.01 sec)

mysql> start group_replication;
Query OK, 0 rows affected (0.01 sec)

mysql> set global group_replication_bootstrap_group=off;
Query OK, 0 rows affected (0.01 sec)

在扩展节点gdb2、gdb3启动mgr

mysql> start group_replication;
Query OK, 0 rows affected (0.01 sec)

五、日常运维

5.1 节点状态查询

mysql> select * from performance_schema.replication_group_members;
+---------------------------+--------------------------------------+----------------+-------------+--------------+-------------+----------------+----------------------------+
| CHANNEL_NAME              | MEMBER_ID                            | MEMBER_HOST    | MEMBER_PORT | MEMBER_STATE | MEMBER_ROLE | MEMBER_VERSION | MEMBER_COMMUNICATION_STACK |
+---------------------------+--------------------------------------+----------------+-------------+--------------+-------------+----------------+----------------------------+
| group_replication_applier | 5301e31b-e3d6-11ed-bc14-00163ece3e28 | 172.17.140.25  |        3310 | ONLINE       | PRIMARY     | 8.0.32         | XCom                       |
| group_replication_applier | e01908f9-e3d8-11ed-be2e-00163e02fd34 | 172.17.140.24  |        3310 | ONLINE       | SECONDARY   | 8.0.32         | XCom                       |
| group_replication_applier | e0e5b2f9-e3d8-11ed-86cf-00163e073778 | 172.17.139.164 |        3310 | ONLINE       | SECONDARY   | 8.0.32         | XCom                       |
+---------------------------+--------------------------------------+----------------+-------------+--------------+-------------+----------------+----------------------------+
3 rows in set (0.00 sec)

5.2 节点复制通道状态查询

mysql> select * from replication_connection_status \G
*************************** 1. row ***************************
                                      CHANNEL_NAME: group_replication_applier
                                        GROUP_NAME: 0762c38f-e3dd-11ed-9fb3-00163ece3e28
                                       SOURCE_UUID: 0762c38f-e3dd-11ed-9fb3-00163ece3e28
                                         THREAD_ID: NULL
                                     SERVICE_STATE: ON
                         COUNT_RECEIVED_HEARTBEATS: 0
                          LAST_HEARTBEAT_TIMESTAMP: 0000-00-00 00:00:00.000000
                          RECEIVED_TRANSACTION_SET: 0762c38f-e3dd-11ed-9fb3-00163ece3e28:1-13,
5301e31b-e3d6-11ed-bc14-00163ece3e28:1-8
                                 LAST_ERROR_NUMBER: 0
                                LAST_ERROR_MESSAGE: 
                              LAST_ERROR_TIMESTAMP: 0000-00-00 00:00:00.000000
                           LAST_QUEUED_TRANSACTION: 0762c38f-e3dd-11ed-9fb3-00163ece3e28:11
 LAST_QUEUED_TRANSACTION_ORIGINAL_COMMIT_TIMESTAMP: 2023-04-26 11:38:02.990181
LAST_QUEUED_TRANSACTION_IMMEDIATE_COMMIT_TIMESTAMP: 2023-04-26 11:38:02.990181
     LAST_QUEUED_TRANSACTION_START_QUEUE_TIMESTAMP: 2023-04-26 11:38:02.990295
       LAST_QUEUED_TRANSACTION_END_QUEUE_TIMESTAMP: 2023-04-26 11:38:02.990340
                              QUEUEING_TRANSACTION: 
    QUEUEING_TRANSACTION_ORIGINAL_COMMIT_TIMESTAMP: 0000-00-00 00:00:00.000000
   QUEUEING_TRANSACTION_IMMEDIATE_COMMIT_TIMESTAMP: 0000-00-00 00:00:00.000000
        QUEUEING_TRANSACTION_START_QUEUE_TIMESTAMP: 0000-00-00 00:00:00.000000
1 row in set (0.00 sec)

更多运维信息详见https://gitee.com/GreatSQL/GreatSQL-Doc/tree/master/deep-dive-mgr

六、问题处理

6.1 异常1

提示信息:The group_replication_group_name option is mandatory

原因:变量group_replication_group_name必须在每个MGR成员中设置,并且保持一致

解决方法:如果是初始化集群,可以查询一个uuid()的值作为group_replication_group_name的值,如果是已经存在的集群,则到其他MGR节点查询该变量,并且在本节点设置

mysql> select uuid();
+--------------------------------------+
| uuid()                               |
+--------------------------------------+
| 0762c38f-e3dd-11ed-9fb3-00163ece3e28 |
+--------------------------------------+
1 row in set (0.01 sec)

mysql> set persist group_replication_group_name='0762c38f-e3dd-11ed-9fb3-00163ece3e28';
Query OK, 0 rows affected (0.00 sec)

mysql> select @@group_replication_group_name;
+--------------------------------------+
| @@group_replication_group_name       |
+--------------------------------------+
| 0762c38f-e3dd-11ed-9fb3-00163ece3e28 |
+--------------------------------------+
1 row in set (0.00 sec)

6.2 异常2

提示信息:[GCS] Invalid hostname or IP address () assigned to the parameter local_node!

原因:变量group_replication_local_address必须在每个MGR成员中设置,并且每个成员的值不一样

解决方法:参数group_replication_local_address设置规则为IP:PORT,一般是设置为本机IP,然后在实例端口前面加1作为MGR监听端口,即文章中的实例端口为3310,此时MGR监听端口设置为13310

mysql> set persist group_replication_local_address='172.17.140.25:13310';
Query OK, 0 rows affected (0.00 sec)

6.3 异常3

提示信息:[GCS] Unable to join the group: peers not configured.

原因:变量group_replication_group_seeds必须在每个MGR成员中设置,并且保持一致

解决方法:设置格式为所有节点的group_replication_local_address值使用逗号拼接上

mysql> set persist group_replication_group_seeds='172.17.140.25:13310,172.17.140.24:13310,172.17.139.164:13310';
Query OK, 0 rows affected (0.01 sec)

6.4 异常4

提示信息:performance_schema.replication_group_members中节点状态一直处于RECOVERING,并且在error log中有如下信息

2023-04-26T11:06:32.048986+08:00 38 [ERROR] [MY-013117] [Repl] Slave I/O for channel 'group_replication_recovery': Fatal error: Invalid (empty) username when attempting to connect to the master server. Connection attempt terminated. Error_code: MY-013117
2023-04-26T11:06:32.179376+08:00 37 [ERROR] [MY-011582] [Repl] Plugin group_replication reported: 'There was an error when connecting to the donor server. Please check that group_replication_recovery channel credentials and all MEMBER_HOST column values of performance_schema.replication_group_members table are correct and DNS resolvable.'
2023-04-26T11:06:32.179493+08:00 37 [ERROR] [MY-011583] [Repl] Plugin group_replication reported: 'For details please check performance_schema.replication_connection_status table and error log messages of Slave I/O for channel group_rep
lication_recovery.'

原因: MGR同步数据账户密码设置不正确,导致节点间无法通信

解决方法:通过下面的命令重新设置同步账号信息

mysql> CHANGE MASTER TO MASTER_USER='mgr_sync', MASTER_PASSWORD='!QAZ2wsx' FOR CHANNEL 'group_replication_recovery';
Query OK, 0 rows affected (0.01 sec)

6.5 异常5

提示信息:执行start group_replication命令返回失败,在错误日志中存在如下信息

2023-04-26T11:27:14.610060+08:00 0 [ERROR] [MY-011526] [Repl] Plugin group_replication reported: 'This member has more executed transactions than those present in the group. Local transactions: 0762c38f-e3dd-11ed-9fb3-00163ece3e28:1-7, 
5301e31b-e3d6-11ed-bc14-00163ece3e28:1-8, e0e5b2f9-e3d8-11ed-86cf-00163e073778:1 > Group transactions: 0762c38f-e3dd-11ed-9fb3-00163ece3e28:1-7, 5301e31b-e3d6-11ed-bc14-00163ece3e28:1-8'

原因:由于新加入的MGR节点存在本地事务,与MGR集群事务冲突,导致无法加入

解决方法有两种:

  1. 确认集群为新节点,并且本地操作未修改数据,可以直接重置本节点的GTID,再启动即可
## 在新节点中执行如下命令
mysql> reset master;
Query OK, 0 rows affected (0.03 sec)

mysql> start group_replication;
Query OK, 0 rows affected (2.06 sec)
  1. 手动从其他正常节点进行数据clone,clone完成后,实例会自动重启,自动加入mgr组
mysql> set global clone_valid_donor_list='172.17.140.25:3310';
Query OK, 0 rows affected (0.00 sec)

mysql> set global super_read_only=off;
Query OK, 0 rows affected (0.00 sec)

mysql> clone instance from mgr_sync@172.17.140.25:3310 identified by '!QAZ2wsx';
Query OK, 0 rows affected (1.62 sec)

mysql> Restarting mysqld...
2023-04-26T03:37:53.502149Z mysqld_safe Number of processes running now: 0
2023-04-26T03:37:53.511862Z mysqld_safe mysqld restarte

Enjoy GreatSQL :)

关于 GreatSQL

GreatSQL是适用于金融级应用的国内自主开源数据库,具备高性能、高可靠、高易用性、高安全等多个核心特性,可以作为MySQL或Percona Server的可选替换,用于线上生产环境,且完全免费并兼容MySQL或Percona Server。

相关链接: GreatSQL社区 Gitee GitHub Bilibili

GreatSQL社区:

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社区有奖建议反馈: https://greatsql.cn/thread-54-1-1.html

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技术干货 | MGR新手超全入门指南 GreatSQL部署MGR集群及运维知识全get! **MGR新手福利来了,一文帮大家快速掌握MGR集群的部署和运维知识点,**新建集群、添加新节点、滚动升级、切主在这全都有,超详细。 0内容提纲 1、运行环境 2、准备my.cnf配置文件 3、初始化MySQL实例 4、构建MGR集群 4.1 构建MGR集群前的准备工作 4.2 配置MGR集群PRIMARY节点 4.3 查看MGR集群状态 4.4 测试数据读写 5、进一步,再增加一个新节点 6、再进一步,切换主节点 6.1 先升级SECONDARY节点 6.2 再升级PRIMA 阅读详情

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此外,GreatSQL社区还提供了MySQL Shell for GreatSQL,这是一个高级客户端和代码编辑器,支持JavaScript和Python脚本功能,以及与MySQL一起使用的API。它可以用来管理数据库,特别是对MGR的支持,可以方便地进行搭建、管理和配置。GreatSQL的架构可以分为连接层、服务层和存储引擎层。连接层负责与客户端建立连接,服务层是GreatSQL的核心,包括连接器、分析器、优化器、执行器等,而存储引擎层则负责数据的存储和提取。

地球空间 3193

GreatSQL 开源数据库 & NVIDIA InfiniBand存算分离池化方案:实现高性能分布式部署

1. 计算节点是无SSD盘的裸金属服务器,运行MySQL数据库服务程序;2. 存储节点提供NVMe SSD资源池,通过软件聚合方式提供高性能Lun实现对于数据库的数据的存储服务;两部分服务器通过Quantum 平台的InfiniBand网络实现对计算节点和存储节点的无损连接,结合NVMe-oF(NVMe over Fabric)高效的数据存储传输协议,将存储节点的Lun挂载到计算节点,实现结算节点本地高性能的数据存储能力。...

GreatDB的博客 414

技术干货 | 图文结合带你搞懂GreatSQL体系架构

所有的数据,数据库、表的定义,表的每一行的内容,索引,都是存在 文件系统上,以文件的方式存在的,并完成与存储引擎的交互。服务层是GreatSQL Server的核心,主要包含连接器、分析器、优化器、执行器等,涵盖 GreatSQL 的大多数核心服务功能,以及所有的内置函数(如日期、时间、数学和加密函数等),所有跨存储引擎的功能都在这一层实现,比如存储过程、触发器、视图等。GreatSQL数据库区别于其他数据库的一个特点就是其可插拔的表存储引擎,特别需要注意的是,存储引擎是基于表的,而不是数据库。

GreatDB的博客 437

GreatSQL与MySQL性能对比测试,带您一同探索数据库的速度差异

在选择适合自己的数据库时,性能是一个至关重要的考虑因素。本文将介绍GreatSQL和MySQL两个常见的关系型数据库,并通过性能测试来比较它们之间的速度差异。除了查询性能之外,我们还可以比较GreatSQL和MySQL在插入和更新操作方面的性能差异。除了查询性能之外,我们还可以比较GreatSQL和MySQL在插入和更新操作方面的性能差异。然后,我们分别在GreatSQL和MySQL中执行了一个查询,并记录了执行时间。然后,我们分别在GreatSQL和MySQL中执行了一个查询,并记录了执行时间。

HackGJN的博客 868

GreatSQL安装篇

操作系统:CentOS7 CPU:X86_64架构 数据库:GreatSQL-8.0.32-26-Linux-glibc2.17-x86_64.tar.xz。

BUTCHER 1123

GreatSQL 从单机到 MGR 扩展纪实

原有的业务系统跑在 MySQL 主从架构中,高可用通过脚本完成,但存在切换数据丢失和切换不及时风险,调研了高可用更稳定的 MGR 后,准备入手一试。本篇文章主要记录 GreatSQL 从单机扩展到 MGR 的详细过程,遇到的问题及解决方法。

Jdjdjjdjdjdje的博客 193

KEPServer EX6的Modbus、MQTT和REST Server一站式配置整理说明

KEPServer EX6的Modbus、MQTT和REST Server一站式配置整理说明

syqenter的专栏 8961

GreatSQL社区2023全年技术文章总结

GreatSQL社区自成立以来一直致力于为广大的数据库爱好者提供一个交流与学习的平台。在2023年,我们见证了社区的蓬勃发展,见证了众多技术文章的诞生与分享。此篇总结呈现GreatSQL社区2023年社区技术文章在优快云发布的全部。这些文章涵盖了GreatSQL、MGR、源码解析、故障解析、SQL优化、安装部署、等众多数据库技术文章。

GreatSQL2021的博客 1223

Databricks Genie:语义编译器架构与企业级智能解析实践

语义层是现代数据平台实现自然语言查询的核心基础,其本质是将业务术语映射为可执行、可治理的数据契约。原理上依赖结构化元数据、值字典与数据血缘三支柱协同,通过分层AI代理(如Intent Parsing、Retriever、Verifier)实现意图识别→语义对齐→安全执行的闭环。技术价值在于降低SQL使用门槛的同时保障审计性、一致性和权限可控性,适用于销售运营、财务分析等一线业务场景。本文深入解析Databricks Genie作为典型语义编译器的落地逻辑,涵盖Unity Catalog知识注入、七层Agen

weixin_29053695的博客 337

微信Dat文件转图片(文末含工具下载)

PC端微信dat图片转换为jpg格式

weixin_40704848的博客 8086

图形化探索:快速改造单实例为双主、MGR、读写分离等架

改造的意义:如果您需要存储和处理大量数据,且读写负载较高,单个MySQL服务器可能无法满足需求,或为了满足跨地域访问的需求,可以将单机数据库,改造成可跨多个数据中心采用异步复制、或半同步、以及强同步等方式,既可以保证数据库的多副本容灾,又可提升业务的读写效率,规避了单点故障对业务的影响和负载。

GreatSQL2021的博客 216

Claude Code Agents 进阶:多代理协作与任务委托

Agent 是一个独立运行的 Claude 实例,它有自己的上下文和任务焦点。│ 主 Claude ││ (协调者) ││ │ │▼ ▼ ▼│ 代码审查 │ │ 测试生成 │ │ 文档编写 ││ │ ││▼汇总结果,完成任务并行处理:多个 Agent 同时工作,提高效率任务隔离:每个 Agent 有独立的上下文专业分工:不同类型的 Agent 擅长不同任务灵活控制:可以指定运行模式、超时等参数。

gaoxiang_120的专栏 336

OceanBase与GreatSQL哪个更适合高并发场景?

OceanBase 是“重剑”,用来解决规模和可靠性的天花板问题;GreatSQL 是“利刃”,用来解决大多数互联网应用的单机性能瓶颈问题。

一火的专栏 506

路由三大分类:直连路由、静态路由、动态路由

直连路由、静态路由和动态路由各有其独特的特点和适用场景。直连路由适用于简单的直接连接网络,静态路由提供了精确的路径控制,而动态路由则在大型复杂网络中展现出强大的适应性和自动化能力。了解和掌握这三种路由类型的配置和管理,对于网络管理员来说,是确保网络高效、安全运行的重要技能。

网络技术联盟站 4698

【YOLOv10改进[Backbone]】使用ADNet改进YOLOv10在噪声干扰下的目标检测效果(图像去噪)+ 含全部代码和详细修改方式 + 手撕结构图 + 全网首发

使用ADNet改进YOLOv10在噪声干扰下的目标检测效果(图像去噪)+ 含全部代码和详细修改方式 + 手撕结构图 + 全网首发 + 遥遥领先

静谧、淡雅 2858

主流国产数据库选型指南:助力企业平稳应对MySQL 5.7停服挑战

随着MySQL 5.7版本正式进入生命周期末期,国内众多企业正面临数据库系统停服带来的运维风险与安全隐忧。在此背景下,关键行业对数据自主可控、系统稳定可靠的要求持续提升,叠加信创政策的深入推进,数据库国产化替代已从“可选项”逐步转变为保障业务连续性的“必选项”。当前,企业在迁移路径上主要聚焦三大方向:升级至MySQL 8.0、采用国内开源增强分支,或切换至具备完整服务体系的国产商业数据库。

jysjfxs的博客 692

① Django手把手从零开发个人博客-前言

手把手教你从零开始玩转手把手教你从零开始玩转Django,全面学习并了解django的运行机制、开发全流程及线上部署。Django,全面学习并了解django的运行机制、开发全流程及线上部署。 专栏介绍 本套教程遵循Django的设计思想,开门见山,快速切入主题,拒绝拖沓,在实战中学习每一个知识点,提高学习的趣味性以及个人成就感,并且能快速将所学运用到实际项目中。 将从零手把手带着你一步步完成博客的每一个功能,通过实现博客文章列表页、文章详情页、作者页面等功能来学习和了解django的视图查询模块相关知.

轻编程的博客 513

CentOS7安装GreatSQL

GreatSQL 数据库是一款 开源免费 数据库,可在普通硬件上满足金融级应用场景,具有 高可用、高性能、高兼容、高安全 等特性,可作为 MySQL 或 Percona Server for MySQL 的理想可选替换。

落的博客 1701

NTN(三) Timing

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asd199086的博客 4828

温度PID控制PLC程序

温度PID稳定控制程序,适用于欧姆龙PLC,对其他PLC也具有很大参考价值

探索卓越性能:GreatSQL - 高效数据库解决方案

探索卓越性能:GreatSQL - 高效数据库解决方案 项目简介 是一个开源的、高性能的关系型数据库管理系统,它源自 MySQL 的 Percona Server for MySQL 分支,并在此基础上进行了一系列优化和增强,旨在提供更优秀的事务处理能力、更高的并发性能以及更好的大数据处理能力。 技术分析 性能提升 GreatSQL 在 MySQL 的基础上,针对 InnoDB 存储引擎进行了深度...

gitblog_00021的博客 645

计算机控制技术及应用

《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

GreatSQL体系架构的了解

通过学习《图文结合带你搞懂GreatSQL体系架构》,能对GreatSQL的基本底层原理有所了解。一张经典的体系架构图,图1_GreatSQL5.7 版本体系架构图由此可以发现,GreatSQL5.7由以下几部分组成,连接池组件系统管理和控制工具SQL接口组件查询解析器查询优化器缓存组件可插拔存储引擎系统和日志文件GreatSQL数据库区别于其他数据库的一个特点就是其可插拔的表存储引擎,特别需要注...

bisal的专栏 409

VC中sqlite的使用

在VC中对sqlite的使用,实现数据库大批数据的快速插入,读取数据库中的数据在ListCtrl中显示

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

MariaDB 和 GreatSQL 性能差异背后的真相

最近项目上遇到了两次 MariaDB 和 GreatSQL 的对比,GreatSQL受到客户质疑,最后经过排查抓到性能差异背后的真相。基于此做个分享。无论是问题一中的100倍性能背后还是插入hang住,其实背后都是对产品的不了解,MariaDB 10.3.39其实对应的是5.7的产品,而且其本身的默认参数调整有作弊嫌疑,默认关闭binlog ,sync_binlog设置为0,checkpoint阈值降低,提高checkpoint的频率等设置,面向测试环境数据是很好看的,但是增加了生产环境的风险和硬件成本。

GreatSQL2021的博客 1668

腾讯 GreatSQL 全链路实战:从国产化选型到 MGR 集群部署

【摘要】GreatSQL是由腾讯主导开发的开源MySQL分支数据库,基于MySQL 8.0内核深度优化,完全兼容MySQL协议与语法,特别适配国产信创环境。其核心优势包括:增强的MGR高可用架构,支持仲裁节点避免脑裂;性能较原生MySQL提升30%;提供国密加密等安全特性;完美适配鲲鹏、飞腾等国产CPU及麒麟、统信等操作系统。安装部署支持二进制包方式,配置与MySQL高度一致,同时针对MGR集群进行了优化预设。GreatSQL的零代码迁移特性使其成为金融、政务等领域替代Oracle/MySQL的理想选择,特

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