MongoDB的管理服务(MMS)是用于监控和备份MongoDB的基础设施服务。其中监控的服务是免费的,备份的服务是需要收费的。 MMS监控 MMS提供实时的报告,可视化,警报,硬件指标,并以直观的Web仪表盘展现数据。只需要安装上一个轻量级的监控代理,来收集mongodb
MongoDB的管理服务(MMS)是用于监控和备份MongoDB的基础设施服务。其中监控的服务是免费的,备份的服务是需要收费的。MMS监控
MMS提供实时的报告,可视化,警报,硬件指标,并以直观的Web仪表盘展现数据。 只需要安装上一个轻量级的监控代理,来收集mongodb运行信息并传回给MMS。MMS用户界面允许用户查看可视化的数据和设置警报。MMS备份
该功能专为mongodb设计的,可以预定恢复快照和时间点。一旦该服务启动并运行,MMS提供一个Web界面来支持备份和恢复,MMS备份还支持水平扩展。 需要安装一个轻量级的备份代理,代理执行初始化同步,并截取复制集primary节点的OPLOG。对于分片集群,备份代理截取每个primary和每个config服务器。然后将初始化同步和oplog数据通过https传回到MMS。 MMS服务通过备份再现每个复制集并应用oplog日志,然后,MMS在硬盘上保持一个独立的mongodb数据库,也称为head,每个复制集的备份。每个head与原始的primary通过代理提供的最后的oplog来保持一致。 该初始化数据和oplog都是使用标准的MongoDB查询来完成的。操作
MMS备份服务使用备份来重新创建每个复制集并应用有代理发送来的oplog日志。对于生产环境上的复制集和分片,并不知道关心复制备份数据,因为这些数据都是使用标准的mongodb查询来完成的。 该服务需要预定所有head的快照,并保留基于用户定义的策略的快照。MMS备份基于OPLOG时间变化bserved的间隔来捕获复制集的快照。分片集群快照暂时通过mongos来停止平衡器,这样可以在所有分片和配置服务器上插入一个标记。当标记出现在备份数据上MMS需要一个快照。 压缩和块级重复数据删除技术降低了快照的数据大小。快照只存储快照之间的差异性数据,快照使用完整快照的一小部分磁盘空间。恢复
恢复适用于复制集特定的快照和时间点恢复还原。集群只能从快照时间的一致性恢复。 快照恢复直接从备份Blockstore数据库读取,通过HTTPS下载链接(pull)或MMS服务通过SSH发送(push)来传输文件。 时间点恢复首先创建一个本地的blockstore快照的恢复。 之后,应用oplog日志直到指定的时间点。然后,该服务通过相同的HTTPS或SSH机制提供时间点备份。 oplog日志保持每个备份是可配置的,对于时间点恢复影响它的可用的时间窗口。原文地址:MMS 功能介绍 (1), 感谢原作者分享。

Stored procedures are precompiled SQL statements in MySQL for improving performance and simplifying complex operations. 1. Improve performance: After the first compilation, subsequent calls do not need to be recompiled. 2. Improve security: Restrict data table access through permission control. 3. Simplify complex operations: combine multiple SQL statements to simplify application layer logic.

The working principle of MySQL query cache is to store the results of SELECT query, and when the same query is executed again, the cached results are directly returned. 1) Query cache improves database reading performance and finds cached results through hash values. 2) Simple configuration, set query_cache_type and query_cache_size in MySQL configuration file. 3) Use the SQL_NO_CACHE keyword to disable the cache of specific queries. 4) In high-frequency update environments, query cache may cause performance bottlenecks and needs to be optimized for use through monitoring and adjustment of parameters.

The reasons why MySQL is widely used in various projects include: 1. High performance and scalability, supporting multiple storage engines; 2. Easy to use and maintain, simple configuration and rich tools; 3. Rich ecosystem, attracting a large number of community and third-party tool support; 4. Cross-platform support, suitable for multiple operating systems.

The steps for upgrading MySQL database include: 1. Backup the database, 2. Stop the current MySQL service, 3. Install the new version of MySQL, 4. Start the new version of MySQL service, 5. Recover the database. Compatibility issues are required during the upgrade process, and advanced tools such as PerconaToolkit can be used for testing and optimization.

MySQL backup policies include logical backup, physical backup, incremental backup, replication-based backup, and cloud backup. 1. Logical backup uses mysqldump to export database structure and data, which is suitable for small databases and version migrations. 2. Physical backups are fast and comprehensive by copying data files, but require database consistency. 3. Incremental backup uses binary logging to record changes, which is suitable for large databases. 4. Replication-based backup reduces the impact on the production system by backing up from the server. 5. Cloud backups such as AmazonRDS provide automation solutions, but costs and control need to be considered. When selecting a policy, database size, downtime tolerance, recovery time, and recovery point goals should be considered.

MySQLclusteringenhancesdatabaserobustnessandscalabilitybydistributingdataacrossmultiplenodes.ItusestheNDBenginefordatareplicationandfaulttolerance,ensuringhighavailability.Setupinvolvesconfiguringmanagement,data,andSQLnodes,withcarefulmonitoringandpe

Optimizing database schema design in MySQL can improve performance through the following steps: 1. Index optimization: Create indexes on common query columns, balancing the overhead of query and inserting updates. 2. Table structure optimization: Reduce data redundancy through normalization or anti-normalization and improve access efficiency. 3. Data type selection: Use appropriate data types, such as INT instead of VARCHAR, to reduce storage space. 4. Partitioning and sub-table: For large data volumes, use partitioning and sub-table to disperse data to improve query and maintenance efficiency.

TooptimizeMySQLperformance,followthesesteps:1)Implementproperindexingtospeedupqueries,2)UseEXPLAINtoanalyzeandoptimizequeryperformance,3)Adjustserverconfigurationsettingslikeinnodb_buffer_pool_sizeandmax_connections,4)Usepartitioningforlargetablestoi


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