MySQL has stand-alone clients that allow users to interact directly with a MySQL database using SQL, but more often, MySQL is used with other programs to implement applications that need relational database capability. This database management system is a component of the LAMPweb applicationsoftware stack (and others), which is an acronym for Linux, Apache, MySQL, Perl/PHP/Python. This database management system is used by many database-driven web applications, including Drupal, Joomla, phpBB, and WordPress. This database management system is also used by many popular websites, including Facebook,Flickr,MediaWiki,Twitter, and YouTube.
This database management system can be built and installed manually from source code, but it is more commonly installed from a binary package unless special customizations are required. On most Linux distributions, the package management system can download and install MySQL with minimal effort, though further configuration is often required to adjust security and optimization settings.
LAMP software bundle, displayed here together with Squid.
Though MySQL began as a low-end alternative to more powerful proprietary databases, it has gradually evolved to support higher-scale needs as well. It is still most commonly used in small to medium scale single-server deployments, either as a component in a LAMP-based web application or as a standalone database server. Much of MySQL’s appeal originates in its relative simplicity and ease of use, which is enabled by an ecosystem of open source tools such as phpMyAdmin. In the medium range, This database management system can be scaled by deploying it on more powerful hardware, such as a multi-processor server with gigabytes of memory.
There are, however, limits to how far performance can scale on a single server (‘scaling up’), so on larger scales, multi-server MySQL (‘scaling out’) deployments are required to provide improved performance and reliability. A typical high-end configuration can include a powerful master database which handles data write operations and is replicated to multiple slaves that handle all read operations. The master server continually pushes binlog events to connected slaves so in the event of failure a slave can be promoted to become the new master, minimizing downtime. Further improvements in performance can be achieved by caching the results from database queries in memory using memcached, or breaking down a database into smaller chunks called shards which can be spread across a number of distributed server clusters.