This section describes connecting to an already configured Memcache server. Information about installation and configuration of Memcached servers can be found here.

There are 2 PEAR modules for connecting to a Memcached server: the Memcache library and the Memcached library.

$db = NewADOConnection($driver='mysqli');
 
/*
 * Enable memcache
 */
$db->memCache = true;
 
/*
 * Add one or more servers, for use in distributed systems
 */
$db->memCacheHost = array('192.168.0.78', '192.168.0.79', '192.168.0.80');
 
/*
 * Optionally add the server port if it differs from the default
 */
$db->memCachePort = 11211;
 
/*
 * Use 'true' to store the item compressed (uses zlib)
 * Note; Compression is only supported using the memcache library. This
 *       parameter will be ignored when using the memcached library
 */
$db->memCacheCompress = false;
 
/*
 * We connect to the database, not the memcache server
 */
$db->Connect('localhost','mysql-user','mysql-password','employees');
 
/*
 * look for a cached query with a life of 2400 seconds
 */
$sql = 'SELECT code,description FROM xref_table';
$db->cacheExecute(2400,$sql);

The size of query results that can be cached by the memcached server is limited by server configuration. The default is quite small. If the query is too large, and debugging is enabled, then an error is displayed.

3849805e4e026b9e7c485999a24a95c6 cache failure: Item with such key doesn't exists on the memcached server.
(this is a notice and not an error)
-----<hr>

(mysqli): SELECT * FROM employees

------<hr>

Notice: MemcachePool::set(): Server 192.168.0.78 (tcp 11211, udp 0) failed with:
SERVER_ERROR object too large for cache
(3) in C:\dev\github\mssqlnative-fixes\adodb-memcache.lib.inc.php on line 94
Failed to save data at the memcached server!

Cache write error

PHP Notice:  MemcachePool::set(): Server 192.168.0.78 (tcp 11211, udp 0) failed with:
SERVER_ERROR object too large for cache (3)
in C:\dev\github\mssqlnative-fixes\adodb-memcache.lib.inc.php on line 94

ADOdb currently has no mechanism for splitting large queries into smaller chunks.