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Consider the following resource-hogging code: // Very expensive, silly way to get node titles. First we get the node IDs // of all published nodes. $query = db_select('node', 'n'); $query->fields('n', array('nid')); $query->condition("n.status", 1); $query->addTag('node_access'); $result = $query->execute(); // Now we do a node_load() on each individual node and save the title. foreach($result as $row) { $node = node_load($row->nid); $titles[] = check_plain($node->title); } Fully loading a node is an expensive operation: hooks run, modules perform database queries to add or modify the node, and memory is used to cache the node in node_load() s internal cache. If you are not depending on modification to the node by a module, it s much faster to do your own query of the node table directly. Certainly this is a contrived example, but the same pattern can often be found, that

is, often data is retrieved via multiple queries that could be combined into a single query, or needless node loading is performed.

In this last step, you need to find all the mapping models and tell the migration manager to do its job for each of them.

.has()

Tip Drupal has an internal caching mechanism (using a static variable) when a node is loaded more than once per request. For example, if node_load(1) was called, node number 1 is fully loaded and cached. When another call to node_load(1) is made during the same web request, Drupal will return the cached results for the previously loaded node having the same node ID.

NSMappingModel *crmMappingModel = [[NSMappingModel alloc] initWithContentsOfURL:crmMappingURL]; BOOL crmDone = [migrationManager migrateStoreFromURL:sourceStoreURL type: sourceStoreType options:nil withMappingModel:crmMappingModel toDestinationURL:destinationStoreURL destinationType:destinationStoreType destinationOptions:nil error:&error]; NSMappingModel *catalogMappingModel = [[NSMappingModel alloc] initWithContentsOfURL:catalogMappingURL]; BOOL catalogDone = [migrationManager migrateStoreFromURL:sourceStoreURL type:sourceStoreType options:nil withMappingModel:catalogMappingModel toDestinationURL:destinationStoreURL destinationType:destinationStoreType destinationOptions:nil error:&error];

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SQL slowness can result from poor implementation of SQL tables in contributed modules. For example, columns without indices may result in slow queries. A quick way to see how queries are executed by MySQL is to take one of the queries you ve captured in your slow query log, prepend the word EXPLAIN to it, and issue the query to MySQL. The result will be a table showing which indices were used. Consult a good book on MySQL for details.

To select an element that contains elements matching a certain pattern, you can use the .has() method. For instance, you would use the following to select all paragraphs and filter the results to only paragraphs that contain a span element: $("p").has("span"); This outputs the following: >>> $("p").has("span"); [ p, p#bar ]

If you have very expensive queries that must be performed, perhaps the results can be manually cached by your module. See 16 for details on Drupal s cache API.

Note that the source and destination store URLs are the same since we are migrating data for the same persistent store. The migration, however, can be done in multiple passes as long as the object graphs are not related, hence offering an opportunity to keep the memory clean.

Two common choices for MySQL storage engines, often called table types, are MyISAM and InnoDB. Drupal uses InnoDB by default. MyISAM uses table-level locking, while InnoDB uses row-level locking. Locking is important to preserve database integrity; it prevents two database processes from trying to update the same data at the same time. In practice, the difference in locking strategies means that access to an entire table is blocked during writes for MyISAM. Therefore, on a busy Drupal site when many comments are being added, all comment reads are blocked while a new comment is inserted. On InnoDB, this is less of a problem, since only the row(s) being written get locked, allowing other server threads to continue to operate on the remaining rows. However, with MyISAM, table reads are faster, and data maintenance and recovery tools are more mature. See http://dev.mysql.com/tech-resources/articles/storageengine/part_1.html or http://dev.mysql.com/doc/refman/5.1/en/storage-engines.html for more information on MySQL s table storage architectures. To test whether table-locking issues are the cause of slow performance, you can analyze lock contention by checking the Table_locks_immediate and Table_locks_waited status variables within MySQL.

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