D.3  Using Clusters for Mapping and High-speed Translations

Clusters arrays can be used to create high-speed translation tables. Every member of a cluster can be used as a lookup key. The findrow() function is used to do cluster key lookups. All data types are supported. Duplicate keys are supported as well

Findrow() is highly optimized and can do over 10 million searches per second. This makes findrow() ideal for tasks that require fast lookups. Given the cluster name, cluster member to search in, and data to searched for, findrow() returns the first row where the key was found or returns a 0 if the key was not found.

To handle duplicate keys, findrow() has an optional third parameter to find the Nth occurrence of a key.

• All members of a cluster, regardless of data type, are searchable as a key
• All keys are changeable
• All keys can contain duplicates
• All key searches take O(1) constant time -- virtually instant lookups

By default, all searches are case-regardless. Set the optional fourth parameter to TRUE if you want a case-sensitive search.

Because all cluster fields are keys, bi-directional lookups are easy to do. In our example we will translate from English to German and German to English.

To define a cluster, use the cluster statement followed by the name of the cluster. In our example, we are calling our cluster trans. Our trans cluster has two fields: english$ and german$. To reference them we use trans->english$ and trans->german$.

cluster trans: english$, german$

The code below uses the trans cluster to translate from English to German and German to English.