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The MyISAM storage engine supports the R-tree index layout for indexing spatial data types. Spatial data types are geographical coordinates or three-dimensional data. Currently, MyISAM is the only storage engine that supports R-tree indexes, in versions of MySQL 4.1 and later. R-tree index layouts are based on the same tree structures as B-tree indexes, but they implement the comparison of values differently.

I revisit the application in this section, replacing TWAIN with JMF, more specifically the JMF Performance Pack for Windows v.2.1.1e (http://java.sun.com/products/java-media/jmf/). The overall class structure is much the same as before, as Figure 9-3 indicates.

Vertex 1: (-0.5, 0.5, 0.5)

4. The name balanced tree index reflects the nature of the indexing algorithm. Whether the B in B-tree actually stands for balanced is debatable, since the creator of the algorithm was Rudolf Bayer (see http://www.nist.gov/dads/HTML/btree.html).

Figure 9-3. Class diagrams for ShowJPics The class names have been changed (to protect the innocent), but ShowJPics is actually identical to ShowPics. PicsJPanel is threaded in the same way as PicsPanel since it repeatedly calls grabImage() in the JMFCapture object. JMFCapture contains the JMF code for accessing the capture device, but externally it looks just like TwainCapture, offering the same method prototypes.

Vertex 3: (0.5, 0.5, 0.5)

In computer lingo, a hash is simply a key/value pair Consequently, a hash table is merely a collection of those key value pairs A hash function is a method by which a supplied search key, k, can be mapped to a distinct set of buckets, where the values paired with the hash key are stored We represent this hashing activity by saying h(k) = {1,m}, where m is the number of buckets and {1,m} represents the set of buckets In performing a hash, the hash function reduces the size of the key value to a smaller subset, which cuts down on memory usage and makes both searches and insertions into the hash table more efficient The InnoDB and MEMORY storage engines support hash index layouts, but only the MEMORY storage engine gives you control over whether a hash index should be used instead of a tree index.

Vertex 0: (-0.5, -0.5, 0.5)

The run() method in JPicsPanel is similar to the one in PicsPanel: // globals private static final int DELAY = 40; private JMFCapture camera; //ms

Each storage engine internally implements a hash function differently As an example, let s say you want to search the product table by name, and you know that product names are always unique Since the value of each record s Name field could be up to 100 bytes long, we know that creating an index on all Name records, along with a record identifier, would be space- and memory-consuming If we had 10,000 products, with a 6-byte record identifier and a 100-byte Name field, a simple list index would be 1,060,000 bytes Additionally, we know that longer string comparisons in our binary search algorithm would be less efficient, since more bytes of data would need to be compared.

Vertex 2: (0.5, -0.5, 0.5)

In a hash index layout, the storage engine s hash function would consume our 100-byte Name field and convert the string data into a smaller integer, which corresponds to a bucket in which the record identifier will be placed For the purpose of this example, suppose the storage engine s particular hash function happens to produce an integer in the range of 0 to 32,768 See Figure 2-7 for an idea of what s going on Don t worry about the implementation of the hash function Just know that the conversion of string keys to an integer occurs consistently across requests for the hash function given a specific key..