Trie saves space, but how?

Question

I am confused as to how the Trie implementation saves space & stores data in most compact form!

If you look at the tree below. When you store a character at any nod

Answer 1

You might deduce that it save space is on a ideal machine where every byte is allocated efficiently. However real machines allocate aligned blocks of memory (8 bytes on Java and 16 bytes on some C++) and so it may not save any space.

Java Strings and collections add relatively high amount of over head so the percentage difference can be very small.

Unless your structure is very large the value of your time out weights the memory cost that using the simplest, most standard and easiest to maintain collection is far more important. e.g. your time can very easily be worth 1000x or more the value of the memory you are try to save.

e.g. say you have 10000 names which you can save 16 bytes each by using a trie. (Assuming this can be proven without taking more time) This equates to 16 KB, which at today's prices is worth 0.1 cents. If your time costs your company $30 per hour, the cost of writing one line of tested code might be $1.

If you have think about it a blink of an eye longer to save 16 KB, its unlikely to be worth it for a PC. (mobile devices are a different story but the same argument applies IMHO)

EDIT: You have inspired me to add an update http://vanillajava.blogspot.com/2011/11/ever-decreasing-cost-of-main-memory.html

Answer 2

To save space when using a trie, one can use a compressed trie (also known as a patricia trie or radix tree), for which one node can represent multiple characters:

In computer science, a radix tree (also patricia trie or radix trie) is a space-optimized trie data structure where each node with only one child is merged with its child. The result is that every internal node has at least two children. Unlike in regular tries, edges can be labeled with sequences of characters as well as single characters. This makes them much more efficient for small sets (especially if the strings are long) and for sets of strings that share long prefixes.

Example of a radix tree:

Note that a trie is usually used as an efficient data structure for prefix matching on a set of strings. A trie can also be used as an associative array (like a hash table) where the key is a string.

Answer 3

Guava may indeed store the key at each level but the point to realize is that the key does not really need to be stored because the path to the node completely defines the key for that node. All that actually needs to be stored at each node is a single boolean indicating whether this is a leaf node or not.

Tries, like any other structure, excel at storing certain types of data. Specifically, tries are best at storing strings that share a common root. Think of storing full-path directory listings for example.

Answer 4

It's not about cheap space in memory, it's about precious space in a file or on a communications link. With an algorithm that builds that trie, we can send 'ten' in three bits, left-right-right. Compared to the 24 bits 'ten' would take up uncompressed, that's a huge savings of valuable disk space or transfer bandwidth.

Answer 5

Space is saved when you've lots of words to be represented by the tree. Because many words share the same path in the tree; the more words you've, more space you would save.

But there is a better data structure if you want to save space. Trie doesn't save space as much as directed acyclic word graph (DAWG) does, because it shares common node throughout the structure, whereas trie doesn't share nodes. The wiki entry explains this much detail, so have a look at it.

Here is the difference (graphically) between Trie and DAWG:

The strings "tap", "taps", "top", and "tops" stored in a Trie (left) and a DAWG (right), EOW stands for End-of-word.

The tree on the left side is Trie, and the tree on the right is DAWG. Compare them and see how DAWG saves space effciently. Trie has duplicate nodes that represent same letter/subword, while DAWG has exactly one node for each letter/subword.