Simple usage example
import com.rklaehn.radixtree._ import scala.io.Source val words = Source.fromURL("http://www-01.sil.org/linguistics/wordlists/english/wordlist/wordsEn.txt").getLines.toArray val pairs = words.map(x => x -> x) val tree = RadixTree(pairs: _*) // print all english words starting with z println(tree.filterPrefix("z").keys.take(10))
Word count using AdditiveMonoid instance
import com.rklaehn.radixtree._ import algebra.ring.AdditiveMonoid import algebra.std.all._ import scala.io.Source val text = Source.fromURL("http://classics.mit.edu/Homer/odyssey.mb.txt").getLines val words = text.flatMap(_.split(' ')).filterNot(_.isEmpty) val m = AdditiveMonoid[RadixTree[String, Int]] val count = words.map(x ⇒ RadixTree(x → 1)).reduce(m.plus) println(count.entries.take(10))
toString, hashCode, equals
None of the methods of java.lang.Object are implemented on RadixTree. Use cats.Show to get a textual representation:
import com.rklaehn.radixtree._ import cats.implicits._ println(RadixTree("a" -> 1).show)
This is an immutable generic radix tree. It works for both immutable objects which override equals and hashcode, and objects that do not override equals and hashcode, such as raw byte arrays.
Predefined RadixTree.Key instances are provided for
Array[T : Order]. But it would be relatively trivial to make this work for e.g.
scodec.bits.ByteVector or even
A radix tree node consists of a prefix of type K, an optional value of type V, and a (possibly empty) array of children. The internal representation is very compact, since it is using a flat array for the children of a radix tree node. But this puts a number of constraints on the key type if maximum efficiency is desired: The key type has to be some kind of sequence, and the element type of the sequence should not have too many distinct values.
E.g. when using String as key type, Char is the element type. Theoretically, it has 2^16 possible values. But in real world strings it is very rare to find a radix tree node of type string with more than 60 children.
When using Array of Byte as the key type, Byte is the element type. So there are a maximum of 256 children in each RadixTree node, which is still OK.
Using a sequence of Longs as key type on the other hand might be less than optimal from a performance POV when creating trees.
A big advantage of a radix tree over a sorted map or hash map is that keys are not stored completely. So when storing a large number of long strings such as urls, a radix tree will have a space advantage over a HashMap or SortedMap. To optimise space usage even more, an interning scheme for keys can be used.
When a tree is used mostly for reading, there is also a method to pack the tree into a very compact representation using interning of keys, values and nodes.
Space usage benchmark
Here is an example of the space usage of different string sets using the radix tree as well as standard scala collections.
Numbers from 0 until 10000 is the textual representation of all numbers from 0 to 10000, e.g. "nine thousand, nine hundred ninety"
English words is a list of words of the english language from here
sbt instrumentedTest/test:run ... [info] Numbers from 0 until 10000: [info] Elements: 1398888 [info] HashSet: 1746600 [info] SortedSet: 1678952 [info] RadixTree: 1229904 [info] RadixTree (packed): 5648 // <= this is not a typo! [info] English words: [info] Elements: 9644728 [info] HashSet: 13833088 [info] SortedSet: 12713112 [info] RadixTree: 11527264 [info] RadixTree (packed): 2098712
The radix tree is much more efficient regarding space usage than the standard scala collections. It consumes just slightly more space than the elements. When packing the radix tree, it consumes extremely little space. Less than the list of words itself!
As you can see from the benchmark results, creation and lookup is faster than with the standard
scala.collection.immutable.SortedMap, which is the closest equivalent from the scala collections. However, filterPrefix is many thousand times faster, since it is one of the optimizations a RadixTree is designed for.
So a very simple example where a RadixTree is very useful is word completion from a very large dictionary.
The benchmarks are the 109583 words of the english language from here. They can be run using
This creates a tree from scratch
Benchmark comparison (in 10.73 s): Create SortedMap vs. RadixTree Significantly different (p ~= 0) Time ratio: 0.81548 95% CI 0.79274 - 0.83821 (n=20) First 47.99 ms 95% CI 47.14 ms - 48.84 ms Second 39.13 ms 95% CI 38.29 ms - 39.98 ms
This looks up all elements
Benchmark comparison (in 9.110 s): Lookup SortedMap vs. RadixTree Significantly different (p ~= 0) Time ratio: 0.34484 95% CI 0.32363 - 0.36605 (n=20) First 46.24 ms 95% CI 44.63 ms - 47.86 ms Second 15.95 ms 95% CI 15.14 ms - 16.75 ms
Benchmark comparison (in 3.930 s): FilterPrefix SortedMap vs. RadixTree Significantly different (p ~= 0) Time ratio: 0.00015 95% CI 0.00014 - 0.00015 (n=20) First 1.843 ms 95% CI 1.803 ms - 1.883 ms Second 267.3 ns 95% CI 262.4 ns - 272.1 ns
A merge of disjoint or mostly disjoint radix trees can be very fast. E.g. if you have a RadixTree with all english words starting with a, and one with all english words starting with b, merging will be O(1) regardless of the size of the subtrees, and will use structural sharing.
Prepending a prefix to all keys in a radix tree is an O(1) operation with a RadixTree, whereas it would be at least O(n log(n)) with a SortedMap or HashMap
Filtering by prefix is extremely fast with a radix tree (worst case O(log(N)), whereas it is worse than O(N) with SortedMap and HashMap. Filtering by prefix will also benefit a lot from structural sharing.
Filters keys containing a substring