by Tim Henderson (tim.tadh@gmail.com)
Copyright 2013, Licensed under the GPL version 2.
To collect many important data structures for usage in go programs. Some of these data structures may have implementations elsewhere but are collected here for completeness and instructional usage.
The library also provided generic types to allow the user to swap out various data structures transparently. The interfaces provide operation for adding, removing, retrieving objects from collections as well as iterating over the collection using functional iterators.
Finally, the tree sub-package provides a variety of generic tree traversals. The tree traversals and other iterators in the package use a functional iteration technique detailed on my blog.
An AVL tree is a height balanced binary search tree. It is commonly taught in algorithms courses
This version of the classic is immutable and should be thread safe due to immutability. However, there is a performance hit:
BenchmarkAvlTree 10000 166657 ns/op
BenchmarkImmutableAvlTree 5000 333709 ns/op
A ternary search trie is a symbol table specialized to byte strings. It can be used to build a suffix tree for full text string indexing. However, even without a suffix tree it is still a great structure for flexible prefix searches.
A B+Tree is a general symbol table usually used for database indices. This implementation is not currently thread safe. It uses the structure detailed in the link and was ported from my file-structures repository.
See hashtable/hashtable.go
. An implementation of the classic hash table with
separate chaining to handle collisions.
See hashtables/linhash.go
. An implementation of Linear
Hashing, a technique usually used
for secondary storage hash tables. Often employed by databases and file systems
for hash indices. This version is mostly instructional see the
accompanying blog post.
If you want the "real" disk backed version you want to check my
file-structures repository. See
the linhash
directory.
Benchmarks Put + Remove
$ go test -v -bench '.*' \
> github.com/timtadh/data-structures/hashtable
> github.com/timtadh/data-structures/tree/...
> github.com/timtadh/data-structures/trie
BenchmarkGoMap 50000 30051 ns/op
BenchmarkMLHash 20000 78840 ns/op
BenchmarkHash 20000 81012 ns/op
BenchmarkTST 10000 149985 ns/op
BenchmarkBpTree 10000 185134 ns/op
BenchmarkAvlTree 10000 193069 ns/op
BenchmarkImmutableAvlTree 5000 367602 ns/op
BenchmarkLHash 1000 2743693 ns/op
Benchmarks Put
BenchmarkGoMap 100000 22036 ns/op
BenchmarkMLHash 50000 52104 ns/op
BenchmarkHash 50000 53426 ns/op
BenchmarkTST 50000 69852 ns/op
BenchmarkBpTree 20000 76124 ns/op
BenchmarkAvlTree 10000 142104 ns/op
BenchmarkImmutableAvlTree 10000 302196 ns/op
BenchmarkLHash 1000 1739710 ns/op
The performance of the in memory linear hash (MLHash) is slightly improved since
the blog post do
to the usage of an AVL Tree tree/avltree.go
instead of an unbalanced binary
search tree.