Go Data Structures
by Tim Henderson (email@example.com)
Copyright 2013, Licensed under the GPL version 2. Please reach out to me directly if you require another licensing option. I am willing to work with you.
To collect many important data structures for usage in go programs. Golang's standard library lacks many useful and important structures. This library attempts to fill the gap. I have implemented data-structure's as I have needed them. If there is a missing structure or even just a missing (or incorrect) method open an issue, send a pull request, or send an email patch.
The library also provides 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.
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.
I hope you find my library useful. If you are using it drop me a line I would love to hear about it.
Similar to a Java ArrayList or a Python or Ruby "list". There is a version
(called Sortable) which integrates with the
"sort" package from the standard
Keeps the ArrayList in sorted order for you.
Built on top of
*list.Sorted, it provides basic set operations. With
set.SortedSet you don't have to write code re-implementing sets with the
map[type] datatype. Supports: intersection, union, set difference and overlap
Construct a set from any
A double ended queue that only allows unique items inside. Constructed from a doubly linked list and a linear hash table.
Fixed Size Lists
list.Sorted have alternative constructors which make them
fixed size. This prevents them from growing beyond a certain size bound and is
useful for implementing other data structures on top of them.
set.SortedSet all can be serialized provided
their contents can be serialized. They are therefore suitable for being sent
over the wire. See this
for how to use the serialization.
Heaps and Priority Queues
This is a binary heap for usage as a priority queue. The priorities are given to items in the queue on insertion and cannot be changed after insertion. It can be used as both a min heap and a max heap.
A priority queue which only allows unique entries.
An AVL Tree is a height balanced binary search tree. Insertion and retrieval are both O(log(n)) where n is the number items in the tree.
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. Ternary Search Tries (TSTs) are a particularly fast version of the more common R-Way Trie. They utilize less memory allowing them to store more data while still retaining all of the flexibility of the R-Way Trie. TSTs can be used to build a suffix tree for full text string indexing by storing every suffix of each string in addition to the string. However, even without storing all of the suffixes it is still a great structure for flexible prefix searches. For instance, TSTs can be used to implement extremely fast auto-complete functionality.
is a general symbol table usually used for database indices. This implementation
is not currently thread safe. However, unlike many B+Trees it fully supports
duplicate keys making it suitable for use as a Multi-Map. There is also a
variant which has unique keys,
bptree.BpMap. B+Trees are storted and efficient
to iterate over making them ideal choices for storing a large amount of data
in sorted order. For storing a very large amount of data please utilize the
fs2 version, fs2/bptree. fs2 utilizes
memory mapped files in order to allow you to store more data than your computer
hashtable/hashtable.go. An implementation of the classic hash table with
separate chaining to handle collisions.
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
If you want a disk backed version check out my
file-structures repository. See
Exceptions, Errors, and Testing
By default, most errors in Go programs to not track where they were created. Many programmers quickly discover they need to have stack traces associated with their errors. This is a light weight package which adds stack traces to errors. It also provides a very very simple logging function that reports where in your code your printed out the log. This is not a full featured logging solution but rather a replacement for using fmt.Printf when debugging.
The test package provides two minimal assertions and a way to get random strings and data. It also seeds the math/rand number generator. I consider this to the bare minimum of what is often needed when testing go code particularly data-structures. Since this package seeks to be entirely self contained with no dependencies no external testing package is used. This package is slowly being improved to encompass more common functionality between the different tests.
provides support for exceptions. They work very similarly to the way unchecked
exceptions work in Java. They are built on top of the built-in
recover functions. See the README in the package for more information or
checkout the documentation. They should play nice with the usual way of handling
errors in Go and provide an easy way to create public APIs which return errors
rather than throwing these non-standard exceptions.
Note: these benchmarsk are fairly old and probably not easy to understand. Look at the relative difference not the absolute numbers as they are misleading. Each benchmark does many operations per "test" which makes it difficult to compare these numbers to numbers found elsewhere.
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
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