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https://github.com/Workiva/go-datastructures

A collection of useful, performant, and threadsafe Go datastructures.
https://github.com/Workiva/go-datastructures

Last synced: 13 days ago
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A collection of useful, performant, and threadsafe Go datastructures.

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go-datastructures

=================



Go-datastructures is a collection of useful, performant, and threadsafe Go

datastructures.



### NOTE: only tested with Go 1.3+.



#### Augmented Tree



Interval tree for collision in n-dimensional ranges.  Implemented via a

red-black augmented tree.  Extra dimensions are handled in simultaneous

inserts/queries to save space although this may result in suboptimal time

complexity.  Intersection determined using bit arrays.  In a single dimension,

inserts, deletes, and queries should be in O(log n) time.



#### Bitarray



Bitarray used to detect existence without having to resort to hashing with

hashmaps.  Requires entities have a uint64 unique identifier.  Two

implementations exist, regular and sparse.  Sparse saves a great deal of space

but insertions are O(log n).  There are some useful functions on the BitArray

interface to detect intersection between two bitarrays. This package also

includes bitmaps of length 32 and 64 that provide increased speed and O(1) for

all operations by storing the bitmaps in unsigned integers rather than arrays.



#### Futures



A helpful tool to send a "broadcast" message to listeners.  Channels have the

issue that once one listener takes a message from a channel the other listeners

aren't notified.  There were many cases when I wanted to notify many listeners

of a single event and this package helps.



#### Queue



Package contains both a normal and priority queue.  Both implementations never

block on send and grow as much as necessary.  Both also only return errors if

you attempt to push to a disposed queue and will not panic like sending a

message on a closed channel.  The priority queue also allows you to place items

in priority order inside the queue.  If you give a useful hint to the regular

queue, it is actually faster than a channel.  The priority queue is somewhat

slow currently and targeted for an update to a Fibonacci heap.



Also included in the queue package is a MPMC threadsafe ring buffer. This is a

block full/empty queue, but will return a blocked thread if the queue is

disposed while a thread is blocked.  This can be used to synchronize goroutines

and ensure goroutines quit so objects can be GC'd.  Threadsafety is achieved

using only CAS operations making this queue quite fast.  Benchmarks can be found

in that package.



#### Fibonacci Heap



A standard Fibonacci heap providing the usual operations. Can be useful in executing Dijkstra or Prim's algorithms in the theoretically minimal time. Also useful as a general-purpose priority queue. The special thing about Fibonacci heaps versus other heap variants is the cheap decrease-key operation. This heap has a constant complexity for find minimum, insert and merge of two heaps, an amortized constant complexity for decrease key and O(log(n)) complexity for a deletion or dequeue minimum. In practice the constant factors are large, so Fibonacci heaps could be slower than Pairing heaps, depending on usage. Benchmarks - in the project subfolder. The heap has not been designed for thread-safety.



#### Range Tree



Useful to determine if n-dimensional points fall within an n-dimensional range.

Not a typical range tree however, as we are actually using an n-dimensional

sorted list of points as this proved to be simpler and faster than attempting a

traditional range tree while saving space on any dimension greater than one.

Inserts are typical BBST times at O(log n^d) where d is the number of

dimensions.



#### Set

Our Set implementation is very simple, accepts items of type `interface{}` and

includes only a few methods. If your application requires a richer Set

implementation over lists of type `sort.Interface`, see

[xtgo/set](https://github.com/xtgo/set) and

[goware/set](https://github.com/goware/set).



#### Threadsafe

A package that is meant to contain some commonly used items but in a threadsafe

way.  Example: there's a threadsafe error in there as I commonly found myself

wanting to set an error in many threads at the same time (yes, I know, but

channels are slow).



#### AVL Tree



This is an example of a branch copy immutable AVL BBST.  Any operation on a node

makes a copy of that node's branch.  Because of this, this tree is inherently

threadsafe although the writes will likely still need to be serialized.  This

structure is good if your use case is a large number of reads and infrequent

writes as reads will be highly available but writes somewhat slow due to the

copying.  This structure serves as a basis for a large number of functional data

structures.



#### X-Fast Trie



An interesting design that treats integers as words and uses a trie structure to

reduce time complexities by matching prefixes.  This structure is really fast

for finding values or making predecessor/successor types of queries, but also

results in greater than linear space consumption.  The exact time complexities

can be found in that package.



#### Y-Fast Trie



An extension of the X-Fast trie in which an X-Fast trie is combined with some

other ordered data structure to reduce space consumption and improve CRUD types

of operations.  These secondary structures are often BSTs, but our implementation

uses a simple ordered list as I believe this improves cache locality.  We also

use fixed size buckets to aid in parallelization of operations.  Exact time

complexities are in that package.



#### Fast integer hashmap



A datastructure used for checking existence but without knowing the bounds of

your data.  If you have a limited small bounds, the bitarray package might be a

better choice.  This implementation uses a fairly simple hashing algorithm

combined with linear probing and a flat datastructure to provide optimal

performance up to a few million integers (faster than the native Golang

implementation).  Beyond that, the native implementation is faster (I believe

they are using a large -ary B-tree).  In the future, this will be implemented

with a B-tree for scale.



#### Skiplist



An ordered structure that provides amortized logarithmic operations but without

the complication of rotations that are required by BSTs.  In testing, however,

the performance of the skip list is often far worse than the guaranteed log n

time of a BBST.  Tall nodes tend to "cast shadows", especially when large

bitsizes are required as the optimum maximum height for a node is often based on

this.  More detailed performance characteristics are provided in that package.



#### Sort



The sort package implements a multithreaded bucket sort that can be up to 3x

faster than the native Golang sort package.  These buckets are then merged using

a symmetrical merge, similar to the stable sort in the Golang package.  However,

our algorithm is modified so that two sorted lists can be merged by using

symmetrical decomposition.



#### Numerics



Early work on some nonlinear optimization problems.  The initial implementation

allows a simple use case with either linear or nonlinear constraints.  You can

find min/max or target an optimal value.  The package currently employs a

probabilistic global restart system in an attempt to avoid local critical points.

More details can be found in that package.



#### B+ Tree



Initial implementation of a B+ tree.  Delete method still needs added as well as

some performance optimization.  Specific performance characteristics can be

found in that package.  Despite the theoretical superiority of BSTs, the B-tree

often has better all around performance due to cache locality.  The current

implementation is mutable, but the immutable AVL tree can be used to build an

immutable version.  Unfortunately, to make the B-tree generic we require an

interface and the most expensive operation in CPU profiling is the interface

method which in turn calls into runtime.assertI2T.  We need generics.



#### Immutable B Tree

A btree based on two principles, immutability and concurrency. 

Somewhat slow for single value lookups and puts, it is very fast for bulk operations.

A persister can be injected to make this index persistent.



#### Ctrie



A concurrent, lock-free hash array mapped trie with efficient non-blocking

snapshots. For lookups, Ctries have comparable performance to concurrent skip

lists and concurrent hashmaps. One key advantage of Ctries is they are

dynamically allocated. Memory consumption is always proportional to the number

of keys in the Ctrie, while hashmaps typically have to grow and shrink. Lookups,

inserts, and removes are O(logn).



One interesting advantage Ctries have over traditional concurrent data

structures is support for lock-free, linearizable, constant-time snapshots.

Most concurrent data structures do not support snapshots, instead opting for

locks or requiring a quiescent state. This allows Ctries to have O(1) iterator

creation and clear operations and O(logn) size retrieval.



#### Dtrie



A persistent hash trie that dynamically expands or shrinks to provide efficient

memory allocation. Being persistent, the Dtrie is immutable and any modification

yields a new version of the Dtrie rather than changing the original. Bitmapped

nodes allow for O(log32(n)) get, remove, and update operations. Insertions are

O(n) and iteration is O(1).



#### Persistent List



A persistent, immutable linked list. All write operations yield a new, updated

structure which preserve and reuse previous versions. This uses a very

functional, cons-style of list manipulation. Insert, get, remove, and size

operations are O(n) as you would expect.



#### Simple Graph



A mutable, non-persistent undirected graph where parallel edges and self-loops are 

not permitted. Operations to add an edge as well as retrieve the total number of 

vertices/edges are O(1) while the operation to retrieve the vertices adjacent to a

target is O(n). For more details see [wikipedia](https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)#Simple_graph)



### Installation



 1. Install Go 1.3 or higher.

 2. Run `go get github.com/Workiva/go-datastructures/...`



### Updating



When new code is merged to master, you can use



	go get -u github.com/Workiva/go-datastructures/...



To retrieve the latest version of go-datastructures.



### Testing



To run all the unit tests use these commands:



	cd $GOPATH/src/github.com/Workiva/go-datastructures

	go get -t -u ./...

	go test ./...



Once you've done this once, you can simply use this command to run all unit tests:



	go test ./...



### Contributing



Requirements to commit here:



 - Branch off master, PR back to master.

 - `gofmt`'d code.

 - Compliance with [these guidelines](https://code.google.com/p/go-wiki/wiki/CodeReviewComments)

 - Unit test coverage

 - [Good commit messages](http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html)



### Maintainers

 - Alexander Campbell <[[email protected]](mailto:[email protected])>

 - Dustin Hiatt <[[email protected]](mailto:[email protected])>

 - Ryan Jackson <[[email protected]](mailto:[email protected])>