Efficient global Symbol table, with Garbage Collection.
Symbols, also known as Atoms or Interned Strings, are a common technique to reduce memory usage and improve performance when using many small strings.
By storing a single copy of each encountered string in a global table and giving out indexes to that table, it is possible to compare strings for equality in constant time, instead of linear (in string size) time.
The main advantages of Symbolize over existing symbol table implementations are:
Garbage collection: Symbols which are no longer used are automatically cleaned up.
Symbol
s have a memory footprint of exactly 1Word
and are nicely unpacked by GHC.Support for any
Textual
type, includingString
, (strict and lazy)Data.Text
, (strict and lazy)Data.ByteString
etc.Thread-safe.
Calls to
lookup
andunintern
are free of atomic memory barriers (and never have to wait on a concurrent thread runningintern
)Support for a maximum of 2^64 symbols at the same time (you'll probably run out of memory before that point).
Please see the full README below or on GitHub at https://github.com/Qqwy/haskell-symbolize#readme
Symbolize
Haskell library implementing a global Symbol Table, with garbage collection.
Symbols, also known as Atoms or Interned Strings, are a common technique to reduce memory usage and improve performance when using many small strings.
By storing a single copy of each encountered string in a global table and giving out indexes to that table, it is possible to compare strings for equality in constant time, instead of linear (in string size) time.
The main advantages of Symbolize over existing symbol table implementations are:
- Garbage collection: Symbols which are no longer used are automatically cleaned up.
Symbol
s have a memory footprint of exactly 1Word
and are nicely unpacked by GHC.- Support for any
Textual
type, includingString
, (strict and lazy)Data.Text
, (strict and lazy)Data.ByteString
etc. - Thread-safe.
- Efficient: Calls to
lookup
andunintern
are free of atomic memory barriers (and never have to wait on a concurrent thread runningintern
) - Support for a maximum of 2^64 symbols at the same time (you'll probably run out of memory before that point).
Basic usage
This module is intended to be imported qualified, e.g.
import Symbolize (Symbol)
import qualified Symbolize
To intern a string, use intern
:
>>> hello = Symbolize.intern "hello"
>>> world = Symbolize.intern "world"
>>> (hello, world)
(Symbolize.intern "hello",Symbolize.intern "world")
Interning supports any Textual
type, so you can also use Data.Text
or Data.ByteString
etc.:
>>> import Data.Text (Text)
>>> niceCheeses = fmap Symbolize.intern (["Roquefort", "Camembert", "Brie"] :: [Text])
>>> niceCheeses
[Symbolize.intern "Roquefort",Symbolize.intern "Camembert",Symbolize.intern "Brie"]
And if you are using OverloadedStrings, you can use the IsString
instance to intern constants:
>>> hello2 = ("hello" :: Symbol)
>>> hello2
Symbolize.intern "hello"
Comparisons between symbols run in O(1) time:
>>> hello == hello2
True
>>> hello == world
False
To get back the textual value of a symbol, use unintern
:
>>> Symbolize.unintern hello
"hello"
If you only want to check whether a string is already interned, use lookup
:
>>> Symbolize.lookup "hello"
Just (Symbolize.intern "hello")
Symbols make great keys for Data.HashMap
and Data.HashSet
. Hashing them is a no-op and they are guaranteed to be unique:
>>> import qualified Data.Hashable as Hashable
>>> Hashable.hash hello
0
>>> fmap Hashable.hash niceCheeses
[2,3,4]
For introspection, you can look at how many symbols currently exist:
>>> Symbolize.globalSymbolTableSize
5
>>> [unintern (intern (show x)) | x <- [1..5]]
["1","2","3","4","5"]
>>> Symbolize.globalSymbolTableSize
10
Unused symbols will be garbage-collected, so you don't have to worry about memory leaks:
>>> System.Mem.performGC
>>> Symbolize.globalSymbolTableSize
5
For deeper introspection, you can look at the Show instance of the global symbol table: /(Note that the exact format is subject to change.)/
>>> Symbolize.globalSymbolTable
GlobalSymbolTable { count = 5, next = 10, contents = [(0,"hello"),(1,"world"),(2,"Roquefort"),(3,"Camembert"),(4,"Brie")] }