An elegant, fully typesafe, generic, customizable, user-friendly, consistent, and very fast standard container library for C99. This is a small headers only library with the most used container components, and a few algorithms:
- carray - Generic dynamic multi-dimensional array
- cstr - Powerful and compact string type
- cbitset - A std::bitset/boost::dynamic_bitset-like bitset type
- clist - Generic circular singly linked list type
- cmap - Generic fast unordered map type
- cset - Generic fast unordered set type
- cvec - Generic dynamic vector type
- cstack - A stack adapter type
- cqueue - A queue adapter type
- cpqueue - A priority queue adapter type
- cptr - Support for pointers and shared pointers in containers
- crandom - A few very efficent modern random number generators
- coption - Implements coption_get(), a getopt_long-like function
- ccommon - Collection of general definitions
The usage of the containers is quite similar to the C++ standard containers, so it should be easy if you are familiar with them.
All containers mentioned above, except cstr_t and cbitset_t, are generic and therefore typesafe (similar to templates in C++). No casting is used. A simple example:
#include <stc/cvec.h>
using_cvec(i, int);
int main(void) {
cvec_i vec = cvec_i_init();
cvec_i_push_back(&vec, 1);
cvec_i_push_back(&vec, 2);
c_foreach (i, cvec_i, vec)
printf(" %d", *i.val);
cvec_i_del(&vec);
}Using containers with complex element types is simple:
#include <stc/cstr.h>
#include <stc/cvec.h>
typedef struct {
cstr_t name; // dynamic string
int id;
} User;
void User_del(User* u)
{ cstr_del(&u->name); }
int User_compare(User* u, User* v)
{ int c = strcmp(u->name.str, v->name.str); return c != 0 ? c : u->id - v->id; }
using_cvec(u, User, User_del, User_compare);
int main(void) {
cvec_u vec = cvec_u_init();
cvec_u_push_back(&vec, (User) {cstr_from("admin"), 0}); // cstr_from() allocates string memory
cvec_u_push_back(&vec, (User) {cstr_from("usera"), 1});
c_foreach (i, cvec_u, vec)
printf("%s: %d\n", i.val->name.str, i.val->id);
cvec_u_del(&vec); // free everything
}The aim of this project was to create a small Standard Container Library for the C99 language. It should
- be easy to use, have intuitive naming and consistency across the library.
- be type safe. Have minimal usage of casting and void* pointers.
- be highly efficient. Both in speed and memory usage.
- be customizable without losing efficiency. E.g. inline replacable compare, hash, allocation functions per container type instantiation.
- have a fairly small code base, and easy to install, deploy and maintain.
- avoid bloat. It should not try to cover all thinkable functions, but limit itself to the most useful and commonly used.
Because it is headers only, files can simply be included in your program. The functions will be inlined by default. You may add the project folder to CPATH environment variable, to let gcc, clang, and tinyc locate the headers.
If containers are extensively used accross many translation units with common instantiated container types, it is recommended to build as a library, to minimize executable size. To enable this mode, specify -DSTC_HEADER as compiler option, and put all the instantiations of the containers used in one C file, like this:
#define STC_IMPLEMENTATION
#include <stc/cstr.h>
#include <stc/cmap.h>
#include <stc/cvec.h>
#include <stc/clist.h>
#include "Vec3.h"
using_cmap(ii, int, int);
using_cset(ix, int64_t);
using_cvec(i, int);
using_clist(v3, Vec3);
...The library is very efficent. Containers have templated intrusive elements. One of the most performance critical containers is the cmap / cset. Luckily, cmap is among the fastest C/C++ map implementations available, see examples/benchmark.c
Compiled with clang.exe -O3 -x c++, v10.0 on windows, Ryzen 7 2700X CPU. Similar results with VC and g++.
- CMAP = stc/cmap
- KMAP = klib/khash
- UMAP = std::unordered_map
- SMAP = spp::sparse_hash_map
- BMAP = ska::bytell_hash_map
- FMAP = ska::flat_hash_map
- RMAP = robin_hood::unordered_map
- HMAP = tsl::hopscotch_map
Random keys are in range [0, 2^24), seed = 1600720716:
Unordered maps: 30000000 repeats of Insert random key + try to remove a random key:
CMAP: time: 4.35, sum: 450000015000000, size: 8153497, erased 10922466
KMAP: time: 3.37, sum: 450000015000000, size: 8153497, erased 10922466
UMAP: time: 11.19, sum: 450000015000000, size: 8153497, erased 10922466
SMAP: time: 14.21, sum: 450000015000000, size: 8153497, erased 10922466
BMAP: time: 5.00, sum: 450000015000000, size: 8153497, erased 10922466
FMAP: time: 4.68, sum: 450000015000000, size: 8153497, erased 10922466
RMAP: time: 3.16, sum: 450000015000000, size: 8153497, erased 10922466
HMAP: time: 4.49, sum: 450000015000000, size: 8153497, erased 10922466
Unordered maps: Insert 30000000 index keys, then remove them in same order:
CMAP: time: 3.27, erased 30000000
KMAP: time: 2.86, erased 30000000
UMAP: time: 15.80, erased 30000000
SMAP: time: 22.60, erased 30000000
BMAP: time: 7.93, erased 30000000
FMAP: time: 5.91, erased 30000000
RMAP: time: 3.22, erased 30000000
HMAP: time: 5.91, erased 30000000
Unordered maps: Insert 30000000 random keys, then remove them in same order:
CMAP: time: 3.16, erased 13971002
KMAP: time: 4.05, erased 13971002
UMAP: time: 10.77, erased 13971002
SMAP: time: 13.77, erased 13971002
BMAP: time: 5.27, erased 13971002
FMAP: time: 4.41, erased 13971002
RMAP: time: 3.21, erased 13971002
HMAP: time: 4.50, erased 13971002
Unordered maps: Iterate 30000000 random keys:
CMAP: time: 0.23, size: 13971002, sum 1344701724191145
UMAP: time: 3.32, size: 13971002, sum 1344701724191145
SMAP: time: 0.33, size: 13971002, sum 1344701724191145
BMAP: time: 0.60, size: 13971002, sum 1344701724191145
FMAP: time: 0.56, size: 13971002, sum 1344701724191145
HMAP: time: 0.51, size: 13971002, sum 1344701724191145
From these tests cmap, robin_hood and khash are almost equally fast. std::unordered_map is horrible. With random numbers in 0 - 2^20 range, khash performs worse, though:
Unordered maps: 30000000 repeats of Insert random key + try to remove a random key:
CMAP: time: 1.93, sum: 450000015000000, size: 524809, erased 14738434
KMAP: time: 7.28, sum: 450000015000000, size: 524809, erased 14738434
Unordered maps: Insert 30000000 random keys, then remove them in same order:
CMAP: time: 1.16, erased 1048576
KMAP: time: 2.62, erased 1048576
The containers are memory efficent, i.e. they occupy as little memory as practical possible.
- cstr, cvec: Type size: one pointer. The size and capacity is stored as part of the heap allocation that also holds the vector elements.
- clist: Type size: one pointer. Each node allocates block storing value and next pointer.
- cmap: Type size: 4 pointers. cmap uses one table of keys+value, and one table of precomputed hash-value/used bucket, which occupies only one byte per bucket. The open hashing has a default max load factor of 85%, and hash table scales by 1.5x when reaching that.
- cset: Same as cmap, but this uses a table of keys only, not (key, value) pairs.
- carray: carray1, carray2 and carray3. Type size: One pointer plus one, two, or three size_t variables to store dimensions. Arrays are allocated as one contiguous block of heap memory.
cmap/cset uses open hashing and is among the fastest hash-tables for C and C++. The default max load-factor is 0.85.
You can customize the destroy-, hash-, equals- functions, but also define a convertion from a raw/literal type to the key-type specified. This is very useful when e.g. having cstr as key, and therefore a few using-macros are pre-defined for cmaps with cstr_t keys and/or values:
- using_cmap_strkey(tag, valuetype)
- using_cmap_strval(tag, keytype)
- using_cmap_str() // cstr_t -> cstr_t
- using_cset_str() // cstr_t set
To customize your own cmap type to work like these, you may want to look at examples/advanced.c. It demonstrates how to use a custom struct as a hash map key, by using the optional parameters to using_cmap().