Sparsehash Save

C++ associative containers

Project README

This directory contains several hash-map implementations, similar in API to SGI's hash_map class, but with different performance characteristics. sparse_hash_map uses very little space overhead, 1-2 bits per entry. dense_hash_map is very fast, particulary on lookup. (sparse_hash_set and dense_hash_set are the set versions of these routines.) On the other hand, these classes have requirements that may not make them appropriate for all applications.

All these implementation use a hashtable with internal quadratic probing. This method is space-efficient -- there is no pointer overhead -- and time-efficient for good hash functions.


To compile test applications with these classes, run ./configure followed by make. To install these header files on your system, run 'make install'. (On Windows, the instructions are different; see README_windows.txt.) See INSTALL for more details.

This code should work on any modern C++ system. It has been tested on Linux (Ubuntu, Fedora, RedHat, Debian), Solaris 10 x86, FreeBSD 6.0, OS X 10.3 and 10.4, and Windows under both VC++7 and VC++8.


See the html files in the doc directory for small example programs that use these classes. It's enough to just include the header file:

#include <sparsehash/sparse_hash_map> // or sparse_hash_set, dense_hash_map, ... google::sparse_hash_set<int, int> number_mapper;

and use the class the way you would other hash-map implementations. (Though see "API" below for caveats.)

By default (you can change it via a flag to ./configure), these hash implementations are defined in the google namespace.


The API for sparse_hash_map, dense_hash_map, sparse_hash_set, and dense_hash_set, are a superset of the API of SGI's hash_map class. See doc/sparse_hash_map.html, et al., for more information about the API.

The usage of these classes differ from SGI's hash_map, and other hashtable implementations, in the following major ways:

  1. dense_hash_map requires you to set aside one key value as the 'empty bucket' value, set via the set_empty_key() method. This MUST be called before you can use the dense_hash_map. It is illegal to insert any elements into a dense_hash_map whose key is equal to the empty-key.

  2. For both dense_hash_map and sparse_hash_map, if you wish to delete elements from the hashtable, you must set aside a key value as the 'deleted bucket' value, set via the set_deleted_key() method. If your hash-map is insert-only, there is no need to call this method. If you call set_deleted_key(), it is illegal to insert any elements into a dense_hash_map or sparse_hash_map whose key is equal to the deleted-key.

  3. These hash-map implementation support I/O. See below.

There are also some smaller differences:

  1. The constructor takes an optional argument that specifies the number of elements you expect to insert into the hashtable. This differs from SGI's hash_map implementation, which takes an optional number of buckets.

  2. erase() does not immediately reclaim memory. As a consequence, erase() does not invalidate any iterators, making loops like this correct: for (it = ht.begin(); it != ht.end(); ++it) if (...) ht.erase(it); As another consequence, a series of erase() calls can leave your hashtable using more memory than it needs to. The hashtable will automatically compact at the next call to insert(), but to manually compact a hashtable, you can call ht.resize(0)


In addition to the normal hash-map operations, sparse_hash_map can read and write hashtables to disk. (dense_hash_map also has the API, but it has not yet been implemented, and writes will always fail.)

In the simplest case, writing a hashtable is as easy as calling two methods on the hashtable: ht.write_metadata(fp); ht.write_nopointer_data(fp);

Reading in this data is equally simple: google::sparse_hash_map<...> ht; ht.read_metadata(fp); ht.read_nopointer_data(fp);

The above is sufficient if the key and value do not contain any pointers: they are basic C types or agglomorations of basic C types. If the key and/or value do contain pointers, you can still store the hashtable by replacing write_nopointer_data() with a custom writing routine. See sparse_hash_map.html et al. for more information.


In addition to the hash-map and hash-set classes, this package also provides sparsetable.h, an array implementation that uses space proportional to the number of elements in the array, rather than the maximum element index. It uses very little space overhead: 2 to 5 bits per entry. See doc/sparsetable.html for the API.


  • sparse_hash_map has memory overhead of about 4 to 10 bits per hash-map entry, assuming a typical average occupancy of 50%.
  • dense_hash_map has a factor of 2-3 memory overhead: if your hashtable data takes X bytes, dense_hash_map will use 3X-4X memory total.

Hashtables tend to double in size when resizing, creating an additional 50% space overhead. dense_hash_map does in fact have a significant "high water mark" memory use requirement, which is 6 times the size of hash entries in the table when resizing (when reaching 50% occupancy, the table resizes to double the previous size, and the old table (2x) is copied to the new table (4x)).

sparse_hash_map, however, is written to need very little space overhead when resizing: only a few bits per hashtable entry.


You can compile and run the included file to examine the performance of sparse_hash_map, dense_hash_map, and your native hash_map implementation on your system. One test against the SGI hash_map implementation gave the following timing information for a simple find() call: SGI hash_map: 22 ns dense_hash_map: 13 ns sparse_hash_map: 117 ns SGI map: 113 ns

See doc/performance.html for more detailed charts on resource usage and performance data.

16 March 2005 (Last updated: 12 September 2010)

Open Source Agenda is not affiliated with "Sparsehash" Project. README Source: sparsehash/sparsehash
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