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Help getting a STL-like hashmap for Sun Workshop 5.0 C++?
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Joe Higgins
May 9, 2000, 3:00:00 AM5/9/00
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I want a hashmap that is reasonably compatible with STL's
map that I can use with WSP5.0.
map that I can use with WSP5.0.
The SGI hash_map that comes with g++ would be ideal, but
my attempts to selectively incorporate hash_map have been
dismal.
WSP5.0 ships with Rogue's Tools.h++, but as far as I can
determine, their hashmap (rw/stdex/hashmap.h) cannot be
used with WSP5.0. Also, using these tools comes with a
classic iostreams issue.
Any suggestions/pointers or help appreciated.
Regards, Joe
1971 powerChina
May 8, 2015, 3:01:26 AM5/8/15
to
Title: The core of the big data solutions -- Map
Author: pengwenwei
Email: pww71 foxmail.com
Language: c++
Platform: Windows, linux
Technology: Perfect hash algorithm
Level: Advanced
Description: A high performance map algorithm
Section MFC c++ map stl
SubSection c++ algorithm
License: (GPLv3)
Map is widely used in c++ programs. And the bottleneck of program performance is often the performance of map. especially in big data field and the scenarios which can't realize data distribution and parallel processing. Therefore, the performance of map becomes the most critical technology.
My many years of work experience in telecommunition and information security industry Is all about big data analysis. especially in the information security industry, the data analysis is the most complicated. They can't work without map. For example, ip table, mac table, telephone numbers table, dns table etc.
Currently, the STL map and Google's hash map are the most popular maps. But they have some disadvantages. The STL map utilizes binary chop, which causes a bad performance. Google Hash map has the best performance at present, but it has probability of repeat collision. For big data analysis, the probability of repeat collision is unacceptable.
Now I would like to publish my algorithms. It includes three different maps for different scenarios:
1. Memory Map(memMap): It resides in memory and has a good access speed. But its size is limited by memory size.
2. Harddisk Map(diskMap): It utilizes hard disk to store data. So it could accept much more data than memory map.
3. Hashmap(hashMap): It has the best performance and a great lookup speed, but it doesn't have 'insert' and 'delete' functionality.
memMap and diskMap could be converted to hashMap by function memMap2HashMap and diskMap2HashMap. According to the test result, my algorithms' collision probability is zero. About performance, memMap has a comparable performance with google, and hashMap's performance is 100 times better than Google's hashmap.
In summary, my algorithms are perfect zero collision probability hash algorithms.You can refer to following artical to find the key index and compress algorithm theory:
http://blog.csdn.net/chixinmuzi/article/details/1727195
Source code and documents:
https://sourceforge.net/projects/pwwhashmap/files/?source=navbar
Author: pengwenwei
Email: pww71 foxmail.com
Language: c++
Platform: Windows, linux
Technology: Perfect hash algorithm
Level: Advanced
Description: A high performance map algorithm
Section MFC c++ map stl
SubSection c++ algorithm
License: (GPLv3)
Map is widely used in c++ programs. And the bottleneck of program performance is often the performance of map. especially in big data field and the scenarios which can't realize data distribution and parallel processing. Therefore, the performance of map becomes the most critical technology.
My many years of work experience in telecommunition and information security industry Is all about big data analysis. especially in the information security industry, the data analysis is the most complicated. They can't work without map. For example, ip table, mac table, telephone numbers table, dns table etc.
Currently, the STL map and Google's hash map are the most popular maps. But they have some disadvantages. The STL map utilizes binary chop, which causes a bad performance. Google Hash map has the best performance at present, but it has probability of repeat collision. For big data analysis, the probability of repeat collision is unacceptable.
Now I would like to publish my algorithms. It includes three different maps for different scenarios:
1. Memory Map(memMap): It resides in memory and has a good access speed. But its size is limited by memory size.
2. Harddisk Map(diskMap): It utilizes hard disk to store data. So it could accept much more data than memory map.
3. Hashmap(hashMap): It has the best performance and a great lookup speed, but it doesn't have 'insert' and 'delete' functionality.
memMap and diskMap could be converted to hashMap by function memMap2HashMap and diskMap2HashMap. According to the test result, my algorithms' collision probability is zero. About performance, memMap has a comparable performance with google, and hashMap's performance is 100 times better than Google's hashmap.
In summary, my algorithms are perfect zero collision probability hash algorithms.You can refer to following artical to find the key index and compress algorithm theory:
http://blog.csdn.net/chixinmuzi/article/details/1727195
Source code and documents:
https://sourceforge.net/projects/pwwhashmap/files/?source=navbar
1971 powerChina
May 8, 2015, 3:01:50 AM5/8/15
to
在 2000年5月9日星期二 UTC+8下午3:00:00，Joe Higgins写道：
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