boost
flat_hash_map
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boost | flat_hash_map | |
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1 | 1,677 | |
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over 13 years ago | 7 months ago | |
C++ | C++ | |
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boost
- Inside boost::unordered_flat_map
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coost v3.0.0 released - A tiny boost library in C++11
coost is a cross-platform C++ basic library with both performance and ease of use. It is like boost, but much smaller, the static library built on linux and mac is only about 1MB in size. Although small, it provides enough powerful features:
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Ask HN: Is ease in getting started the key for Python's success?
Not so much ease, as flexibility.
In the end, the thing that matters the most for software is being able to get logic into code as efficiently as possible. This includes being able to write concise code, being able to execute it and see results, debug it efficiently, use libraries easily, and deploy it to production. Python has all of this.
The rest of the stuff, like strong typing, memory safety, e.t.c are at best academic. The supposed advantages of those just don't hold up once you start to look into the real world. Linux, which runs on most devices that support an os hardware wise, is written purely in C. Python is used as a backend for very big projects like Youtube, Instagram, Spotify, e.t.c. Its also used to run Openpilot (https://github.com/commaai/openpilot), which has performance on par with Teslas autopilot.
Meanwhile in Java world, with strict typing, you have egregious vulnerabilities like log4shell, amongst others (https://java-0day.com/).
Language evolution is also a thing to look at with this stuff. The more "strict" you try to make a language, the worse its going to become as people are necessarily going to find hacks around it. With java, type safety strict features like having getters and setters get abstracted away behind an annotation processor that hacks the AST (Lombok), and thats not only considered ok, but is encouraged to be used. With C++, template metaprogramming got extremely out of hand with https://www.boost.org/, where the error messages for one thing used to be pages long. Rust manage to sneak this under the radar with the unsafe clause, which is going to see standard use in many codebases, thus negating any of its advantages.
In the end, good code comes from good developers, full stop. Every codebase will necessarily have tests for production deployment, and anything that language features don't catch during compilation or static checking can be checked with testing if you have developers that understand what they are doing and can write appropriate testing frameworks.
And based on that, its pretty attractive to use Python especially when you consider developer time. And the flexibility means you can write your code in different forms to suit your use case, where it be OOP with MyPy type checking, functional, imperative, or super complex if you want.
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Compile-Time Hash in Plain C (Not Only C++) is Now Possible!
For those who didn't know what is Boost, it's a C++ library that helps to prevent re-inventing the wheel while trying to program something quite complex as example looping only with macro, Boost Preprocessor. Fortunately, Boost Preprocessor Repeat also works with plain C, not only C++. So, my OrangePi board can calculate hash at compile-time. Unfortunately, my SIX Hash algorithm requires sizeof(input) and Boost... won't... work... with it. Hours of workarounds, no luck.
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How do I connect a REST API with C++?
If you have the ability to use third-party libraries (though if you can't this project is going to be a nightmare, lol...) I would recommend using the Beast library from the Boost collection of libraries. It's a little bit more verbose than some options, but not that much more, and it's better maintained. REST webservices are built on top of the HTTP framework, so it's just a matter of sending a HTTP GET request to a server (or POST/UPDATE/DELETE, depending on how exactly the api on the other end is implemented) and reading the response you get back. This is a very basic sample of a client sending a GET request to a server. If you need to change this to do a POST (or some other kind of request), there's only two real changes that need to be made:
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Can anyone explain the differences of Conda vs Pip?
The person you replied to used slightly confusing terminology. Conda deals with non-python packages. As in if you wanted to install boost for C++.
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Looking to download/use Boost
I'm not sure if its just me but I'm finding I can't access any of the download links on the Boost Website.
- Resources for experienced C programmer for C++20/17/13
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How to write reflection for C++
rich standard library and Boost;
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Where to read about modern C++ features which you should use?
Boost is also another ubiquitous library. Lots of code that doesn't make it into the standard kind of ends up here. Lots of code that gets into the standard starts here. Boost.Asio might end up being our network API in 23.
flat_hash_map
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Effortless Performance Improvements in C++: std:unordered_map
If you don't need all the guarantees provided by std::unordered_map (pointer stability is usually the big one), you can go a /lot/ faster with a map that uses open addressing.
Some of my favorite alternative hash map implementations are ska::flat_hash_map and ska::bytell_hash_map from https://github.com/skarupke/flat_hash_map. They're fast, and the single header implementation makes them easy to add to a project. For my use cases they generally offer similar performance to abseil and folly F14.
Don't be fooled by the fact that they haven't been updated in ~5 years. I've been using them for nearly that long and have yet to find any bugs.
- Inside boost::unordered_flat_map
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A fast & densely stored hashmap and hashset based on robin-hood backward shift deletion
When int64 is the key, then the winner remains the unorder map from Malte Skarupke if (and only if) associated with a custom allocator.
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boost::unordered map is a new king of data structures
Unordered hash map shootout CMAP = https://github.com/tylov/STC KMAP = https://github.com/attractivechaos/klib PMAP = https://github.com/greg7mdp/parallel-hashmap FMAP = https://github.com/skarupke/flat_hash_map RMAP = https://github.com/martinus/robin-hood-hashing HMAP = https://github.com/Tessil/hopscotch-map TMAP = https://github.com/Tessil/robin-map UMAP = std::unordered_map Usage: shootout [n-million=40 key-bits=25] Random keys are in range [0, 2^25). Seed = 1656617916: T1: Insert/update random keys: KMAP: time: 1.949, size: 15064129, buckets: 33554432, sum: 165525449561381 CMAP: time: 1.649, size: 15064129, buckets: 22145833, sum: 165525449561381 PMAP: time: 2.434, size: 15064129, buckets: 33554431, sum: 165525449561381 FMAP: time: 2.112, size: 15064129, buckets: 33554432, sum: 165525449561381 RMAP: time: 1.708, size: 15064129, buckets: 33554431, sum: 165525449561381 HMAP: time: 2.054, size: 15064129, buckets: 33554432, sum: 165525449561381 TMAP: time: 1.645, size: 15064129, buckets: 33554432, sum: 165525449561381 UMAP: time: 6.313, size: 15064129, buckets: 31160981, sum: 165525449561381 T2: Insert sequential keys, then remove them in same order: KMAP: time: 1.173, size: 0, buckets: 33554432, erased 20000000 CMAP: time: 1.651, size: 0, buckets: 33218751, erased 20000000 PMAP: time: 3.840, size: 0, buckets: 33554431, erased 20000000 FMAP: time: 1.722, size: 0, buckets: 33554432, erased 20000000 RMAP: time: 2.359, size: 0, buckets: 33554431, erased 20000000 HMAP: time: 0.849, size: 0, buckets: 33554432, erased 20000000 TMAP: time: 0.660, size: 0, buckets: 33554432, erased 20000000 UMAP: time: 2.138, size: 0, buckets: 31160981, erased 20000000 T3: Remove random keys: KMAP: time: 1.973, size: 0, buckets: 33554432, erased 23367671 CMAP: time: 2.020, size: 0, buckets: 33218751, erased 23367671 PMAP: time: 2.940, size: 0, buckets: 33554431, erased 23367671 FMAP: time: 1.147, size: 0, buckets: 33554432, erased 23367671 RMAP: time: 1.941, size: 0, buckets: 33554431, erased 23367671 HMAP: time: 1.135, size: 0, buckets: 33554432, erased 23367671 TMAP: time: 1.064, size: 0, buckets: 33554432, erased 23367671 UMAP: time: 5.632, size: 0, buckets: 31160981, erased 23367671 T4: Iterate random keys: KMAP: time: 0.748, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 CMAP: time: 0.627, size: 23367671, buckets: 33218751, repeats: 8, sum: 4465059465719680 PMAP: time: 0.680, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 FMAP: time: 0.735, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 RMAP: time: 0.464, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 HMAP: time: 0.719, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 TMAP: time: 0.662, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 UMAP: time: 6.168, size: 23367671, buckets: 31160981, repeats: 8, sum: 4465059465719680 T5: Lookup random keys: KMAP: time: 0.943, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 CMAP: time: 0.863, size: 23367671, buckets: 33218751, lookups: 34235332, found: 29040438 PMAP: time: 1.635, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 FMAP: time: 0.969, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 RMAP: time: 1.705, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 HMAP: time: 0.712, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 TMAP: time: 0.584, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 UMAP: time: 1.974, size: 23367671, buckets: 31160981, lookups: 34235332, found: 29040438
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Updating map_benchmarks: Send your hashmaps!
I believe that when the number of elements is larger than 4 (a rough estimation), the associative linear table won't be faster than ska::flat_hash_map or fph-table with the identity hash function. If you look at the benchmark results, you will find that the average lookup time may well be less than 2 nanoseconds when item number is smaller than one thousand on morden CPUs. For these two hash tables, there are only about ten instructions in the critical path of lookup. And this should be faster than the linear search in a associative table, where there are a lot of branches and comparing instructions. However, you should benchmark it youself to get the real conclusion. This is just a simple analysis on paper from mine. By the way, the associative table can be faster if it is implemented with hardware circuits or SIMD instructions.
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Will std::set and std::unordered_set implement extra optimizations for low-number of expected unique values with too many duplicates?
I have done similar benchmarks (with a lot of care) a few months ago, but the results were different than yours when using a very fast hash map. I was surprised that even for small maps, flat_hash_map was faster than searching small arrays (searching int64_t).
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A truly fast Map implementation?
You should look for flat-hash-maps. This is a good implementation, skarupke/flat_hash_map. The author also has a talk about the implementation at one of the boost conferences on youtube.
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Dolphin Emulator - Dolphin MEGA Progress Report: April and May 2021
You may want to give a try to Skarupke's HashMaps.
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Fast insert-only hash map
You can either use Abseil's Swiss Table, or Facebook's F14, or Skarupke's flat_hash_map.
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C++: How a simple question helped me form a New Year's Resolution
The state of the art for hash-based containers would be either Abseil's or Skarupke's.
What are some alternatives?
jackson-databind - General data-binding package for Jackson (2.x): works on streaming API (core) implementation(s)
parallel-hashmap - A family of header-only, very fast and memory-friendly hashmap and btree containers.
coost - A tiny boost library in C++11.
unordered - Boost.org unordered module
cppinsights - C++ Insights - See your source code with the eyes of a compiler
dense_hash_map - A simple replacement for std::unordered_map
GSL - Guidelines Support Library
robin-map - C++ implementation of a fast hash map and hash set using robin hood hashing
simdjson - Parsing gigabytes of JSON per second : used by Facebook/Meta Velox, the Node.js runtime, ClickHouse, WatermelonDB, Apache Doris, Milvus, StarRocks
unordered_dense - A fast & densely stored hashmap and hashset based on robin-hood backward shift deletion
restclient-cpp - C++ client for making HTTP/REST requests
robin-hood-hashing - Fast & memory efficient hashtable based on robin hood hashing for C++11/14/17/20