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The erasure benchmarks erase traverse the `n` elements and erase those with odd key (50% on average).
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image::benchmarks-set/vs/scattered successful looukp.xlsx.practice non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered successful looukp.xlsx.practice non-unique 5.png]
image::benchmarks-set/vs/scattered successful looukp.xlsx.practice non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered successful looukp.xlsx.practice non-unique 5.png]
:284
h|non-duplicate elements h|duplicate elements h|duplicate elements, + max load factor 5
h|非重复元素 h|重复元素 h|重复元素, + 最大负载因子 5
:291
Unsuccessful lookup
未命中查找
image::benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice.png]
image::benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice.png]
image::benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique.png]
image::benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique.png]
image::benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique 5.png]
image::benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered unsuccessful looukp.xlsx.practice non-unique 5.png]
:301
h|non-duplicate elements h|duplicate elements h|duplicate elements, + max load factor 5
h|非重复元素 h|重复元素 h|重复元素, + 最大负载因子 5
boost::unordered_(flat|node)_map
boost::unordered_(flat|node)_map
:310
All benchmarks were created using:
所有基准测试均为使用以下链接来创建的：
`https://abseil.io/docs/cpp/guides/container[absl::flat_hash_map^]<uint64_t, uint64_t>`
https://abseil.io/docs/cpp/guides/container[`absl::flat++_++hash++_++map`^]`++&lt;++uint64++_++t, uint64++_++t++&gt;++`
`boost::unordered_map<uint64_t, uint64_t>`
`boost::unordered_map<uint64_t, uint64_t="">`</uint64_t,>
`boost::unordered_flat_map<uint64_t, uint64_t>`
`boost::unordered_flat_map<uint64_t, uint64_t="">`</uint64_t,>
`boost::unordered_node_map<uint64_t, uint64_t>`
`boost::unordered_node_map<uint64_t, uint64_t="">`</uint64_t,>
The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_flat_map[found here^].
源代码可 https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_flat_map[在此处获取] 。
The insertion benchmarks insert `n` random values, where `n` is between 10,000 and 10 million.
插入基准测试的操作将插入 `n` 个随机值，其中 `n` 的范围在10,000至1000万之间。
The erasure benchmarks erase traverse the `n` elements and erase those with odd key (50% on average).
擦除基准测试会遍历全部 `n` 个元素，并删除其中键值为奇数的元素（平均约占50%）。
:323
The successful lookup benchmarks are done by looking up all `n` values, in their original insertion order.
成功查找基准测试通过按照元素的原始插入顺序查找全部 `n` 个值来完成。
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The unsuccessful lookup benchmarks use `n` randomly generated integers but using a different seed value.
未命中查找基准测试使用不同种子值生成的 `n` 个随机整数进行。
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GCC 12, x64
GCC 12, x64
image::benchmarks-flat_map/gcc-x64/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x64/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x64/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x64/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x64/Scattered unsuccessful looukp.xlsx.plot.png]
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h|running insertion h|running erasure h|successful lookup h|unsuccessful lookup
h|插入的执行 h|擦除的执行 h|成功查找 h|未命中查找
:347
Clang 15, x64
Clang 15, x64
image::benchmarks-flat_map/clang-x64/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png]
:359
h|running insertion h|running erasure h|successful lookup h|unsuccessful lookup
h|插入的执行 h|擦除的执行 h|成功查找 h|未命中查找
:366
Visual Studio 2022, x64
Visual Studio 2022, x64
English Chinese (Simplified Han script) Actions
image::benchmarks-set/vs/scattered erasure.xlsx.practice non-unique.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered erasure.xlsx.practice non-unique.png]
image::benchmarks-set/vs/scattered erasure.xlsx.practice non-unique.png[width=250,window=_blank,link=_images/benchmarks-set/vs/scattered erasure.xlsx.practice non-unique.png]
image::benchmarks-flat_map/clang-arm64/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-arm64/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/clang-arm64/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-arm64/Running insertion.xlsx.plot.png]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-set/clang_libcpp/running insertion.xlsx.practice norehash non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/running insertion.xlsx.practice norehash non-unique 5.png]
image::benchmarks-set/clang_libcpp/running insertion.xlsx.practice norehash non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/running insertion.xlsx.practice norehash non-unique 5.png]
image::benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice.png]
image::benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice.png]
image::benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique.png]
image::benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique.png]
image::benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique 5.png]
image::benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique 5.png[width=250,window=_blank,link=_images/benchmarks-set/clang_libcpp/scattered successful looukp.xlsx.practice non-unique 5.png]
boost::unordered_(flat|node)_map
boost::unordered_(flat|node)_map
The erasure benchmarks erase traverse the `n` elements and erase those with odd key (50% on average).
擦除基准测试会遍历全部 `n` 个元素，并删除其中键值为奇数的元素（平均约占50%）。
image::benchmarks-flat_map/clang-x86/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Running insertion.xlsx.plot.png]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.99.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.99.png"]
image::benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.5M, 0.99.png"]
image::benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.5M, 0.99.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-arm64/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.500k, 0.99.png"]
boost::unordered_[multi]set
boost::unordered_[multi]set
All benchmarks were created using `unordered_set<unsigned int>` (non-duplicate) and `unordered_multiset<unsigned int>` (duplicate). The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_set[found here^].
所有基准测试均使用 `unordered++_++set++&lt;++unsigned int++&gt;++`(非重复元素） 和 `unordered++_++multiset++&lt;++unsigned int++&gt;++` （重复元素）创建。源代码可 https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_set[在此处获取] 。
The insertion benchmarks insert `n` random values, where `n` is between 10,000 and 3 million. For the duplicated benchmarks, the same random values are repeated an average of 5 times.
插入基准测试会插入 `n` 个随机值，其中 `n` 的范围在10,000至300万之间。在重复值测试场景中，同一组随机值会平均重复插入 5 次。
h|non-duplicate elements h|duplicate elements h|duplicate elements, + max load factor 5 |===
h|非重复元素 h|重复元素 h|重复元素, + 最大负载因子 5 |===
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x86/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.99.png"]
image::benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x86/Parallel workload.xlsx.5M, 0.99.png"]

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The erasure benchmarks erase traverse the `n` elements and erase those with odd key (50% on average).
The erasure benchmarks erase traverse the `n` elements and erase those with odd key (50% on average).擦除基准测试会遍历全部 `n` 个元素，并删除其中键值为奇数的元素（平均约占50%）。
06/05/2026
User avatar admin

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The erasure benchmarks erase traverse the `n` elements and erase those with odd key (50% on average).
The erasure benchmarks erase traverse the `n` elements and erase those with odd key (50% on average).
06/05/2026
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safe-html, strict-same
Unit identifier
50674
Source string location
:321
String age
06/05/2026
Last updated
06/06/2026
Source string age
06/05/2026
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doc/modules/ROOT/pages/benchmarks_zh_Hans.adoc, string 101