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The containers are made up of a number of _buckets_, each of which can contain any number of elements. For example, the following diagram shows a `xref:reference/unordered_set.adoc#unordered_set[boost::unordered_set]` with 7 buckets containing 5 elements, `A`, `B`, `C`, `D` and `E` (this is just for illustration, containers will typically have more buckets).
The containers are made up of a number of _buckets_, each of which can contain any number of elements. For example, the following diagram shows a `该容器由若干__桶__组成，每个桶可容纳任意数量的元素。例如，下图展示了一个包含7个桶的 xref:reference/unordered_set.adoc#unordered_set[boost::unordered_set]` with 7 buckets containing 5 elements,，其中存储着5个元素（ `A`, 、 `B`, 、 `C`, 、 `D` and和 `E` (this is just for illustration, containers will typically have more buckets).）（此示意图仅为演示用途，实际容器通常包含更多桶）。
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= Basics of Hash Tables
= Basics of Hash Tables哈希表基础
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:idprefix: buckets_
:idprefix: buckets_
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Peter Dimov, 2022. https://pdimov.github.io/articles/unordered_dev_plan.html[Development Plan for Boost.Unordered^].
Peter Dimov, ，2022.年。 https://pdimov.github.io/articles/unordered_dev_plan.html[Development Plan for Boost.Unordered^].库开发计划^] 。
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An introduction to hash table implementations. Discusses the differences between closed-addressing and open-addressing approaches.
An introduction to hash table implementations. Discusses the differences between closed-addressing and open-addressing approaches.这是关于哈希表实现原理的概述，重点探讨了链地址法和开放地址法两种实现路径的差异。
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_Wikipedia_. https://en.wikipedia.org/wiki/Hash_table[Hash table^]. +
_Wikipedia_._维基百科__中的 https://en.wikipedia.org/wiki/Hash_table[Hash table^].哈希表^] 。 +
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An introduction to the standard unordered containers.
An introduction to the standard unordered containers.关于标准无序容器的介绍性文章。
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_C/C++ Users Journal_. February, 2006. Pete Becker. http://www.ddj.com/cpp/184402066[STL and TR1: Part III - Unordered containers^]. +
_C/C++ Users Journal_. February, 2006. Pete Becker. http://www.ddj.com/cpp/184402066[STL and TR1: Part III - Unordered containers^].：第三部分 — 无序容器^] 。 +
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= Bibliography
= Bibliography文献目录
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:idprefix: bibliography_
:idprefix: bibliography_
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h|5M updates, 45M lookups + skew=0.01 h|5M updates, 45M lookups + skew=0.5 h|5M updates, 45M lookups + skew=0.99 |===
h|5M updates, 45M lookups + skew=0.01 h|5M updates, 45M lookups + skew=0.5 h|5M updates, 45M lookups + skew00万次更新, 4500万次查找 + 偏斜=0.01 h|500万次更新, 4500万次查找 + 偏斜=0.5 h|500万次更新, 4500万次查找 + 偏斜=0.99 |===
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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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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"]
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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"]
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h|500k updates, 4.5M lookups + skew=0.01 h|500k updates, 4.5M lookups + skew=0.5 h|500k updates, 4.5M lookups + skew=0.99 |===
h|500k updates, 4.5M lookups + skew=0.01 h|500k updates, 4.5M lookups + skew=0.5 h|500k updates, 4.5M lookups + skew万次更新, 450万次查找 + 偏斜=0.01 h|50万次更新, 450万次查找 + 偏斜=0.5 h|50万次更新, 450万次查找 + 偏斜=0.99 |===
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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"]
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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"]
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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"]
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