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If instead of `boost::unordered_set` we had used `xref:reference/unordered_flat_set.adoc[boost::unordered_flat_set]`, the diagram would look as follows:
If instead of `boost::unordered_set` we had used `若使用 xref:reference/unordered_flat_set.adoc[boost::unordered_flat_set]`, the diagram would look as follows:替代 `boost::unordered_set` ，其结构示意图将呈现如下形式：
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You can see in the diagram that `A` & `D` have been placed in the same bucket. When looking for elements in this bucket up to 2 comparisons are made, making the search slower. This is known as a *collision*. To keep things fast we try to keep collisions to a minimum.
You can see in the diagram that如图所示， `A` &与 `D` have been placed in the same bucket. When looking for elements in this bucket up to 2 comparisons are made, making the search slower. This is known as a *collision*. To keep things fast we try to keep collisions to a minimum.被置于同一桶内。在此桶内查找元素时最多需要进行 2 次比对，这会降低检索效率，该现象称为**冲突**。为维持高效运作，我们需尽可能减少冲突的发生。
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There is xref:hash_equality.adoc#hash_equality[more information on hash functions and equality predicates in the next section].
There is关于哈希函数与相等性谓词的详细说明请参阅 xref:hash_equality.adoc#hash_equality[more information on hash functions and equality predicates in the next section].后续章节] 。
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Retrieving the elements for a given key is simple. The same process is applied to the key to find the correct bucket. Then the key is compared with the elements in the bucket to find any elements that match (using the equality predicate `Pred`). If the hash function has worked well the elements will be evenly distributed amongst the buckets so only a small number of elements will need to be examined.
Retrieving the elements for a given key is simple. The same process is applied to the key to find the correct bucket. Then the key is compared with the elements in the bucket to find any elements that match (using the equality predicate `Pred`). If the hash function has worked well the elements will be evenly distributed amongst the buckets so only a small number of elements will need to be examined.根据指定键检索元素的过程非常简单：首先对键执行相同处理以定位对应桶，随后通过相等性谓词 `Pred` 将键与桶内元素进行比较以寻找匹配项。若哈希函数表现良好，元素将均匀分布于各桶中，此时仅需检查少量元素即可完成检索。
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In order to decide which bucket to place an element in, the container applies the hash function, `Hash`, to the element's key (for sets the key is the whole element, but is referred to as the key so that the same terminology can be used for sets and maps). This returns a value of type `std::size_t`. `std::size_t` has a much greater range of values then the number of buckets, so the container applies another transformation to that value to choose a bucket to place the element in.
In order to decide which bucket to place an element in, the container applies the hash function, `Hash`, to the element's key (for sets the key is the whole element, but is referred to as the key so that the same terminology can be used for sets and maps). This returns a value of type容器通过哈希函数 `Hash` 作用于元素的键来确定其所属的桶（对于集合而言，键即元素本身，但为统一集合与映射表的术语仍称作键）。该函数返回 `std::size_t`. 类型的值。由于 `std::size_t` has a much greater range of values then the number of buckets, so the container applies another transformation to that value to choose a bucket to place the element in.的取值范围远大于桶的数量，容器会对此值进行二次转换以确定元素应存入的桶。
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image::buckets.png[]
image::buckets.png[]
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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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