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This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container.
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`visit_while` and `erase_if` can also be parallelized. Note that, in order to increase efficiency, whole-table visitation operations do not block the table during execution: this implies that elements may be inserted, modified or erased by other threads during visitation. It is advisable not to assume too much about the exact global state of a concurrent container at any point in your program.
`visit++_++while` 与 `erase++_++if` 同样支持并行化。需要注意的是，为提升执行效率，全表遍历操作在运行期间不会锁定整个容器：这意味着在遍历过程中，其他线程可能同时执行元素的插入、修改或删除操作。建议在程序中避免对并发容器在任意时间点的全局状态做过强的假设。
Bulk visitation
批量访问
Suppose you have an `std::array` of keys you want to look up for in a concurrent map:
假设有一个 `std::array` 存储了需要在并发映射中查找的键：
_Bulk visitation_ allows us to pass all the keys in one operation:
__批量访问__允许用户通过单次操作传入所有键：
This functionality is not provided for mere syntactic convenience, though: by processing all the keys at once, some internal optimizations can be applied that increase performance over the regular, one-at-a-time case (consult the xref:benchmarks.adoc#benchmarks_boostconcurrent_flatnode_map[benchmarks]). In fact, it may be beneficial to buffer incoming keys so that they can be bulk visited in chunks:
提供此功能并非仅出于语法便利性考量：通过一次性处理所有键，可应用内部优化提升性能（详见 xref:benchmarks.adoc#benchmarks_boostconcurrent_flatnode_map[基准测试]）。实际上，用户可通过缓冲输入键值以实现分块批量访问，从而进一步提升效率：
There's a latency/throughput tradeoff here: it will take longer for incoming keys to be processed (since they are buffered), but the number of processed keys per second is higher. `bulk_visit_size` is the recommended chunk size —smaller buffers may yield worse performance.
这里存在延迟与吞吐量的权衡：缓冲机制会延长单个键的处理延迟，但每秒处理的键总数（吞吐量）会更高。 `bulk++_++visit++_++size` 是推荐的缓冲区块大小——过小的缓冲区可能导致性能下降。
Blocking Operations
阻塞式操作
Concurrent containers can be copied, assigned, cleared and merged just like any other Boost.Unordered container. Unlike most other operations, these are _blocking_, that is, all other threads are prevented from accesing the tables involved while a copy, assignment, clear or merge operation is in progress. Blocking is taken care of automatically by the library and the user need not take any special precaution, but overall performance may be affected.
并发容器可像其他 Boost.Unordered 容器一样支持复制、赋值、清空和合并操作。与大多数其他操作不同，这些属于__阻塞式操作__：在执行期间，其他线程将被阻止访问相关容器。该阻塞机制由库自动处理，用户无需采取特殊预防措施，但整体性能可能受到影响。
Another blocking operation is _rehashing_, which happens explicitly via `rehash`/`reserve` or during insertion when the table's load hits `max_load()`. As with non-concurrent containers, reserving space in advance of bulk insertions will generally speed up the process.
另一阻塞操作是__重哈希__，该操作可通过 `rehash` / `reserve` 显式触发，或在插入过程中当容器负载达到 `max++_++load()` 时自动执行。与非并发容器类似，在批量插入前预先分配空间通常能加速处理过程。
Interoperability with non-concurrent containers
与非并发容器的互操作性
As open-addressing and concurrent containers are based on the same internal data structure, they can be efficiently move-constructed from their non-concurrent counterpart, and vice versa.
由于开放寻址容器与并发容器基于相同的内部数据结构，它们可以高效地通过移动构造从其非并发对应容器转换而来，反之亦然。
^|`boost::concurrent_node_set` ^|`boost::unordered_node_set`
^|`boost::concurrent_node_set` ^|`boost::unordered_node_set`
^|`boost::concurrent_node_map` ^|`boost::unordered_node_map`
^|`boost::concurrent_node_map` ^|`boost::unordered_node_map`
^|`boost::concurrent_flat_set` ^|`boost::unordered_flat_set`
^|`boost::concurrent_flat_set` ^|`boost::unordered_flat_set`
^|`boost::concurrent_flat_map` ^|`boost::unordered_flat_map`
^|`boost::concurrent_flat_set` ^|`boost::unordered_flat_set`
This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container.
此互操作性适用于多阶段场景：部分数据处理环节需要并行执行，而其他步骤采用非并发（或只读）模式。下例中，我们需要从一个庞大的单词输入向量构建词频统计直方图：填充阶段用 `boost::concurrent++_++flat++_++map` 并行执行，随后将结果转移至最终容器。
English Chinese (Simplified Han script) Actions
Traversal can be interrupted midway:
遍历过程可中途中断：
There is one last whole-table visitation operation, `erase_if`:
最后一项全表访问操作是 `erase++_++if` ：
`visit_while` and `erase_if` can also be parallelized. Note that, in order to increase efficiency, whole-table visitation operations do not block the table during execution: this implies that elements may be inserted, modified or erased by other threads during visitation. It is advisable not to assume too much about the exact global state of a concurrent container at any point in your program.
`visit++_++while` 与 `erase++_++if` 同样支持并行化。需要注意的是，为提升执行效率，全表遍历操作在运行期间不会锁定整个容器：这意味着在遍历过程中，其他线程可能同时执行元素的插入、修改或删除操作。建议在程序中避免对并发容器在任意时间点的全局状态做过强的假设。
Bulk visitation
批量访问
Suppose you have an `std::array` of keys you want to look up for in a concurrent map:
假设有一个 `std::array` 存储了需要在并发映射中查找的键：
_Bulk visitation_ allows us to pass all the keys in one operation:
__批量访问__允许用户通过单次操作传入所有键：
This functionality is not provided for mere syntactic convenience, though: by processing all the keys at once, some internal optimizations can be applied that increase performance over the regular, one-at-a-time case (consult the xref:benchmarks.adoc#benchmarks_boostconcurrent_flatnode_map[benchmarks]). In fact, it may be beneficial to buffer incoming keys so that they can be bulk visited in chunks:
提供此功能并非仅出于语法便利性考量：通过一次性处理所有键，可应用内部优化提升性能（详见 xref:benchmarks.adoc#benchmarks_boostconcurrent_flatnode_map[基准测试]）。实际上，用户可通过缓冲输入键值以实现分块批量访问，从而进一步提升效率：
There's a latency/throughput tradeoff here: it will take longer for incoming keys to be processed (since they are buffered), but the number of processed keys per second is higher. `bulk_visit_size` is the recommended chunk size —smaller buffers may yield worse performance.
这里存在延迟与吞吐量的权衡：缓冲机制会延长单个键的处理延迟，但每秒处理的键总数（吞吐量）会更高。 `bulk++_++visit++_++size` 是推荐的缓冲区块大小——过小的缓冲区可能导致性能下降。
Blocking Operations
阻塞式操作
Concurrent containers can be copied, assigned, cleared and merged just like any other Boost.Unordered container. Unlike most other operations, these are _blocking_, that is, all other threads are prevented from accesing the tables involved while a copy, assignment, clear or merge operation is in progress. Blocking is taken care of automatically by the library and the user need not take any special precaution, but overall performance may be affected.
并发容器可像其他 Boost.Unordered 容器一样支持复制、赋值、清空和合并操作。与大多数其他操作不同，这些属于__阻塞式操作__：在执行期间，其他线程将被阻止访问相关容器。该阻塞机制由库自动处理，用户无需采取特殊预防措施，但整体性能可能受到影响。
As open-addressing and concurrent containers are based on the same internal data structure, they can be efficiently move-constructed from their non-concurrent counterpart, and vice versa.
由于开放寻址容器与并发容器基于相同的内部数据结构，它们可以高效地通过移动构造从其非并发对应容器转换而来，反之亦然。
^|`boost::concurrent_node_set` ^|`boost::unordered_node_set`
^|`boost::concurrent_node_set` ^|`boost::unordered_node_set`
^|`boost::concurrent_node_map` ^|`boost::unordered_node_map`
^|`boost::concurrent_node_map` ^|`boost::unordered_node_map`
^|`boost::concurrent_flat_set` ^|`boost::unordered_flat_set`
^|`boost::concurrent_flat_set` ^|`boost::unordered_flat_set`
^|`boost::concurrent_flat_map` ^|`boost::unordered_flat_map`
^|`boost::concurrent_flat_set` ^|`boost::unordered_flat_set`
This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container.
此互操作性适用于多阶段场景：部分数据处理环节需要并行执行，而其他步骤采用非并发（或只读）模式。下例中，我们需要从一个庞大的单词输入向量构建词频统计直方图：填充阶段用 `boost::concurrent++_++flat++_++map` 并行执行，随后将结果转移至最终容器。

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This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container.
This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container.此互操作性适用于多阶段场景：部分数据处理环节需要并行执行，而其他步骤采用非并发（或只读）模式。下例中，我们需要从一个庞大的单词输入向量构建词频统计直方图：填充阶段用 `boost::concurrent++_++flat++_++map` 并行执行，随后将结果转移至最终容器。
06/05/2026
User avatar admin

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This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container.
This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container.
06/05/2026
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safe-html, strict-same
Unit identifier
50143
Source string location
:300
String age
06/05/2026
Last updated
06/06/2026
Source string age
06/05/2026
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doc/modules/ROOT/pages/concurrent_zh_Hans.adoc, string 36