190 lines
6.5 KiB
Plaintext
190 lines
6.5 KiB
Plaintext
[[modules-threadpool]]
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== Thread Pool
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A node holds several thread pools in order to improve how threads memory consumption
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are managed within a node. Many of these pools also have queues associated with them,
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which allow pending requests to be held instead
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of discarded.
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There are several thread pools, but the important ones include:
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`generic`::
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For generic operations (e.g., background node discovery).
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Thread pool type is `scaling`.
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`index`::
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For index/delete operations. Thread pool type is `fixed`
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with a size of `# of available processors`,
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queue_size of `200`. The maximum size for this pool
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is `1 + # of available processors`.
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`search`::
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For count/search/suggest operations. Thread pool type is
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`fixed_auto_queue_size` with a size of
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`int((# of available_processors * 3) / 2) + 1`, and initial queue_size of
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`1000`.
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`get`::
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For get operations. Thread pool type is `fixed`
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with a size of `# of available processors`,
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queue_size of `1000`.
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`bulk`::
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For bulk operations. Thread pool type is `fixed`
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with a size of `# of available processors`,
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queue_size of `200`. The maximum size for this pool
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is `1 + # of available processors`.
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`snapshot`::
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For snapshot/restore operations. Thread pool type is `scaling` with a
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keep-alive of `5m` and a max of `min(5, (# of available processors)/2)`.
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`warmer`::
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For segment warm-up operations. Thread pool type is `scaling` with a
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keep-alive of `5m` and a max of `min(5, (# of available processors)/2)`.
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`refresh`::
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For refresh operations. Thread pool type is `scaling` with a
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keep-alive of `5m` and a max of `min(10, (# of available processors)/2)`.
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`listener`::
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Mainly for java client executing of action when listener threaded is set to true.
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Thread pool type is `scaling` with a default max of `min(10, (# of available processors)/2)`.
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Changing a specific thread pool can be done by setting its type-specific parameters; for example, changing the `index`
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thread pool to have more threads:
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[source,yaml]
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--------------------------------------------------
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thread_pool:
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index:
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size: 30
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--------------------------------------------------
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[float]
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[[types]]
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=== Thread pool types
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The following are the types of thread pools and their respective parameters:
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[float]
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==== `fixed`
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The `fixed` thread pool holds a fixed size of threads to handle the
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requests with a queue (optionally bounded) for pending requests that
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have no threads to service them.
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The `size` parameter controls the number of threads, and defaults to the
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number of cores times 5.
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The `queue_size` allows to control the size of the queue of pending
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requests that have no threads to execute them. By default, it is set to
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`-1` which means its unbounded. When a request comes in and the queue is
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full, it will abort the request.
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[source,yaml]
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--------------------------------------------------
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thread_pool:
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index:
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size: 30
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queue_size: 1000
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--------------------------------------------------
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[float]
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==== `fixed_auto_queue_size`
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experimental[]
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The `fixed_auto_queue_size` thread pool holds a fixed size of threads to handle
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the requests with a bounded queue for pending requests that have no threads to
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service them. It's similar to the `fixed` threadpool, however, the `queue_size`
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automatically adjusts according to calculations based on
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https://en.wikipedia.org/wiki/Little%27s_law[Little's Law]. These calculations
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will potentially adjust the `queue_size` up or down by 50 every time
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`auto_queue_frame_size` operations have been completed.
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The `size` parameter controls the number of threads, and defaults to the
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number of cores times 5.
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The `queue_size` allows to control the initial size of the queue of pending
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requests that have no threads to execute them.
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The `min_queue_size` setting controls the minimum amount the `queue_size` can be
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adjusted to.
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The `max_queue_size` setting controls the maximum amount the `queue_size` can be
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adjusted to.
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The `auto_queue_frame_size` setting controls the number of operations during
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which measurement is taken before the queue is adjusted. It should be large
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enough that a single operation cannot unduly bias the calculation.
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The `target_response_time` is a time value setting that indicates the targeted
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average response time for tasks in the thread pool queue. If tasks are routinely
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above this time, the thread pool queue will be adjusted down so that tasks are
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rejected.
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[source,yaml]
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--------------------------------------------------
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thread_pool:
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search:
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size: 30
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queue_size: 500
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min_queue_size: 10
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max_queue_size: 1000
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auto_queue_frame_size: 2000
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target_response_time: 1s
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--------------------------------------------------
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[float]
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==== `scaling`
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The `scaling` thread pool holds a dynamic number of threads. This
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number is proportional to the workload and varies between the value of
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the `core` and `max` parameters.
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The `keep_alive` parameter determines how long a thread should be kept
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around in the thread pool without it doing any work.
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[source,yaml]
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--------------------------------------------------
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thread_pool:
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warmer:
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core: 1
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max: 8
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keep_alive: 2m
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--------------------------------------------------
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[float]
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[[processors]]
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=== Processors setting
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The number of processors is automatically detected, and the thread pool
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settings are automatically set based on it. In some cases it can be
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useful to override the number of detected processors. This can be done
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by explicitly setting the `processors` setting.
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[source,yaml]
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--------------------------------------------------
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processors: 2
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--------------------------------------------------
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There are a few use-cases for explicitly overriding the `processors`
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setting:
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. If you are running multiple instances of Elasticsearch on the same
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host but want Elasticsearch to size its thread pools as if it only has a
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fraction of the CPU, you should override the `processors` setting to the
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desired fraction (e.g., if you're running two instances of Elasticsearch
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on a 16-core machine, set `processors` to 8). Note that this is an
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expert-level use-case and there's a lot more involved than just setting
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the `processors` setting as there are other considerations like changing
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the number of garbage collector threads, pinning processes to cores,
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etc.
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. Sometimes the number of processors is wrongly detected and in such
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cases explicitly setting the `processors` setting will workaround such
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issues.
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In order to check the number of processors detected, use the nodes info
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API with the `os` flag.
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