OpenSearch/docs/reference/modules/threadpool.asciidoc

[[modules-threadpool]]
== Thread Pool

A node holds several thread pools in order to improve how threads memory consumption
are managed within a node. Many of these pools also have queues associated with them,
which allow pending requests to be held instead
of discarded.


There are several thread pools, but the important ones include:

`generic`::
    For generic operations (e.g., background node discovery).
    Thread pool type is `scaling`.

`search`::
    For count/search/suggest operations. Thread pool type is
    `fixed_auto_queue_size` with a size of
    `int((# of available_processors * 3) / 2) + 1`, and initial queue_size of
    `1000`.

`get`::
    For get operations. Thread pool type is `fixed`
    with a size of `# of available processors`,
    queue_size of `1000`.

`analyze`::
    For analyze requests. Thread pool type is `fixed` with a size of 1, queue size of 16.

`write`::
    For single-document index/delete/update and bulk requests. Thread pool type
    is `fixed` with a size of `# of available processors`, queue_size of `200`.
    The maximum size for this pool is `1 + # of available processors`.

`snapshot`::
    For snapshot/restore operations. Thread pool type is `scaling` with a
    keep-alive of `5m` and a max of `min(5, (# of available processors)/2)`.

`warmer`::
    For segment warm-up operations. Thread pool type is `scaling` with a
    keep-alive of `5m` and a max of `min(5, (# of available processors)/2)`.

`refresh`::
    For refresh operations. Thread pool type is `scaling` with a
    keep-alive of `5m` and a max of `min(10, (# of available processors)/2)`.

`listener`::
    Mainly for java client executing of action when listener threaded is set to true.
    Thread pool type is `scaling` with a default max of `min(10, (# of available processors)/2)`.

Changing a specific thread pool can be done by setting its type-specific parameters; for example, changing the `bulk`
thread pool to have more threads:

[source,yaml]
--------------------------------------------------
thread_pool:
    bulk:
        size: 30
--------------------------------------------------

[float]
[[types]]
=== Thread pool types

The following are the types of thread pools and their respective parameters:

[float]
==== `fixed`

The `fixed` thread pool holds a fixed size of threads to handle the
requests with a queue (optionally bounded) for pending requests that
have no threads to service them.

The `size` parameter controls the number of threads, and defaults to the
number of cores times 5.

The `queue_size` allows to control the size of the queue of pending
requests that have no threads to execute them. By default, it is set to
`-1` which means its unbounded. When a request comes in and the queue is
full, it will abort the request.

[source,yaml]
--------------------------------------------------
thread_pool:
    bulk:
        size: 30
        queue_size: 1000
--------------------------------------------------

[float]
==== `fixed_auto_queue_size`

experimental[]

The `fixed_auto_queue_size` thread pool holds a fixed size of threads to handle
the requests with a bounded queue for pending requests that have no threads to
service them. It's similar to the `fixed` threadpool, however, the `queue_size`
automatically adjusts according to calculations based on
https://en.wikipedia.org/wiki/Little%27s_law[Little's Law]. These calculations
will potentially adjust the `queue_size` up or down by 50 every time
`auto_queue_frame_size` operations have been completed.

The `size` parameter controls the number of threads, and defaults to the
number of cores times 5.

The `queue_size` allows to control the initial size of the queue of pending
requests that have no threads to execute them.

The `min_queue_size` setting controls the minimum amount the `queue_size` can be
adjusted to.

The `max_queue_size` setting controls the maximum amount the `queue_size` can be
adjusted to.

The `auto_queue_frame_size` setting controls the number of operations during
which measurement is taken before the queue is adjusted. It should be large
enough that a single operation cannot unduly bias the calculation.

The `target_response_time` is a time value setting that indicates the targeted
average response time for tasks in the thread pool queue. If tasks are routinely
above this time, the thread pool queue will be adjusted down so that tasks are
rejected.

[source,yaml]
--------------------------------------------------
thread_pool:
    search:
        size: 30
        queue_size: 500
        min_queue_size: 10
        max_queue_size: 1000
        auto_queue_frame_size: 2000
        target_response_time: 1s
--------------------------------------------------

[float]
==== `scaling`

The `scaling` thread pool holds a dynamic number of threads. This
number is proportional to the workload and varies between the value of
the `core` and `max` parameters.

The `keep_alive` parameter determines how long a thread should be kept
around in the thread pool without it doing any work.

[source,yaml]
--------------------------------------------------
thread_pool:
    warmer:
        core: 1
        max: 8
        keep_alive: 2m
--------------------------------------------------

[float]
[[processors]]
=== Processors setting
The number of processors is automatically detected, and the thread pool
settings are automatically set based on it. In some cases it can be
useful to override the number of detected processors. This can be done
by explicitly setting the `processors` setting.

[source,yaml]
--------------------------------------------------
processors: 2
--------------------------------------------------

There are a few use-cases for explicitly overriding the `processors`
setting:

. If you are running multiple instances of Elasticsearch on the same
host but want Elasticsearch to size its thread pools as if it only has a
fraction of the CPU, you should override the `processors` setting to the
desired fraction (e.g., if you're running two instances of Elasticsearch
on a 16-core machine, set `processors` to 8). Note that this is an
expert-level use-case and there's a lot more involved than just setting
the `processors` setting as there are other considerations like changing
the number of garbage collector threads, pinning processes to cores,
etc.
. Sometimes the number of processors is wrongly detected and in such
cases explicitly setting the `processors` setting will workaround such
issues.

In order to check the number of processors detected, use the nodes info
API with the `os` flag.