OpenSearch/docs/reference/modules/threadpool.asciidoc

[[modules-threadpool]]
=== Thread pools

A node uses several thread pools to manage memory consumption.
Queues associated with many of the thread pools enable pending requests
to be held instead of discarded.

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

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

[[search-threadpool]]
`search`::
    For count/search/suggest operations. Thread pool type is
    `fixed_auto_queue_size` with a size of `int((`<<node.processors,
    `# of allocated processors`>>`pass:[ * ]3) / 2) + 1`, and initial queue_size of
    `1000`.

[[search-throttled]]`search_throttled`::
    For count/search/suggest/get operations on `search_throttled indices`.
    Thread pool type is `fixed_auto_queue_size` with a size of `1`, and initial
    queue_size of `100`.

`get`::
    For get operations. Thread pool type is `fixed`
    with a size of <<node.processors, `# of allocated 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 <<node.processors, `# of allocated processors`>>,
    queue_size of `10000`. The maximum size for this pool is
    `pass:[1 + ]`<<node.processors, `# of allocated processors`>>.

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

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

`fetch_shard_started`::
    For listing shard states.
    Thread pool type is `scaling` with keep-alive of `5m` and a default maximum
    size of `pass:[2 * ]`<<node.processors, `# of allocated processors`>>.

`fetch_shard_store`::
    For listing shard stores.
    Thread pool type is `scaling` with keep-alive of `5m` and a default maximum
    size of `pass:[2 * ]`<<node.processors, `# of allocated processors`>>.

`flush`::
    For <<indices-flush,flush>>, <<indices-synced-flush-api,synced flush>>, and
    <<index-modules-translog, translog>> `fsync` operations. Thread pool type is
    `scaling` with a keep-alive of `5m` and a default maximum size of `min(5, (`
    <<node.processors, `# of allocated processors`>>`) / 2)`.

`force_merge`::
    For <<indices-forcemerge,force merge>> operations.
    Thread pool type is `fixed` with a size of 1 and an unbounded queue size.

`management`::
    For cluster management.
    Thread pool type is `scaling` with a keep-alive of `5m` and a default
    maximum size of `5`.

`system_read`::
    For read operations on system indices.
    Thread pool type is `fixed` and a default maximum size of
    `min(5, (`<<node.processors, `# of allocated processors`>>`) / 2)`.

Changing a specific thread pool can be done by setting its type-specific
parameters; for example, changing the number of threads in the `write` thread
pool:

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

[[thread-pool-types]]
==== Thread pool types

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

[[fixed-thread-pool]]
===== `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.

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:
    write:
        size: 30
        queue_size: 1000
--------------------------------------------------

[[fixed-auto-queue-size]]
===== `fixed_auto_queue_size`

experimental[]

deprecated[7.7.0,The experimental `fixed_auto_queue_size` thread pool type is
deprecated and will be removed in 8.0.]

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.

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
--------------------------------------------------

[[scaling-thread-pool]]
===== `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
--------------------------------------------------

[[node.processors]]
==== Allocated 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
`node.processors` setting.

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

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

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

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