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Elastic Docs ›Elasticsearch Guide [8.5] ›Set up Elasticsearch ›Configuring Elasticsearch

Thread pools

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Thread pools

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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
For count/search/suggest operations. Thread pool type is fixed with a size of int((# of allocated processors * 3) / 2) + 1, and queue_size of 1000.
search_throttled
For count/search/suggest/get operations on search_throttled indices. Thread pool type is fixed with a size of 1, and queue_size of 100.
search_coordination
For lightweight search-related coordination operations. Thread pool type is fixed with a size of a max of min(5, (# of allocated processors) / 2), and queue_size of 1000.
get
For get operations. Thread pool type is fixed with a size of # 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 # of allocated processors, queue_size of 10000. The maximum size for this pool is 1 + # 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, (# of allocated processors) / 2).
snapshot_meta
For snapshot repository metadata read operations. Thread pool type is scaling with a keep-alive of 5m and a max of min(50, (# of allocated processors* 3)).
warmer
For segment warm-up operations. Thread pool type is scaling with a keep-alive of 5m and a max of min(5, (# 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, (# 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 2 * # 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 2 * # of allocated processors.
flush
For flush and translog fsync operations. Thread pool type is scaling with a keep-alive of 5m and a default maximum size of min(5, (# of allocated processors) / 2).
force_merge
For force merge operations. Thread pool type is fixed with a size of max(1, (# of allocated processors) / 8) 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 with a default maximum size of min(5, (# of allocated processors) / 2).
system_write
For write operations on system indices. Thread pool type is fixed with a default maximum size of min(5, (# of allocated processors) / 2).
system_critical_read
For critical read operations on system indices. Thread pool type is fixed with a default maximum size of min(5, (# of allocated processors) / 2).
system_critical_write
For critical write operations on system indices. Thread pool type is fixed with a default maximum size of min(5, (# of allocated processors) / 2).
watcher
For watch executions. Thread pool type is fixed with a default maximum size of min(5 * (# of allocated processors), 50) and queue_size of 1000.

Thread pool settings are static and can be changed by editing elasticsearch.yml. 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:

thread_pool:
    write:
        size: 30

Thread pool types

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

fixed

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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.

thread_pool:
    write:
        size: 30
        queue_size: 1000

scaling

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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.

thread_pool:
    warmer:
        core: 1
        max: 8
        keep_alive: 2m

Allocated processors setting

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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. This setting is bounded by the number of available processors and accepts floating point numbers, which can be useful in environments where the Elasticsearch nodes are configured to run with CPU limits, such as cpu shares or quota under Cgroups.

node.processors: 2

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

  1. 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 node.processors setting to the desired fraction, for example, if you’re running two instances of Elasticsearch 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.
  2. 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.

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