[ML] Introduce a setting for the process connect timeout #43234
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This change introduces a new setting, xpack.ml.process_connect_timeout, to enable the timeout for one of the external ML processes to connect to the ES JVM to be increased. The timeout may need to be increased if many processes are being started simultaneously on the same machine. This is unlikely in clusters with many ML nodes, as we balance the processes across the ML nodes, but can happen in clusters with a single ML node and a high value for xpack.ml.node_concurrent_job_allocations.
Collaborator
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Pinging @elastic/ml-core |
Author
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I deliberately didn't use the configurable timeout for the |
lcawl
reviewed
Jun 14, 2019
| such an external process. | ||
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| `xpack.ml.process_connect_timeout` (<<cluster-update-settings,Dynamic>>):: | ||
| Some {ml} processing is done by processes that run separately to the {es} JVM. |
Contributor
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It would be nice to have a brief introductory explanation here, akin to what we have in other timeout definitions.
e.g.
The connect(ion?) timeout for {ml} processes that run separately from the {es} JVM. Defaults to
10s. When such processes...
lcawl
reviewed
Jun 14, 2019
lcawl
reviewed
Jun 14, 2019
Author
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Jenkins run elasticsearch-ci/1 |
droberts195
pushed a commit
that referenced
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Jun 26, 2019
This change introduces a new setting, xpack.ml.process_connect_timeout, to enable the timeout for one of the external ML processes to connect to the ES JVM to be increased. The timeout may need to be increased if many processes are being started simultaneously on the same machine. This is unlikely in clusters with many ML nodes, as we balance the processes across the ML nodes, but can happen in clusters with a single ML node and a high value for xpack.ml.node_concurrent_job_allocations.
droberts195
pushed a commit
that referenced
this pull request
Jun 26, 2019
This change introduces a new setting, xpack.ml.process_connect_timeout, to enable the timeout for one of the external ML processes to connect to the ES JVM to be increased. The timeout may need to be increased if many processes are being started simultaneously on the same machine. This is unlikely in clusters with many ML nodes, as we balance the processes across the ML nodes, but can happen in clusters with a single ML node and a high value for xpack.ml.node_concurrent_job_allocations.
droberts195
pushed a commit
that referenced
this pull request
Jun 26, 2019
This change introduces a new setting, xpack.ml.process_connect_timeout, to enable the timeout for one of the external ML processes to connect to the ES JVM to be increased. The timeout may need to be increased if many processes are being started simultaneously on the same machine. This is unlikely in clusters with many ML nodes, as we balance the processes across the ML nodes, but can happen in clusters with a single ML node and a high value for xpack.ml.node_concurrent_job_allocations.
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This change introduces a new setting,
xpack.ml.process_connect_timeout, to enable
the timeout for one of the external ML processes
to connect to the ES JVM to be increased.
The default timeout of 10 seconds is the same
as the hardcoded timeout in previous versions.
The timeout may need to be increased if many
processes are being started simultaneously on
the same machine. This is unlikely in clusters
with many ML nodes, as we balance the processes
across the ML nodes, but can happen in clusters
with a single ML node and a high value for
xpack.ml.node_concurrent_job_allocations.