- X-Pack Reference for 6.0-6.2 and 5.x:
- Introduction
- Setting Up X-Pack
- Breaking Changes
- X-Pack APIs
- Graphing Connections in Your Data
- Profiling your Queries and Aggregations
- Reporting from Kibana
- Securing the Elastic Stack
- Getting Started with Security
- How Security Works
- Setting Up User Authentication
- Configuring SAML Single-Sign-On on the Elastic Stack
- Configuring Role-based Access Control
- Auditing Security Events
- Encrypting Communications
- Restricting Connections with IP Filtering
- Cross Cluster Search, Tribe, Clients and Integrations
- Reference
- Monitoring the Elastic Stack
- Alerting on Cluster and Index Events
- Machine Learning in the Elastic Stack
- Troubleshooting
- Getting Help
- X-Pack security
- Can’t log in after upgrading to 6.2.4
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- X-Pack Watcher
- X-Pack monitoring
- X-Pack machine learning
- Limitations
- License Management
- Release Notes
WARNING: Version 6.2 of the Elastic Stack has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Stopping Machine Learning
editStopping Machine Learning
editAn orderly shutdown of machine learning ensures that:
- Datafeeds are stopped
- Buffers are flushed
- Model history is pruned
- Final results are calculated
- Model snapshots are saved
- Jobs are closed
This process ensures that jobs are in a consistent state in case you want to subsequently re-open them.
Stopping Datafeeds
editWhen you stop a datafeed, it ceases to retrieve data from Elasticsearch. You can stop a
datafeed by using Kibana or the
stop datafeeds API. For example, the following
request stops the feed1
datafeed:
POST _xpack/ml/datafeeds/feed1/_stop
You must have manage_ml
, or manage
cluster privileges to stop datafeeds.
For more information, see Security Privileges.
A datafeed can be started and stopped multiple times throughout its lifecycle.
For examples of stopping datafeeds in Kibana, see Managing Jobs.
Stopping All Datafeeds
editIf you are upgrading your cluster, you can use the following request to stop all datafeeds:
POST _xpack/ml/datafeeds/_all/_stop
Closing Jobs
editWhen you close a job, it cannot receive data or perform analysis operations. If a job is associated with a datafeed, you must stop the datafeed before you can close the jobs. If the datafeed has an end date, the job closes automatically on that end date.
You can close a job by using the close job API. For
example, the following request closes the job1
job:
POST _xpack/ml/anomaly_detectors/job1/_close
You must have manage_ml
, or manage
cluster privileges to stop datafeeds.
For more information, see Security Privileges.
A job can be opened and closed multiple times throughout its lifecycle.
Closing All Jobs
editIf you are upgrading your cluster, you can use the following request to close all open jobs on the cluster:
POST _xpack/ml/anomaly_detectors/_all/_close
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