/elasticsearch-anomaly-detection

name: elasticsearch-anomaly-detection
description: >
  Create and manage Elastic ML anomaly detection jobs via the API. Use when setting
  up jobs on an index or data stream, configuring jobs and datafeeds, or opening,
  starting, or stopping them.
compatibility: >
  Requires Elasticsearch 8.x+ or Elastic Cloud Serverless with ML anomaly detection.
  Uses the ML REST API only. User needs manage_ml privilege to create and manage jobs.
metadata:
  author: elastic
  version: 1.1.0
  universal: true

Elasticsearch Anomaly Detection

Create, open, and start ML anomaly detection jobs on time-series data. Choose the right count-family detector direction, configure bucket span and time field, wire the datafeed to the correct index, and confirm running state from stats — not from assumptions.

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Environment Configuration

This skill executes Elasticsearch operations through the `elastic` CLI. If the [`elastic` CLI](https://github.com/elastic/cli#configuration) is not installed, tell the user what it is needed for. Do not guess credentials, call the HTTP API directly, or attempt other workarounds.

This skill references operations in HTTP-shorthand form (e.g., `GET /`, `GET /_cat/indices`, `GET /{index}/_mapping`, `GET /{index}/_settings/index.mode`, `POST /_query`). The [Operations](#operations) table at the end of this document maps each shorthand to the equivalent `elastic` CLI command — always use the CLI rather than calling the HTTP API directly.

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> **Prerequisite:** ML anomaly detection requires a Platinum-equivalent license on self-managed clusters. Serverless > projects include ML. The caller needs `manage_ml` to create and manage jobs. > > **Related skill:** For interpreting anomaly scores, influencers, and model behavior after a job is running, use > `elasticsearch-anomaly-detection-explainer` — not this skill.

Process

1. **Discover the target index and time field.** List candidate indices with `GET /_cat/indices` (pass a pattern when the user names one). Fetch field types for the chosen index with `GET /{index}/_mapping`. The decision: confirm the index exists, identify the time field (often `@timestamp`), and verify document volume is sufficient for baseline learning. Never guess index or field names — they vary across deployments.

2. **Choose detector function and direction.** Match the user's intent to a count-family detector in `analysis_config.detectors`:

• **Spike, surge, unusual increase in event volume** → `high_count` (or `count`, which flags both directions but is

acceptable when the user cares about spikes). Do **not** use `low_count` — it will miss spikes.

• **Drop, outage, absence of events, traffic stops** → `low_count`. Do **not** use `high_count` — it will miss drops

and silence.

• **Metric deviation** (CPU, latency, a numeric field) → mean-family functions (`mean`, `high_mean`, `low_mean`) with

`field_name` set — only when the user asks about a numeric metric, not raw event volume.

The decision: pick one primary detector whose direction matches the anomaly type. For volume spike/drop questions on document counts, stay in the count family — mean detectors are unsuited to "how many events" questions.

3. **Set immutable job shape before creation.** These fields cannot change after `PUT /_ml/anomaly_detectors/{job_id}`:

• `analysis_config.bucket_span` — use the interval the user specifies (e.g. `15m` for 15-minute buckets). Match the

granularity of anomalies they care about; too short is noisy, too long is slow to detect.

• `data_description.time_field` — the time field from the mapping (commonly `@timestamp`).
• `analysis_config.detectors` — the function and direction from step 2.

Example job body for a volume-spike detector:

   {
     "analysis_config": {
       "bucket_span": "15m",
       "detectors": [{ "function": "high_count" }]
     },
     "data_description": { "time_field": "@timestamp" }
   }

Example for an outage / drop detector:

   {
     "analysis_config": {
       "bucket_span": "15m",
       "detectors": [{ "function": "low_count" }]
     },
     "data_description": { "time_field": "@timestamp" }
   }

4. **Create the job.** Call `PUT /_ml/anomaly_detectors/{job_id}` with the job id the user requested (or a descriptive id you propose). The job starts in `closed` state — creating it does not start analysis.

5. **Create the datafeed.** Call `PUT /_ml/datafeeds/datafeed-{job_id}` immediately after job creation. Set `job_id` to the same id, `indices` to the target index (exact name or pattern from step 1), and a query that selects the relevant documents (typically `match_all`). The datafeed id convention is `datafeed-{job_id}`.

   {
     "job_id": "{job_id}",
     "indices": ["{index}"],
     "query": { "match_all": {} }
   }

6. **Open the job, then start the datafeed — in that order.** This sequence is mandatory; do not skip or reorder: 1. `POST /_ml/anomaly_detectors/{job_id}/_open` — transitions the job to `opened`. 2. `POST /_ml/datafeeds/datafeed-{job_id}/_start` — transitions the datafeed to `started`.

Opening before the datafeed exists fails. Starting the datafeed before opening the job fails. Do not report success after only creating resources — the job is not running until both are active.

7. **Confirm running state from stats.** Verify the outcome with:

• `GET /_ml/anomaly_detectors/{job_id}/_stats` — expect `state: "opened"`.
• `GET /_ml/datafeeds/datafeed-{job_id}/_stats` — expect `state: "started"`.

Optionally call `GET /_ml/anomaly_detectors/{job_id}` to confirm configuration (detectors, `bucket_span`, `time_field`, datafeed indices). Report both stats states explicitly — "created" is not the same as "opened" and "started".

Teardown

When stopping or deleting a job, reverse the startup order:

1. `POST