Machine learning anomaly detection in Elasticsearch

Introduction

Machine learning anomaly detection helps find unusual patterns in data automatically. It spots things that don't fit normal behavior.

Detecting unusual spikes in website traffic that might mean a problem or attack.

Finding errors or faults in machines by monitoring sensor data.

Spotting fraud in financial transactions by noticing strange activity.

Monitoring server logs to catch unexpected errors or failures.

Checking user behavior to identify potential security breaches.

Syntax

Elasticsearch

POST _ml/anomaly_detectors/<job_id>
{
  "description": "Detect anomalies in data",
  "analysis_config": {
    "bucket_span": "15m",
    "detectors": [
      {
        "function": "mean",
        "field_name": "value"
      }
    ]
  },
  "data_description": {
    "time_field": "timestamp"
  }
}

job_id is a unique name for your anomaly detection job.

bucket_span defines the time window size for analysis, like 15 minutes.

Examples

This example creates a job to find unusual total sales per hour.

Elasticsearch

POST _ml/anomaly_detectors/sales_anomaly_job
{
  "description": "Detect anomalies in sales data",
  "analysis_config": {
    "bucket_span": "1h",
    "detectors": [
      {
        "function": "sum",
        "field_name": "sales_amount"
      }
    ]
  },
  "data_description": {
    "time_field": "sale_time"
  }
}

This job looks for high CPU usage every 5 minutes.

Elasticsearch

POST _ml/anomaly_detectors/cpu_usage_job
{
  "description": "Detect CPU usage spikes",
  "analysis_config": {
    "bucket_span": "5m",
    "detectors": [
      {
        "function": "max",
        "field_name": "cpu_percent"
      }
    ]
  },
  "data_description": {
    "time_field": "timestamp"
  }
}

Sample Program

This example creates an anomaly detection job for temperature data, opens the job, starts a datafeed to read from the 'sensor_data' index, and then fetches detected anomalies.

Elasticsearch

POST _ml/anomaly_detectors/temperature_anomaly_job
{
  "description": "Detect temperature anomalies",
  "analysis_config": {
    "bucket_span": "10m",
    "detectors": [
      {
        "function": "mean",
        "field_name": "temperature"
      }
    ]
  },
  "data_description": {
    "time_field": "timestamp"
  }
}

POST _ml/anomaly_detectors/temperature_anomaly_job/_open

POST _ml/datafeeds/datafeed-temperature_anomaly_job
{
  "job_id": "temperature_anomaly_job",
  "indices": ["sensor_data"]
}

POST _ml/datafeeds/datafeed-temperature_anomaly_job/_start

GET _ml/anomaly_detectors/temperature_anomaly_job/results/anomalies

Important Notes

Always choose a bucket_span that matches how often your data updates.

After creating a job, you must open it before starting the datafeed.

Check anomaly scores to decide if a result is important; higher scores mean more unusual.

Summary

Machine learning anomaly detection finds unusual data patterns automatically.

Use it to monitor systems, detect fraud, or find errors early.

In Elasticsearch, create a job, open it, start a datafeed, then check results.

Practice

1. What is the main purpose of machine learning anomaly detection in Elasticsearch?

easy

A. To automatically find unusual patterns in data

B. To store large amounts of data efficiently

C. To create visual dashboards for data

D. To backup Elasticsearch clusters

Solution

1. Step 1: Understand anomaly detection goal

   Machine learning anomaly detection is designed to find unusual or unexpected patterns in data automatically.

2. Step 2: Compare options with purpose

   Options B, C, and D describe other Elasticsearch features, not anomaly detection.

3. Final Answer:

   To automatically find unusual patterns in data -> Option A

4. Quick Check:

   Purpose of anomaly detection = find unusual patterns [OK]

Hint: Anomaly detection finds unusual data automatically [OK]

Common Mistakes:

• Confusing anomaly detection with data storage
• Thinking anomaly detection creates dashboards
• Mixing anomaly detection with backup tasks

2. Which Elasticsearch API call starts the anomaly detection process by feeding data to the job?

easy

A. POST _ml/anomaly_detectors/<job_id>/_start_datafeed

B. GET _ml/anomaly_detectors/<job_id>/results

C. PUT _ml/anomaly_detectors/<job_id>

D. DELETE _ml/anomaly_detectors/<job_id>

Solution

1. Step 1: Identify datafeed start API

   The API to start feeding data to an anomaly detection job is POST _ml/anomaly_detectors/<job_id>/_start_datafeed.

2. Step 2: Eliminate other options

   GET retrieves results, PUT creates or updates jobs, DELETE removes jobs.

3. Final Answer:

   POST _ml/anomaly_detectors/<job_id>/_start_datafeed -> Option A

4. Quick Check:

   Start datafeed = POST _start_datafeed [OK]

Hint: Start datafeed uses POST with _start_datafeed endpoint [OK]

Common Mistakes:

• Using GET instead of POST to start datafeed
• Confusing job creation with starting datafeed
• Deleting job instead of starting datafeed

3. Given this Elasticsearch ML job result snippet:

{"job_id":"sales_anomaly","results":[{"timestamp":1680000000000,"anomaly_score":75},{"timestamp":1680003600000,"anomaly_score":5}]}

Which timestamp shows a likely anomaly?

medium

A. Neither timestamp

B. 1680003600000

C. Both timestamps

D. 1680000000000

Solution

1. Step 1: Understand anomaly score meaning

   Higher anomaly scores indicate more unusual data points. A score of 75 is high, 5 is low.

2. Step 2: Identify timestamp with high score

   The timestamp 1680000000000 has anomaly_score 75, indicating a likely anomaly.

3. Final Answer:

   1680000000000 -> Option D

4. Quick Check:

   High anomaly score = likely anomaly [OK]

Hint: Higher anomaly_score means more likely anomaly [OK]

Common Mistakes:

• Choosing low anomaly score as anomaly
• Selecting both timestamps without checking scores
• Ignoring anomaly_score values

4. You created an anomaly detection job but see no results after starting the datafeed. What is a likely cause?

medium

A. The job was deleted before starting

B. The Elasticsearch cluster is offline

C. The datafeed is not running or has stopped

D. The anomaly scores are all zero

Solution

1. Step 1: Check datafeed status

   If no results appear, the datafeed may not be running or has stopped feeding data to the job.

2. Step 2: Evaluate other options

   Job deletion would prevent starting datafeed; cluster offline causes broader failures; zero scores still produce results.

3. Final Answer:

   The datafeed is not running or has stopped -> Option C

4. Quick Check:

   No results usually mean datafeed stopped [OK]

Hint: No results? Check if datafeed is running [OK]

Common Mistakes:

• Assuming zero scores mean no results
• Ignoring datafeed status
• Blaming cluster offline without checking datafeed

5. You want to detect unusual spikes in website traffic using Elasticsearch ML anomaly detection. Which steps should you follow to set this up correctly?

hard

A. Backup traffic data, create index pattern, then visualize spikes

B. Create a job with traffic data, start datafeed, then analyze anomaly results

C. Create a dashboard, upload traffic logs, then run anomaly detection manually

D. Delete old data, create job without datafeed, then check results

Solution

1. Step 1: Create ML job with traffic data

   Define an anomaly detection job using the website traffic data to analyze patterns.

2. Step 2: Start the datafeed to feed data into the job

   Start the datafeed so the job can process incoming traffic data continuously.

3. Step 3: Analyze the anomaly detection results

   Review the results to identify unusual spikes or anomalies in traffic.

4. Final Answer:

   Create a job with traffic data, start datafeed, then analyze anomaly results -> Option B

5. Quick Check:

   Job + datafeed + analyze = correct setup [OK]

Hint: Job creation + datafeed start + result check = setup [OK]

Common Mistakes:

• Skipping datafeed start step
• Confusing dashboards with anomaly detection setup
• Deleting data before analysis