Machine learning anomaly detection in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is anomaly detection in machine learning?

Anomaly detection is the process of finding data points that do not fit the normal pattern. These unusual points are called anomalies or outliers.

beginner

How does Elasticsearch use machine learning for anomaly detection?

Elasticsearch uses machine learning jobs to analyze data streams and automatically find unusual patterns without needing explicit rules.

intermediate

What is a 'bucket' in Elasticsearch anomaly detection?

A bucket is a time interval in which Elasticsearch groups data points to analyze patterns and detect anomalies over time.

beginner

What role does the 'anomaly score' play in Elasticsearch machine learning?

The anomaly score shows how unusual a data point or bucket is. Scores closer to 100 mean very unusual behavior.

beginner

Name one common use case for machine learning anomaly detection in Elasticsearch.

One common use case is detecting unusual spikes in website traffic that might indicate a cyber attack or system problem.

What does an anomaly detection job in Elasticsearch analyze?

A Static configuration files

B Data patterns over time

C User passwords

D Only the latest data point

Anomaly detection jobs analyze data patterns over time to find unusual behavior.

What does a high anomaly score indicate?

A Normal behavior

B Data is missing

C Unusual or rare behavior

D System error

A high anomaly score means the data is unusual or rare compared to normal patterns.

In Elasticsearch, what is a 'bucket' used for?

A Grouping data by time intervals

B Storing user credentials

C Saving machine learning models

D Backing up data

Buckets group data by time intervals to analyze changes and detect anomalies.

Which of these is NOT a typical use case for anomaly detection?

A Monitoring system health

B Finding unusual network traffic

C Detecting fraud in transactions

D Sorting emails alphabetically

Sorting emails alphabetically is not related to anomaly detection.

What type of data does Elasticsearch machine learning typically work with for anomaly detection?

A Time series data

B Static images

C Text documents only

D Audio files

Elasticsearch machine learning mainly analyzes time series data for anomaly detection.

Explain how Elasticsearch uses machine learning to detect anomalies in data.

Think about how data is grouped and scored for unusual behavior.

Describe a real-life example where machine learning anomaly detection in Elasticsearch could help.

Consider monitoring website traffic or system logs.

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