15 Kibana Interview Questions and Answers

Kibana is a powerful data visualization and exploration tool used primarily for log and time-series analytics. It is an integral part of the Elastic Stack, enabling users to create dynamic dashboards and perform complex queries with ease. Kibana’s intuitive interface and robust features make it a popular choice for monitoring and analyzing large datasets in real-time.

This article offers a curated selection of interview questions designed to test your knowledge and proficiency with Kibana. By working through these questions, you will gain a deeper understanding of Kibana’s capabilities and be better prepared to demonstrate your expertise in a professional setting.

Kibana Interview Questions and Answers

1. Explain how to build and customize a dashboard.

Building and customizing a dashboard in Kibana involves creating visualizations based on your data using various charts, graphs, and maps. Once your visualizations are ready, you can add them to a dashboard by navigating to the Dashboard section and clicking “Create new dashboard.” Add your visualizations by clicking “Add” and selecting the ones you want. You can arrange and resize them as needed.

Customization options include applying filters to focus on specific data subsets and using the time picker to adjust the time range. You can also add markdown widgets for text, images, or links. The “Options” menu allows you to adjust the dashboard’s appearance, set the theme, and configure the layout. Save your dashboard for future use and share it by generating a link or embedding it in an external application.

2. What is Timelion and how do you use it for time-series data analysis?

Timelion is a time-series data visualization tool in Kibana that uses a simple syntax for advanced data analysis. It is useful for analyzing trends and patterns over time. Timelion allows users to chain functions and operations to query and visualize time-series data.

To use Timelion, write expressions in the Timelion expression language, which can include functions for data retrieval and transformations. For example, the .es() function fetches data from Elasticsearch, and you can apply transformations like moving averages.

Example:

.es(index=my_index, timefield=@timestamp, metric=avg:response_time).movingaverage(5)

Timelion supports multiple data sources, allowing you to combine data from different indices or external APIs. You can create multi-series visualizations by chaining expressions and using functions like .label().

3. Write a Kibana Query Language (KQL) query to filter documents where the status is “error”.

To filter documents in Kibana where the status is “error,” use the following KQL query:

status: "error"

This query searches for all documents where the “status” field has the value “error.”

4. How do you create a scripted field to calculate the difference between two date fields?

In Kibana, scripted fields allow you to create new fields based on existing ones. These fields are computed at query time using the Painless scripting language.

To create a scripted field to calculate the difference between two date fields:

• Navigate to the “Management” section in Kibana.
• Select the index pattern where you want to create the scripted field.
• Click on the “Scripted Fields” tab and then “Add Scripted Field.”
• Enter a name for the scripted field, such as “date_difference.”
• Choose the type of the scripted field, typically “number.”
• In the script editor, use the following Painless script to calculate the difference between two date fields (e.g., date1 and date2) in days:

if (doc['date1'].size() != 0 && doc['date2'].size() != 0) {
    return (doc['date2'].value.getMillis() - doc['date1'].value.getMillis()) / (1000 * 60 * 60 * 24);
} else {
    return null;
}

This script checks if both date fields are present, calculates the difference in milliseconds, and converts it to days.

5. What are the key security features available?

Kibana offers several security features:

• Authentication: Supports various methods, including native, LDAP, Active Directory, and SSO with SAML and OpenID Connect.
• Authorization: Role-based access control (RBAC) allows defining roles and assigning permissions to users.
• Encryption: Supports TLS/SSL for data in transit.
• Auditing: Tracks and logs user activities, including access and configuration changes.
• Spaces: Segments data and dashboards into different workspaces for organization and security.

6. Describe the process of setting up an alert for when a specific threshold is met.

To set up an alert in Kibana for when a specific threshold is met, use the Watcher feature:

• Create a Watch: Navigate to the Watcher section and create a new watch.
• Define the Conditions: Specify the criteria for the alert, such as a threshold for a metric.
• Set Up Actions: Define actions to take when the alert triggers, like sending an email or logging the event.
• Test and Activate the Watch: Test the watch before activating it to ensure it works as expected.

7. Write an advanced KQL query to find documents where the response time is greater than 500ms and the status is not “success”.

To find documents where the response time is greater than 500ms and the status is not “success,” use the following KQL query:

response_time > 500 and not status: "success"

This query filters documents to include only those meeting these criteria.

8. How do you use the REST API to automate dashboard creation?

Kibana’s REST API allows for programmatic interaction, enabling automation of tasks like dashboard creation. To automate dashboard creation:

• Define the dashboard structure in JSON format.
• Use the REST API to send a request to Kibana to create the dashboard.

Example:

import requests
import json

kibana_url = 'http://localhost:5601'
api_endpoint = '/api/saved_objects/dashboard'

dashboard_json = {
    "attributes": {
        "title": "Automated Dashboard",
        "panelsJSON": "[]",
        "optionsJSON": "{}",
        "version": 1
    }
}

headers = {
    'kbn-xsrf': 'true',
    'Content-Type': 'application/json'
}

response = requests.post(kibana_url + api_endpoint, headers=headers, data=json.dumps(dashboard_json))

if response.status_code == 200:
    print("Dashboard created successfully")
else:
    print("Failed to create dashboard:", response.text)

9. What techniques can be used to optimize performance for large datasets?

To optimize performance for large datasets in Kibana:

• Efficient Data Indexing: Use appropriate mappings and settings.
• Shard Allocation: Balance load across the Elasticsearch cluster.
• Query Optimization: Use filters instead of queries where possible.
• Data Aggregation: Summarize and reduce data volume.
• Index Lifecycle Management (ILM): Manage indices based on age, size, or other criteria.
• Hardware and Resource Allocation: Ensure adequate resources and configure settings properly.

10. How do you configure and use cross-cluster search?

Cross-cluster search in Kibana allows searching across multiple Elasticsearch clusters from a single instance. To configure it:

• Configure remote clusters in the Elasticsearch configuration file (elasticsearch.yml).
• Ensure remote clusters are accessible from the local cluster.
• Use cross-cluster search syntax in Kibana to query data from remote clusters.

Example configuration in elasticsearch.yml:

cluster:
  remote:
    cluster_one:
      seeds: ["127.0.0.1:9300"]
    cluster_two:
      seeds: ["127.0.0.2:9300"]

Example query in Kibana:

GET /cluster_one:index_name/_search
{
  "query": {
    "match_all": {}
  }
}

11. Describe a troubleshooting approach for resolving a situation where no data is displayed.

When troubleshooting a situation where no data is displayed in Kibana:

1. Check Data Ingestion: Ensure data is being ingested into Elasticsearch.
2. Verify Index Patterns: Confirm correct index patterns are defined in Kibana.
3. Time Filter Settings: Check the time filter settings in Kibana.
4. Field Mappings: Verify fields in the index pattern are correctly mapped.
5. Visualization Configuration: Ensure visualizations are configured correctly.
6. Elasticsearch Cluster Health: Check the health of the Elasticsearch cluster.

12. How do you visualize relationships between different datasets?

Kibana provides tools to visualize relationships between datasets. The Graph feature enables exploring connections between entities. The Vega visualization allows for advanced and customized visualizations to represent relationships in detail.

13. Explain the concept of Role-Based Access Control (RBAC).

Role-Based Access Control (RBAC) restricts system access based on user roles. In Kibana, RBAC manages permissions to ensure users access only relevant data and functionalities.

RBAC involves:

• Roles: Define permissions for users.
• Users: Accounts assigned one or more roles.
• Permissions: Specific actions within Kibana.

RBAC simplifies permission management and enhances security by ensuring users have necessary access.

14. What is Index Lifecycle Management (ILM) and how do you configure it?

Index Lifecycle Management (ILM) automates index management by defining policies for index transitions through phases: hot, warm, cold, and delete. Each phase can have specific actions like rollover, shrink, freeze, or delete.

To configure ILM:

• Define an ILM Policy: Create a policy specifying phases and actions.
• Assign the Policy to an Index Template: Apply the policy to an index template.
• Monitor and Adjust: Use Kibana’s tools to track indices and adjust the policy as needed.

Example JSON configuration for an ILM policy:

{
  "policy": {
    "phases": {
      "hot": {
        "actions": {
          "rollover": {
            "max_size": "50GB",
            "max_age": "30d"
          }
        }
      },
      "warm": {
        "actions": {
          "shrink": {
            "number_of_shards": 1
          },
          "forcemerge": {
            "max_num_segments": 1
          }
        }
      },
      "cold": {
        "actions": {
          "freeze": {}
        }
      },
      "delete": {
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

15. How do you set up anomaly detection?

To set up anomaly detection in Kibana, use the Machine Learning feature:

• Navigate to the Machine Learning Section: Go to the Machine Learning section in Kibana.
• Create a New Job: Click “Create Job” and select the job type.
• Select the Data Source: Choose the index pattern for analysis.
• Configure the Job: Define fields and metrics to monitor.
• Set Detectors: Add detectors for anomalies.
• Job Settings: Configure job name, description, and custom URLs.
• Run the Job: Start the job to begin anomaly detection.