Dashboard creation in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ visualization

intermediate

Identify the correct Kibana visualization type for time series data

You want to create a dashboard showing sales trends over time using Elasticsearch data in Kibana. Which visualization type is best suited for this purpose?

A Pie chart

B Tag cloud

C Data table

D Line chart

💡 Hint

Think about which chart shows changes over time clearly.

Line charts are ideal for showing trends over time because they connect data points chronologically, making it easy to see increases or decreases.

❓ dax_lod_result

intermediate

Calculate total sales for a specific region using Elasticsearch SQL

Given an Elasticsearch SQL query to calculate total sales for the 'West' region, what is the correct query to get the sum of sales?

Elasticsearch

SELECT region, SUM(sales) AS total_sales FROM sales_data WHERE region = 'West' GROUP BY region

A SELECT SUM(sales) AS total_sales FROM sales_data WHERE region = 'West'

B SELECT region, SUM(sales) FROM sales_data GROUP BY region HAVING region = 'West'

C SELECT SUM(sales) FROM sales_data WHERE region = 'West' GROUP BY region

D SELECT region, sales FROM sales_data WHERE region = 'West' SUM(sales)

💡 Hint

Focus on summing sales only for the 'West' region without grouping by region.

Option A correctly sums sales filtered by region 'West' without unnecessary grouping.

❓ data_modeling

advanced

Best practice for indexing time-based data in Elasticsearch for dashboards

You have large volumes of time-stamped log data. What is the best practice for indexing this data to optimize dashboard performance?

A Store all data in one index and filter with queries only

B Use a single index for all data regardless of time

C Create time-based indices, such as daily or monthly indices

D Index data without timestamps to reduce index size

💡 Hint

Think about how Elasticsearch handles large datasets efficiently over time.

Creating time-based indices allows efficient querying and management of large time-series data, improving dashboard responsiveness.

🔧 Debug

advanced

Fix the Kibana dashboard filter not applying correctly

You created a dashboard filter for 'product_category' but it does not filter the visualizations. What is the most likely cause?

A The field 'product_category' is not mapped as keyword in Elasticsearch

B The filter syntax in Kibana is correct but the dashboard needs refresh

C The filter is applied but visualizations are set to ignore filters

D The Elasticsearch cluster is down

💡 Hint

Check how fields are mapped in Elasticsearch for filtering.

Fields not mapped as keyword cannot be filtered properly in Kibana filters because they are analyzed text fields.

🎯 Scenario

expert

Design a dashboard to monitor real-time error rates with alerting

You need to build a Kibana dashboard that shows real-time error rates from logs and triggers alerts when error counts exceed thresholds. Which approach is best?

A Build a tag cloud of error messages and rely on visual inspection

B Use a line chart with a date histogram aggregation and set up Kibana alerting on the error count metric

C Use a data table with all log entries and filter errors manually

D Create a pie chart showing error types and manually check the dashboard hourly

💡 Hint

Consider automation and real-time monitoring capabilities.

Line charts with date histograms show trends over time, and Kibana alerting automates notifications when thresholds are crossed.

Practice

1. What is the main purpose of a dashboard in Elasticsearch's Kibana?

easy

A. To display multiple visualizations together for easy data analysis

B. To write complex Elasticsearch queries

C. To store raw data from Elasticsearch indexes

D. To manage user permissions for Elasticsearch

Solution

1. Step 1: Understand dashboard function

   A dashboard groups visualizations so users can see data insights in one place.

2. Step 2: Compare options

   Options A, B, and C describe other tasks not related to dashboard display.

3. Final Answer:

   To display multiple visualizations together for easy data analysis -> Option A

4. Quick Check:

   Dashboard = multiple visualizations [OK]

Hint: Dashboards show many visuals together for quick insights [OK]

Common Mistakes:

• Confusing dashboards with query writing
• Thinking dashboards store raw data
• Mixing dashboards with user management

2. Which syntax correctly adds a saved visualization to a Kibana dashboard?

easy

A. dashboard.addVisualization('vis_id')

B. dashboard.add('vis_id')

C. Dashboard.addVisualization('vis_id')

D. Dashboard.add('vis_id')

Solution

1. Step 1: Recall Kibana dashboard API

   The correct method to add a visualization is Dashboard.add('vis_id') with capital D.

2. Step 2: Check case sensitivity and method name

   dashboard.add('vis_id') uses lowercase dashboard object; options C and D use incorrect method name 'addVisualization'.

3. Final Answer:

   <code>Dashboard.add('vis_id')</code> -> Option D

4. Quick Check:

   Correct method is Dashboard.add() [OK]

Hint: Dashboard object is capitalized; method is add() [OK]

Common Mistakes:

• Using lowercase 'dashboard' instead of 'Dashboard'
• Using wrong method name like addVisualization
• Confusing method parameters

3. Given this Elasticsearch query used in a visualization:

{"query": {"match": {"status": "error"}}}

What will the visualization show when added to a dashboard?

medium

A. All documents with status 'error' count or details

B. All documents regardless of status

C. Documents with status 'success' only

D. An error message due to invalid query

Solution

1. Step 1: Analyze the query filter

   The query matches documents where the field 'status' equals 'error'.

2. Step 2: Understand visualization output

   The visualization will display data filtered to only those documents with status 'error'.

3. Final Answer:

   All documents with status 'error' count or details -> Option A

4. Quick Check:

   Query filters status='error' so visualization shows those docs [OK]

Hint: Match query filters data shown in visualization [OK]

Common Mistakes:

• Assuming it shows all documents
• Confusing 'error' with 'success'
• Thinking query syntax is invalid

4. You tried to add a visualization to a Kibana dashboard but it does not appear. Which is the most likely cause?

medium

A. The dashboard is already full and cannot add more visualizations

B. The Elasticsearch cluster is offline

C. The visualization ID used in the add command is incorrect

D. The visualization was created in a different tool

Solution

1. Step 1: Check visualization ID correctness

   If the ID is wrong, the dashboard cannot find and add the visualization.

2. Step 2: Evaluate other options

   Cluster offline would cause broader failures; dashboards do not have fixed limits; visualizations must be from Kibana.

3. Final Answer:

   The visualization ID used in the add command is incorrect -> Option C

4. Quick Check:

   Wrong ID means visualization won't load [OK]

Hint: Verify visualization ID matches exactly [OK]

Common Mistakes:

• Assuming dashboard has max visualization limit
• Ignoring ID typos
• Blaming Elasticsearch cluster without checking

5. You want to create a dashboard that shows error counts by hour and success counts by hour side by side. Which approach is best?

hard

A. Create a dashboard with only one visualization and switch filters manually

B. Create two visualizations with filters for 'error' and 'success', then add both to the dashboard

C. Create one visualization with a combined filter for 'error' and 'success' together

D. Create visualizations in different dashboards and link them

Solution

1. Step 1: Understand requirement for side-by-side comparison

   Two separate visualizations filtered by 'error' and 'success' allow clear side-by-side display.

2. Step 2: Evaluate other options

   Create one visualization with a combined filter for 'error' and 'success' together mixes filters, losing clarity; A requires manual switching; D separates data, not side-by-side.

3. Final Answer:

   Create two visualizations with filters for 'error' and 'success', then add both to the dashboard -> Option B

4. Quick Check:

   Separate filtered visuals show side-by-side data clearly [OK]

Hint: Use separate filtered visuals for clear side-by-side comparison [OK]

Common Mistakes:

• Combining filters in one visualization losing clarity
• Using one visualization and switching filters manually
• Splitting visuals across dashboards