Visualization types in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Bar Chart in Elasticsearch visualizations?

A Bar Chart displays data using rectangular bars where the length of each bar is proportional to the value it represents. It is useful for comparing different categories or groups.

beginner

Describe a Pie Chart and its use in Elasticsearch.

A Pie Chart shows data as slices of a circle, where each slice represents a part of the whole. It is good for showing proportions or percentages of categories.

beginner

What is a Line Chart used for in Elasticsearch visualizations?

A Line Chart connects data points with lines to show trends over time or continuous data. It helps to see how values change.

beginner

Explain the purpose of a Data Table visualization.

A Data Table displays raw data in rows and columns. It is useful for detailed views and exact values rather than visual trends.

intermediate

What is a Heat Map in Elasticsearch visualizations?

A Heat Map uses colors to represent data density or intensity in a grid layout. It helps to quickly spot areas with high or low values.

Which visualization type is best to show parts of a whole in Elasticsearch?

A Bar Chart

B Line Chart

C Data Table

D Pie Chart

Pie Charts show proportions of categories as slices of a circle, making them ideal for parts of a whole.

What visualization would you use to track changes over time?

A Line Chart

B Heat Map

C Pie Chart

D Bar Chart

Line Charts connect points over time to show trends and changes clearly.

Which visualization shows exact values in rows and columns?

A Bar Chart

B Heat Map

C Data Table

D Pie Chart

Data Tables display raw data in a structured format for detailed examination.

A Heat Map uses what to represent data intensity?

A Colors

B Lines

C Slices

D Bars

Heat Maps use colors to show how dense or intense data values are in different areas.

Which visualization is best for comparing categories side by side?

A Line Chart

B Bar Chart

C Pie Chart

D Heat Map

Bar Charts display categories as bars, making it easy to compare their values visually.

List and describe three common visualization types used in Elasticsearch.

Think about charts that show comparisons, parts of a whole, and trends.

Explain when you would use a Heat Map versus a Data Table in Elasticsearch visualizations.

Consider visual patterns versus detailed numbers.

Practice

1. Which visualization type is best to show how parts make up a whole in Elasticsearch dashboards?

easy

A. Bar chart

B. Line chart

C. Pie chart

D. Data table

Solution

1. Step 1: Understand visualization purpose

   Pie charts are designed to show parts of a whole by dividing a circle into slices.

2. Step 2: Match visualization to data type

   Since the question asks for parts of a whole, pie chart fits best over line or bar charts which show trends or comparisons.

3. Final Answer:

   Pie chart -> Option C

4. Quick Check:

   Parts of whole = Pie chart [OK]

Hint: Parts of whole? Think pie chart slices [OK]

Common Mistakes:

• Choosing bar chart for parts of whole
• Confusing line chart with pie chart
• Using data table instead of visual chart

2. Which of the following is the correct Elasticsearch aggregation type to use for a bar chart showing counts per category?

easy

A. terms aggregation

B. date_histogram aggregation

C. avg aggregation

D. max aggregation

Solution

1. Step 1: Identify aggregation for categories

   Terms aggregation groups data by unique values, perfect for categories.

2. Step 2: Match aggregation to bar chart data

   Bar charts often show counts per category, so terms aggregation is correct.

3. Final Answer:

   terms aggregation -> Option A

4. Quick Check:

   Category counts = terms aggregation [OK]

Hint: Use terms aggregation for category counts [OK]

Common Mistakes:

• Using avg or max aggregation for counts
• Choosing date_histogram for non-date data
• Confusing aggregation types

3. Given this Elasticsearch aggregation result for a line chart showing sales over time:

{"buckets": [{"key_as_string": "2024-01-01", "doc_count": 10}, {"key_as_string": "2024-01-02", "doc_count": 15}]}

What will the line chart display?

medium

A. A line rising from 10 to 15 between Jan 1 and Jan 2

B. A flat line at 10 for both days

C. A line dropping from 15 to 10 between Jan 1 and Jan 2

D. No line because data format is incorrect

Solution

1. Step 1: Read aggregation buckets

   The buckets show counts 10 on Jan 1 and 15 on Jan 2.

2. Step 2: Interpret line chart trend

   The line chart plots these points over time, so it rises from 10 to 15.

3. Final Answer:

   A line rising from 10 to 15 between Jan 1 and Jan 2 -> Option A

4. Quick Check:

   Counts increase over time = rising line [OK]

Hint: Line chart shows trend from low to high values [OK]

Common Mistakes:

• Assuming flat line despite different counts
• Thinking data format is invalid
• Reversing the trend direction

4. You created a pie chart in Kibana but it shows only one slice with 100% instead of multiple categories. What is the most likely cause?

medium

A. The date range filter is too wide

B. The aggregation used is a single metric, not a terms aggregation

C. The pie chart visualization is not supported in Kibana

D. The data has no documents

Solution

1. Step 1: Understand pie chart data needs

   Pie charts require terms aggregation to split data into categories.

2. Step 2: Identify cause of single slice

   If a single metric aggregation is used, it returns one value, so pie chart shows one slice.

3. Final Answer:

   The aggregation used is a single metric, not a terms aggregation -> Option B

4. Quick Check:

   Single slice = single metric aggregation [OK]

Hint: Use terms aggregation for multiple pie slices [OK]

Common Mistakes:

• Blaming Kibana for unsupported visualization
• Assuming no data causes single slice
• Thinking date range affects slice count

5. You want to create a dashboard showing monthly sales trends and category sales distribution side by side. Which combination of visualization types and aggregations should you use?

hard

A. Bar chart with avg aggregation for trends, data table with max aggregation for categories

B. Data table with sum aggregation for trends, bar chart with avg aggregation for categories

C. Pie chart with date_histogram aggregation for trends, line chart with terms aggregation for categories

D. Line chart with date_histogram aggregation for trends, pie chart with terms aggregation for categories

Solution

1. Step 1: Choose visualization for monthly trends

   Line chart is best for showing trends over time; date_histogram groups data by month.

2. Step 2: Choose visualization for category distribution

   Pie chart shows parts of whole; terms aggregation groups by category.

3. Final Answer:

   Line chart with date_histogram aggregation for trends, pie chart with terms aggregation for categories -> Option D

4. Quick Check:

   Trends = line + date_histogram; categories = pie + terms [OK]

Hint: Trends = line + date_histogram; parts = pie + terms [OK]

Common Mistakes:

• Mixing pie chart with date_histogram aggregation
• Using avg or max aggregation for category grouping
• Choosing data table instead of visual charts