Visualization types in Elasticsearch

Introduction

Visualizations help you see your data clearly. They turn numbers into pictures that are easy to understand.

You want to see trends in your website visits over time.

You need to compare sales numbers between different products.

You want to find out which categories have the most errors.

You want to show the distribution of response times for your server.

You want to create a dashboard to monitor key metrics at a glance.

Syntax

Elasticsearch

In Elasticsearch Kibana, you create visualizations by choosing a type and configuring data sources and options.

Common visualization types include:
- Bar chart
- Line chart
- Pie chart
- Data table
- Metric
- Heat map
- Gauge

Each type has settings for fields, aggregations, and display options.

You pick the visualization type based on what you want to show.

Most visualizations use aggregations to group and summarize data.

Examples

Example: Number of sales per product category.

Elasticsearch

Bar chart: Shows data as vertical bars.
Use it to compare values across categories.

Example: Website visits per day over a month.

Elasticsearch

Line chart: Shows data points connected by lines.
Use it to show trends over time.

Example: Percentage of errors by error type.

Elasticsearch

Pie chart: Shows parts of a whole as slices.
Use it to show percentage breakdowns.

Example: List of top users with their scores.

Elasticsearch

Data table: Shows raw or aggregated data in rows and columns.
Use it to see exact numbers.

Sample Program

This Elasticsearch query groups sales by category. You can use the results to create a bar chart showing sales per category.

Elasticsearch

POST /sales/_search
{
  "size": 0,
  "aggs": {
    "sales_per_category": {
      "terms": { "field": "category.keyword" }
    }
  }
}

# This query gets sales count per category.
# In Kibana, you would use this aggregation in a bar chart visualization.

Important Notes

Choose visualization types that match your data story.

Use filters to focus on important data before visualizing.

Combine multiple visualizations in dashboards for a full picture.

Summary

Visualizations turn data into easy-to-understand pictures.

Pick the right type: bar for comparisons, line for trends, pie for parts of a whole.

Use Elasticsearch aggregations to prepare data for visualizations.

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