Visualization types in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify a common visualization type in Elasticsearch.

Elasticsearch

{
  "visualization": {
    "type": "[1]"
  }
}

Drag options to blanks, or click blank then click option'

Atable

Bline_chart

Cpie_chart

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'table' which is not a chart type.

Selecting 'map' which is for geospatial data.

The line_chart is a common visualization type used to show trends over time in Elasticsearch.

2fill in blank

medium

Complete the code to define a visualization type that shows parts of a whole.

Elasticsearch

{
  "visualization": {
    "type": "[1]"
  }
}

Drag options to blanks, or click blank then click option'

Abar_chart

Bheatmap

Cpie_chart

Dgauge

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'bar_chart' which shows comparisons, not parts of a whole.

Selecting 'heatmap' which shows intensity, not proportions.

The pie_chart visualization shows how different parts make up a whole.

3fill in blank

hard

Fix the error in the visualization type to correctly specify a map visualization.

Elasticsearch

{
  "visualization": {
    "type": "[1]"
  }
}

Drag options to blanks, or click blank then click option'

Aheatmap

Bhistogram

Cscatter_plot

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'heatmap' which shows intensity but not geographic locations.

Selecting 'histogram' which is for distribution, not maps.

The map visualization is used to display geospatial data in Elasticsearch.

4fill in blank

hard

Fill both blanks to create a visualization that shows data distribution over time using a bar chart.

Elasticsearch

{
  "visualization": {
    "type": "[1]",
    "x_axis": "[2]"
  }
}

Drag options to blanks, or click blank then click option'

Abar_chart

Btime

Cdate

Dline_chart

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'line_chart' instead of 'bar_chart' for this task.

Using 'time' instead of 'date' which is the correct field name.

The bar_chart is used to show data distribution, and the date field is commonly used on the x-axis for time-based data.

5fill in blank

hard

Fill all three blanks to define a gauge visualization with a field and a threshold value.

Elasticsearch

{
  "visualization": {
    "type": "[1]",
    "field": "[2]",
    "threshold": [3]
  }
}

Drag options to blanks, or click blank then click option'

Agauge

Bcpu_usage

C75

Dline_chart

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'line_chart' which is not a gauge.

Setting threshold as a string instead of a number.

The gauge visualization shows a single value like cpu_usage with a threshold of 75 to indicate limits.

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