Visualization types in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Elasticsearch aggregation visualization query?

Given the following Elasticsearch aggregation query for a bar chart visualization, what will be the count of documents in the bucket for the term "status:200"?

Elasticsearch

{
  "size": 0,
  "aggs": {
    "status_codes": {
      "terms": {
        "field": "status",
        "size": 3
      }
    }
  }
}

A The bucket for status:200 will contain the count of all documents with status 200.

B The bucket for status:200 will contain the count of all documents with status 404.

C The bucket for status:200 will be empty because terms aggregation does not support numeric fields.

D The bucket for status:200 will contain the total count of all documents regardless of status.

💡 Hint

Terms aggregation groups documents by unique values of a field.

Terms aggregation groups documents by unique values of the specified field. Here, it groups by the 'status' field, so the bucket for 'status:200' contains all documents with status 200.

🧠 Conceptual

intermediate

What type of visualization is best for showing trends over time in Elasticsearch?

You want to visualize the number of sales per day over the last month using Elasticsearch aggregations. Which visualization type is most appropriate?

A Gauge showing total sales count.

B Data table listing all sales documents.

C Pie chart showing sales distribution by product category.

D Line chart showing sales count per day over time.

💡 Hint

Trends over time are best shown with continuous time-based charts.

Line charts are ideal for showing trends over time because they plot data points sequentially along a time axis.

🔧 Debug

advanced

Why does this Elasticsearch pie chart show only one slice?

You created a pie chart visualization with a terms aggregation on the field 'category'. However, the pie chart shows only one slice labeled 'Other'. What is the most likely cause?

Elasticsearch

{
  "size": 0,
  "aggs": {
    "categories": {
      "terms": {
        "field": "category",
        "size": 5,
        "min_doc_count": 10
      }
    }
  }
}

A There are no documents with category values having at least 10 documents, so all are grouped into 'Other'.

B The field 'category' is missing from all documents, so only 'Other' appears.

C The size parameter is too large, causing aggregation to fail and show only 'Other'.

D Pie charts cannot display terms aggregations with min_doc_count set.

💡 Hint

min_doc_count filters out buckets with fewer documents.

The min_doc_count:10 means only terms with at least 10 documents appear as separate slices. If none meet this, all are grouped into 'Other'.

🧠 Conceptual

advanced

Which Elasticsearch visualization type is best for showing hierarchical data?

You want to visualize data that has multiple levels, such as country > state > city. Which visualization type is best suited for this?

A Data table with flat rows for each city.

B Sunburst chart showing nested levels of location.

C Bar chart showing total counts per country only.

D Gauge showing total number of cities.

💡 Hint

Hierarchical data needs nested visualization.

Sunburst charts display hierarchical data as concentric rings, making it easy to see nested relationships.

📝 Syntax

expert

What error does this Elasticsearch aggregation query produce?

Examine the following aggregation query and identify the error it will cause.

Elasticsearch

{
  "size": 0,
  "aggs": {
    "sales_over_time": {
      "date_histogram": {
        "field": "sale_date",
        "calendar_interval": "1d"
      }
    }
  }
}

A No error; the query runs successfully and returns daily buckets.

B TypeError: 'date_histogram' requires a numeric field, but 'sale_date' is a date.

C Deprecation warning: 'interval' is deprecated; use 'calendar_interval' or 'fixed_interval' instead.

D RuntimeError: 'size' cannot be zero in aggregation queries.

💡 Hint

Elasticsearch changed the 'interval' parameter in date_histogram.

The 'interval' parameter in date_histogram is deprecated in recent Elasticsearch versions. It must be replaced with 'calendar_interval' or 'fixed_interval'.

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