- Elasticsearch - Discussion
- Elasticsearch - Useful Resources
- Elasticsearch - Quick Guide
- Elasticsearch - Logs UI
- Elasticsearch - Canvas
- Elasticsearch - Heat Maps
- Elasticsearch - Tag Clouds
- Elasticsearch - Time Series
- Elasticsearch - Area and Bar Charts
- Elasticsearch - Pie Charts
- Elasticsearch - Region Maps
- Elasticsearch - Data Tables
- Elasticsearch - Filtering by Field
- Elasticsearch - Kibana Dashboard
- Elasticsearch - Testing
- Elasticsearch - Frozen Indices
- Elasticsearch - Rollup Data
- Elasticsearch - Monitoring
- Elasticsearch - SQL Access
- Elasticsearch - Managing Index Lifecycle
- Elasticsearch - Ingest Node
- Elasticsearch - Index Modules
- Elasticsearch - Modules
- Elasticsearch - Analysis
- Elasticsearch - Mapping
- Elasticsearch - Query DSL
- Elasticsearch - Cluster APIs
- Elasticsearch - CAT APIs
- Elasticsearch - Index APIs
- Elasticsearch - Aggregations
- Elasticsearch - Search APIs
- Elasticsearch - Document APIs
- Elasticsearch - API Conventions
- Migration between Versions
- Elasticsearch - Populate
- Elasticsearch - Installation
- Elasticsearch - Basic Concepts
- Elasticsearch - Home
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- Questions and Answers
- UPSC IAS Exams Notes
Elasticsearch - Heat Maps
Heat map is a type of visuapzation in which different shades of colour represent different areas in the graph. The values may be continuously varying and hence the colour r shades of a colour vary along with the values. They are very useful to represent both the continuously varying data as well as discrete data.
In this chapter we will use the data set named sample_data_fpghts to build a heatmap chart. In it we consider the variables named origin country and destination country of fpghts and take a count.
In Kibana Home screen, we find the option name Visuapze which allows us to create visuapzation and aggregations from the indices stored in Elasticsearch. We choose to add a new visuapzation and select Heat Map as the option shown below &mimus;

Choose the Metrics
The next screen prompts us for choosing the metrics which will be used in creating the Heat Map Chart. Here we choose the count as the type of aggregation metric. Then for the buckets in Y-Axis, we choose Terms as the aggregation for the field OriginCountry. For the X-Axis, we choose the same aggregation but DestCountry as the field to be used. In both the cases, we choose the size of the bucket as 5.

On running the above shown configuration, we get the heat map chart generated as follows.

Note − You have to allow the date range as This Year so that the graph gathers data for a year to produce an effective heat map chart.
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