6 EQL Use Cases

This section describes how to handle various business scenarios using EQL. The examples in this section are not based on a single data schema.

• Re-normalization
  Re-normalization is important in denormalized data models in the Dgraph, as well as when analyzing multi-value attributes.
• Grouping by range buckets
  To create value range buckets, divide the records by the bucket size, and then use FLOOR or CEIL if needed to round to the nearest integer.
• Manipulating records in a dynamically computed range value
  The following scenario describes how to manipulate records in a dynamically computed range value.
• Grouping data into quartiles
  EQL allows you to group your data into quartiles.
• Combining multiple sparse fields into one
  EQL allows you to combine multiple sparse fields into a single field.
• Joining data from different types of records
  You can use EQL to join data from different types of records.
• Linear regressions in EQL
  Using the syntax described in this topic, you can produce linear regressions in EQL.
• Using an IN filter for pie chart segmentation
  This query shows how the IN filter can be used to populate a pie chart showing sales divided into six segments: one segment for each of the five largest customers, and one segment showing the aggregate sales for all other customers.
• Running sum
  A running (or cumulative) sum calculation can be useful in warranty scenarios.
• Query by age
  In this example, records are tagged with a Date attribute on initial ingest. No updates are necessary.
• Calculating percent change between most recent month and previous month
  The following example finds the most recent month in the data that matches the current filters, and compares it to the prior month, again in the data that matches the current filters.