description: Use transformations to rename fields, join series data, apply mathematical operations, and more
description: Use transformations to rename fields, join time series/SQL-like data, apply mathematical operations, and more
weight: 100
---
@ -48,7 +48,7 @@ weight: 100
Transformations are a powerful way to manipulate data returned by a query before the system applies a visualization. Using transformations, you can:
- Rename fields
- Join time series data
- Join time series/SQL-like data
- Perform mathematical operations across queries
- Use the output of one transformation as the input to another transformation
@ -761,13 +761,13 @@ Use this transformation to merge multiple results into a single table, enabling
This is especially useful for converting multiple time series results into a single wide table with a shared time field.
#### Inner join
#### Inner join (for Time Series or SQL-like data)
An inner join merges data from multiple tables where all tables share the same value from the selected field. This type of join excludes data where values do not match in every result.
Use this transformation to combine the results from multiple queries (combining on a passed join field or the first time column) into one result, and drop rows where a successful join cannot occur.
Use this transformation to combine the results from multiple queries (combining on a passed join field or the first time column) into one result, and drop rows where a successful join cannot occur. This is not optimized for large Time Series datasets.
In the following example, two queries return table data. It is visualized as two separate tables before applying the inner join transformation.
In the following example, two queries return Time Series data. It is visualized as two separate tables before applying the inner join transformation.
**Query A:**
@ -792,7 +792,39 @@ The result after applying the inner join transformation looks like the following
The inner join only includes rows where there is a match between the "StudentID" in both tables. In this case, the result does not include "Jennifer" from the "Students" table because there are no matching enrollments for her in the "Enrollments" table.
#### Outer join (for Time Series data)
An outer join includes all data from an inner join and rows where values do not match in every input. While the inner join joins Query A and Query B on the time field, the outer join includes all rows that don't match on the time field.
@ -831,6 +863,38 @@ I applied a transformation to join the query results using the time field. Now I
{{<figuresrc="/static/img/docs/transformations/join-fields-after-7-0.png"class="docs-image--no-shadow"max-width="1100px"alt="A table visualization showing results for multiple servers">}}
#### Outer join (for SQL-like data)
A tabular outer join combining tables so that the result includes matched and unmatched rows from either or both tables.
| StudentID | Name | Major |
| --------- | -------- | ---------------- |
| 1 | John | Computer Science |
| 2 | Emily | Mathematics |
| 3 | Michael | Physics |
| 4 | Jennifer | Chemistry |
Can now be joined with:
| StudentID | CourseID | Grade |
| --------- | -------- | ----- |
| 1 | CS101 | A |
| 1 | CS102 | B |
| 2 | MATH201 | A |
| 3 | PHYS101 | B |
| 5 | HIST101 | B |
The result after applying the outer join transformation looks like the following:
Jev Forsberg
2 years ago
committed by