Dimension is a concept used by SlicingDice to describe a way to group columns for a database.
Each database can contain multiple dimensions and each dimension can contain multiple columns.
Watch this quick video below to learn what a dimension is, or go to the text explanation right below.
A dimension is a way to group columns for a database. Each database can contain multiple dimensions and each dimension can contain multiple columns.
Differently from a relational database, where you normally create tables and build relations between them, SlicingDice doesn't have the concept of a table, because it doesn't have to.
As SlicingDice supports storing both types of data, attributes (non-time-series) and events (time-series) in the same dimension (that can be seen like a single table), without affecting the query performance or database storage size, there is no need to split/normalize the data into multiple tables, as it's commonly done in other databases.
The purpose of a dimension is simply to group all columns created, similarly how a table work in a database. We just don't call it "table", because that could give the impression that we expect you to model your schema in a relational way, and that is unnecessary using SlicingDice.
SlicingDice automatically creates a
default dimension for you when you create a new Database to store data. You can rename this
default dimension name at any time. As you'll see, you can add new dimensions when creating a
database or at any time in the future.
There are two ways for you to create dimension, one of them will be covered on this page:
- Using the SlicingDice's Control Panel;
You can learn the other way to create columns by checking the SlicingDice's API Docs.
- Using the
"auto-create": ["dimension"]parameter when inserting data using the SlicingDice API;
Watch this quick video below to learn step-by-step how to create dimensions using the Control Panel.
You might want to create a new
dimension when at least one of the following conditions apply:
The column and data you want to store are totally unrelated to the columns and data stored on the existing dimension, even if the entities are the same.
You want to insert data related to a different kind of
entityin order to analyze other dimensions of the data. For example: associate website page views information to a
web pagesentity instead of