Query Engines


Performance is a critical part of the user's experience, which is why we have designed features to reduce query response times.

Field usage

This requires a version of the Vizzly Query Engine > 0.2.11.

One feature of our data sets, allows you to choose which fields can be used as dimensions and measures, and limit the granularity that can be applied to date time fields. For example, you could only allow grouping by hour, day or year instead of per second or per minute.

If you have an SQL integration, this can be used to your advantage by creating indexes based exclusively from the fields you intend to be used as dimensions.

Another time to consider turning to indexes is for fields you think will be commonly used in filters and sorts. However, be aware that whilst indexes are optimal for read heavy tables, they can impact write performance.

Usage in the config

Each field in a data set can optionally provide a canBeDimension or canBeMeasure key with a boolean value. For example;

  "canBeDimension": true,
  "canBeMeasure": false

By default, the values of these properties are both true, and setting false will remove the field as an option in the chart editor.

NOTE: This requires a version of the Vizzly Query Engine > 0.2.11 and of our dashboard react library (opens in a new tab) > 0.0.35

SQL table structure

Although we do support joins in SQL integrations, they can be the cause of slow queries and therefore we'd recommend avoiding them if at all possible.

In an ideal world, each Vizzly data set would pull data from its own table, thus eliminating the need for joins.

Query engine


The docker image we provide does not do computationally heavy lifting. The noteworthy operations include validating JWTs and transforming Vizzly queries into the language your underlying database requires.

Hardware considerations

The recommended hardware requirements will depend on a number of different factors, including:

  • The number of concurrent users you expect to be viewing the dashboard.
  • How many charts a typical user has on their dashboard.
  • How many additional data controls exist on each chart.

To increase performance, consider scaling horizontally by adding more containers running the Vizzly docker image, or vertically by provisioning larger compute resource.


The query engine in the docker image is stateless and so it can be easily scaled horizontally to reduce performance or resiliency concerns and easily auto-scaled on all major cloud providers if your traffic pattern is spiky.