Batch Data

Rerun has built-in support for batch data. Whenever you have a collection of things that all have the same type, rather than logging each element individually, you can log the entire collection together as a single "Batch". This provides significant benefits in terms of storage and compute.

Some examples of batched data include points in a point cloud, bounding boxes for detected objects, tracked keypoints in a skeleton, or a collection of line strips.

In the Python APIs, the majority of archetypes are named with the plural form, for example rr.Points3D. They accept both single elements (internally treated as an N=1 batch) or arrays corresponding to the batches.


  • An entity is a collection of components (see Entities and Components).
  • When an entity is batched, it's components individual elements are called instances.
  • Each instance is identified within the entity by its instance key.
  • When every instance within an entity has the same value for a component, that component is called a splat. This is a common pattern and has dedicated support for it (see the Splats section below). For instance, you can set all the colors of a point cloud to the same color by passing a single color value to the color parameter.
  • During queries, a batch always has a primary component. The primary component is what determines how many instances exist in the batch.


When using batched entities there are a few restrictions:

  • Because there is a one-to-one mapping between batches and entities:
    • If data needs to span multiple entity paths, it needs to be split up into separate batches.
    • If data needs to be split into multiple batches, each must be logged to a different path.
  • Whenever you log a batched entity, for any component that is updated, you must provide values for every instance.
    • It is not possible to only update a subset of the instances in the batch.

Batch join rules

Rerun lets you choose which components in an entity you want to log at any point in time. If you don't log to a component, then in general it is not updated. For example, if you log a point cloud with positions and colors and then later log just new positions, when the viewer displays that point cloud it will still look up the last colors that were logged (we refer to this as the latest at semantics).

This can be quite convenient since updating different components at different times puts far fewer restrictions on the organization of your code. It even means if a component on an entity is static, you only need to log it once.

However, if both a batch of colors and a batch of positions have been logged at two different points in time, we need a way to know which point receives which color. This is what Rerun uses the instance keys for. When a component batch is logged it is always assigned a set of instance keys. By default, this key is based on the sequential index within the logged array. When querying a batched component, the component-values are joined together based on these keys. Logically, this happens as a left-join using the primary component for the entity. For example, if you log 3 points and then later log 5 colors, you will still only see 3 points in the viewer.

In the future you will be able to specify the instance keys manually while logging (#1309).


As mentioned, Rerun has special handling for splats within batches. This is what happens when you mix arrays and single values in an API call. The non-array value is instead logged as a splat. Behind the scenes, the splat is stored as a single value rather than a full array. Then, when doing the join at lookup time the splat is repeated across every instance in the batch.