Migrating from 0.27 to 0.28

Pose* component types have been removed pose-component-types-have-been-removed

The following component types have been removed in favor of their more general counterparts:

  • components.PoseTranslation3Dcomponents.Translation3D
  • components.PoseRotationQuatcomponents.RotationQuat
  • components.PoseTransformMat3x3components.TransformMat3x3
  • components.PoseRotationAxisAnglecomponents.RotationAxisAngle
  • components.PoseScale3Dcomponents.Scale3D

Existing .rrd files will be automatically migrated when opened.

Transform3D no longer supports axis_length for visualizing coordinate axes transform3d-no-longer-supports-axislength-for-visualizing-coordinate-axes

The axis_length parameter/method has been moved from Transform3D to a new TransformAxes3D archetype, which you can log alongside of Transform3D. This new archetype also works with the CoordinateFrame archetype.

Existing .rrd recordings will be automatically migrated when opened (the migration converts Transform3D:axis_length components to TransformAxes3D:axis_length).

CoordinateFrame::frame_id has been renamed to CoordinateFrame::frame coordinateframeframeid-has-been-renamed-to-coordinateframeframe

The frame_id component of CoordinateFrame has been renamed to just frame, because the component type TransformFrameId already conveys the information that this is an id.

Existing .rrd recordings will be automatically migrated when opened (the migration renames the frame_id component).

Changes to Transform3D/InstancePose3D and Pinhole's transform properties are now treated transactionally by the Viewer changes-to-transform3dinstancepose3d-and-pinholes-transform-properties-are-now-treated-transactionally-by-the-viewer

If you previously updated only certain components of Transform3D/InstancePose3D and relied on previously logged values remaining present, you must now re-log those previous values every time you update the Transform3D/InstancePose3D.

If you always logged the same transform components on every log/send call or used the standard constructor of Transform3D, no changes are required!

import rerun as rr

rr.log("simple", rr.Transform3D(translation=[1.0, 2.0, 3.0]))

# Note that we explicitly only set the scale here:
# Previously, this would have meant that we keep the translation.
# However, in 0.27 the Viewer will no longer show apply the previous translation regardless.
rr.log("simple", rr.Transform3D.from_fields(scale=2))

Pinhole's transform properties, resolution & image_from_plane as well its new parent_frame & child_frame, fields are also affected by this change. Again, this means that any change to any of Pinhole's resolution/image_from_plane/parent_frame/child_frame, will reset all of these fields.

Details & motivation details--motivation

We changed the way Transform3D, InstancePose3D & Pinhole are queried under the hood!

Usually, when querying any collection of components with latest-at semantics, we look for the latest update of each individual component. This is useful, for example, when you log a mesh and only change its texture over time: a latest-at query at any point in time gets all the same vertex information, but the texture that is active at any given point in time may change.

However, for Transform3D, this behavior can be very surprising, as the typical expectation is that logging a Transform3D with only a rotation will not inherit previously logged translations to the same path. Previously, to work around this, all SDKs implemented the constructor of Transform3D such that it set all components to empty arrays, thereby clearing everything that was logged before. This caused significant memory (and networking) bloat, as well as needlessly convoluted displays in the viewer. With the arrival of explicit ROS-style transform frames, per-component latest-at semantics can cause even more surprising side effects.

Therefore, we decided to change the semantics of Transform3D such that any change to any of its components fully resets the transform state.

For example, if you change its rotation and scale fields but do not write to translation, we will not look further back in time to find the previous value of translation. Instead, we assume that translation is not set at all (i.e., zero), deriving the new overall transform state only from rotation and scale. Naturally, if any update to a transform always changes the same components, this does not cause any changes other than the simplification of not having to clear out all other components that may ever be set, thus reducing memory bloat both on send and query!

Python SDK: "partition" renamed to "segment" in catalog APIs python-sdk-partition-renamed-to-segment-in-catalog-apis

In the rerun.catalog module, all APIs using "partition" terminology have been renamed to use "segment" instead. The old APIs are deprecated and will be removed in a future release.

Old APINew API
DatasetEntry.partition_ids()DatasetEntry.segment_ids()
DatasetEntry.partition_table()DatasetEntry.segment_table()
DatasetEntry.partition_url()DatasetEntry.segment_url()
DatasetEntry.download_partition()DatasetEntry.download_segment()
DatasetEntry.default_blueprint_partition_id()DatasetEntry.default_blueprint_segment_id()
DatasetEntry.set_default_blueprint_partition_id()DatasetEntry.set_default_blueprint_segment_id()
DataframeQueryView.filter_partition_id()DataframeQueryView.filter_segment_id()

The DataFusion utility functions in rerun.utilities.datafusion.functions.url_generation have also been renamed:

Old APINew API
partition_url()segment_url()
partition_url_udf()segment_url_udf()
partition_url_with_timeref_udf()segment_url_with_timeref_udf()

The partition table columns have also been renamed from rerun_partition_id to rerun_segment_id.

Additionally, the partition_id field on viewer event classes has been renamed to segment_id:

# Before (0.27)
def on_event(event):
    print(event.partition_id)

# After (0.28)
def on_event(event):
    print(event.segment_id)

This affects PlayEvent, PauseEvent, TimeUpdateEvent, TimelineChangeEvent, SelectionChangeEvent, and RecordingOpenEvent.

Python SDK: catalog entry listing APIs renamed python-sdk-catalog-entry-listing-apis-renamed

The CatalogClient methods for listing catalog entries have been renamed for clarity:

Old APINew API
CatalogClient.all_entries()CatalogClient.entries()
CatalogClient.dataset_entries()CatalogClient.datasets()
CatalogClient.table_entries()CatalogClient.tables()

The old methods are deprecated and will be removed in a future release.

Additionally, the new methods accept an optional include_hidden parameter:

  • datasets(include_hidden=True): includes blueprint datasets
  • tables(include_hidden=True): includes system tables (e.g., __entries)
  • entries(include_hidden=True): includes both

Python SDK: removed DataFrame-returning entry listing methods python-sdk-removed-dataframereturning-entry-listing-methods

The following methods that returned datafusion.DataFrame objects have been removed without deprecation:

Removed methodReplacement
CatalogClient.entries() (returning DataFrame)CatalogClient.get_table(name="__entries").df()
CatalogClient.datasets() (returning DataFrame)CatalogClient.get_table(name="__entries").df() filtered by entry kind
CatalogClient.tables() (returning DataFrame)CatalogClient.get_table(name="__entries").df() filtered by entry kind

The new entries(), datasets(), and tables() methods now return lists of entry objects (DatasetEntry and TableEntry) instead of DataFrames. If you need DataFrame access to the raw entries table, use client.get_table(name="__entries").df().

Python SDK: entry name listing methods now support include_hidden python-sdk-entry-name-listing-methods-now-support-includehidden

The CatalogClient methods for listing entry names now accept an optional include_hidden parameter, matching the behavior of entries(), datasets(), and tables():

  • entry_names(include_hidden=True): includes hidden entries (blueprint datasets and system tables like __entries)
  • dataset_names(include_hidden=True): includes blueprint datasets
  • table_names(include_hidden=True): includes system tables (e.g., __entries)

Python SDK: entry access methods renamed python-sdk-entry-access-methods-renamed

The CatalogClient methods for accessing individual entries have been renamed:

Old APINew API
CatalogClient.get_dataset_entry()CatalogClient.get_dataset()
CatalogClient.get_table_entry()CatalogClient.get_table()
CatalogClient.create_table_entry()CatalogClient.create_table()

The existing CatalogClient.create_dataset() method is already aligned with the new naming scheme and remains unchanged. The old methods are deprecated and will be removed in a future release.

Python SDK: get_table() now returns TableEntry instead of DataFrame python-sdk-gettable-now-returns-tableentry-instead-of-dataframe

The CatalogClient.get_table() method has been changed to return a TableEntry object instead of a datafusion.DataFrame. This is a breaking change.

# Before (0.27)
df = client.get_table(name="my_table")  # returns DataFrame

# After (0.28)
table_entry = client.get_table(name="my_table")  # returns TableEntry
df = table_entry.df()  # call df() to get the DataFrame

This change aligns get_table() with get_dataset(), which returns a DatasetEntry. Both methods now consistently return entry objects that provide access to metadata and data.

Python SDK: table write operations moved to TableEntry python-sdk-table-write-operations-moved-to-tableentry

Write operations for tables have been moved from CatalogClient to TableEntry. The new methods provide a cleaner API that operates directly on table entries:

Old APINew API
CatalogClient.write_table(name, batches, mode)TableEntry.append(batches)
TableEntry.overwrite(batches)
TableEntry.upsert(batches)
CatalogClient.append_to_table(name, batches)TableEntry.append(batches)
CatalogClient.update_table(name, batches)TableEntry.upsert(batches)

The old methods are deprecated and will be removed in a future release.

# Before (0.27)
client.write_table("my_table", batches, TableInsertMode.APPEND)
client.append_to_table("my_table", batches)
client.update_table("my_table", batches)

# After (0.28)
table = client.get_table(name="my_table")
table.append(batches)
table.overwrite(batches)
table.upsert(batches)

The new TableEntry methods also support writing Python objects directly via keyword arguments:

table.append(col1=[1, 2, 3], col2=["a", "b", "c"])

Note: TableInsertMode is no longer needed with the new API and will be removed in a future release.

Python SDK: schema and column types moved to rerun.catalog python-sdk-schema-and-column-types-moved-to-reruncatalog

The Schema class and related column descriptor/selector types have moved from rerun.dataframe to rerun.catalog.

Old import (0.27)New import (0.28)
from rerun.dataframe import Schemafrom rerun.catalog import Schema
from rerun.dataframe import ComponentColumnDescriptorfrom rerun.catalog import ComponentColumnDescriptor
from rerun.dataframe import ComponentColumnSelectorfrom rerun.catalog import ComponentColumnSelector
from rerun.dataframe import IndexColumnDescriptorfrom rerun.catalog import IndexColumnDescriptor
from rerun.dataframe import IndexColumnSelectorfrom rerun.catalog import IndexColumnSelector

The previous import paths are still supported but will be removed in a future release.

Python SDK: new DatasetView API for filtering datasets python-sdk-new-datasetview-api-for-filtering-datasets

A new DatasetView class has been introduced for filtering and reading from datasets. It provides a cleaner, lazily-evaluated API for working with subsets of dataset data.

Creating a DatasetView creating-a-datasetview

Use filter_segments() or filter_contents() on a DatasetEntry to create a DatasetView:

from rerun.catalog import CatalogClient

client = CatalogClient("rerun+http://localhost:51234")
dataset = client.get_dataset(name="my_dataset")

# Filter to specific segments
view = dataset.filter_segments(["recording_0", "recording_1"])

# Filter to specific entity paths
view = dataset.filter_contents(["/points/**"])

# Chain filters
view = dataset.filter_segments(["recording_0"]).filter_contents(["/points/**", "-/text/**"])

Reading data reading-data

Use reader() to get a DataFusion DataFrame:

df = view.reader(index="timeline")

Available methods available-methods

MethodDescription
filter_segments(segment_ids)Filter to specific segment IDs (list or DataFrame with rerun_segment_id column)
filter_contents(exprs)Filter to specific entity paths (supports wildcards like /points/**)
segment_ids()Get the list of segment IDs in this view
segment_table()Get segment metadata as a DataFusion DataFrame
schema()Get the filtered schema
arrow_schema()Get the filtered Arrow schema
reader(index=...)Create a DataFusion DataFrame reader
get_index_ranges(index)Get min/max values per segment for an index
download_segment(segment_id)Download a specific segment as a Recording

Deprecation of dataframe_query_view() deprecation-of-dataframequeryview

The DatasetEntry.dataframe_query_view() method is deprecated. Use the new DatasetView API instead:

# Before (deprecated)
view = dataset.dataframe_query_view(index="timeline", contents={"/points": ["Position2D"]})
df = view.df()

# After
view = dataset.filter_contents(["/points/**"])
df = view.reader(index="timeline")