wehub-resource-sync
95 lines
3.8 KiB
Markdown
95 lines
3.8 KiB
Markdown
---
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title: Migrating from 0.24 to 0.25
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order: 985
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---
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<!-- ^^^ this number must be _decremented_ when you copy/paste this file -->
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## Removed deprecated `--serve` CLI argument
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Use `--web-viewer` instead.
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## Removed the `--drop-at-latency` CLI argument
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This feature has been defunct for a while. A better replacement can be tracked [in this issue](https://github.com/rerun-io/rerun/issues/11024).
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## Removed the `-o` CLI argument shorthand for `--stdout` in `script_add_args`
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Use `--stdout` directly instead.
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## Flush takes an optional timeout, and returns errors
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When flushing a recording stream you can now give it a maximum time for how long it should block.
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The flush will block until either it completes, fails (e.g. because of connection loss), or the timeout is reached.
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Previously this could only be configured for gRPC sinks, and it was configured once upon setting up the connection.
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In the C++ and Python APIs, negative timeouts used to have special meaning. Now they are no longer permitted.
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The Python flush calls now raises an error if the flushing did not complete successfully.
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The timeout behavior is also improved: it will only block as long as there is _hope of progress_. If the gRPC connection is severed, the flush will aborted with an error. This means it should be very rare that you need to configure a flush timeout, as it will only block for a long time if there is a very slow connection.
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Removed:
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* Python: `flush_timeout_sec` argument of `connect_grpc`
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* Rust: `flush_timeout` argument of `connect_grpc_opts`
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* C++: `rerun::GrpcSink::flush_timeout_sec`
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## ❗ Deprecations
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### Python 3.9
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Support for Python 3.9 is being deprecated. Python 3.9 is past end-of-life. See: https://devguide.python.org/versions/
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In the next release, we will fully drop support and switch to Python 3.10 as the minimum supported version.
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See an overview for supported python versions [here](https://ref.rerun.io/docs/python/main/common#supported-python-versions).
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### `archetype` specification in `AnyValues`
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Previously, logging two `AnyValues` with the same field name but different archetype name under the same entity would lead to an inconsistency where the viewer would disambiguate them, but not the dataframe API.
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```python
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arbitrary_int = 10
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example = AnyValues()
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example.with_field(ComponentDescriptor("component_name", "archetype_name"), arbitrary_int)
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example.with_field(ComponentDescriptor("component_name", "different_archetype"), arbitrary_int + 1)
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rr.log("/path", example)
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```
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In the viewer we would see two `component_name` entries under different archetypes but they would not be uniquely queryable.
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```python
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from rerun.dataframe import load_recording
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rec = load_recording("<path_to_logs_above>.rrd")
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rec.view(index="log_time", contents="/path").select().schema
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# Only shows one `component_name` component
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```
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To address that, we split this functionality into two utilities:
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- `AnyValues`, which has no archetype name
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- `DynamicArchetype`, which requires an archetype name
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When using `DynamicArchetype`, the dataframe API will include the archetype the column names (similar to how built-in components are handled), which reduces the possibility for ambiguity.
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In the next release we will remove the ability to specify an `archetype` when creating `AnyValues` to finalize the transition.
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```python
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arbitrary_int = 10
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example = DynamicArchetype("archetype_name")
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example.with_component_from_data("component_name", arbitrary_int)
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another_example = DynamicArchetype("another_archetype")
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another_example.with_field("component_name", arbitrary_int + 1)
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rr.log("/path", example)
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rr.log("/path", another_example)
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```
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```python
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from rerun.dataframe import load_recording
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rec = load_recording("<path_to_logs_above>.rrd")
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rec.view(index="log_time", contents="/path").select().schema
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# Only shows two `component_name` components deduplicated by archetype!
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```
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