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Posted to commits@beam.apache.org by jr...@apache.org on 2023/11/16 15:16:47 UTC
(beam) branch master updated: Add logging changes to CHANGES.md (#29442)
This is an automated email from the ASF dual-hosted git repository.
jrmccluskey pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/beam.git
The following commit(s) were added to refs/heads/master by this push:
new b995211561e Add logging changes to CHANGES.md (#29442)
b995211561e is described below
commit b995211561e50ed56fd89178119f88ca0316a8c3
Author: Jack McCluskey <34...@users.noreply.github.com>
AuthorDate: Thu Nov 16 10:16:38 2023 -0500
Add logging changes to CHANGES.md (#29442)
* Add logging changes to CHANGES.md
* Add link to PR
* Apply suggestions from code review
Co-authored-by: tvalentyn <tv...@users.noreply.github.com>
---------
Co-authored-by: tvalentyn <tv...@users.noreply.github.com>
---
CHANGES.md | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/CHANGES.md b/CHANGES.md
index 466ad546860..39339e606e6 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -135,6 +135,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
* In Python, [RunInference](https://beam.apache.org/documentation/sdks/python-machine-learning/#why-use-the-runinference-api) now supports loading many models in the same transform using a [KeyedModelHandler](https://beam.apache.org/documentation/sdks/python-machine-learning/#use-a-keyed-modelhandler) ([#27628](https://github.com/apache/beam/issues/27628)).
* In Python, the [VertexAIModelHandlerJSON](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.vertex_ai_inference.html#apache_beam.ml.inference.vertex_ai_inference.VertexAIModelHandlerJSON) now supports passing in inference_args. These will be passed through to the Vertex endpoint as parameters.
* Added support to run `mypy` on user pipelines ([#27906](https://github.com/apache/beam/issues/27906))
+* Python SDK worker start-up logs and crash logs are now captured by a buffer and logged at appropriate levels via Beam logging API. Dataflow Runner users might observe that most `worker-startup` log content is now captured by the `worker` logger. Users who relied on `print()` statements for logging might notice that some logs don't flush before pipeline succeeds - we strongly advise to use `logging` package instead of `print()` statements for logging. ([#28317](https://github.com/apache [...]
## Breaking Changes
@@ -221,7 +222,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
* Python Pipelines using BigQuery IO or `orjson` dependency might experience segmentation faults or get stuck: [#28318](https://github.com/apache/beam/issues/28318).
* Beam Python containers rely on a version of Debian/aom that has several security vulnerabilities: [CVE-2021-30474](https://nvd.nist.gov/vuln/detail/CVE-2021-30474), [CVE-2021-30475](https://nvd.nist.gov/vuln/detail/CVE-2021-30475), [CVE-2021-30473](https://nvd.nist.gov/vuln/detail/CVE-2021-30473), [CVE-2020-36133](https://nvd.nist.gov/vuln/detail/CVE-2020-36133), [CVE-2020-36131](https://nvd.nist.gov/vuln/detail/CVE-2020-36131), [CVE-2020-36130](https://nvd.nist.gov/vuln/detail/CVE-202 [...]
* Python SDK's cross-language Bigtable sink mishandles records that don't have an explicit timestamp set: [#28632](https://github.com/apache/beam/issues/28632). To avoid this issue, set explicit timestamps for all records before writing to Bigtable.
-
+* Python SDK worker start-up logs, particularly PIP dependency installations, that are not logged at warning or higher are suppressed. This suppression is reverted in 2.51.0.
# [2.49.0] - 2023-07-17