Thanks for sharing the learnings Ahmed!
> The solution lies in keeping the retry of each step separate. A good
example of this is in how steps 2 and 3 are implemented [3]. They are
separated into different DoFns and step 3 can start only after step 2
completes successfully. This way, any failure
Thank you Moritz for updating the docs!
On Wed, Sep 7, 2022 at 3:06 AM Moritz Mack wrote:
> Sorry for the confusion. Beam migrated to using Github issues just
> recently and the confluence docs haven’t been updated yet.
>
>
>
> Please create a new issue under
I don't think there's a reason for this, it's just that these logical types
were defined after the Avro <-> Beam schema conversion. I think it would be
worthwhile to add support for them, but we'd also need to look at the
reverse (avro to beam) direction, which would map back to the catch-all
Thanks for writing this up Valentyn!
I'm curious Jarek, does Airflow take any dependencies on popular libraries
like pandas, numpy, pyarrow, scipy, etc... which users are likely to have
their own dependency on? I think these dependencies are challenging in a
different way than the client
I proposed https://github.com/apache/beam/pull/23877 to address this.
On Thu, Oct 27, 2022 at 2:12 PM Sachin Agarwal wrote:
> No objections here. The latter (the surviving) is the one linked in
> the top navigation bar and has the x-lang details that help.
>
> On Thu, Oct 27, 2022 at 2:09 PM
Could we just use the same set of reviewers as pr-bot in the main repo [1]?
I don't think that we could avoid duplicating the data though.
[1]
https://github.com/apache/beam/blob/728e8ecc8a40d3d578ada7773b77eca2b3c68d03/.github/REVIEWERS.yml
On Thu, Oct 27, 2022 at 12:20 PM David Cavazos via dev
Hm, it seems like we need to drop
https://beam.apache.org/documentation/io/built-in/ as it's been superseded
by https://beam.apache.org/documentation/io/connectors/
Would there be any objections to that?
On Thu, Oct 27, 2022 at 2:04 PM Sachin Agarwal via dev
wrote:
> JDBCIO is available as a
In SQL we just don't support cross joins currently [1]. I'm not aware of an
existing implementation of a cross join/cartesian product.
> My team has an internal implementation of a CartesianProduct transform,
based on using hashing to split a pcollection into a finite number of
groups and
I got to thinking about this again and ran some benchmarks. The result is
documented in the GitHub issue [1].
tl;dr: we can't realize a huge benefit since we don't actually have an
out-of-band path for exchanging the buffers. However, pickle 5 can yield
improved in-band performance as well, and I
Is there somewhere we could document this?
On Thu, Sep 15, 2022 at 6:45 AM Moritz Mack wrote:
> Thank you, Andrew!
>
> Exactly what I was looking for, that’s awesome!
>
>
>
> On 15.09.22, 06:37, "Alexey Romanenko" wrote:
>
>
>
>
>
> Ahh, great! I didn’t know that 'beam-perf’ label is used for
I agree with Austin on this one, it makes sense to be realistic, but I'm
concerned about just blanket reducing the priority on all flakes. Two
classes of issues that could certainly be dropped to P2:
- Issues tracking flakes that have not been sickbayed yet (e.g.
On Tue, Oct 4, 2022 at 8:58 AM Alexey Romanenko
wrote:
> Thanks for your feedback.
>
> At the time, using a Google website search was a simplest solution since,
> before, we didn’t have a search at all. I agree that it could be
> frustrating to have ad links before the actual results (not sure
Thanks Borris, that is helpful feedback. I filed an issue [1] to track
improving this.
[1] https://github.com/apache/beam/issues/23472
On Mon, Oct 3, 2022 at 2:32 PM Borris wrote:
> This is my experience of trying the search capability.
>
>- I know I want to read about dataframes (I was
I wanted to share that Ryan gave a presentation about his (and Charles')
work on Pangeo Forge at Scipy 2022 (in Austin just before Beam Summit!),
with a couple mentions of their transition to Beam [1]. There were also a
couple of other talks about Pangeo [2,3] with some Beam/xarray-beam
references
Thanks Cham! I really like the proposal, I left a few comments. I also had
one higher-level point I wanted to elevate here:
> Pipeline SDKs can generate user-friendly stub-APIs based on transforms
registered with an expansion service, eliminating the need to develop
language-specific wrappers.
Hi Andy,
Thanks for writing this up! This seems like something that Batched DoFns
could help with. Could we make a BatchConverter [1] that represents the
necessary transformations here, and define RunInference as a Batched DoFn?
Note that the Numpy BatchConverter already enables users to specify
Ah sorry, I forgot that INT64 is encoded with VarIntCoder, so we can't
simulate TimestampCoder with a logical type.
I think the ideal end state would be to have a well-defined
beam:logical_type:millis_instant that we use for cross-language (when
appropriate), and never use DATETIME at
Hi everyone,
In a recent code review we noticed that we are not consistent when
describing python type hints in documentation. Depending on who wrote the
patch, we switch between typehint, type-hint, and "type hint" [1].
I think we should standardize on "type hint" as this is what Guido used in
These have all been addressed. I went through and merged all of them,
except for the slf4j-jdk14 dependency in Java and Kotlin. After consulting
with Luke [1] I told dependabot to ignore this dependency.
[1]
https://github.com/apache/beam-starter-java/pull/26#issuecomment-130263Java9941
Well deserved! Congratulations Yi
On Wed, Nov 9, 2022 at 11:25 AM Valentyn Tymofieiev via dev <
dev@beam.apache.org> wrote:
> I am with the Beam PMC on this, congratulations and very well deserved, Yi!
>
> On Wed, Nov 9, 2022 at 11:08 AM Byron Ellis via dev
> wrote:
>
>> Congratulations!
>>
>>
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