> Wondering if we should just take a bunch of payload configs and deprecate
these flags
I have the same feeling, there are already so many config options in
Hoodie, the maintain work for developers or users is hard.
Vinoth Chandar 于2021年1月18日周一 下午11:40写道：
> +1 as well.
>
> Slightly orthogonal p
+1?? Currently we have encountered such scenarios and look forward to
supporting
-- --
??:
"dev"
Hi guys,
*I open this thread to discuss if we can separate the attributes and
behaviors of HoodieTable, and rethink the abstraction of the client.*
Currently, in the hudi-client-common module, there is a HoodieTable class,
which contains a set of attributes and behaviors. For different engines, i
Hi
We can use transformer to have a combination of multiple ordering fields.
However custom Comparable implementation is not possible in that case. So
overall a +1 from my side as well.
On Tue, Jan 19, 2021 at 1:58 PM 刘金辉 <965147...@qq.com> wrote:
> +1， Currently we have encountered such scenari
> It contains three components:
- Two objects: a table, a batch of records;
For the Spark client, it is true because no matter Spark or Spark streaming
engine, they write as batches, but things are different for pure streaming
engines like Flink, Flink writes per-record, it does not accumulate
Folks, travis has been very slow and now or builds have stopped running
altogether. Any one with some ideas?
On Mon, Jan 18, 2021 at 7:37 AM Vinoth Chandar wrote:
> Winding down.
> We have landed two major fixes based on testing
>
> 1. https://github.com/apache/hudi/pull/2441#issuecomment-76174
Checked a few other apache projects. All of them facing the same. Release
branch has to wait further I guess, until travis comes back
Will keep this thread posted
On Tue, Jan 19, 2021 at 7:32 PM Vinoth Chandar wrote:
> Folks, travis has been very slow and now or builds have stopped running
> al
>> For the Spark client, it is true because no matter Spark or Spark
streaming
engine, they write as batches, but things are different for pure streaming
engines like Flink, Flink writes per-record, it does not accumulate
buffers.
Yes, what I mean about the "batch" is not about the behavior or mec
