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Thanks @HyukjinKwon @ueshin and @viirya !
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retest this please
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Github user HyukjinKwon commented on the issue:
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Looks pretty solid. Will take a another look today (KST) and merge this one
in few days if there are no more comments and/or other committers are busy to
take a look and merge.
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@ueshin @HyukjinKwon does this look ready to merge? cc @cloud-fan
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Jenkins, retest this please.
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I made [SPARK-22417](https://issues.apache.org/jira/browse/SPARK-22417) for
fixing reading from timestamps without arrow
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Github user cloud-fan commented on the issue:
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I think it is a bug, we should fix it first.
BTW I'm fine to upgrade arrow, just make sure we get everything we need at
the arrow version we wanna upgrade, then remove all the hacks at
Github user BryanCutler commented on the issue:
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After incorporating date and timestamp types for this, I had to refactor a
little to use `_create_batch` from serializers to make Arrow batches from
Columns even when the user doesn't specify
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@ueshin if possible I'd like to have #18664 merged first and then I can fix
this PR up if needed, thanks!
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Github user ueshin commented on the issue:
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I guess this pr is almost ready to be merged.
I'd cc @gatorsmile @cloud-fan for another look.
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Merged in PR from @ueshin and added case for when schema is a string single
datatype. In addition using a `StructType`, now this handles specifying the
schema with the following:
```
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Yes, I meant to ask for some clarification from @ueshin
for https://github.com/apache/spark/pull/19459#discussion_r145034007
> Btw, do we also need to support schema like ['name',
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Github user HyukjinKwon commented on the issue:
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BTW, https://github.com/apache/spark/pull/19459#discussion_r145034007 looks
missed :).
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Thanks for reviewing @viirya !
I just had some followup questions at
https://github.com/apache/spark/pull/19459#discussion_r144930424 and
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LGTM too but let me leave it to @ueshin just in case.
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LGTM with few minor comments.
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Thanks for the reviews @ueshin and @HyukjinKwon! I added `to_arrow_schema`
conversion for when a schema is passed into `createDataFrame` and added some
new tests to verify it. Please take
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Benchmarks for running in local mode 16 GB memory, i7-4800MQ CPU @ 2.70GHz
à 8 cores
using default Spark configuration
data is 10 columns of doubles with 100,000 rows
Code:
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