Github user HyukjinKwon commented on the issue:
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Another concern is, it doesn't sound to me straightforward that readsupport
is created and executed in write path.
So, at least, we should just read the schema when it's needed (when
Github user dilipbiswal commented on the issue:
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Thank you very much @srowen @fjh100456
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From
https://spark.apache.org/docs/2.3.2/api/java/org/apache/spark/sql/streaming/DataStreamWriter.html
Github user HyukjinKwon commented on the issue:
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Other datasources such as Parquet, ORC and JDBC won't be adoptable to the
current design since we can't read the schema from the information given the
code I provided above.
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Let's close it for now.
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The point is not write only datasource, @cloud-fan. For instance,
`spark.read.range(1).format("source").write("non-existent-path")`. There's no
way to read the schema. There's no complaints so
Github user srowen commented on the issue:
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I like the change. This test is down to 5 seconds now. Unfortunately I
don't see speedup in other Kafka tests, but I think we should leave this in.
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Have a look at `Column.scala` and `Dataset.scala` in
`org.apache.spark.sql`. But, on a second look, this is how I see the lists
render:
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@srowen Guessing you're referring to some other files? Could you link?
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--- Diff: docs/configuration.md ---
@@ -1583,6 +1583,14 @@ Apart from these, the following properties are also
available, and may be useful
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Sorry, was out of town, will update
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Github user cloud-fan commented on the issue:
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Do we have users complain about it? In the new write API design, a data
source must provide a schema.
I don't think it's practical that people need a write-only data source
which can
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/**
*
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> Then we don't need any randomness here, just pick one timezone(like PST?)
and test it.
I took 8 out of 627. Going to do the same in the PR:
https://github.com/apache/spark/pull/22379
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* user-registered
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Hello. I've just found this PR while trying to debug serialization errors
for a case class containing value classes. This would be very helpful!
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Thanx a lot @aditanase ! I will update the PR.
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* limitations under the
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thanks, merging to master!
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It's OK. Close this one.
Thanks for reviewing @viirya @HyukjinKwon
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@HyukjinKwon Sorry. Happened to find that some code is not elegant
according to my taste. I have not got the fact that massive changes are not
good for backporting. As a result, I spent some time
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@HyukjinKwon Could you look at this PR one more time, please.
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@HyukjinKwon May I ask you to look at this PR.
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Yes sure , i will create a ticket for this issue and Keep you guys in
loop. Thanks
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Github user viirya commented on the issue:
https://github.com/apache/spark/pull/22687
I prefer not to add code that will not run. Let's see others options too.
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