Github user HyukjinKwon commented on the issue:
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Hm, actually I thought this makes sense tho.
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Github user vinodkc commented on the issue:
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@viirya , Current issue occurs only in the case of 0 values, none zero
values with higher scale are still save in non scientific notation.
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Github user HyukjinKwon commented on the issue:
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BTW, was wondering if we should call the current way a .. kind of Java
standard? IIRC, Python's decimal representation doesn't use a scientific
notation by default. I thought this makes sense
Github user viirya commented on the issue:
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Scientific notation is more efficient on saving the values in CSV. If there
are many zero values of high scale decimal type, this non scientific notation
can cost storage space and loading time.
Github user HyukjinKwon commented on the issue:
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Hm, I don't think there's standard notation for numbers in CSV since the
datatype is specific to text if I remember the RFC 4180 correctly. Might be
good to double check.
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Github user cloud-fan commented on the issue:
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Is there a standard about how should CSV store decimal values?
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Github user vinodkc commented on the issue:
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@gatorsmile @HyukjinKwon @viirya , I rechecked the customer scenario.
It seems dataframe is saved as csv file and then netezza loads the csv data
into netezza table. In csv output 0 values,
Github user gatorsmile commented on the issue:
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@vinodkc Could you answer the question from @cloud-fan ?
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retest this please
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retest this please
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Github user HyukjinKwon commented on the issue:
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Yea, it should better be clarified. Same question.
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Github user viirya commented on the issue:
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> How can this happen? When Spark writes decimal out, the external systems
will get decimal values, not string values.
I have the same question.
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Github user cloud-fan commented on the issue:
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I don't have a strong opinion about the display, scientific notation is
fine to me.
I'm curious about "... but also in dataset write operations. External
databases like netezza fails to
Github user HyukjinKwon commented on the issue:
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cc @cloud-fan and @gatorsmile, WDYT? actually I happened to meet this and
thought it's better be fixed.
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@rxin, I recall https://github.com/apache/spark/pull/14560 where we used
Postgres as reference. WDYT?
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Github user vinodkc commented on the issue:
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@viirya, This issue is not only related to Dataset.show but also in dataset
write operations. External databases like netezza fails save the result due
to scientific notation on "0" values .
Github user viirya commented on the issue:
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So this is an issue only related to `Dataset.show`?
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Github user HyukjinKwon commented on the issue:
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@vinodkc, I think you can mark up the code bit via
``
```
spark.sql("create table test (a decimal(10,7), b decimal(10,6), c
decimal(10,8))")
...
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