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LGTM
Thanks! Merged to master
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@gatorsmile @cloud-fan @HyukjinKwon @gengliangwang Could you look at the PR
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@gatorsmile @cloud-fan @HyukjinKwon @gengliangwang May I ask you to look at
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I understand the default value of `enforceSchema` must be true to keep
backward compatibility, but shall we suggest users to set it to false to avoid
incorrect result in the document?
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Right, will proceed reviewing this.
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Github user gatorsmile commented on the issue:
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In this PR, when documenting the option `enforceSchema`, we need to
emphasize the schemas are checked by positions instead of by names.
@ssimeonov `UNION` is a good example. Many Spark us
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cc @cloud-fan, I would like to listen what you think too.
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Can we check if there's any committer positive on this change before going
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ping @gengliangwang
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@gengliangwang @gatorsmile May I ask you to look at this PR one more time.
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retest this please
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Github user HyukjinKwon commented on the issue:
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Documentation does solve the problem in a way because we are going to claim
what Spark supports and does. How about adding a script or something to find
out by reading ahead or another way?
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@HyukjinKwon we are one of the Spark users experiencing this problem in the
real world: dealing with dirty data produced by a variety of third party
systems. Documentation doesn't solve anything h
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I don't have a particularly better solution for now. Documentation _might
be_ one option in such case given past discussions and suggestions from other
committers and reviewers.
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> For example, there are so many options that can be potentially added
(other univocity parser options).
You are right, so many things can be added but in this particular case we
are trying
Github user HyukjinKwon commented on the issue:
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I understood the rationale, problem and the approach and I don't feel
strongly - I am less sure since 1. this option is specific to CSV about
columns. It sounds adding complexity. 2. however, I
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@HyukjinKwon @gengliangwang @gatorsmile Please, have a look at it again.
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> The case exists in all data source format, right?
Not in all, for example, JSON datasource is more tolerant to field order in
json records. Let's say if you have the schema:
```
va
Github user gengliangwang commented on the issue:
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Some questions here:
1. The case exists in all data source format, right?
2. If user didn't provide schema, should we check the header among CSV
files?
3. Users should be responsibl
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cc @gengliangwang Could you review this CSV PR?
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jenkins, retest this, please
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