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CC @davies
By the way do you have any measurements that show the speed up? I imagine
it's faster, but mostly curious if it's still a win for short strings.
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thanks, merging to master!
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@jkbradley I split this PR, removed the code for "dump models to disk", so
the PR will be smaller and easier to review. When this PR merged, I will create
follow-up PR for "dump models to disk"
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Merged into master, thanks for all reviewing.
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many thanks @gatorsmile
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Hi @jerryshao thanks for your reviewing.
>So it somehow reflects that CPU core contention is the main issue for
memory pre-occupation
I have modified the code, now it will not request m
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S
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customSchema
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@zhengruifeng Yeah, it is better. Actually the difference between running
multiple `withColumn` and one `withColumns` is mainly in the cost of query
analysis and plan/dataset initialization. I will
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In the test code, should we use `model.transform(df).count` instead?
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Add a test for it?
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[SPARK-22003][SQL] support array column in vectorized reader with UDF
## What changes were proposed in this pull request?
The UDF needs to deserialize the `UnsafeRow`. When the column typ
GitHub user wangyum opened a pull request:
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[SPARK-22002][SQL] Read JDBC table use custom schema support specify
partial fields.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/18266 add a ne
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FYI, the `withColumns` API was proposed in #17819.
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Ran the similar benchmark as
https://github.com/apache/spark/pull/18902#issuecomment-321727416:
numColums | Old Mean | Old Median | New Mean | New Median
-- | -- | -- | -- | --
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[SPARK-22001][ML][SQL] ImputerModel can do withColumn for all input columns
at one pass
## What changes were proposed in this pull request?
SPARK-21690 makes one-pass `Imputer` by paralleli
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