Github user kiszk commented on the issue:
https://github.com/apache/spark/pull/11956
@davies, thank you for your comment. I hope that you will have bandwidth
soon since Spark 2.0 was released.
[this PR](https://github.com/apache/spark/pull/13899/files) does the same
thing. In particular, generated code for reading a column is almost the same.
The difference is to use the conventional `CachedBatch` that uses
`Array[Byte]` or to use the new `CachedBatchByte` that may use `ColumnarBatch`
created by [generated code
](https://gist.github.com/andrewor14/a9ed9d942029457a0f953e809ac26ee9). I like
simplify my PR by using the idea in [the
PR](https://github.com/apache/spark/pull/13899/files). For example, I can throw
away new files `ByteBufferColumnVector.java` and `PassThroughSuite.scala`.
I have two question in [the
PR](https://github.com/apache/spark/pull/13899/files).
1. Do we use the conventional `CachedBatch` or `ColumnarBatch` for cache?
2. In this implementation, how a cache content in `ColumnarBatch` will be
serialized when it must be flushed into a disk?
3. What test cases were failed? Links to test results are not valid now.
4. Will we support compression scheme in the future while we use
`ColumnarBatch`?
What do you think?
2.
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