GitHub user nongli opened a pull request:
https://github.com/apache/spark/pull/10593
[SPARK-12644][SQL] Update parquet reader to be vectorized.
This inlines a few of the Parquet decoders and adds vectorized APIs to
support decoding in batch.
There are a few particulars in the Parquet encodings that make this much
more efficient. In
particular, RLE encodings are very well suited for batch decoding. The
Parquet 2.0 encodings are
also very suited for this.
This is a work in progress and does not affect the current execution. In
subsequent patches, we will
support more encodings and types before enabling this.
Simple benchmarks indicate this can decode single ints about > 3x faster.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/nongli/spark spark-12644
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/10593.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #10593
----
commit 7eeff58298ceac076779a5cae05ca674ed0ac51a
Author: Nong <non...@gmail.com>
Date: 2015-12-31T22:45:30Z
[SPARK-12636][SQL] Update UnsafeRowParquetRecordReader to support reading
paths directly.
As noted in the code, this change is to make this componenet easier to
test in isolation.
commit 22afd1f0115b86cdb5ba661dd2c0714ff6a4243b
Author: Nong <non...@gmail.com>
Date: 2016-01-01T00:26:34Z
[SPARK-12640][SQL] Add simple benchmarking utility class and add Parquet
scan benchmarks.
We've run benchmarks ad hoc to measure the scanner performance. We will
continue to invest in this
and it makes sense to get these benchmarks into code. This adds a simple
benchmarking utility to do
this.
commit 3e41ed43ebc16f4ea0f2a642dbf3a5e40a8bd0d9
Author: Nong <non...@gmail.com>
Date: 2016-01-01T05:12:44Z
[SPARK-12635][SQL] Add ColumnarBatch, an in memory columnar format for
execution.
There are many potential benefits of having an efficient in memory columnar
format as an alternate
to UnsafeRow. This patch introduces ColumnarBatch/ColumnarVector which
starts this effort. The
remaining implementation can be done as follow up patches.
As stated in the in the JIRA, there are useful external components that
operate on memory in a
simple columnar format. ColumnarBatch would serve that purpose and could
server as a
zero-serialization/zero-copy exchange for this use case.
This patch supports running the underlying data either on heap or off heap.
On heap runs a bit
faster but we would need offheap for zero-copy exchanges. Currently, this
mode is hidden behind one
interface (ColumnVector).
This differs from Parquet or the existing columnar cache because this is
*not* intended to be used
as a storage format. The focus is entirely on CPU efficiency as we expect
to only have 1 of these
batches in memory per task.
commit d99659d89a7709df8223ab86b1edd244b1e63086
Author: Nong <non...@gmail.com>
Date: 2016-01-01T07:28:06Z
[SPARK-12644][SQL] Update parquet reader to be vectorized.
This inlines a few of the Parquet decoders and adds vectorized APIs to
support decoding in batch.
There are a few particulars in the Parquet encodings that make this much
more efficient. In
particular, RLE encodings are very well suited for batch decoding. The
Parquet 2.0 encodings are
also very suited for this.
This is a work in progress and does not affect the current execution. In
subsequent patches, we will
support more encodings and types before enabling this.
Simple benchmarks indicate this can decode single ints about > 3x faster.
----
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