JoshRosen opened a new pull request #27089: [SPARK-30414][SQL][WIP] 
ParquetRowConverter optimizations: arrays, maps, and constant factors
URL: https://github.com/apache/spark/pull/27089
 
 
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   ### What changes were proposed in this pull request?
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   This PR implements multiple performance optimizations for 
`ParquetRowConverter`, achieving some modest constant-factor wins for all 
fields and larger wins for map and array fields: 
   
   - Add `private[this]` to several `val`s 
(90cebf080a5d3857ea8cf2a89e8e060b8b5a2fbf)
   - Keep a `fieldUpdaters` array, saving two`.updater()` calls per field 
(7318785d350cc924198d7514e40973fd76d54ad5): I suspect that these are often 
megamorphic calls, so cutting these out seems like it could be a relatively 
large performance win.
   - Only call `currentRow.numFields` once per `start()` call 
(e05de150813b639929c18af1df09ec718d2d16fc): previously we'd call it once per 
field and this had a significant enough cost that it was visible during 
profiling.
   - Re-use buffers in array and map converters 
(c7d1534685fbad5d2280b082f37bed6d75848e76): previously we would create a 
brand-new Scala `ArrayBuffer` for each field read, but this isn't actually 
necessary because the data is already copied into a fresh array when `end()` 
constructs a `GenericArrayData`. Here, I've replaced this with re-used Java 
`ArrayList`s.
   
   ### Why are the changes needed?
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   To improve Parquet read performance; this is complementary to #26993's 
(orthogonal) improvements for nested struct read performance.
   
   ### Does this PR introduce any user-facing change?
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   No.
   
   ### How was this patch tested?
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   I'm marking this as `[WIP]` pending more benchmarking. Early benchmarks with 
both synthetic and realistic schemas (similar to the ones in #26993) suggest 
moderate gains (10%+ in certain cases).

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