emaynardigs opened a new pull request #27155: [SPARK-17636][SPARK-25557][SQL] 
Parquet and ORC predicate pushdown in nested fields
URL: https://github.com/apache/spark/pull/27155
 
 
   Firstly, much of this PR is a rebase of PR#22535, much thanks to @dbtsai for 
his work.
   ### What changes were proposed in this pull request?
   Spark can now push down predicates on struct columns when reading Parquet 
and ORC tables.
   
   ### Why are the changes needed?
   There are significant performance gains to be had from pushing down 
predicates.
   
   ### Does this PR introduce any user-facing change?
   No
   
   ### How was this patch tested?
   Existing UT were extended to cover the new functionality.
   
   Sanity check tests:
   ```
   //// Setup ////
   spark.range(1000 * 1000).toDF("id").selectExpr("id", "STRUCT(id x, 
STRUCT(CAST(id AS STRING) z) y) 
nested").write.mode("overwrite").parquet("/tmp/data")
   spark.range(1000 * 1000).toDF("id").selectExpr("id", "STRUCT(id x, 
STRUCT(CAST(id AS STRING) z) y) 
nested").write.mode("overwrite").orc("/tmp/data_orc")
   def hack_benchmark(f: (() => Any)): Double = {
        (0 to 100).map(i => {
                val start = System.currentTimeMillis
                f()
                (System.currentTimeMillis - start)
        }).sum / 100.0
   }
   
   
   //// Without patch ////
   scala> spark.read.parquet("/tmp/data").filter("nested.x = 100").explain
   == Physical Plan ==
   *(1) Project [id#0L, nested#1]
   +- *(1) Filter (isnotnull(nested#1) && (nested#1.x = 100))
      +- *(1) FileScan parquet [id#0L,nested#1] Batched: false, Format: 
Parquet, Location: InMemoryFileIndex[file:/tmp/data], PartitionFilters: [], 
PushedFilters: [IsNotNull(nested)], ReadSchema: 
struct<id:bigint,nested:struct<x:bigint,y:string>>
   
   scala> spark.read.orc("/tmp/data_orc").filter("nested.x = 100").explain
   == Physical Plan ==
   *(1) Project [id#9253L, nested#9254]
   +- *(1) Filter (isnotnull(nested#9254) && (nested#9254.x = 100))
      +- *(1) FileScan orc [id#9253L,nested#9254] Batched: false, Format: ORC, 
Location: InMemoryFileIndex[file:/tmp/data_orc], PartitionFilters: [], 
PushedFilters: [IsNotNull(nested)], ReadSchema: 
struct<id:bigint,nested:struct<x:bigint,y:string>>
   
   scala> hack_benchmark(spark.read.parquet("/tmp/data").filter("nested.x < 
100").count _)
   res0: Double = 419.82 
   
   scala> hack_benchmark(spark.read.orc("/tmp/data_orc").filter("nested.x < 
100").count _)
   res5: Double = 1525.83  
   
   //// With patch ////
   scala> spark.read.parquet("/tmp/data").filter("nested.x = 100").explain
   == Physical Plan ==
   *(1) Project [id#54L, nested#55]
   +- *(1) Filter (isnotnull(nested#55) AND (nested#55.x = 100))
      +- BatchScan[id#54L, nested#55] ParquetScan Location: 
InMemoryFileIndex[file:/tmp/data], ReadSchema: 
struct<id:bigint,nested:struct<x:bigint,y:string>>, PushedFilters: 
[EqualTo(nested.x,100)]
   
   scala> spark.read.orc("/tmp/data_orc").filter("nested.x = 100").explain
   == Physical Plan ==
   *(1) Project [id#0L, nested#1]
   +- *(1) Filter (isnotnull(nested#1) AND (nested#1.x = 100))
      +- BatchScan[id#0L, nested#1] OrcScan Location: 
InMemoryFileIndex[file:/tmp/data_orc], ReadSchema: 
struct<id:bigint,nested:struct<x:bigint,y:struct<z:string>>>, PushedFilters: 
[EqualTo(nested.x,100)]
               
   scala> hack_benchmark(spark.read.parquet("/tmp/data").filter("nested.x < 
100").count _)
   res0: Double = 192.15                                                        
   
   
   scala> hack_benchmark(spark.read.orc("/tmp/data_orc").filter("nested.x < 
100").count _)
   res1: Double = 1029.57                                                   
   ```
   
   Note the significant performance improvement and the inclusion of the filter 
in `PushedFilters` in both cases.
   

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