[SPARK-15639] [SPARK-16321] [SQL] Push down filter at RowGroups level for parquet reader

## What changes were proposed in this pull request?

The base class `SpecificParquetRecordReaderBase` used for vectorized parquet reader will try to get pushed-down filters from the given configuration. This pushed-down filters are used for RowGroups-level filtering. However, we don't set up the filters to push down into the configuration. In other words, the filters are not actually pushed down to do RowGroups-level filtering. This patch is to fix this and tries to set up the filters for pushing down to configuration for the reader.

The benchmark that excludes the time of writing Parquet file:

    test("Benchmark for Parquet") {
      val N = 500 << 12
        withParquetTable((0 until N).map(i => (101, i)), "t") {
          val benchmark = new Benchmark("Parquet reader", N)
          benchmark.addCase("reading Parquet file", 10) { iter =>
            sql("SELECT _1 FROM t where t._1 < 100").collect()
          }
          benchmark.run()
      }
    }

`withParquetTable` in default will run tests for vectorized reader non-vectorized readers. I only let it run vectorized reader.

When we set the block size of parquet as 1024 to have multiple row groups. The benchmark is:

Before this patch:

The retrieved row groups: 8063

    Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
    Intel(R) Core(TM) i7-5557U CPU  3.10GHz
    Parquet reader:                          Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
    ------------------------------------------------------------------------------------------------
    reading Parquet file                           825 / 1233          2.5         402.6       1.0X

After this patch:

The retrieved row groups: 0

    Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
    Intel(R) Core(TM) i7-5557U CPU  3.10GHz
    Parquet reader:                          Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
    ------------------------------------------------------------------------------------------------
    reading Parquet file                           306 /  503          6.7         149.6       1.0X

Next, I run the benchmark for non-pushdown case using the same benchmark code but with disabled pushdown configuration. This time the parquet block size is default value.

Before this patch:

    Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
    Intel(R) Core(TM) i7-5557U CPU  3.10GHz
    Parquet reader:                          Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
    ------------------------------------------------------------------------------------------------
    reading Parquet file                           136 /  238         15.0          66.5       1.0X

After this patch:

    Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic
    Intel(R) Core(TM) i7-5557U CPU  3.10GHz
    Parquet reader:                          Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
    ------------------------------------------------------------------------------------------------
    reading Parquet file                           124 /  193         16.5          60.7       1.0X

For non-pushdown case, from the results, I think this patch doesn't affect normal code path.

I've manually output the `totalRowCount` in `SpecificParquetRecordReaderBase` to see if this patch actually filter the row-groups. When running the above benchmark:

After this patch:
    `totalRowCount = 0`

Before this patch:
    `totalRowCount = 1024000`

## How was this patch tested?
Existing tests should be passed.

Author: Liang-Chi Hsieh <simonh@tw.ibm.com>

Closes #13701 from viirya/vectorized-reader-push-down-filter2.

1 files changed, 18 insertions, 0 deletions

diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java
index dfe6967647..06cd9ea2d2 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java
@@ -31,6 +31,8 @@ import java.util.List;
 import java.util.Map;
 import java.util.Set;
 
+import scala.Option;
+
 import static org.apache.parquet.filter2.compat.RowGroupFilter.filterRowGroups;
 import static org.apache.parquet.format.converter.ParquetMetadataConverter.NO_FILTER;
 import static org.apache.parquet.format.converter.ParquetMetadataConverter.range;
@@ -59,8 +61,12 @@ import org.apache.parquet.hadoop.metadata.ParquetMetadata;
 import org.apache.parquet.hadoop.util.ConfigurationUtil;
 import org.apache.parquet.schema.MessageType;
 import org.apache.parquet.schema.Types;
+import org.apache.spark.TaskContext;
+import org.apache.spark.TaskContext$;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.sql.types.StructType$;
+import org.apache.spark.util.AccumulatorV2;
+import org.apache.spark.util.LongAccumulator;
 
 /**
  * Base class for custom RecordReaders for Parquet that directly materialize to `T`.
@@ -145,6 +151,18 @@ public abstract class SpecificParquetRecordReaderBase<T> extends RecordReader<Vo
     for (BlockMetaData block : blocks) {
       this.totalRowCount += block.getRowCount();
     }
+
+    // For test purpose.
+    // If the predefined accumulator exists, the row group number to read will be updated
+    // to the accumulator. So we can check if the row groups are filtered or not in test case.
+    TaskContext taskContext = TaskContext$.MODULE$.get();
+    if (taskContext != null) {
+      Option<AccumulatorV2<?, ?>> accu = (Option<AccumulatorV2<?, ?>>) taskContext.taskMetrics()
+        .lookForAccumulatorByName("numRowGroups");
+      if (accu.isDefined()) {
+        ((LongAccumulator)accu.get()).add((long)blocks.size());
+      }
+    }
   }
 
   /**