[spark] branch branch-3.1 updated: [SPARK-34212][SQL][FOLLOWUP] Parquet vectorized reader can read decimal fields with a larger precision

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gurwls223 pushed a commit to branch branch-3.1
in repository https://gitbox.apache.org/repos/asf/spark.git


The following commit(s) were added to refs/heads/branch-3.1 by this push:
     new bb0efc1  [SPARK-34212][SQL][FOLLOWUP] Parquet vectorized reader can read decimal fields with a larger precision
bb0efc1 is described below

commit bb0efc16a435346db8d4a6a0bae7f3e647f9f186
Author: Wenchen Fan <we...@databricks.com>
AuthorDate: Wed Feb 3 09:26:36 2021 +0900

    [SPARK-34212][SQL][FOLLOWUP] Parquet vectorized reader can read decimal fields with a larger precision
    
    ### What changes were proposed in this pull request?
    
    This is a followup of https://github.com/apache/spark/pull/31357
    
    #31357 added a very strong restriction to the vectorized parquet reader, that the spark data type must exactly match the physical parquet type, when reading decimal fields. This restriction is actually not necessary, as we can safely read parquet decimals with a larger precision. This PR releases this restriction a little bit.
    
    ### Why are the changes needed?
    
    To not fail queries unnecessarily.
    
    ### Does this PR introduce _any_ user-facing change?
    
    Yes, now users can read parquet decimals with mismatched `DecimalType` as long as the scale is the same and precision is larger.
    
    ### How was this patch tested?
    
    updated test.
    
    Closes #31443 from cloud-fan/improve.
    
    Authored-by: Wenchen Fan <we...@databricks.com>
    Signed-off-by: HyukjinKwon <gu...@apache.org>
    (cherry picked from commit 00120ea53748d84976e549969f43cf2a50778c1c)
    Signed-off-by: HyukjinKwon <gu...@apache.org>
---
 .../sql/execution/datasources/parquet/VectorizedColumnReader.java | 4 +++-
 sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala  | 8 ++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java
index 7a10aa0..119af8d 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java
@@ -111,7 +111,9 @@ public class VectorizedColumnReader {
   private boolean isDecimalTypeMatched(DataType dt) {
     DecimalType d = (DecimalType) dt;
     DecimalMetadata dm = descriptor.getPrimitiveType().getDecimalMetadata();
-    return dm != null && dm.getPrecision() == d.precision() && dm.getScale() == d.scale();
+    // It's OK if the required decimal precision is larger than or equal to the physical decimal
+    // precision in the Parquet metadata, as long as the decimal scale is the same.
+    return dm != null && dm.getPrecision() <= d.precision() && dm.getScale() == d.scale();
   }
 
   private boolean canReadAsIntDecimal(DataType dt) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index d2a578b..5ce236c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -3785,6 +3785,14 @@ class SQLQuerySuite extends QueryTest with SharedSparkSession with AdaptiveSpark
       val df = sql("SELECT 1.0 a, CAST(1.23 AS DECIMAL(17, 2)) b, CAST(1.23 AS DECIMAL(36, 2)) c")
       df.write.parquet(path.toString)
 
+      Seq(true, false).foreach { vectorizedReader =>
+        withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> vectorizedReader.toString) {
+          // We can read the decimal parquet field with a larger precision, if scale is the same.
+          val schema = "a DECIMAL(9, 1), b DECIMAL(18, 2), c DECIMAL(38, 2)"
+          checkAnswer(readParquet(schema, path), df)
+        }
+      }
+
       withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> "false") {
         val schema1 = "a DECIMAL(3, 2), b DECIMAL(18, 3), c DECIMAL(37, 3)"
         checkAnswer(readParquet(schema1, path), df)


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