[GitHub] [arrow-datafusion] liukun4515 commented on a diff in pull request #2960: test: add test for decimal and pruning for decimal column

[GitBox <gi...@apache.org>]

liukun4515 commented on code in PR #2960:
URL: https://github.com/apache/arrow-datafusion/pull/2960#discussion_r928940950


##########
datafusion/core/src/physical_optimizer/pruning.rs:
##########
@@ -1418,6 +1452,74 @@ mod tests {
         Ok(())
     }
 
+    #[test]
+    fn prune_decimal_data() {
+        // decimal(9,2)
+        let schema = Arc::new(Schema::new(vec![Field::new(
+            "s1",
+            DataType::Decimal(9, 2),
+            true,
+        )]));
+        // s1 > 5
+        let expr = col("s1").gt(lit(ScalarValue::Decimal128(Some(500), 9, 2)));
+        // If the data is written by spark, the physical data type is INT32 in the parquet
+        // So we use the INT32 type of statistic.
+        let statistics = TestStatistics::new().with(
+            "s1",
+            ContainerStats::new_i32(
+                vec![Some(0), Some(4), None, Some(3)], // min
+                vec![Some(5), Some(6), Some(4), None], // max
+            ),
+        );
+        let p = PruningPredicate::try_new(expr, schema).unwrap();
+        let result = p.prune(&statistics).unwrap();
+        let expected = vec![false, true, false, true];
+        assert_eq!(result, expected);
+
+        // decimal(18,2)
+        let schema = Arc::new(Schema::new(vec![Field::new(
+            "s1",
+            DataType::Decimal(18, 2),
+            true,
+        )]));
+        // s1 > 5
+        let expr = col("s1").gt(lit(ScalarValue::Decimal128(Some(500), 18, 2)));
+        // If the data is written by spark, the physical data type is INT64 in the parquet
+        // So we use the INT32 type of statistic.
+        let statistics = TestStatistics::new().with(
+            "s1",
+            ContainerStats::new_i64(
+                vec![Some(0), Some(4), None, Some(3)], // min
+                vec![Some(5), Some(6), Some(4), None], // max
+            ),
+        );
+        let p = PruningPredicate::try_new(expr, schema).unwrap();
+        let result = p.prune(&statistics).unwrap();
+        let expected = vec![false, true, false, true];
+        assert_eq!(result, expected);
+
+        // decimal(23,2)
+        let schema = Arc::new(Schema::new(vec![Field::new(
+            "s1",
+            DataType::Decimal(23, 2),
+            true,
+        )]));
+        // s1 > 5
+        let expr = col("s1").gt(lit(ScalarValue::Decimal128(Some(500), 23, 2)));
+        let statistics = TestStatistics::new().with(
+            "s1",
+            ContainerStats::new_decimal128(
+                vec![Some(0), Some(400), None, Some(300)], // min
+                vec![Some(500), Some(600), Some(400), None], // max
+                23,
+                2,
+            ),
+        );

Review Comment:
   This test case is just used to test the pruning logic.
   I will file follow-up pull request to fix the https://github.com/apache/arrow-datafusion/issues/2962 with parquet rowgroup filter/prune.



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