[GitHub] [iceberg] dramaticlly commented on a diff in pull request #8394: Spark 3.4: Rewrite procedure throw better exception when filter expression cannot translate

["dramaticlly (via GitHub)" <gi...@apache.org>]

dramaticlly commented on code in PR #8394:
URL: https://github.com/apache/iceberg/pull/8394#discussion_r1326513355


##########
spark/v3.4/spark-extensions/src/test/java/org/apache/iceberg/spark/extensions/TestRewriteDataFilesProcedure.java:
##########
@@ -763,6 +764,35 @@ public void testDefaultSortOrder() {
     assertEquals("Data after compaction should not change", expectedRecords, actualRecords);
   }
 
+  @Test
+  public void testRewriteDataFilesWithSystemFunctions() {
+    Assumptions.assumeThat(catalogName).isNotEqualTo("spark_catalog");
+
+    sql(
+        "CREATE TABLE %s (c1 INT, c2 STRING, c3 TIMESTAMP) "
+            + "USING iceberg "
+            + "PARTITIONED BY (days(c3)) "
+            + "TBLPROPERTIES ('%s' '%s')",
+        tableName,
+        TableProperties.WRITE_DISTRIBUTION_MODE,
+        TableProperties.WRITE_DISTRIBUTION_MODE_NONE);
+
+    sql(
+        "INSERT INTO TABLE %s VALUES (0, 'data-0', CAST('2017-11-22T09:20:44.294658+00:00' AS TIMESTAMP))",
+        tableName);
+    sql(
+        "INSERT INTO TABLE %s VALUES (1, 'data-1', CAST('2017-11-23T03:15:32.194356+00:00' AS TIMESTAMP))",
+        tableName);
+    // Test with invalid filter column col1
+    Assertions.assertThatThrownBy(
+            () ->
+                sql(
+                    "CALL %s.system.rewrite_data_files(table => '%s', where => '%s.system.years(c3) = 2017')",

Review Comment:
   looks like comment suggest filter on column col1 but predicate in where clause is actually on year transform of c3, just want to make sure if it's desired



##########
spark/v3.4/spark/src/main/scala/org/apache/spark/sql/execution/datasources/SparkExpressionConverter.scala:
##########
@@ -35,7 +35,14 @@ object SparkExpressionConverter {
     // Currently, it is a double conversion as we are converting Spark expression to Spark filter
     // and then converting Spark filter to Iceberg expression.
     // But these two conversions already exist and well tested. So, we are going with this approach.
-    SparkFilters.convert(DataSourceStrategy.translateFilter(sparkExpression, supportNestedPredicatePushdown = true).get)
+    DataSourceStrategy.translateFilter(sparkExpression, supportNestedPredicatePushdown = true) match {

Review Comment:
   thank you and I think it's much better than `null.get` when I saw it in my change



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