diff --git a/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala b/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala
index 729778f1d..b0ab4fc2a 100644
--- a/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala
+++ b/spark/src/main/scala/org/apache/spark/sql/comet/CometNativeScanExec.scala
@@ -74,78 +74,82 @@ case class CometNativeScanExec(
   override def hashCode(): Int = Objects.hashCode(output)
 
   override lazy val metrics: Map[String, SQLMetric] = {
-    Map(
-      "time_elapsed_opening" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Wall clock time elapsed for file opening"),
-      "time_elapsed_scanning_until_data" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Wall clock time elapsed for file scanning +" +
-            "first record batch of decompression + decoding"),
-      "time_elapsed_scanning_total" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Total elapsed wall clock time for for scanning + record batch decompression / decoding"),
-      "time_elapsed_processing" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Wall clock time elapsed for data decompression + decoding"),
-      "file_open_errors" ->
-        SQLMetrics.createMetric(sparkContext, "Count of errors opening file"),
-      "file_scan_errors" ->
-        SQLMetrics.createMetric(sparkContext, "Count of errors scanning file"),
-      "predicate_evaluation_errors" ->
-        SQLMetrics.createMetric(
-          sparkContext,
-          "Number of times the predicate could not be evaluated"),
-      "row_groups_matched_bloom_filter" ->
-        SQLMetrics.createMetric(
-          sparkContext,
-          "Number of row groups whose bloom filters were checked and matched (not pruned)"),
-      "row_groups_pruned_bloom_filter" ->
-        SQLMetrics.createMetric(sparkContext, "Number of row groups pruned by bloom filters"),
-      "row_groups_matched_statistics" ->
-        SQLMetrics.createMetric(
-          sparkContext,
-          "Number of row groups whose statistics were checked and matched (not pruned)"),
-      "row_groups_pruned_statistics" ->
-        SQLMetrics.createMetric(sparkContext, "Number of row groups pruned by statistics"),
-      "bytes_scanned" ->
-        SQLMetrics.createSizeMetric(sparkContext, "Total number of bytes scanned"),
-      "pushdown_rows_pruned" ->
-        SQLMetrics.createMetric(
-          sparkContext,
-          "Total rows filtered out by predicates pushed into parquet scan"),
-      "pushdown_rows_matched" ->
-        SQLMetrics.createMetric(
-          sparkContext,
-          "Total rows passed predicates pushed into parquet scan"),
-      "row_pushdown_eval_time" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Total time spent evaluating row-level pushdown filters"),
-      "statistics_eval_time" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Total time spent evaluating row group-level statistics filters"),
-      "bloom_filter_eval_time" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Total time spent evaluating row group Bloom Filters"),
-      "page_index_rows_pruned" ->
-        SQLMetrics.createMetric(sparkContext, "Total rows filtered out by parquet page index"),
-      "page_index_rows_matched" ->
-        SQLMetrics.createMetric(sparkContext, "Total rows passed through the parquet page index"),
-      "page_index_eval_time" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Total time spent evaluating parquet page index filters"),
-      "metadata_load_time" ->
-        SQLMetrics.createNanoTimingMetric(
-          sparkContext,
-          "Total time spent reading and parsing metadata from the footer"))
+    CometMetricNode.baselineMetrics(sparkContext) ++
+      Map(
+        "time_elapsed_opening" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Wall clock time elapsed for file opening"),
+        "time_elapsed_scanning_until_data" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Wall clock time elapsed for file scanning +" +
+              "first record batch of decompression + decoding"),
+        "time_elapsed_scanning_total" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Total elapsed wall clock time for for scanning " +
+              "+ record batch decompression / decoding"),
+        "time_elapsed_processing" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Wall clock time elapsed for data decompression + decoding"),
+        "file_open_errors" ->
+          SQLMetrics.createMetric(sparkContext, "Count of errors opening file"),
+        "file_scan_errors" ->
+          SQLMetrics.createMetric(sparkContext, "Count of errors scanning file"),
+        "predicate_evaluation_errors" ->
+          SQLMetrics.createMetric(
+            sparkContext,
+            "Number of times the predicate could not be evaluated"),
+        "row_groups_matched_bloom_filter" ->
+          SQLMetrics.createMetric(
+            sparkContext,
+            "Number of row groups whose bloom filters were checked and matched (not pruned)"),
+        "row_groups_pruned_bloom_filter" ->
+          SQLMetrics.createMetric(sparkContext, "Number of row groups pruned by bloom filters"),
+        "row_groups_matched_statistics" ->
+          SQLMetrics.createMetric(
+            sparkContext,
+            "Number of row groups whose statistics were checked and matched (not pruned)"),
+        "row_groups_pruned_statistics" ->
+          SQLMetrics.createMetric(sparkContext, "Number of row groups pruned by statistics"),
+        "bytes_scanned" ->
+          SQLMetrics.createSizeMetric(sparkContext, "Total number of bytes scanned"),
+        "pushdown_rows_pruned" ->
+          SQLMetrics.createMetric(
+            sparkContext,
+            "Total rows filtered out by predicates pushed into parquet scan"),
+        "pushdown_rows_matched" ->
+          SQLMetrics.createMetric(
+            sparkContext,
+            "Total rows passed predicates pushed into parquet scan"),
+        "row_pushdown_eval_time" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Total time spent evaluating row-level pushdown filters"),
+        "statistics_eval_time" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Total time spent evaluating row group-level statistics filters"),
+        "bloom_filter_eval_time" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Total time spent evaluating row group Bloom Filters"),
+        "page_index_rows_pruned" ->
+          SQLMetrics.createMetric(sparkContext, "Total rows filtered out by parquet page index"),
+        "page_index_rows_matched" ->
+          SQLMetrics.createMetric(
+            sparkContext,
+            "Total rows passed through the parquet page index"),
+        "page_index_eval_time" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Total time spent evaluating parquet page index filters"),
+        "metadata_load_time" ->
+          SQLMetrics.createNanoTimingMetric(
+            sparkContext,
+            "Total time spent reading and parsing metadata from the footer"))
   }
 }