fix: rename reserved word columns (min/max/sum/one) to avoid Dremio SQL conflicts

Co-Authored-By: Itamar Hartstein <haritamar@gmail.com>

Author: devin-ai-integration[bot]

.github/workflows/test-warehouse.yml

@@ -387,23 +387,15 @@ jobs:
           fi
           dbt run --target "${{ inputs.warehouse-type }}" $EXTRA_ARGS || true
 
-          # Validate run_results.json: only expected models should be non-success.
-          # error_model is always expected to fail (intentionally broken SQL).
-          # Dremio also fails on models using SQL reserved words as column names
-          # (min, max, sum, one) which cannot be fixed without breaking other adapters.
-          EXPECTED_FAILURES='["model.elementary_integration_tests.error_model"]'
-          if [ "${{ inputs.warehouse-type }}" = "dremio" ]; then
-            EXPECTED_FAILURES='["model.elementary_integration_tests.error_model","model.elementary_integration_tests.numeric_column_anomalies","model.elementary_integration_tests.one","model.elementary_integration_tests.nested"]'
-          fi
-
-          jq -e --argjson expected "$EXPECTED_FAILURES" '
+          # Validate run_results.json: only error_model should be non-success
+          jq -e '
             [.results[] | select(.status != "success") | .unique_id]
-            | sort == ($expected | sort)
+            | length == 1 and .[0] == "model.elementary_integration_tests.error_model"
           ' target/run_results.json > /dev/null
           jq_exit=$?
 
           if [ $jq_exit -eq 0 ]; then
-            echo "✅ Validation passed: only expected models failed."
+            echo "✅ Validation passed: only error_model failed."
           else
             echo "❌ Validation failed. Unexpected failures:"
             jq '[.results[] | select(.status != "success") | .unique_id] | join(", ")' target/run_results.json

tests/e2e/report/fixtures/elementary_output.json

@@ -0,0 +1,201 @@
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+1969-12-31 00:00:00,1969-12-30 17:00:00,42,232,0,0,109,85,98,303
+1969-12-31 00:00:00,1969-12-30 17:00:00,62,300,0,0,105,96,85,368
+1969-12-31 00:00:00,1969-12-30 17:00:00,111,107,0,0,105,114,106,329
+1969-12-31 00:00:00,1969-12-30 17:00:00,181,265,0,0,108,88,97,305
+1969-12-31 00:00:00,1969-12-30 17:00:00,68,192,0,0,103,88,91,344
+1969-12-31 00:00:00,1969-12-30 17:00:00,182,135,0,127,101,94,102,309
+1969-12-31 00:00:00,1969-12-30 17:00:00,51,300,0,0,104,109,98,347
+1969-12-31 00:00:00,1969-12-30 17:00:00,55,164,0,0,103,83,81,330
+1969-12-31 00:00:00,1969-12-30 17:00:00,155,174,0,178,101,98,97,343
+1969-12-31 00:00:00,1969-12-30 17:00:00,191,216,0,0,106,110,89,322
+1969-12-31 00:00:00,1969-12-30 17:00:00,50,248,0,195,108,109,86,321
+1969-12-31 00:00:00,1969-12-30 17:00:00,188,300,0,198,106,102,111,306
+1969-12-31 00:00:00,1969-12-30 17:00:00,13,272,0,0,103,92,101,330
+1969-12-31 00:00:00,1969-12-30 17:00:00,33,233,0,146,102,105,95,349

tests/e2e_dbt_project/data/training/numeric_column_anomalies_training.csv

@@ -132,8 +132,8 @@ def get_training_row(date, row_index, rows_count):
         return {
             "updated_at": date.strftime(DATE_FORMAT),
             "occurred_at": (date - timedelta(hours=1)).strftime(DATE_FORMAT),
-            "min": random.randint(100, 200),
-            "max": random.randint(100, 200),
+            "min_val": random.randint(100, 200),
+            "max_val": random.randint(100, 200),
             "zero_count": 0
             if row_index < (3 / 100 * rows_count)
             else random.randint(100, 200),
@@ -143,16 +143,16 @@ def get_training_row(date, row_index, rows_count):
             "average": random.randint(99, 101),
             "standard_deviation": random.randint(99, 101),
             "variance": random.randint(99, 101),
-            "sum": random.randint(100, 200),
+            "sum_val": random.randint(100, 200),
         }
 
     def get_validation_row(date, row_index, rows_count):
         row_index += -(rows_count / 2)
         return {
             "updated_at": date.strftime(DATE_FORMAT),
             "occurred_at": (date - timedelta(hours=7)).strftime(DATE_FORMAT),
-            "min": random.randint(10, 200),
-            "max": random.randint(100, 300),
+            "min_val": random.randint(10, 200),
+            "max_val": random.randint(100, 300),
             "zero_count": 0
             if row_index < (80 / 100 * rows_count)
             else random.randint(100, 200),
@@ -162,20 +162,20 @@ def get_validation_row(date, row_index, rows_count):
             "average": random.randint(101, 110),
             "standard_deviation": random.randint(80, 120),
             "variance": random.randint(80, 120),
-            "sum": random.randint(300, 400),
+            "sum_val": random.randint(300, 400),
         }
 
     numeric_columns = [
         "updated_at",
         "occurred_at",
-        "min",
-        "max",
+        "min_val",
+        "max_val",
         "zero_count",
         "zero_percent",
         "average",
         "standard_deviation",
         "variance",
-        "sum",
+        "sum_val",
     ]
     dates = generate_rows_timestamps(base_date=EPOCH - timedelta(days=2))
     training_rows = generate_rows(rows_count_per_day, dates, get_training_row)

tests/e2e_dbt_project/data/validation/numeric_column_anomalies_validation.csv

@@ -1 +1 @@
-select 1 as one
+select 1 as col1

tests/e2e_dbt_project/generate_data.py

@@ -16,14 +16,14 @@ source as (
      select
          updated_at,
          occurred_at,
-         min,
-         max,
+         min_val,
+         max_val,
          zero_count,
          zero_percent,
          average,
          standard_deviation,
          variance,
-         sum
+         sum_val
      from source
  )
 

tests/e2e_dbt_project/models/nested/models/tree/nested.sql

@@ -1 +1 @@
-select 1 as one
+select 1 as col1

tests/e2e_dbt_project/models/numeric_column_anomalies.sql

@@ -7,7 +7,7 @@ models:
     meta:
       owner: "{{ var('one_owner', none) }}"
     columns:
-      - name: one
+      - name: col1
         tests:
           - accepted_values:
               meta:
@@ -255,7 +255,7 @@ models:
             - average_length
             - null_count
     columns:
-      - name: min
+      - name: min_val
         tests:
           - elementary.column_anomalies:
               tags: ["numeric_column_anomalies", "column_anomalies"]
@@ -269,7 +269,7 @@ models:
               tags: ["numeric_column_anomalies", "column_anomalies"]
               column_anomalies:
                 - average
-      - name: max
+      - name: max_val
         tests:
           - elementary.column_anomalies:
               tags: ["numeric_column_anomalies", "column_anomalies"]
@@ -327,7 +327,7 @@ models:
         tests:
           - elementary.column_anomalies:
               tags: ["numeric_column_anomalies", "column_anomalies"]
-      - name: sum
+      - name: sum_val
         tests:
           - elementary.column_anomalies:
               column_anomalies:

tests/e2e_dbt_project/models/one.sql

@@ -1,2 +1,2 @@
 {% set relation = api.Relation.create(database=elementary.target_database(), schema=target.schema, identifier='numeric_column_anomalies') %}
-select min from {{ relation }} where min < 100
+select min_val from {{ relation }} where min_val < 100