Add data_freshness_sla and volume_threshold tests

integration_tests/tests/test_data_freshness_sla.py

@@ -0,0 +1,123 @@
+from datetime import datetime, timedelta
+
+from data_generator import DATE_FORMAT
+from dbt_project import DbtProject
+
+TEST_NAME = "elementary.data_freshness_sla"
+TIMESTAMP_COLUMN = "updated_at"
+
+
+def test_fresh_data_passes(test_id: str, dbt_project: DbtProject):
+    """Data updated today should pass when the SLA deadline has already passed."""
+    utc_now = datetime.utcnow()
+    data = [
+        {TIMESTAMP_COLUMN: utc_now.strftime(DATE_FORMAT)},
+        {TIMESTAMP_COLUMN: (utc_now - timedelta(hours=1)).strftime(DATE_FORMAT)},
+    ]
+    test_args = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "sla_time": "11:59pm",
+        "timezone": "UTC",
+    }
+    test_result = dbt_project.test(test_id, TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "pass"
+
+
+def test_stale_data_fails(test_id: str, dbt_project: DbtProject):
+    """Data only from previous days should fail when today's SLA deadline has passed."""
+    utc_now = datetime.utcnow()
+    yesterday = utc_now - timedelta(days=2)
+    data = [
+        {TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)},
+        {TIMESTAMP_COLUMN: (yesterday - timedelta(hours=5)).strftime(DATE_FORMAT)},
+    ]
+    # Use a deadline early in the day so it has certainly passed
+    test_args = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "sla_time": "12:01am",
+        "timezone": "UTC",
+    }
+    test_result = dbt_project.test(test_id, TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "fail"
+
+
+def test_no_data_fails(test_id: str, dbt_project: DbtProject):
+    """An empty table (after WHERE filter) should fail when deadline has passed."""
+    utc_now = datetime.utcnow()
+    # Seed with data that will be excluded by the where_expression
+    data = [
+        {TIMESTAMP_COLUMN: utc_now.strftime(DATE_FORMAT), "category": "excluded"},
+    ]
+    test_args = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "sla_time": "12:01am",
+        "timezone": "UTC",
+        "where_expression": "category = 'included'",
+    }
+    test_result = dbt_project.test(test_id, TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "fail"
+
+
+def test_deadline_not_passed_does_not_fail(test_id: str, dbt_project: DbtProject):
+    """Even if data is stale, the test should pass if the deadline hasn't passed yet."""
+    utc_now = datetime.utcnow()
+    yesterday = utc_now - timedelta(days=2)
+    data = [
+        {TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)},
+    ]
+    # Set the deadline far in the future so it hasn't passed yet.
+    # Etc/GMT-14 is UTC+14, the farthest-ahead timezone, so 11:59pm there
+    # is well into the future from UTC's perspective.
+    test_args = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "sla_time": "11:59pm",
+        "timezone": "Etc/GMT-14",
+    }
+    test_result = dbt_project.test(test_id, TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "pass"
+
+
+def test_with_where_expression(test_id: str, dbt_project: DbtProject):
+    """The where_expression should filter which rows count toward freshness."""
+    utc_now = datetime.utcnow()
+    yesterday = utc_now - timedelta(days=2)
+    data = [
+        # Fresh data for category A
+        {TIMESTAMP_COLUMN: utc_now.strftime(DATE_FORMAT), "category": "a"},
+        # Stale data for category B
+        {TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT), "category": "b"},
+    ]
+    # Test with category A (fresh data) -> should pass
+    test_args = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "sla_time": "11:59pm",
+        "timezone": "UTC",
+        "where_expression": "category = 'a'",
+    }
+    test_result = dbt_project.test(test_id, TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "pass"
+
+    # Test with category B (stale data) and early deadline -> should fail
+    test_args_stale = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "sla_time": "12:01am",
+        "timezone": "UTC",
+        "where_expression": "category = 'b'",
+    }
+    test_result = dbt_project.test(test_id, TEST_NAME, test_args_stale)
+    assert test_result["status"] == "fail"
+
+
+def test_with_timezone(test_id: str, dbt_project: DbtProject):
+    """Test that timezone conversion works correctly."""
+    utc_now = datetime.utcnow()
+    data = [
+        {TIMESTAMP_COLUMN: utc_now.strftime(DATE_FORMAT)},
+    ]
+    test_args = {
+        "timestamp_column": TIMESTAMP_COLUMN,
+        "sla_time": "11:59pm",
+        "timezone": "America/New_York",
+    }
+    test_result = dbt_project.test(test_id, TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "pass"

integration_tests/tests/test_volume_threshold.py

@@ -0,0 +1,218 @@
+from datetime import datetime, timedelta
+
+from data_generator import DATE_FORMAT, generate_dates
+from dbt_project import DbtProject
+
+TIMESTAMP_COLUMN = "updated_at"
+DBT_TEST_NAME = "elementary.volume_threshold"
+DBT_TEST_ARGS = {
+    "timestamp_column": TIMESTAMP_COLUMN,
+    "time_bucket": {"period": "day", "count": 1},
+    "days_back": 14,
+    "backfill_days": 14,
+}
+
+
+def _generate_stable_data(rows_per_day=100, days_back=14):
+    """Generate data with a consistent number of rows per day bucket."""
+    utc_today = datetime.utcnow().date()
+    data = []
+    for cur_date in generate_dates(base_date=utc_today, days_back=days_back):
+        for _ in range(rows_per_day):
+            data.append({TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT)})
+    return data
+
+
+def test_stable_volume_passes(test_id: str, dbt_project: DbtProject):
+    """Consistent row counts across buckets should pass."""
+    data = _generate_stable_data(rows_per_day=100)
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, DBT_TEST_ARGS, data=data)
+    assert test_result["status"] == "pass"
+
+
+def test_large_spike_fails(test_id: str, dbt_project: DbtProject):
+    """A large spike in row count (>10% default error threshold) should fail."""
+    utc_today = datetime.utcnow().date()
+    yesterday = utc_today - timedelta(days=1)
+    data = []
+    # Previous days: 100 rows each
+    for cur_date in generate_dates(base_date=utc_today, days_back=14):
+        if cur_date < yesterday:
+            for _ in range(100):
+                data.append({TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT)})
+    # Yesterday (current bucket): 100 rows
+    for _ in range(100):
+        data.append({TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)})
+    # Today (current bucket): 150 rows (50% spike)
+    for _ in range(150):
+        data.append({TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT)})
+
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, DBT_TEST_ARGS, data=data)
+    assert test_result["status"] != "pass"
+
+
+def test_large_drop_fails(test_id: str, dbt_project: DbtProject):
+    """A large drop in row count (>10% default error threshold) should fail."""
+    utc_today = datetime.utcnow().date()
+    yesterday = utc_today - timedelta(days=1)
+    data = []
+    # Previous days: 100 rows each
+    for cur_date in generate_dates(base_date=utc_today, days_back=14):
+        if cur_date < yesterday:
+            for _ in range(100):
+                data.append({TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT)})
+    # Yesterday (previous bucket): 100 rows
+    for _ in range(100):
+        data.append({TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)})
+    # Today (current bucket): 50 rows (50% drop)
+    for _ in range(50):
+        data.append({TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT)})
+
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, DBT_TEST_ARGS, data=data)
+    assert test_result["status"] != "pass"
+
+
+def test_direction_spike_ignores_drop(test_id: str, dbt_project: DbtProject):
+    """With direction=spike, a drop should not trigger a failure."""
+    utc_today = datetime.utcnow().date()
+    yesterday = utc_today - timedelta(days=1)
+    data = []
+    # Previous days: 100 rows each
+    for cur_date in generate_dates(base_date=utc_today, days_back=14):
+        if cur_date < yesterday:
+            for _ in range(100):
+                data.append({TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT)})
+    # Yesterday: 100 rows
+    for _ in range(100):
+        data.append({TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)})
+    # Today: 50 rows (50% drop)
+    for _ in range(50):
+        data.append({TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT)})
+
+    test_args = {**DBT_TEST_ARGS, "direction": "spike"}
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "pass"
+
+
+def test_direction_drop_ignores_spike(test_id: str, dbt_project: DbtProject):
+    """With direction=drop, a spike should not trigger a failure."""
+    utc_today = datetime.utcnow().date()
+    yesterday = utc_today - timedelta(days=1)
+    data = []
+    # Previous days: 100 rows each
+    for cur_date in generate_dates(base_date=utc_today, days_back=14):
+        if cur_date < yesterday:
+            for _ in range(100):
+                data.append({TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT)})
+    # Yesterday: 100 rows
+    for _ in range(100):
+        data.append({TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)})
+    # Today: 150 rows (50% spike)
+    for _ in range(150):
+        data.append({TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT)})
+
+    test_args = {**DBT_TEST_ARGS, "direction": "drop"}
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, test_args, data=data)
+    assert test_result["status"] == "pass"
+
+
+def test_min_row_count_skips_small_baseline(test_id: str, dbt_project: DbtProject):
+    """When previous bucket has fewer rows than min_row_count, check is skipped (pass)."""
+    utc_today = datetime.utcnow().date()
+    yesterday = utc_today - timedelta(days=1)
+    data = []
+    # Previous days: only 5 rows each (below default min_row_count=100)
+    for cur_date in generate_dates(base_date=utc_today, days_back=14):
+        if cur_date < yesterday:
+            for _ in range(5):
+                data.append({TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT)})
+    # Yesterday: 5 rows
+    for _ in range(5):
+        data.append({TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)})
+    # Today: 50 rows (huge spike but baseline is too small)
+    for _ in range(50):
+        data.append({TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT)})
+
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, DBT_TEST_ARGS, data=data)
+    assert test_result["status"] == "pass"
+
+
+def test_custom_thresholds(test_id: str, dbt_project: DbtProject):
+    """Custom thresholds should control the sensitivity of the test."""
+    utc_today = datetime.utcnow().date()
+    yesterday = utc_today - timedelta(days=1)
+    data = []
+    # Previous days: 100 rows each
+    for cur_date in generate_dates(base_date=utc_today, days_back=14):
+        if cur_date < yesterday:
+            for _ in range(100):
+                data.append({TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT)})
+    # Yesterday: 100 rows
+    for _ in range(100):
+        data.append({TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT)})
+    # Today: 108 rows (8% change)
+    for _ in range(108):
+        data.append({TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT)})
+
+    # With default thresholds (warn=5, error=10), 8% should warn but not error
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, DBT_TEST_ARGS, data=data)
+    assert test_result["status"] == "warn"
+
+    # With high thresholds (warn=20, error=50), 8% should pass
+    test_args_high = {
+        **DBT_TEST_ARGS,
+        "warn_threshold_percent": 20,
+        "error_threshold_percent": 50,
+    }
+    test_result = dbt_project.test(
+        test_id,
+        DBT_TEST_NAME,
+        test_args_high,
+        test_vars={"force_metrics_backfill": True},
+    )
+    assert test_result["status"] == "pass"
+
+
+def test_where_expression(test_id: str, dbt_project: DbtProject):
+    """The where_expression should filter which rows are counted."""
+    utc_today = datetime.utcnow().date()
+    yesterday = utc_today - timedelta(days=1)
+    data = []
+    # Previous days: 100 rows of category A each
+    for cur_date in generate_dates(base_date=utc_today, days_back=14):
+        if cur_date < yesterday:
+            for _ in range(100):
+                data.append(
+                    {TIMESTAMP_COLUMN: cur_date.strftime(DATE_FORMAT), "category": "a"}
+                )
+    # Yesterday: 100 rows of category A
+    for _ in range(100):
+        data.append(
+            {TIMESTAMP_COLUMN: yesterday.strftime(DATE_FORMAT), "category": "a"}
+        )
+    # Today: 100 rows of category A (stable) + 200 rows of category B (noise)
+    for _ in range(100):
+        data.append(
+            {TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT), "category": "a"}
+        )
+    for _ in range(200):
+        data.append(
+            {TIMESTAMP_COLUMN: utc_today.strftime(DATE_FORMAT), "category": "b"}
+        )
+
+    # Without filter: total today = 300 vs 100 yesterday -> big spike -> fail
+    test_result = dbt_project.test(test_id, DBT_TEST_NAME, DBT_TEST_ARGS, data=data)
+    assert test_result["status"] != "pass"
+
+    # With filter on category A: 100 today vs 100 yesterday -> stable -> pass
+    test_args_filtered = {
+        **DBT_TEST_ARGS,
+        "where_expression": "category = 'a'",
+    }
+    test_result = dbt_project.test(
+        test_id,
+        DBT_TEST_NAME,
+        test_args_filtered,
+        test_vars={"force_metrics_backfill": True},
+    )
+    assert test_result["status"] == "pass"