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Comparison Monitor Reference

Detailed reference for building createComparisonMonitorMac tool calls.

When to Use

Use a comparison monitor when the user wants to:

  • Compare data between two tables (e.g., source vs target, dev vs prod)
  • Validate data consistency after migration or replication
  • Check row count parity across environments
  • Compare field-level metrics between tables (null counts, sums, distributions)

Pre-Step: Verify Both Tables and Fields

Before constructing alert conditions, you MUST verify that both tables exist and that any referenced fields are real columns. This is the most common source of comparison monitor failures.

  1. Resolve both MCONs. Use search to find the source and target tables. If the user provided database:schema.table format, search for each to get the MCON.
  2. Get full schemas. Call getTable with include_fields: true on BOTH the source table and the target table. You need the column lists from both.
  3. For field-level metrics, verify fields exist on both sides. Confirm that sourceField exists in the source table's column list AND targetField exists in the target table's column list. Field names are case-sensitive on most warehouses.
  4. Check field type compatibility. The metric must be compatible with the column types on both sides. For example, NUMERIC_MEAN requires numeric columns in both the source and target tables. If the source column is numeric but the target is a string, the comparison will fail.
  5. If any field does not exist or types are incompatible, stop and ask the user to clarify. Do not guess.

Required Parameters

Parameter Type Description
name string Unique identifier for the monitor. Use a descriptive slug (e.g., orders_dev_prod_compare).
description string Human-readable description of what the monitor checks.
source_table string Source table MCON (preferred) or database:schema.table format. If not MCON, also pass source_warehouse.
target_table string Target table MCON (preferred) or database:schema.table format. If not MCON, also pass target_warehouse.
alert_conditions array List of comparison conditions (see Alert Conditions below).

Optional Parameters

Parameter Type Description
source_warehouse string Warehouse name or UUID for the source table. Required if source_table is not an MCON.
target_warehouse string Warehouse name or UUID for the target table. Required if target_table is not an MCON.
segment_fields array of string Fields to segment the comparison by. Must exist in BOTH tables with the same name.
domain_id string (uuid) Domain UUID (use getDomains to list). Only one domain can be assigned per monitor.

Cross-Warehouse Comparisons

When the source and target tables live in different warehouses (e.g., comparing a Snowflake staging table against a BigQuery production table), you MUST provide both source_warehouse and target_warehouse explicitly. The tool cannot auto-resolve warehouses when tables are in different environments.

Even when both tables are MCONs, if they belong to different warehouses, pass both warehouse parameters to be safe. Omitting them in cross-warehouse scenarios causes silent failures or incorrect results.

Common cross-warehouse patterns:

  • Dev vs prod: same warehouse type, different databases or schemas
  • Migration validation: source in old warehouse, target in new warehouse
  • Replication checks: primary warehouse vs replica or downstream warehouse

Alert Conditions

Each condition compares a metric between the source and target tables.

Field Type Required Description
metric string Yes The metric to compare (see Metrics Reference below).
sourceField string For field-level metrics Column in the source table. Required for ALL metrics except ROW_COUNT.
targetField string For field-level metrics Column in the target table. Required for ALL metrics except ROW_COUNT.
thresholdValue number No Threshold for acceptable difference between source and target.
isThresholdRelative boolean No false = absolute difference (default), true = percentage difference.
customMetric object No Custom SQL expressions for source and target (see Custom Metrics below).

ROW_COUNT and Fields: A Critical Rule

NEVER pass sourceField or targetField when using the ROW_COUNT metric.

ROW_COUNT is a table-level metric -- it counts all rows in the table, not values in a column. Passing field names with ROW_COUNT causes the API call to fail or produce unexpected behavior.

This is the single most common mistake with comparison monitors. Before submitting any alert condition with ROW_COUNT, verify that sourceField and targetField are both absent from the condition object.

Metric Fields needed? What happens if you pass fields?
ROW_COUNT No -- NEVER pass fields API error or undefined behavior
All other metrics Yes -- always pass both fields Required for the comparison to work

Metrics Reference

Table-level metric (no fields needed)

Metric Description
ROW_COUNT Compare total row counts between source and target.

Field-level metrics (require sourceField and targetField)

Uniqueness and duplicates

Metric Description
UNIQUE_COUNT Count of distinct values.
DUPLICATE_COUNT Count of duplicate (non-unique) values.
APPROX_DISTINCT_COUNT Approximate distinct count (faster on large tables).

Null and empty checks

Metric Description
NULL_COUNT Count of null values.
NON_NULL_COUNT Count of non-null values.
EMPTY_STRING_COUNT Count of empty string values.
TEXT_ALL_SPACES_COUNT Count of values that are all whitespace.
NAN_COUNT Count of NaN values.
TEXT_NULL_KEYWORD_COUNT Count of values containing null-like keywords (e.g., "NULL", "None").

Numeric statistics

Metric Description
NUMERIC_MEAN Mean of numeric field.
NUMERIC_MEDIAN Median of numeric field.
NUMERIC_MIN Minimum value.
NUMERIC_MAX Maximum value.
NUMERIC_STDDEV Standard deviation.
SUM Sum of numeric field.
ZERO_COUNT Count of zero values.
NEGATIVE_COUNT Count of negative values.

Percentiles

Metric Description
PERCENTILE_20 20th percentile value.
PERCENTILE_40 40th percentile value.
PERCENTILE_60 60th percentile value.
PERCENTILE_80 80th percentile value.

Text statistics

Metric Description
TEXT_MAX_LENGTH Maximum string length.
TEXT_MIN_LENGTH Minimum string length.
TEXT_MEAN_LENGTH Mean string length.
TEXT_STD_LENGTH Standard deviation of string length.

Text format checks

Metric Description
TEXT_NOT_INT_COUNT Count of values not parseable as integers.
TEXT_NOT_NUMBER_COUNT Count of values not parseable as numbers.
TEXT_NOT_UUID_COUNT Count of values not matching UUID format.
TEXT_NOT_SSN_COUNT Count of values not matching SSN format.
TEXT_NOT_US_PHONE_COUNT Count of values not matching US phone format.
TEXT_NOT_US_STATE_CODE_COUNT Count of values not matching US state codes.
TEXT_NOT_US_ZIP_CODE_COUNT Count of values not matching US zip codes.
TEXT_NOT_EMAIL_ADDRESS_COUNT Count of values not matching email format.
TEXT_NOT_TIMESTAMP_COUNT Count of values not parseable as timestamps.

Boolean

Metric Description
TRUE_COUNT Count of true values.
FALSE_COUNT Count of false values.

Timestamp

Metric Description
FUTURE_TIMESTAMP_COUNT Count of timestamps in the future.
PAST_TIMESTAMP_COUNT Count of timestamps unreasonably far in the past.
UNIX_ZERO_COUNT Count of timestamps equal to Unix epoch zero (1970-01-01).

Choosing the Right Metric

User intent Correct metric Fields needed?
Row count parity ROW_COUNT No -- never pass fields
Distinct values in a column UNIQUE_COUNT Yes
Null values in a column NULL_COUNT Yes
Sum, average, min, max SUM, NUMERIC_MEAN, NUMERIC_MIN, NUMERIC_MAX Yes
Data completeness NON_NULL_COUNT Yes
String format validation TEXT_NOT_EMAIL_ADDRESS_COUNT, TEXT_NOT_UUID_COUNT, etc. Yes
Custom computed expressions Use customMetric instead of metric No (SQL handles it)

Custom Metrics

Use custom metrics when:

  • Column names differ between source and target and you need a computed expression (not just a direct field comparison).
  • You need a derived calculation like SUM(quantity * unit_price) rather than a simple column metric.
  • Standard metrics do not cover the comparison (e.g., comparing a ratio, a conditional aggregate, or a windowed calculation).

If the columns simply have different names but you want a standard metric (e.g., compare SUM of revenue in source vs total_revenue in target), you do NOT need a custom metric -- just use the standard metric with different sourceField and targetField values.

Custom metric structure:

{
  "customMetric": {
    "displayName": "Revenue Sum",
    "sourceSqlExpression": "SUM(revenue)",
    "targetSqlExpression": "SUM(total_revenue)"
  }
}
Field Type Required Description
displayName string Yes Human-readable name for the metric in alerts and dashboards.
sourceSqlExpression string Yes SQL expression evaluated against the source table.
targetSqlExpression string Yes SQL expression evaluated against the target table.

When using customMetric, do NOT also pass metric, sourceField, or targetField in the same alert condition. The custom metric replaces all of those.

Threshold Guidance

Absolute thresholds (isThresholdRelative: false or omitted)

The thresholdValue is the maximum acceptable absolute difference between the source and target metric values.

  • thresholdValue: 0 -- source and target must match exactly.
  • thresholdValue: 100 -- up to 100 units of difference is acceptable.

Relative (percentage) thresholds (isThresholdRelative: true)

The thresholdValue is the maximum acceptable percentage difference.

  • thresholdValue: 5 -- up to 5% difference is acceptable.
  • thresholdValue: 0.1 -- up to 0.1% difference is acceptable.

When to use each

Scenario Recommended threshold type
Exact replication (row counts must match) Absolute, thresholdValue: 0
Near-real-time sync with small lag Absolute, small value (e.g., 10-100)
Tables at different scales Relative, percentage-based
Aggregated metrics (sums, means) Relative, to handle floating-point differences

Examples

Row count parity with absolute threshold

Compare row counts between dev and prod, alerting if they differ by more than 100 rows.

{
  "name": "orders_dev_prod_row_count",
  "description": "Verify dev and prod orders tables have similar row counts",
  "source_table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++dev_warehouse:core.orders",
  "target_table": "MCON++b2c3d4e5-f6a7-8901-bcde-f12345678901++1++1++prod_warehouse:core.orders",
  "alert_conditions": [
    {
      "metric": "ROW_COUNT",
      "thresholdValue": 100,
      "isThresholdRelative": false
    }
  ]
}

Note: no sourceField or targetField -- ROW_COUNT is table-level.

Row count parity with percentage threshold

Alert if row counts differ by more than 5%.

{
  "name": "orders_replication_check",
  "description": "Verify replicated orders table is within 5% of source row count",
  "source_table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++primary:sales.orders",
  "target_table": "MCON++b2c3d4e5-f6a7-8901-bcde-f12345678901++1++1++replica:sales.orders",
  "alert_conditions": [
    {
      "metric": "ROW_COUNT",
      "thresholdValue": 5,
      "isThresholdRelative": true
    }
  ]
}

Field-level comparison (different column names)

Compare the sum of revenue in the source table against total_revenue in the target table.

{
  "name": "revenue_source_target_sum",
  "description": "Verify revenue sums match between staging and production",
  "source_table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++staging:finance.transactions",
  "target_table": "MCON++b2c3d4e5-f6a7-8901-bcde-f12345678901++1++1++production:finance.transactions",
  "alert_conditions": [
    {
      "metric": "SUM",
      "sourceField": "revenue",
      "targetField": "total_revenue",
      "thresholdValue": 1,
      "isThresholdRelative": true
    }
  ]
}

Segmented comparison

Compare null counts on email between source and target, segmented by country. The country field must exist in both tables.

{
  "name": "email_nulls_by_country",
  "description": "Compare email null counts by country between ETL source and target",
  "source_table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++raw:crm.contacts",
  "target_table": "MCON++b2c3d4e5-f6a7-8901-bcde-f12345678901++1++1++analytics:crm.contacts",
  "segment_fields": ["country"],
  "alert_conditions": [
    {
      "metric": "NULL_COUNT",
      "sourceField": "email",
      "targetField": "email",
      "thresholdValue": 0,
      "isThresholdRelative": false
    }
  ]
}

Cross-warehouse comparison with explicit warehouses

When source and target are in different warehouses, both warehouse parameters must be provided.

{
  "name": "migration_users_row_count",
  "description": "Validate user row counts match after Snowflake to BigQuery migration",
  "source_table": "snowflake_db:public.users",
  "source_warehouse": "snowflake-prod",
  "target_table": "bigquery_project:public.users",
  "target_warehouse": "bigquery-prod",
  "alert_conditions": [
    {
      "metric": "ROW_COUNT",
      "thresholdValue": 0,
      "isThresholdRelative": false
    }
  ]
}

Custom metric comparison

Compare a computed revenue expression when the SQL differs between source and target.

{
  "name": "computed_revenue_compare",
  "description": "Compare total revenue computation between legacy and new schema",
  "source_table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++warehouse:legacy.orders",
  "target_table": "MCON++b2c3d4e5-f6a7-8901-bcde-f12345678901++1++1++warehouse:v2.orders",
  "alert_conditions": [
    {
      "customMetric": {
        "displayName": "Total Revenue",
        "sourceSqlExpression": "SUM(quantity * unit_price)",
        "targetSqlExpression": "SUM(total_amount)"
      },
      "thresholdValue": 0.01,
      "isThresholdRelative": true
    }
  ]
}

Multiple alert conditions

Compare both row counts and field-level metrics in a single monitor.

{
  "name": "orders_full_comparison",
  "description": "Full comparison of orders between staging and production",
  "source_table": "MCON++a1b2c3d4-e5f6-7890-abcd-ef1234567890++1++1++staging:core.orders",
  "target_table": "MCON++b2c3d4e5-f6a7-8901-bcde-f12345678901++1++1++production:core.orders",
  "domain_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "alert_conditions": [
    {
      "metric": "ROW_COUNT",
      "thresholdValue": 0,
      "isThresholdRelative": false
    },
    {
      "metric": "NULL_COUNT",
      "sourceField": "customer_id",
      "targetField": "customer_id",
      "thresholdValue": 0,
      "isThresholdRelative": false
    },
    {
      "metric": "SUM",
      "sourceField": "amount",
      "targetField": "amount",
      "thresholdValue": 0.1,
      "isThresholdRelative": true
    }
  ]
}

Note: the ROW_COUNT condition has no fields, while the field-level conditions each specify both sourceField and targetField.