How Contains Email Checks Work

Definition

Asserts that every value in the selected field contains an email address.

Overview

The Contains Email rule scans the text of a single field and requires that each value contains an email address. The match is a containment test, not an equality test: the value passes as soon as an email address appears anywhere inside it, so surrounding text does not cause a failure. The pattern accepts the usual local@domain shape found anywhere inside the value.

Typical use cases:

• Validate that a customer contact column really holds an address.
• Check that notification or billing recipients are usable before a send.
• Audit a legacy free-text column that is being migrated into a proper email field.

Field Scope

Single: The rule evaluates exactly one field per check.

Accepted Types

On an array field every element is tested and the row fails as soon as one element does not contain the pattern. That evaluation runs as a field-level check, so it reports a Shape Anomaly for the field instead of per-row Record Anomalies, whatever the coverage is set to.

General Properties

The filter allows you to define a subset of data upon which the rule will operate.

It requires a valid Spark SQL expression that determines the criteria rows in the DataFrame should meet. This means the expression specifies which rows the DataFrame should include based on those criteria. Since it's applied directly to the Spark DataFrame, traditional SQL constructs like WHERE clauses are not supported.

O_TOTALPRICE > 1000

C_MKTSEGMENT = 'BUILDING'

WHERE O_TOTALPRICE > 1000

WHERE C_MKTSEGMENT = 'BUILDING'

O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

(L_SHIPDATE = '1998-09-02' OR L_RECEIPTDATE = '1998-09-01') AND L_RETURNFLAG = 'R'

WHERE O_ORDERPRIORITY = '1-URGENT' AND O_ORDERSTATUS = 'O'

O_TOTALPRICE > 1000, O_ORDERSTATUS = 'O'

RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - INSTR('-', O_ORDERPRIORITY)
) = 'URGENT'

LEVENSHTEIN(C_NAME, 'Supplier#000000001') < 7

RIGHT(
    O_ORDERPRIORITY,
    LENGTH(O_ORDERPRIORITY) - CHARINDEX('-', O_ORDERPRIORITY)
) = 'URGENT'

EDITDISTANCE(C_NAME, 'Supplier#000000001') < 7

O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM {{_qualytics_self}}
    WHERE O_TOTALPRICE > 1000
)

O_ORDERSTATUS IN (
    SELECT DISTINCT O_ORDERSTATUS
    FROM ORDERS
    WHERE O_TOTALPRICE > 1000
)

Anomaly Types

Evaluation Flow

Every Contains Email check follows the same four-step evaluation flow:

1. Apply the filter clause. If the check has a filter set, only rows matching the filter expression continue to the next step. Rows outside the filter are ignored and cannot contribute to the violation count.
2. Read the field value as text. The platform reads the row's value for the selected field.
3. Look for an email address. The value passes when the pattern is found anywhere inside it, and fails when it is not.
4. Apply coverage. At 100% coverage, any failing row causes the check to fail. Below 100% coverage, the check fails only when the passing fraction drops below the threshold (see Coverage and Tolerance).

NULL Handling

The check passes NULL values: a row with NULL in the evaluated field is not counted as a violation. Contains Email only asserts that present values contain an email address and does not enforce mandatory presence on the field.

If the field must also be populated, pair it with a Not Null check on the same field. An empty string is a present value with no match, so it is reported as a violation.

Array Fields

On an array field, every element is tested and the row fails as soon as one element does not contain the pattern. A NULL array passes, and so does an empty array, since there is nothing to evaluate. NULL elements inside an array are skipped rather than failing the row.

The Filter Clause

The filter clause is a SQL WHERE expression applied before the evaluation. Filtered-out rows are ignored entirely (they cannot trigger a violation and are not counted in the totals).

When a filter is set, both the Record Anomaly and the Shape Anomaly messages end with [filter: <expression>] so the evaluated scope is visible in the alert.

Coverage and Tolerance

Coverage is a fractional value between 0 and 1 that defines the minimum fraction of evaluated rows that must pass:

• 1.0 (100%, default): every row in the filtered set must pass. Any failing row causes the check to fail. This is the strictest setting.
• < 1.0: the check tolerates a fraction of rows failing. The check fails only when the fraction of passing rows drops below the threshold.

Lower coverage values are useful when a small, known fraction of failures is expected. Use coverage carefully: a 0.5% tolerance can mask a real regression that happens to fall just under the threshold.

See Also