How Is Type Checks Work

Definition

Asserts that every value in a text field can be read as the configured data type.

Overview

The Is Type rule tests whether the text in a column really represents a typed value. You pick the expected Field Type, and each row passes when its value can be read as that type. The check exists because a column typed as text in the source can still be meant to hold numbers or dates: a CSV column, a staging table, or an API payload flattened into strings. Is Type is how you find the rows that would break the moment something tries to convert them.

Typical use cases:

• Validate a text column in a staging table before it is cast into a typed column downstream.
• Catch values that would fail a conversion, such as N/A in a column meant to hold numbers.
• Check that a date-like column from a flat file really parses as a date.

Field Scope

Single: The rule evaluates exactly one field per check. The field itself must be a text field, or an array of text values.

Accepted Types

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
)

Specific Properties

Is Type has one rule-specific property:

Anomaly Types

Evaluation Flow

Every Is Type 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. Test it against the expected type. The row passes when the text can be read as the configured Field Type, and fails when it cannot.
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).

The Five Testable Types

The Field Type property accepts the types that can be recognized in text:

Text itself is not in the list: every value is already text, so String would pass unconditionally. Structured types (arrays of structs, maps) are not testable this way either.

The Column Stays Text

Is Type reports which values could be converted; it does not convert anything. The column keeps its text type, and downstream consumers still have to cast it. Use the check as the gate that makes a later cast safe, and pair it with a Computed Field when you want the typed value available in the platform.

NULL Handling

The check passes NULL values: a row with NULL in the evaluated field is not counted as a violation. Is Type only asserts that present values are readable as the chosen type.

An empty string is a present value that cannot be read as any of the testable types, so it is reported. If the field must also be populated, pair the check with Not Null.

Array Fields

On an array of text values, every element is tested against the expected type and the row fails as soon as one element cannot be read as that type. Empty arrays and NULL arrays pass, since there is nothing to evaluate.

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