---
title: "TEST_EXECUTION_RESULTS (Single-Value Tests)"
slug: "results-single-value-tests"
updated: 2026-06-16T21:41:23Z
published: 2026-06-16T21:41:23Z
canonical: "docs.validatar.com/results-single-value-tests"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.validatar.com/llms.txt
> Use this file to discover all available pages before exploring further.

# TEST_EXECUTION_RESULTS (Single-Value Tests)

## Overview

`RESULTS.TEST_EXECUTION_RESULTS` stores the row-level output for **single-value tests** — tests that compare one scalar value from the test dataset against one scalar value from the control dataset. Examples include row count comparisons, aggregate checks (SUM, AVG, MIN, MAX), and any test where the data set column type is set to produce a single-row result.

Each row in this table represents one comparison within a test execution. Most single-value tests produce exactly one row per execution, but some (like profile-based tests with a coverage flag) may produce multiple rows.

## Schema

| Column | Type | Nullable | Description |
| --- | --- | --- | --- |
| TEST_EXECUTION_ID | NUMBER | No | Links to VW_DATASET_TEST_EXECUTIONS — identifies which execution produced this result |
| TEST_ID | NUMBER | No | The test ID |
| TEST_VERSION_ID | NUMBER | No | The test version ID |
| ROW_INDEX | NUMBER | No | Row position within this execution (usually 1 for single-value tests) |
| COVERAGE_FLAG | NUMBER | No | Coverage type: 3 = Matched (both source and target returned data) |
| OVERFLOW_FLAG | NUMBER | No | 0 = normal, 1 = value truncated due to size |
| COMBINED_KEY | VARCHAR | Yes | Combined key value (usually NULL for single-value tests) |
| NUMERIC_SOURCE_VALUE | DECIMAL(27,8) | Yes | Numeric value from the test (source) dataset |
| NUMERIC_TARGET_VALUE | DECIMAL(27,8) | Yes | Numeric value from the control (target) dataset |
| ACTUAL_DIFFERENCE | DECIMAL(27,8) | Yes | Source minus target |
| ABSOLUTE_DIFFERENCE | DECIMAL(27,8) | Yes | Absolute value of the difference |
| PERCENT_DIFFERENCE | DECIMAL(18,9) | Yes | Percentage difference (as a decimal, e.g., 0.05 = 5%) |
| EVALUATION_RESULT_ID | NUMBER | Yes | Result code: 24 = Passed, 25 = Failed, 26 = Error |
| EVALUATION_RESULT_NAME | VARCHAR(100) | Yes | "PASSED", "FAILED", or "ERROR" |
| STRING_SOURCE_VALUE | VARCHAR | Yes | String value from the source (when comparing strings) |
| STRING_TARGET_VALUE | VARCHAR | Yes | String value from the target |
| DATE_SOURCE_VALUE | VARCHAR | Yes | Date value from the source (stored as string) |
| DATE_TARGET_VALUE | VARCHAR | Yes | Date value from the target |
| SF_DATE_CREATED | TIMESTAMP | No | When this row was loaded into Snowflake |
| SF_IMPORT_FILENAME | VARCHAR(500) | Yes | Staging file that delivered this row |

## How Values Are Stored

The table uses three pairs of columns depending on the data type being compared:

| Comparison Type | Source Column | Target Column | Difference Columns |
| --- | --- | --- | --- |
| Numeric | NUMERIC_SOURCE_VALUE | NUMERIC_TARGET_VALUE | ACTUAL_DIFFERENCE, ABSOLUTE_DIFFERENCE, PERCENT_DIFFERENCE |
| String | STRING_SOURCE_VALUE | STRING_TARGET_VALUE | (not applicable — pass/fail only) |
| Date | DATE_SOURCE_VALUE | DATE_TARGET_VALUE | (not applicable — pass/fail only) |

For numeric comparisons, the difference columns are pre-calculated. For string and date comparisons, the result is strictly pass/fail based on equality.

## Relationships

```
RESULTS.TEST_EXECUTION_RESULTS
    │
    ├── TEST_EXECUTION_ID → REPOSITORY.VW_DATASET_TEST_EXECUTIONS.TEST_EXECUTION_ID
    ├── TEST_ID → REPOSITORY.VW_DATASET_TESTS.TEST_ID
    └── TEST_VERSION_ID → REPOSITORY.VW_DATASET_TESTS.TEST_VERSION_ID
```

## Example Queries

### Get Source/Target/Difference for a Specific Execution

```
SELECT
    TEST_EXECUTION_ID,
    ROW_INDEX,
    NUMERIC_SOURCE_VALUE,
    NUMERIC_TARGET_VALUE,
    ACTUAL_DIFFERENCE,
    PERCENT_DIFFERENCE,
    EVALUATION_RESULT_NAME
FROM RESULTS.TEST_EXECUTION_RESULTS
WHERE TEST_EXECUTION_ID = 3913133;
```

### Join to Test Executions for Context

```
SELECT
    te.TEST_NAME,
    te.PROJECT_NAME,
    te.DATE_COMPLETED,
    r.NUMERIC_SOURCE_VALUE AS SOURCE_VALUE,
    r.NUMERIC_TARGET_VALUE AS TARGET_VALUE,
    r.PERCENT_DIFFERENCE,
    r.EVALUATION_RESULT_NAME
FROM REPOSITORY.VW_DATASET_TEST_EXECUTIONS te
JOIN RESULTS.TEST_EXECUTION_RESULTS r
    ON r.TEST_EXECUTION_ID = te.TEST_EXECUTION_ID
WHERE te.LATEST_TEST_EXECUTION_INDEX = 1
    AND te.TEST_NAME = 'Row Count - DIM_CUSTOMER';
```

### Trend Analysis: Track Values Over Time

```
SELECT
    te.TEST_NAME,
    te.DATE_COMPLETED,
    r.NUMERIC_SOURCE_VALUE,
    r.NUMERIC_TARGET_VALUE,
    r.PERCENT_DIFFERENCE,
    r.EVALUATION_RESULT_NAME
FROM REPOSITORY.VW_DATASET_TEST_EXECUTIONS te
JOIN RESULTS.TEST_EXECUTION_RESULTS r
    ON r.TEST_EXECUTION_ID = te.TEST_EXECUTION_ID
WHERE te.TEST_ID = 58874
ORDER BY te.DATE_COMPLETED;
```

### Find All Failed Single-Value Tests with Their Differences

```
SELECT
    te.TEST_NAME,
    te.TEST_PRIMARY_METADATA_LINK_OBJECT,
    te.DATE_COMPLETED,
    r.NUMERIC_SOURCE_VALUE,
    r.NUMERIC_TARGET_VALUE,
    r.ACTUAL_DIFFERENCE,
    ROUND(r.PERCENT_DIFFERENCE * 100, 2) AS PERCENT_DIFF_DISPLAY
FROM REPOSITORY.VW_DATASET_TEST_EXECUTIONS te
JOIN RESULTS.TEST_EXECUTION_RESULTS r
    ON r.TEST_EXECUTION_ID = te.TEST_EXECUTION_ID
WHERE te.LATEST_TEST_EXECUTION_INDEX = 1
    AND r.EVALUATION_RESULT_NAME = 'FAILED'
ORDER BY ABS(r.PERCENT_DIFFERENCE) DESC;
```

## Databricks Notes

- Table structure and column names are identical between Snowflake and Databricks
- `ROUND()` function syntax is the same
- The `SF_DATE_CREATED` and `SF_IMPORT_FILENAME` columns are Snowflake-specific metadata; in Databricks these may be named differently or absent depending on the loading mechanism