Row Count Growth

volumehigh

Asserts that the current row count has not decreased more than the specified percentage compared to the previous run's row count. Detects accidental data loss, failed incremental loads, or unintended deletions between pipeline runs.

v1.0.0by dqhub204 downloads4.1 (20)

volumerow-countgrowthregressiondata-losscomparison

Try This Rule

Parameters

column_namestringrequired

The column containing email addresses

thresholdfloatdefault: 0.99

Minimum fraction of valid emails (0.0 to 1.0)

Install

soda

checks for {{table_name}}:
  - invalid_percent({{column_name}}) < {{(1 - threshold) * 100}}:
      valid regex: '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'

dbt

{% test valid_email(model, column_name) %}
select {{ column_name }}
from {{ model }}
where {{ column_name }} not regexp '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
{% endtest %}

sql

SELECT COUNT(*) as total,
  SUM(CASE WHEN {{column_name}} REGEXP
    '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
    THEN 1 ELSE 0 END) as valid
FROM {{table_name}}

Great Expectations

{
  "expectation_type": "expect_column_values_to_match_regex",
  "kwargs": {
    "column": "{{column_name}}",
    "regex": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$",
    "mostly": {{threshold}}
  }
}

spark

from pyspark.sql.functions import col
pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
invalid = df.filter(~col("{{column_name}}").rlike(pattern)).count()

Test Data

Passing Examples

id	value
1	alice@example.com
2	bob.smith@company.co.uk
3	charlie+tag@domain.org

Failing Examples

id	value
1	not-an-email
2	@missing-local.com
3	spaces in@email.com

CLI

Terminal

npx dqhub install row-count-growth --format soda --table YOUR_TABLE
npx dqhub install row-count-growth --format dbt --model YOUR_MODEL
npx dqhub install row-count-growth --format sql --dialect snowflake