Numeric metrics

Last modified on 10-Jul-24

Use a numeric metric in a check to perform basic calculations on the data in your dataset.

checks for retail_products:
  - avg(size) between 100 and 300 
  - avg_length(manufacturer) > 10
  - duplicate_count(product_id) = 0
  - duplicate_percent(user_id) < 2%
  - max(size) <= 500
  - max_length(manufacturer) = 25
  - min(size) >= 50
  - min_length(manufacturer) = 5
  - row_count > 0
  - percentile(size, 0.95) > 50

checks for retail_orders_postgres:
  - stddev(order_quantity) > 0
  - stddev_pop(order_quantity) between 3 and 4
  - stddev_samp(order_quantity) not between 3 and 4
  - sum(discount) < 120
  - variance(discount) > 0
  - var_pop(discount) between 0 and 5
  - var_samp(discount) not between 0 and 5

✖️    Requires Soda Core Scientific (included in a Soda Agent)
✔️    Supported in Soda Core
✔️    Supported in Soda Library + Soda Cloud
✔️    Supported in Soda Cloud Agreements + Soda Agent
✔️    Some available as a no-code check with a self-hosted Soda Agent connected to any
        Soda-supported data source, except Spark, and Dask and Pandas
        OR
        with a Soda-hosted Agent connected to a BigQuery, Databricks SQL, MS SQL Server,
        MySQL, PostgreSQL, Redshift, or Snowflake data source

Define checks with numeric metrics
    Failed row samples
Optional check configurations
List of numeric metrics
List of comparison symbols and phrases
Change-over-time thresholds
Go further

Define checks with numeric metrics

In the context of Soda check types, you use numeric metrics in Standard checks. Refer to Standard check types for exhaustive configuration details.

You can use the row_count metric in checks that apply to entire datasets.

checks for dim_reseller:
  - row_count > 0

You can use all numeric metrics in checks that apply to individual columns in a dataset. Identify the column by adding a value in the argument between brackets in the check.

checks for dim_reseller:
  - duplicate_count(phone) = 0

You can use some numeric metrics in checks with either fixed or change-over-time thresholds. See Change-over-time thresholds for more detail.

checks for dim_reseller:
# a check with a fixed threshold
  - duplicate_count(phone) = 0
# a check with a dynamic threshold
  - change avg last 7 for row_count < 50

Failed row samples

Checks that use the duplicate_count or duplicate_percent metrics automatically collect samples of any failed rows to display in Soda Cloud. The default number of failed row samples that Soda collects and displays is 100.

If you wish to limit or broaden the sample size, you can use the samples limit configuration in a check with a validity metric. You can add this configuration to your checks YAML file for Soda Library, or when writing checks as part of an agreement in Soda Cloud.

checks for dim_customer:
  - duplicate_count(email_address) < 50:
      samples limit: 2

For security, you can add a configuration to your data source connection details to prevent Soda from collecting failed rows samples from specific columns that contain sensitive data. Refer to Disable failed rows sampling for specific columns.

Alternatively, you can set the samples limit to 0 to prevent Soda from collecting and sending failed rows samples for an individual check, as in the following example.

checks for dim_customer:
  - duplicate_count(email_address) < 50:
      samples limit: 0

You can also use a samples columns configuration to a check to specify the columns for which Soda must implicitly collect failed row sample values, as in the following example. Soda only collects this check’s failed row samples for the columns you specify in the list.

Note that the comma-separated list of samples columns does not support wildcard characters (%).

checks for dim_customer:
  - duplicate_count(email_address) < 50:
      samples columns: [last_name, first_name]

To review the failed rows in Soda Cloud, navigate to the Checks dashboard, then click the row for a check for duplicate values. Examine failed rows in the Failed Rows tab; see Examine failed row samples for further details.

Optional check configurations

Example with alert configuration

checks for dim_reseller:
  - duplicate_count(phone):
      warn: when > 5
      fail: when >= 10  

Example with check name

checks for dim_reseller:
  - duplicate_count(phone) = 0:
      name: Duplicate phone numbers

Example with in-check filter

checks for dim_employee:
  - max(vacation_hours) < 80:
      name: Too many vacation hours for sales territory US
      filter: sales_territory_key = 11

Example with quotes

checks for dim_reseller:
  - duplicate_count("phone") = 0

Example with dataset filter

filter CUSTOMERS [daily]:
  where: TIMESTAMP '{ts_start}' <= "ts" AND "ts" < TIMESTAMP '${ts_end}'

checks for CUSTOMERS [daily]:
  - duplicate_count(phone) > 10

Example with for each

for each dataset T:
  datasets:
    - dim_product
    - dim_customer
    - dim reseller
  checks:
    - row_count > 0

List of numeric metrics

List of comparison symbols and phrases

 = 
 < 
 >
 <=
 >=
 !=
 <> 
 between 
 not between 

Change-over-time thresholds

Numeric metrics can specify a fixed threshold which is not relative to any other threshold. row_count > 0 is an example of a check with a fixed threshold as the threshold value, 0, is absolute. Refer to Checks with fixed thresholds for details.

Only checks that use numeric metrics can specify a change-over-time threshold, a value that is relative to a previously-measured, or historic, value. Sometimes referred to as a dynamic threshold or historic metrics, you use these change-over-time thresholds to gauge changes to the same metric over time. Most of the examples below use the row_count metric, but you can use any numeric metric in checks that use change-over-time thresholds.

The most basic of change-over-time threshold checks has three or four mutable parts:

The example below defines a check that applies to the entire dataset and counts the rows in the dataset, then compares that value to the preceding value contained in the Cloud Metric Store. If the row_count at present is greater than the previously-recorded historic value for row_count by more than 50 or less than -20, the check fails.

Use between for checks with change-over-time thresholds as much as possible to trigger check failures when the measurement falls outside of a range of acceptable values. This practice ensures that you get visibility into changes that either exceed or fall short of threshold expectations.

checks for dim_customer:
  - change for row_count between -20 and +50

You can also use use a change-over-time threshold to compare check results relative to the same day in the previous week. The example below uses change-over-time to compare today’s value with the same check result from last week to confirm that the delta is greater than 10.

checks for dim_customer:
  - change same day last week for row_count > 10

The example below defines a check that applies to the entire dataset and counts the rows in the dataset, then compares that value to the preceding value contained in the Cloud Metric Store. If the row_count at present is greater than the previously-recorded historic value for row_count by more than 50%, the check fails.

For example, the previously-recorded historic measurement for row count is 80, and the newly-recorded value is 100, the relative change is 25%, which is less than the 50% specified in the threshold, so the check passes.

• Percentage thresholds are between 0 and 100, not between 0 and 1.
• If you wish, you can add a % character to the threshold for a change-over-time threshold for improved readability.
• If the previous measurement value is 0 and the new value is 0, Soda calculates the relative change as 0%. However, if the previous measurement value is 0 and the new value is not 0, then Soda indicates the check as NOT EVALUATED because the calculation is a division by zero.

checks for dim_customer:
  - change percent for row_count > 50%

The example below applies to only the phone column in the dataset and counts the rows that contain duplicate values, then compares that value to the preceding value contained in the Cloud Metric Store. If the number of duplicate phone numbers at present is greater than the preceding historic values for duplicate_count by more than 20, the check fails.

checks for dim_customer:
  - change for duplicate_count(phone) < 20

A more complex change-over-time threshold check includes two more optional mutable parts:

checks for dim_customer:
  - change avg last 7 for row_count < 50
  - change min last 7 for row_count < 50
  - change max last 7 percent for row_count < 50

The example above defines three checks, one for each type of calculation available to use, avg, min, and max, all of which apply to the entire dataset.

The first check counts the rows in the dataset, then compares that value to the calculated average of the preceding seven measurement values for that metric contained in the Cloud Metric Store. If the row_count at present is greater than the average of the seven preceding historic values by more than 50, the check fails. The only valid historical value definiton you can use is seven.

The second check in the example determines the minimum value of the preceding seven historic values, then uses that value to compare to the present measurement value.

The third check in the example determines the maximum value of the preceding seven historic values, then uses that value and the present measurement value to calculate the percentage of change.

Go further

• Use numeric metrics in checks with alert configurations to establish warn and fail zones
• Use numeric metrics in checks to define ranges of acceptable thresholds using boundary thresholds.
• Need help? Join the Soda community on Slack.
• Reference tips and best practices for SodaCL.
  

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Documentation always applies to the latest version of Soda products
Last modified on 10-Jul-24

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