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Validity metrics

Use a validity metric in a check to surface invalid or unexpected values in your dataset.
Read more about SodaCL metrics and checks in general.

checks for dim_customer:
  - invalid_count(email_address) = 0:
      valid format: email
  - invalid_percent(english_education) = 0:
      valid length: 100
  - invalid_percent(total_children) <= 2:
      valid max: 6
  - invalid_percent(marital_status) = 0:
      valid max length: 10
  - invalid_count(number_cars_owned) = 0:
      valid min: 1
  - invalid_percent(marital_status) = 0:
      valid min length: 1
  - invalid_percent(birthday) < 5%:
      valid regex: (0?[0-9]|1[012])[/](0?[0-9]|[12][0-9]|3[01])[/](0000|(19|20)?\d\d)
  - invalid_count(house_owner_flag) = 0:
      valid values: ['0', '1']

Define checks with validity metrics
Optional check configurations
List of validity metrics
List of configuration keys
List of valid formats
List of comparison symbols and phrases
Go further

Define checks with validity metrics

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

You can use all validity metrics in checks that apply to individual columns in a dataset; you cannot use validity metrics in checks that apply to entire datasets. Identify the column(s) by adding one or more values in the argument between brackets in the check.

  • You must use a configuration key:value pair to define what qualifies as an valid value.
  • If you wish, you can add a % character to the threshold for a invalid_percent metric for improved readability.
checks for dim_customer
  - invalid_count(number_cars_owned) = 0:
      valid min: 1

You can use validity metrics in checks with fixed thresholds, or relative thresholds, but not dynamic thresholds. See Checks with fixed thresholds for more detail.

checks for dim_reseller:
# a check with a fixed threshold
  - invalid_count(email_address) = 0:
      valid format: email
# a check with a relative threshold
  - invalid_percent(english_education) < 3%:
      valid max length: 100
What is a relative threshold? When it scans a column in your dataset, Soda automatically separates all values in the column into one of three categories:
  • missing
  • invalid
  • valid
Soda then performs two calculations. The sum of the count for all categories in a column is always equal to the total row count for the dataset.
missing_count(column_name) + invalid_count(column_name) + valid_count(column_name) = row_count
Similarly, a calculation that uses percentage always adds up to a total of 100 for the column.
missing_percent(name) + invalid_percent(name) + valid_percent(name) = 100
These calculations enable you to write checks that use relative thresholds.

In the example above, the invalid values of the english_education column must be less than three percent of the total row count, or the check fails.

Percentage thresholds are between 0 and 100, not between 0 and 1.

Specify valid values

Use a nested configuration key:value pair to provide your own definition of a valid value. There are several configuration keys that you can use to define what qualifies as valid; the examples below illustrate the use of just a few config keys. See a complete List of configuration keys below.

A check that uses a validity metric has six mutable parts:

a metric
an argument
a comparison symbol or phrase
a threshold
a configuration key
a configuration value


The example below defines two checks. The first check applies to the column house_owner_flag. The valid values configuration key specifies that if a row in that column contains anything other than the two valid values in the list, Soda registers them as invalid. The check fails if Soda discovers more than five values that are not 0 or 1.

  • Values in a list must be enclosed in square brackets.
  • Numeric characters in a valid values list must be enclosed in single quotes.

The second check uses a regular expression to define what qualifies as a valid value in the birthday column so that any values that do not match the pattern defined by the regex qualify as invalid.

checks for dim_customer:
  - invalid_count(house_owner_flag) = 0:
      valid values: ['0', '1']
  - invalid_count(birthday) = 0:
      valid regex: ^\d{4}-(0[1-9]|1[0-2])-(0[1-9]|[12][0-9]|3[01])$

First check:

metric invalid_count
argument house_owner_flag
comparison symbol =
threshold 0
configuration key valid values
configuration value(s) '0', '1'

Second check:

metric invalid_count
argument birthday
comparison symbol or phrase =
threshold 0
configuration key valid regex
configuration value(s) (0?[0-9]|1[012])[/](0?[0-9]|[12][0-9]|3[01])[/](0000|(19|20)?\d\d)


Specify valid format

If the data type of the column you are checking is TEXT (such as character, character varying, or string) then you can use the valid format configuration key. This config key uses built-in values that test the data in the column for specific formats, such as email address format, date format, or uuid format. See List of valid formats below.

The check below validates that all values in the email_address column conform to an email address format.

checks for dim_customer:
  - invalid_percent(email_address) = 0:
      valid format: email
metric invalid_percent
argument email_address
comparison symbol or phrase =
threshold 0
configuration key valid format
configuration value(s) email


Troubleshoot valid format

Problem: You are using a valid format to test the format of values in a column and the CLI returns the following error message when you run a scan.

  | HINT:  No operator matches the given name and argument types. You might need to add explicit type casts.

Error occurred while executing scan.
  | unsupported operand type(s) for *: 'Undefined' and 'int'

Solution: The error indicates that the data type of the column is not TEXT. Adjust your check to use a different configuration key, instead.


Display failed rows in Soda Cloud

Checks with validity metrics automatically collect samples of any failed rows to display Soda Cloud. The default number of failed row samples that Soda collects and displays is 1000.

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

failed-invalid-count

Optional check configurations

Supported Configuration Documentation
Define a name for a check with validity metrics; see example. Customize check names
Define alert configurations to specify warn and fail thresholds; see example. Add alert configurations
Apply an in-check filter to return results for a specific portion of the data in your dataset; see example. Add an in-check filter to a check
Use quotes when identifying dataset or column names; see example Use quotes in a check
  Use wildcard characters ( % or * ) in values in the check. -
Use for each to apply checks with validity metrics to multiple datasets in one scan; see example. Apply checks to multiple datasets
Apply a dataset filter to partition data during a scan; see example. Scan a portion of your dataset

Example with check name

checks for dim_customer:
  - invalid_count(first_name) = 0 :
      valid min length: 2
      name: First name has 2 or more characters

Example with alert configuration

  - invalid_count(house_owner_flag):
      valid values: ['0', '1']
      warn: when between 1 and 5
      fail: when > 6  

Example with in-check filter

checks for dim_customer:
  - invalid_percent(marital_status) = 0:
      valid max length: 1
      filter: total_children = 0

Example with quotes

checks for dim_customer:
  - invalid_count("number_cars_owned") = 0:
      valid min: 1

Example with for each

for each dataset T:
  datasets:
    - dim_customer
    - dim_customer_%
  checks:
    - invalid_count(email_address) = 0:
        valid format: email

Example with dataset filter

coming soon


List of validity metrics

Metric Column config keys Description Supported data type Supported data sources
invalid_count valid format
valid length
valid max
valid max length
valid min
valid min length
valid regex
valid values
The number of
rows in a
column that
contain values
that are not valid.
number,
text,
time
Athena
Redshift
Apache Spark DataFrames
Big Query
DB2
SQL Server
PostgreSQL
Snowflake
invalid_percent valid format
valid length
valid max
valid max length
valid min
valid min length
valid regex
valid values
The percentage
of rows in a
column, relative to the total
row count, that
contain values
that are not
valid.
number,
text,
time
Athena
Redshift
Apache Spark DataFrames
Big Query
DB2
SQL Server
PostgreSQL
Snowflake

List of configuration keys

The column configuration key:value pair defines what SodaCL ought to consider as valid values.

Column config key Description Values
valid format Defines the format of a value that Soda ought to register as valid.
Only works with columns that contain data type TEXT.
See List of valid formats.
valid length Specifies a valid length for a string.
Only works with columns that contain data type TEXT.
integer
valid max Specifies a maximum numerical value for valid values. integer or float
valid max length Specifies a valid maximum length for a string.
Only works with columns that contain data type TEXT.
integer
valid min Specifies a minimum numerical value for valid values. integer or float
valid min length Specifies a valid minimum length for a string.
Only works with columns that contain data type TEXT.
integer
valid regex Specifies a regular expression to define your own custom valid values. regex, no forward slash delimiters
valid values Specifies the values that Soda is to consider valid. Numeric characters in a valid values list must be enclosed in single quotes. values in a list

List of valid formats

Valid formats apply only to columns using data type TEXT.

Valid format value Format
credit card number Four four-digit numbers separated by spaces.
Four four-digit numbers separated by dashes.
Sixteen-digit number.
Four five-digit numbers separated by spaces.
date eu Validates date only, not time.
dd/mm/yyyy
date inverse Validates date only, not time.
yyyy/mm/dd
date iso 8601 Validates date and/or time according to ISO 8601 format .
2021-04-28T09:00:00+02:00
date us Validates date only, not time.
mm/dd/yyyy
decimal Number uses a , or . as a decimal indicator.
decimal comma Number uses , as decimal indicator.
decimal point Number uses . as decimal indicator.
email name@domain.extension
integer Number is whole.
ip address Four whole numbers separated by .
ipv4 address Four whole numbers separated by .
ipv6 address Eight values separated by :
money A money pattern with currency symbol + decimal point or comma + currency abbreviation.
money comma A money pattern with currency symbol + decimal comma + currency abbreviation.
money point A money pattern with currency symbol + decimal point + currency abbreviation.
negative decimal Negative number uses a , or . as a decimal indicator.
negative decimal comma Negative number uses , as decimal indicator.
negative decimal point Negative number uses . as decimal indicator.
negative integer Number is negative and whole.
negative percentage Negative number is a percentage.
negative percentage comma Negative number is a percentage with a , decimal indicator.
negative percentage point Negative number is a percentage with a . decimal indicator.
percentage comma Number is a percentage with a , decimal indicator.
percentage point Number is a percentage with a . decimal indicator.
percentage Number is a percentage.
phone number +12 123 123 1234
123 123 1234
+1 123-123-1234
+12 123-123-1234
+12 123 123-1234
555-2368
555-ABCD
positive decimal Postive number uses a , or . as a decimal indicator.
positive decimal comma Positive number uses , as decimal indicator.
positive decimal point Positive number uses . as decimal indicator.
positive integer Number is positive and whole.
positive percentage Positive number is a percentage.
positive percentage comma Positive number is a percentage with a , decimal indicator.
positive percentage point Positive number is a percentage with a . decimal indicator.
time 12h Validates against the 12-hour clock.
hh:mm:ss
time 12h nosec Validates against the 12-hour clock.
hh:mm
time 24h Validates against the 244-hour clock.
hh:mm:ss
time 24h nosec Validates against the 24-hour clock.
hh:mm
timestamp 12h Validates against the 12-hour clock.
hh:mm:ss
timestamp 24h Validates against the 24-hour clock.
hh:mm:ss
uuid Universally unique identifier.

List of comparison symbols and phrases

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

Go further


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Last modified on 30-Sep-22