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Reconciliation checks

Last modified on 23-Jul-24

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Use a reconciliation check to validate that target data matches source data before and/or after migrating between data sources.

For example, if you must migrate data from a MySQL data source to a Snowflake data source, you can use reconciliation checks to make sure the MySQL data appears intact in Snowflake in staging before conducting the migration in production.

reconciliation Production:
  label: "Reconcile MySQL to Snowflake"
  attributes:
     priority: 3
  datasets:
    source:
      dataset: dim_customer
      datasource: mysql_adventureworks
    target:
      dataset: dim_customer
      datasource: snowflake_retail

  checks:
  # Metric reconciliation checks
    - row_count diff = 0
    - duplicate_count(last_name):
        fail: when diff > 10%
        warn: when diff < 5%
    - avg(total_children) diff < 10
    - name_combo diff = 0:
        name: Name Combo
        source query: |
          SELECT count(*)
          FROM dim_customer
          WHERE first_name = 'Rob' or last_name = 'Walters'
        target query: |
          SELECT count(*)
          FROM dim_customer
          WHERE last_name = 'Walters'

  # Record reconciliation checks
    - rows diff < 5:
        key columns: [customer_key]
    - rows diff = 0:
        strategy: deepdiff
        source columns: [customer_key, region_id]
        target columns: [customer_base_key, region]

  # Schema reconciliation check
    - schema

✖️    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

✖️    Available as a no-code check

Prerequisites
Types of reconciliation checks
    Best practice for using reconciliation checks
Define reconciliation checks
    Metric reconciliation checks
    Record reconciliation checks
    Schema reconciliation checks
    Add attributes
    Add a filter
    Failed row samples
    List of compatible metrics and checks
Optional check configurations
Limitations and constraints
Go further

Prerequisites

  • Python version 3.9.x or greater.
  • A Soda Cloud account connected to Soda Library via API keys. See Take a sip of Soda.
  • Soda Library; install one Soda Library package for each of the source and target data sources involved in your migration. See step 1, below.

Types of reconciliation checks

Soda supports three types of reconciliation checks:

  • metric reconciliation checks
  • record reconciliation checks
  • schema reconciliation checks

A metric reconciliation check calculates the measurement of a metric such as sum or avg on data in the same dataset in two different data sources; where the delta between calculated measurements differs to the extent that it exceeds the threshold you set in the check, the check fails. Note, you can also compare data between datasets within the same data source.

In other words, the check validates the delta between calculated measurements of a metric in multiple datasets.

In the following example, the metric reconciliation check calculates the sum of column 1 in dataset X in both data source A and data source B. The calculated value of each is the measurement for the sum metric. It then compares the calculated measurements and gauges the difference between them. In this example, the difference between measurements is 4, so the check passes.

reconciliation Production:
  label: "Recon metric check"
  datasets:
    source:
      dataset: dataset X
      datasource: Data source A
    target:
      dataset: dataset X
      datasource: Data source B
  checks:
    - sum(column1) < 5

recon metric

Read more about metrics, measurements, and thresholds in general.

A record reconciliation check performs a row-to-row comparison of the contents of each column, or specific columns, in datasets in two different data sources; where the values do not match exactly, the check fails. The numeric value the check result produces represents the number of rows with different, additional, or missing contents.

For example, the following check compares the entire contents of dataset Y in data source A and dataset Y in data source B. Though the contents of the rows match exactly, one dataset contains additional rows, so it is not an exact match and the reconciliation check fails with a numeric value of 2.

reconciliation Production:
  label: "Recon diff check"
  datasets:
    source:
      dataset: dataset Y
      datasource: Data source A
    target:
      dataset: dataset Y
      datasource: Data source B
  checks:
    - rows diff = 0:
        key columns: [Planet]

recon diff

Read more about the strategies and optional configurations you can add to a record reconciliation check.

A schema reconciliation check compares the columns of two datasets to reveal any differences between target and source; where the columns names differ, or the data type has changed, Soda registers a mismatch and the check fails.

reconciliation Production:
  label: "Recon diff check"
  datasets:
    source:
      dataset: dataset Y
      datasource: Data source A
    target:
      dataset: dataset Y
      datasource: Data source B
  checks:
    - schema

recon schema

Best practice for using reconciliation checks

To efficiently use resources at scan time, best practice dictates that you first configure and run metric reconcilitation checks, then use the output to write refined record reconciliation checks to fine-tune the comparison.

Depending on the volume of data on which you must perform reconciliation checks, metric recon checks run considerably faster and use much fewer resources. Start by defining metric reconciliation checks that test grouping, filters, and joins to get meaningful insight into whether your ingestion or transformation works as expected. Where these checks do not surface all the details you need, or does not provide enough confidence in the output, then proceed with record reconciliation checks.

For running record reconciliation checks, if primary keys exist in your dataset, best practice recommends that you use a simple strategy for executing a record-by-record comparison. This strategy loads rows into memory in batches, thereby reducing the risk of system overload and increasing the speed with which Soda can execute the comparison. See Record reconciliation checks for details about strategies.

Read more about Limitations and constraints.

Define reconciliation checks

The following outlines the basic steps to configure and execute reconciliation checks.

  1. Install a Soda Library package for both the migration source and target data sources. For the very first example above, you would install both soda-mysql and soda-snowflake. If you use a Soda Agent and connect data sources via Soda Cloud, add both data sources to your account.
  2. Configure both data sources in a configuration YAML file, and add your soda_cloud configuration. For the very first example above, you would add both MySQL and Snowflake connection configuration details to a configuration YAML file.
  3. Prepare a recon.yml file or new Soda agreement and configure the reconciliation metadata; see details below.
  4. Define reconciliation checks to compare data between data sources; see details below.
  5. Run a Soda scan against either the source or target data source to execute the reconciliation checks and review results in the command-line output and in Soda Cloud. Note that check results are associated with the target dataset in Soda Cloud.
soda scan -d mysql_adventureworks -c configuration.yml recon.yml


To define reconciliation checks, best practice dictates that you prepare a separate agreement or a recon.yml file separate from your checks YAML file which contains regular, non-reconciliation checks for data quality in your data source. Technically, you can use one YAML file or agreement to contain all recon and regular SodaCL checks, but troubleshooting and issue investigation is easier if you use separate files.

In a recon.yml file, you must first provide reconciliation metadata for the checks, as per the configuration in the example and table below.

reconciliation my_project_name:
  label: "Reconcile MySQL to Snowflake"
  attributes:
     priority: 3
  datasets:
    source:
      dataset: dim_customer
      datasource: mysql_adventureworks
    target:
      dataset: dim_customer
      datasource: snowflake_retail

  checks:
    - row_count diff = 0
reconciliation my_project_name required An identifier for the reconciliation project.
label required An identifier that prepends check result name identifiers in Soda Cloud.
attributes optional A list of attributes that Soda applies to the reconciliation project’s check results in Soda Cloud so that you can filter and find the project’s results.
See: Add attributes
datasets required A subheading to contain the list of datasets to apply your reconciliation checks.
source required Key-value pairs to identify the dataset and data source of the source, or origin location of the data to be migrated. You can provide a comma-separated list of datasets; identify only one data source.
target required Key-value pairs to identify the dataset and data source of the target, or destination location of the data to be migrated. You can provide a comma-separated list of datasets; identify only one data source.
checks required A subheading to contain the checks that reconcile the data between source and target. In this section, you can define any number of both metric and record reconciliation checks; see details below.

Metric reconciliation checks

The syntax of metric reconciliation checks follows the basic patterns of standard SodaCL metrics and checks with the addition of diff in the syntax. Metric reconciliation checks do not support all SodaCL metrics and checks; see compatible checks and metrics below.

For example, you define a regular SodaCL check for data quality that checks for duplicate values in a last_name column as follows:

checks for dim_customer:
  - duplicate_count(last_name) = 0

For a metric reconciliation check, you add the word diff to indicate that it ought to compare the count of duplicate values between the source dataset and the target dataset to confirm that the delta between those counts is zero. Refer to examples below.

Note that with reconciliation checks, there is no need to identify the dataset as you specified both source and target datasets in the project metadata configuration.

reconciliation Production:
...
  checks: 
    - duplicate_count(last_name) diff = 0  
    - avg(total_children) diff < 10
    - freshness(date_first_purchase) diff < 100h
    - row_count:
        fail: when diff > 10%
        warn: when diff between 5% and 9%
    - missing_count(middle_name) diff = 0:
        samples columns: [last_name, first_name]

When you run a scan against either the source or target data source, the Scan summary in the output indicates the check value, which is the calculated delta between measurements, the measurement value of each metric or check for both the source and target datasets, along with the diff value and percentage, and the absolute value and percentage.

soda scan -d adventureworks -c configuration.yml recon2.yml
Soda Library 1.x.x
Soda Core 3.0.xx
xxx 
Sending failed row samples to Soda Cloud
Sending failed row samples to Soda Cloud
Sending failed row samples to Soda Cloud
Scan summary:
3/5 checks PASSED: 
    dim_customer in aws_postgres_retail
      Recon Test: duplicate_count(last_name) diff = 0 [PASSED]
      Recon Test: avg(total_children) diff < 10 [PASSED]
      freshness(date_first_purchase) diff < 100h [PASSED]
1/5 checks WARNED: 
    dim_customer in aws_postgres_retail
      Recon Test: row_count warn when diff < 5% fail when diff > 10% [WARNED]
        check_value: 0.0
        source_row_count: 18484
        target_row_count: 18484
        diff_value: 0
        diff_percentage: 0.0%
1/5 checks FAILED: 
    dim_customer in aws_postgres_retail
      Recon Test: missing_count(middle_name) diff = 0 [FAILED]
        check_value: 7830
        source_missing_count: 7830
        target_missing_count: 0
        diff_value: 7830
        diff_percentage: 100.0%
Oops! 1 failure. 1 warning. 0 errors. 3 pass.
Sending results to Soda Cloud
Soda Cloud Trace: 6925***98


To customize your reconciliation checks, you can borrow from the syntax of failed rows checks to execute SQL queries on the source and target datasets. You can also write a user-defined check to define a SQL query or a common table expression (CTE) that Soda executes on both datasets to reconcile data; see examples below.

reconciliation Production:
...
  checks:
    - name_combo diff = 0:
        name: Name Combo
        source query: |
          SELECT count(*)
          FROM dim_customer
          WHERE first_name = 'Rob' or last_name = 'Walters'
        target query: |
          SELECT count(*)
          FROM dim_customer
          WHERE last_name = 'Walters'
    
    - average_children diff = 0:
        average_children expression: avg(total_children)

Learn about reconciliation check Limitations and constraints.

Record reconciliation checks

Requires Soda Library 1.2.0 or greater
The syntax of record reconciliation checks expects a rows diff input to perform a record-by-record comparison of data between datasets. Choose between two strategies to refine how this type of check executes during a Soda scan:

  • simple
  • deepdiff
reconciliation Production:
  label: "Reconcile Planet Info"
  datasets:
    source:
      dataset: dataset_Y
      datasource: datasource_A
    target:
      dataset: dataset_Y
      datasource: datasource_B

  checks:
  # simple strategy with default page and batch sizes
  # If not explicitly defined, Soda defaults to simple strategy
    - rows diff = 0:
        key columns: [Planet, Size]
  # simple strategy with custom page and batch sizes
    - rows diff = 0:
        key columns: [Planet, Size]
        batch size: 100
        page size: 1000
  # simple strategy with different primary key column names
    - rows diff < 5:
        source key columns: [Planet, Hotness]
        target key columns: [Planet, Relative Temp]
  # deepdiff strategy
    - rows diff = 0:
        strategy: deepdiff

The simple strategy works by processing record comparisons according to one or more primary key identifiers in batches and pages. This type of processing serves to temper large-scale comparisons by loading rows into memory in batches so that a system is not overloaded; it is typically faster than the deepdiff strategy.

  • If you do not specify a strategy, Soda executes the record reconciliation check using the simple strategy.
  • If you do not specify batch size and/or page size, Soda applies default values of 1 and 100000, respectively.

The deepdiff strategy works by processing record comparisons of entire datasets by loading all rows into memory at once. This type of processing is more memory-heavy but allows you to work without primary key identifiers, or without specifying any other details about the data to be compared; it is typically slower than the simple strategy.

Record reconciliation strategy comparison

  Simple strategy Deepdiff strategy
Default strategy  
Processing Loads rows into memory one by one, or by batch for comparison Loads all rows into memory for comparison
Specify key columns Required; can be one or more keys Optional
Specify batch and page sizes Optional N/A
Specify column-constrained comparisons Optional Optional
Best for Standard comparisons in which a primary key exists in the data Comparisons in which no primary key exists in the data
Benchmark:
    10 columns
    1% changes in target
    500K rows
<80MB RAM
9s to execute diff
8GB RAM
136s to execute diff
Benchmark:
    360 columns
    1% changes in target
    100K rows
<80MB RAM
1m to execute diff
8GB RAM
~6m to execute diff
Benchmark:
    360 columns
    1% changes in target
    1M rows
<80MB RAM
35m to execute diff
does not compute on 16GB RAM machine


Beyond choosing a strategy, you can configure a number of granular details for Soda to refine its execution of a record reconciliation check.

Configuration Compares Description and example
Column-constrained Only the data in a specified list of columns. In the example below, the first check applies a deepdiff strategy and compares only the contents of the listed columns, mapping the columns according to the order in which they appear in the list– Planet to Planet, Hotness to Relative Temp. This check passes because the values of the mapped columns are the same.
With composite primary key The entire contents of the datasets, specifying columns to define a primary key in the source. In the example below, the second check applies a simple strategy by default and uses the key columns you identify to form a primary key in the source that defines a single record. Soda uses the key to map records between datasets. Note that you can list column names as comma-separated values in square brackets, or as an unordered list as in the example. This check fails because of the mismatched value for Jupiter’s size.
With different primary keys in source and target The entire contents of the datasets, specifying columns to define mutiple primary keys in both the source and target. This is useful when the column names in your datasets are different. In the example below, the third check applies a simple strategy by default and enables you to define the primary keys in both the source and target datasets. Soda uses the key to map records between datasets. This check passes because with only one failed row, it does not exceed the threshold of 5 that the check sets.
reconciliation Production:
...
  checks:
    # Column-constrained
    - rows diff = 0:
        strategy: deepdiff
        source columns: [Planet, Hotness]
        target columns: [Planet, Relative Temp]
    # With composite primary key
    - rows diff = 0:
        key columns:
          - Planet
          - Size
    # With different primary keys in source and target
    - rows diff < 5:
        source key columns: [Planet, Hotness]
        target key columns: [Planet, Relative Temp]

recon diff2


Custom value comparator

If you use programmtic Soda scans to execute reconciliation checks, you may wish to use a custom value comparator, an example of which follows.

from soda.scan import Scan

from soda.execution.compare.value_comparator import ValueComparator

from datetime import datetime

class CustomValueComparator(ValueComparator):

    def equals(self, x, y):

        # Ignore microsecond difference less than 4ms in datetimes

        if isinstance(x, datetime) and isinstance(y, datetime):

            xms = x.microsecond

            yms = y.microsecond

            ms_diff = abs(xms - yms)

            if ms_diff > 0 and ms_diff <= 4000:

                x = x.replace(microsecond=0)

                y = y.replace(microsecond=0)

        return x == y

if __name__ == "__main__":

    s = Scan()

    s.value_comparator = CustomValueComparator()

    s.set_scan_definition_name("test_scan")

    s.set_verbose(True)

    s.set_data_source_name("soda_demo")

    s.add_configuration_yaml_file("configuration.yml")

    s.add_sodacl_yaml_file("perf.yml")

    s.execute()

    print(s.get_logs_text())

    print(s.build_scan_results())


Schema reconciliation checks

The syntax of schema reconciliation checks is simple, and without configuration details beyond the check identifier.

reconciliation Production:
...
  checks:
    - schema

Optionally, you can add a mapping configuration to a schema check to properly compare columns that use different data types. For example, you can use this configuration to map the comparison of a Snowflake column that uses Boolean and an MSSQL Server column that uses bit.

reconciliation Production:
  label: "Reconcile MS SQL to Snowflake"
  datasets:
    source:
      dataset: opt-in-campaign
      datasource: sqlserver1
    target:
      dataset: optin-campaign
      datasource: snowflake_retail

  checks:
    - schema:
        types:
          - source: bit
            target: boolean
          - source: enum
            target: string

Add attributes

Add attributes to reconciliation checks to organize your checks and alert notifications in Soda Cloud. For example, you can apply attributes to checks to label and sort check results by department, priority, location, etc.

You can add custom attributes to reconciliation checks in two ways:

  • in bulk, so that Soda adds the attribute to all checks in the reconciliation project
  • individually, so that Soda adds the attribute to individual reconciliation checks in the project

After following the instructions to create a check attribute in Soda Cloud, you can add the attribute to a reconciliation project, and/or to individual checks, as in the following example.

Where attribute values for the project and the individual check conflict or overlap, Soda uses the value for the individual check.

reconciliation Production:
  label: "Reconcile MySQL to Snowflake"
  # Soda adds this attribute to each check in the reconciliation project
  attributes:
     priority: 3
  datasets:
    source:
      dataset: dim_customer
      datasource: mysql_adventureworks
    target:
      dataset: dim_customer
      datasource: snowflake_retail
  checks:
    - row_count diff = 0:
        # Soda adds this attribute to this check, only.
        attributes:
           department: [Marketing]
    - rows diff:
        # Soda adds this attribute to this check, only.
        name: Row diff check
        attributes:
            department: [Development]
        fail: when > 10
        warn: when between 5 and 9


Add a filter

You can add a filter to a reconciliation project’s configuration to constrain the data on which Soda executes the reconciliation checks. Refer to the example below.

Best practice dictates that you add filters when using record reconciliation checks to mitigate heavy memory usage and long scan times when performing record-to-record comparisons of data. See Limitations and constraints.

reconciliation Production:
  label: "Recon Test"
  datasets:
    source:
      dataset: dim_customer
      datasource: adventureworks
      filter: total_children > 3
    target:
      dataset: dim_customer
      datasource: aws_postgres_retail

  checks:
    - row_count diff = 0

Output:

soda scan -d adventureworks -c configuration.yml recon2.yml
Soda Library 1.x.x
Soda Core 3.0.x
...
Scan summary:
1/1 check FAILED: 
    dim_customer in aws_postgres_retail
      row_count diff = 0 [FAILED]
        check_value: 14757
        source_row_count: 3727
        target_row_count: 18484
        diff_value: 14757
        diff_percentage: 395.95%
Oops! 1 failures. 0 warnings. 0 errors. 0 pass.
Sending results to Soda Cloud
Soda Cloud Trace: 4380***10


Failed row samples

Record reconciliation checks and metric reconcilication checks that borrow from failed rows check syntax such as the name_combo check in the example above, explicitly 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.

Read more About failed row samples.


If you wish to limit or broaden the sample size, you can add the samples limit configuration to a check. Read more about Setting a sample limit.

checks:  
  - rows diff = 0:
      samples limit: 20


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:  
  - rows diff = 0:
      samples limit: 0


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


List of compatible metrics and checks for metric reconciliation checks

Metric or check Supported data sources
avg all
avg_length all
duplicate_count all
duplicate_percent all
failed rows all
freshness all
invalid_count Athena
BigQuery
DB2
SQL Server
PostgreSQL
Redshift
Snowflake
Spark DataFrames
invalid_percent Athena
BigQuery
DB2
SQL Server
PostgreSQL
Redshift
Snowflake
Spark DataFrames
max all
max_length all
min all
min_length all
missing_count Athena
BigQuery
DB2
SQL Server
PostgreSQL
Redshift
Snowflake
Spark DataFrames
missing_percent Athena
BigQuery
DB2
SQL Server
PostgreSQL
Redshift
Snowflake
Spark DataFrames
percentile PostgreSQL
Snowflake
row_count all
stddev Athena
BigQuery
PostgreSQL
Redshift
Snowflake
stddev_pop Athena
BigQuery
PostgreSQL
Redshift
Snowflake
stddev_samp Athena
BigQuery
PostgreSQL
Redshift
Snowflake
sum all
user-defined all
variance Athena
BigQuery
PostgreSQL
Redshift
Snowflake
var_pop Athena
BigQuery
PostgreSQL
Redshift
Snowflake
var_samp Athena
BigQuery
PostgreSQL
Redshift
Snowflake

Optional check configurations

Supported Configuration Documentation
Define a name for a reconciliation check; see example. Customize check names
Add an identity to a check; see example. Add a check identity
Define alert configurations to specify warn and fail alert conditions; see example.
Exception: schema reconciliation checks do not support alert configurations.
Add alert configurations
  Apply an in-check filter to return results for a specific portion of the data in your dataset. -
Use quotes when identifying dataset or column names; see example.
Note that the type of quotes you use must match that which your data source uses. For example, BigQuery uses a backtick (`) as a quotation mark.
Use quotes in a check
  Use wildcard characters ( % or * ) in values in the check. -
  Use for each to apply reconciliation checks to multiple datasets in one scan. -
  Apply a dataset filter to partition data during a scan. -

Example with name

  checks:
    - rows diff between 35000 and 36000:
        name: Simple row diff

Example with identity

  checks:
    - duplicate_count(last_name) diff < 1:
        identity: 05229d67-e3f0-***-a327-b2***84

Example with alerts

  checks:
    - row_count:
        fail: when diff > 10%
        warn: when diff between 5% and 9%

Example with quotes

  checks:
    - duplicate_count("last_name") diff = 0


Limitations and constraints

  • The Python environment in which deepdiff record reconciliation checks run consumes more time/CPU/memory because this type of check loads all data into memory to execute a comparison. Because record-to-record comparison is dense, exercise caution when executing scans with record reconciliation checks as they can cause usage spikes in the data source, and cost spikes in case of cloud-managed data sources. Best practice dictates that you add filters and use column-constrained record reconciliation checks whenever possible to mitigate cost and performance issues. See also: Best practice for using reconciliation checks.
  • Reconciliation checks on TEXT type columns are case sensitive.
  • Record reconciliation checks do not support samples columns configuration.
  • Reconciliation checks do not support exclude columns in the data source configuration in a configuration YAML file; see Disable failed rows sampling for specific columns.
  • Known issue: Do not define a threshold as a percentage % if you expect the measurement of a metric to equal 0. Using a percentage for a threshold causes an error for an absolute check; the check evaluates correctly but the error persists with a non-zero exit command.

Go further


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Documentation always applies to the latest version of Soda products
Last modified on 23-Jul-24