Data Validation Tool

The Data Validation Tool is an open sourced Python CLI tool based on the Ibis framework that compares heterogeneous data source tables with multi-leveled validation functions.

Data validation is a critical step in a data warehouse, database, or data lake migration project where data from both the source and the target tables are compared to ensure they are matched and correct after each migration step (e.g. data and schema migration, SQL script translation, ETL migration, etc.). The Data Validation Tool (DVT) provides an automated and repeatable solution to perform this task.

DVT supports the following validations:

Column validation (count, sum, avg, min, max, group by)
Row validation (BQ, Hive, Teradata, Oracle, SQL Server, Postgres, Mysql only)
Schema validation
Custom Query validation
Ad hoc SQL exploration

DVT supports the following connection types:

The Connections page provides details about how to create and list connections for the validation tool.

Disclaimer

This is not an officially supported Google product. Please be aware that bugs may lurk, and that we reserve the right to make small backwards-incompatible changes. Feel free to open bugs or feature requests, or contribute directly (see CONTRIBUTING.md for details).

Installation

The Installation page describes the prerequisites and setup steps needed to install and use the Data Validation Tool.

Usage

Before using this tool, you will need to create connections to the source and target tables. Once the connections are created, you can run validations on those tables. Validation results can be printed to stdout (default) or outputted to BigQuery (recommended). DVT also allows you to save or edit validation configurations in a YAML file. This is useful for running common validations or updating the configuration.

Managing Connections

Before running validations, DVT requires setting up a source and target connection. These connections can be stored locally or in a GCS directory. To create connections, please review the Connections page.

Running Validations

The CLI is the main interface to use this tool and it has several different commands which can be used to create and run validations. Below are the command syntax and options for running validations.

Alternatives to running DVT in the CLI include deploying DVT to Cloud Run, Cloud Functions, or Airflow (Examples Here). See the Validation Logic section to learn more about how DVT uses the CLI to generate SQL queries.

Column Validations

Below is the command syntax for column validations. To run a grouped column validation, simply specify the --grouped-columns flag.

You can specify a list of string columns for aggregations in order to calculate an aggregation over the length(string_col). Running an aggregation over all columns ('*') will only run over numeric columns, unless the --wildcard-include-string-len flag is present.

data-validation (--verbose or -v) (--log-level or -ll) validate column
  --source-conn or -sc SOURCE_CONN
                        Source connection details
                        See: *Data Source Configurations* section for each data source
  --target-conn or -tc TARGET_CONN
                        Target connection details
                        See: *Connections* section for each data source
  --tables-list or -tbls SOURCE_SCHEMA.SOURCE_TABLE=TARGET_SCHEMA.TARGET_TABLE
                        Comma separated list of tables in the form schema.table=target_schema.target_table
                        Target schema name and table name are optional.
                        i.e 'bigquery-public-data.new_york_citibike.citibike_trips'
  [--grouped-columns or -gc GROUPED_COLUMNS]
                        Comma separated list of columns for Group By i.e col_a,col_b
  [--primary-keys or -pk PRIMARY_KEYS]
                        Comma separated list of columns to use as primary keys
                        (Note) Only use with grouped column validation. See *Primary Keys* section. 
  [--count COLUMNS]     Comma separated list of columns for count or * for all columns
  [--sum COLUMNS]       Comma separated list of columns for sum or * for all numeric
  [--min COLUMNS]       Comma separated list of columns for min or * for all numeric
  [--max COLUMNS]       Comma separated list of columns for max or * for all numeric
  [--avg COLUMNS]       Comma separated list of columns for avg or * for all numeric
  [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE]
                        BigQuery destination for validation results. Defaults to stdout.
                        See: *Validation Reports* section
  [--service-account or -sa PATH_TO_SA_KEY]
                        Service account to use for BigQuery result handler output.
  [--wildcard-include-string-len or -wis]
                        If flag is present, include string columns in aggregation as len(string_col)
  [--cast-to-bigint or -ctb]
                        If flag is present, cast all int32 columns to int64 before aggregation
  [--filters SOURCE_FILTER:TARGET_FILTER]
                        Colon separated string values of source and target filters.
                        If target filter is not provided, the source filter will run on source and target tables.
                        See: *Filters* section
  [--config-file or -c CONFIG_FILE]
                        YAML Config File Path to be used for storing validations.
  [--threshold or -th THRESHOLD]
                        Float value. Maximum pct_difference allowed for validation to be considered a success. Defaults to 0.0
  [--labels or -l KEY1=VALUE1,KEY2=VALUE2]
                        Comma-separated key value pair labels for the run.
  [--format or -fmt]    Format for stdout output. Supported formats are (text, csv, json, table).
                        Defaults to table.
  [--filter-status or -fs STATUSES_LIST]
                        Comma separated list of statuses to filter the validation results. Supported statuses are (success, fail). If no list is provided, all statuses are returned.

The default aggregation type is a 'COUNT *'. If no aggregation flag (i.e count, sum , min, etc.) is provided, the default aggregation will run.

The Examples page provides many examples of how a tool can be used to run powerful validations without writing any queries.

Row Validations

(Note: Row hash validation is currently supported for BigQuery, Teradata, Impala/Hive, Oracle, SQL Server, Redshift, Postgres, Mysql, Db2 and Alloy DB. Struct and array data types are not currently supported. In addition, please note that SHA256 is not a supported function on Teradata systems. If you wish to perform this comparison on Teradata you will need to deploy a UDF to perform the conversion.)

Below is the command syntax for row validations. In order to run row level validations you need to pass a --primary-key flag which defines what field(s) the validation will be compared on, as well as either the --comparison-fields flag or the --hash flag. See Primary Keys section

The --comparison-fields flag specifies the values (e.g. columns) whose raw values will be compared based on the primary key join. The --hash flag will run a checksum across specified columns in the table. This will include casting to string, sanitizing the data (ifnull, rtrim, upper), concatenating, and finally hashing the row.

Under the hood, row validation uses Calculated Fields to apply functions such as IFNULL() or RTRIM(). These can be edited in the YAML config to customize your row validation.

data-validation (--verbose or -v) (--log-level or -ll) validate row
  --source-conn or -sc SOURCE_CONN
                        Source connection details
                        See: *Data Source Configurations* section for each data source
  --target-conn or -tc TARGET_CONN
                        Target connection details
                        See: *Connections* section for each data source
  --tables-list or -tbls SOURCE_SCHEMA.SOURCE_TABLE=TARGET_SCHEMA.TARGET_TABLE
                        Comma separated list of tables in the form schema.table=target_schema.target_table
                        Target schema name and table name are optional.
                        i.e 'bigquery-public-data.new_york_citibike.citibike_trips'
  --primary-keys or -pk PRIMARY_KEYS
                        Comma separated list of columns to use as primary keys.  See *Primary Keys* section
  --comparison-fields or -comp-fields FIELDS
                        Comma separated list of columns to compare. Can either be a physical column or an alias
                        See: *Calculated Fields* section for details
  --hash COLUMNS        Comma separated list of columns to hash or * for all columns
  --concat COLUMNS      Comma separated list of columns to concatenate or * for all columns (use if a common hash function is not available between databases)
  [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE]
                        BigQuery destination for validation results. Defaults to stdout.
                        See: *Validation Reports* section
  [--service-account or -sa PATH_TO_SA_KEY]
                        Service account to use for BigQuery result handler output.
  [--filters SOURCE_FILTER:TARGET_FILTER]
                        Colon separated string values of source and target filters.
                        If target filter is not provided, the source filter will run on source and target tables.
                        See: *Filters* section
  [--config-file or -c CONFIG_FILE]
                        YAML Config File Path to be used for storing validations.
  [--labels or -l KEY1=VALUE1,KEY2=VALUE2]
                        Comma-separated key value pair labels for the run.
  [--format or -fmt]    Format for stdout output. Supported formats are (text, csv, json, table).
                        Defaults to table.
  [--use-random-row or -rr]
                        Finds a set of random rows of the first primary key supplied.
  [--random-row-batch-size or -rbs]
                        Row batch size used for random row filters (default 10,000).
  [--filter-status or -fs STATUSES_LIST]
                        Comma separated list of statuses to filter the validation results. Supported statuses are (success, fail). If no list is provided, all statuses are returned.

Generate Table Partitions for Large Table Row Validations

When performing row validations, Data Validation Tool brings each row into memory and can run into MemoryError. Below is the command syntax for generating table partitions in order to perform row validations on large tables to alleviate MemoryError. Each partition contains a range of primary key(s) and the ranges of keys across partitions are distinct. The partitions have nearly equal number of rows. See Primary Keys section

The command generates and stores multiple YAML configs that represent chunks of the large table using filters (WHERE primary_key(s) >= X AND primary_key(s) < Y). You can then run the configs in the directory serially (or in parallel in multiple containers, VMs) with the data-validation configs run --config-dir PATH command as described here.

The command takes the same parameters as required for Row Validation plus a few parameters to support partitioning. Single and multiple primary keys are supported and keys can be of any indexable type. A parameter used in earlier versions, partition-key is no longer supported.

data-validation (--verbose or -v) (--log-level or -ll) generate-table-partitions

  --source-conn or -sc SOURCE_CONN
                        Source connection details
                        See: *Data Source Configurations* section for each data source
  --target-conn or -tc TARGET_CONN
                        Target connection details
                        See: *Connections* section for each data source
  --tables-list or -tbls SOURCE_SCHEMA.SOURCE_TABLE=TARGET_SCHEMA.TARGET_TABLE
                        Comma separated list of tables in the form schema.table=target_schema.target_table
                        Target schema name and table name are optional.
                        i.e 'bigquery-public-data.new_york_citibike.citibike_trips'
  --primary-keys PRIMARY_KEYS, -pk PRIMARY_KEYS
                        Comma separated list of primary key columns 'col_a,col_b'.  See *Primary Keys* section
  --comparison-fields or -comp-fields FIELDS
                        Comma separated list of columns to compare. Can either be a physical column or an alias
                        See: *Calculated Fields* section for details
  --hash COLUMNS        Comma separated list of columns to hash or * for all columns
  --concat COLUMNS      Comma separated list of columns to concatenate or * for all columns (use if a common hash function is not available between databases)
  --config-dir CONFIG_DIR, -cdir CONFIG_DIR
                        Directory Path to store YAML Config Files
                        GCS: Provide a full gs:// path of the target directory. Eg: `gs://<BUCKET>/partitions_dir`
                        Local: Provide a relative path of the target directory. Eg: `partitions_dir`
  --partition-num [1-1000], -pn [1-1000]
                        Number of partitions/config files to generate
                        In case this value exceeds the row count of the source/target table, it will be decreased to max(source_row_count, target_row_count)
  [--filters SOURCE_FILTER:TARGET_FILTER]
                        Colon separated string values of source and target filters.
                        If target filter is not provided, the source filter will run on source and target tables.
                        See: *Filters* section

Schema Validations

Below is the syntax for schema validations. These can be used to compare case insensitive column names and types between source and target.

data-validation (--verbose or -v) (--log-level or -ll) validate schema
  --source-conn or -sc SOURCE_CONN
                        Source connection details
                        See: *Data Source Configurations* section for each data source
  --target-conn or -tc TARGET_CONN
                        Target connection details
                        See: *Connections* section for each data source
  --tables-list or -tbls SOURCE_SCHEMA.SOURCE_TABLE=TARGET_SCHEMA.TARGET_TABLE
                        Comma separated list of tables in the form schema.table=target_schema.target_table
                        Target schema name and table name are optional.
                        e.g.: 'bigquery-public-data.new_york_citibike.citibike_trips'
  [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE]
                        BigQuery destination for validation results. Defaults to stdout.
                        See: *Validation Reports* section
  [--service-account or -sa PATH_TO_SA_KEY]
                        Service account to use for BigQuery result handler output.
  [--config-file or -c CONFIG_FILE]
                        YAML Config File Path to be used for storing validations.
  [--format or -fmt]    Format for stdout output. Supported formats are (text, csv, json, table).
                        Defaults  to table.
  [--filter-status or -fs STATUSES_LIST]
                        Comma separated list of statuses to filter the validation results. Supported statuses are (success, fail).
                        If no list is provided, all statuses are returned.
  [--exclusion-columns or -ec EXCLUSION_COLUMNS]
                        Comma separated list of columns to be excluded from the schema validation, e.g.: col_a,col_b.
  [--allow-list or -al ALLOW_LIST]
                        Comma separated list of data-type mappings of source and destination data sources which will be validated in case of missing data types in destination data source. e.g: "decimal(4,2):decimal(5,4),string[non-nullable]:string"
  [--allow-list-file ALLOW_LIST_FILE, -alf ALLOW_LIST_FILE]
                        YAML file containing default --allow-list mappings. Can be used in conjunction with --allow-list.
                        e.g.: samples/allow_list/oracle_to_bigquery.yaml or gs://dvt-allow-list-files/oracle_to_bigquery.yaml
                        See example files in samples/allow_list/.

Custom Query Column Validations

Below is the command syntax for custom query column validations.

data-validation (--verbose or -v) (--log-level or -ll) validate custom-query column
  --source-conn or -sc SOURCE_CONN
                        Source connection details
                        See: *Data Source Configurations* section for each data source
  --target-conn or -tc TARGET_CONN
                        Target connection details
                        See: *Connections* section for each data source
  --source-query SOURCE_QUERY, -sq SOURCE_QUERY
                        Source sql query
                        Either --source-query or --source-query-file must be provided
  --source-query-file  SOURCE_QUERY_FILE, -sqf SOURCE_QUERY_FILE
                        File containing the source sql commands
  --target-query TARGET_QUERY, -tq TARGET_QUERY
                        Target sql query
                        Either --target-query or --target-query-file must be provided
  --target-query-file TARGET_QUERY_FILE, -tqf TARGET_QUERY_FILE
                        File containing the target sql commands
  [--count COLUMNS]     Comma separated list of columns for count or * for all columns
  [--sum COLUMNS]       Comma separated list of columns for sum or * for all numeric
  [--min COLUMNS]       Comma separated list of columns for min or * for all numeric
  [--max COLUMNS]       Comma separated list of columns for max or * for all numeric
  [--avg COLUMNS]       Comma separated list of columns for avg or * for all numeric
  [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE]
                        BigQuery destination for validation results. Defaults to stdout.
                        See: *Validation Reports* section
  [--service-account or -sa PATH_TO_SA_KEY]
                        Service account to use for BigQuery result handler output.
  [--labels or -l KEY1=VALUE1,KEY2=VALUE2]
                        Comma-separated key value pair labels for the run.
  [--format or -fmt]    Format for stdout output. Supported formats are (text, csv, json, table).
                        Defaults to table.
  [--filter-status or -fs STATUSES_LIST]
                        Comma separated list of statuses to filter the validation results. Supported statuses are (success, fail). If no list is provided, all statuses are returned.

The default aggregation type is a 'COUNT *'. If no aggregation flag (i.e count, sum , min, etc.) is provided, the default aggregation will run.

The Examples page provides few examples of how this tool can be used to run custom query validations.

Custom Query Row Validations

(Note: Custom query row validation is currently only supported for BigQuery, Teradata, SQL Server, PostgreSQL, Oracle, Redshift, DB2, AlloyDB, and Impala/Hive. Struct and array data types are not currently supported.)

Below is the command syntax for row validations. In order to run row level validations you need to pass --hash flag which will specify the fields of the custom query result that will be concatenated and hashed. The primary key should be included in the SELECT statement of both source_query.sql and target_query.sql. See Primary Keys section

Below is the command syntax for custom query row validations.

data-validation (--verbose or -v) (--log-level or -ll) validate custom-query row
  --source-conn or -sc SOURCE_CONN
                        Source connection details
                        See: *Data Source Configurations* section for each data source
  --target-conn or -tc TARGET_CONN
                        Target connection details
                        See: *Connections* section for each data source
  --source-query SOURCE_QUERY, -sq SOURCE_QUERY
                        Source sql query
                        Either --source-query or --source-query-file must be provided
  --source-query-file SOURCE_QUERY_FILE, -sqf SOURCE_QUERY_FILE
                        File containing the source sql commands
  --target-query TARGET_QUERY, -tq TARGET_QUERY
                        Target sql query
                        Either --target-query or --target-query-file must be provided
  --target-query-file TARGET_QUERY_FILE, -tqf TARGET_QUERY_FILE
                        File containing the target sql commands
  --comparison-fields or -comp-fields FIELDS
                        Comma separated list of columns to compare. Can either be a physical column or an alias
                        See: *Calculated Fields* section for details
  --hash '*'            '*' to hash all columns.
  --concat COLUMNS      Comma separated list of columns to concatenate or * for all columns
                        (use if a common hash function is not available between databases)
  --primary-key or -pk JOIN_KEY
                       Common column between source and target tables for join
  [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE]
                        BigQuery destination for validation results. Defaults to stdout.
                        See: *Validation Reports* section
  [--service-account or -sa PATH_TO_SA_KEY]
                        Service account to use for BigQuery result handler output.
  [--labels or -l KEY1=VALUE1,KEY2=VALUE2]
                        Comma-separated key value pair labels for the run.
  [--format or -fmt]    Format for stdout output. Supported formats are (text, csv, json, table).
                        Defaults to table.
  [--filter-status or -fs STATUSES_LIST]
                        Comma separated list of statuses to filter the validation results. Supported statuses are (success, fail). If no list is provided, all statuses are returned.

The Examples page provides few examples of how this tool can be used to run custom query row validations.

Dry Run Validation

The validate command takes a --dry-run command line flag that prints source and target SQL to stdout as JSON in lieu of performing a validation:

data-validation (--verbose or -v) (--log-level or -ll) validate
  [--dry-run or -dr]    Prints source and target SQL to stdout in lieu of performing a validation.

For example, this flag can be used as follows:

> data-validation validate --dry-run row \
  -sc my_bq_conn \
  -tc my_bq_conn \
  -tbls bigquery-public-data.new_york_citibike.citibike_stations \
  --primary-keys station_id \
  --hash '*'
{
    "source_query": "SELECT `hash__all`, `station_id`\nFROM ...",
    "target_query": "SELECT `hash__all`, `station_id`\nFROM ..."
}

YAML Configuration Files

You can customize the configuration for any given validation by providing use case specific CLI arguments or editing the YAML configuration file.

For example, the following command creates a YAML file for the validation of the new_york_citibike table: data-validation validate column -sc my_bq_conn -tc my_bq_conn -tbls bigquery-public-data.new_york_citibike.citibike_trips -c citibike.yaml.

The vaildation config file is saved to the GCS path specified by the PSO_DV_CONFIG_HOME env variable if that has been set; otherwise, it is saved to wherever the tool is run.

You can now edit the YAML file if, for example, the new_york_citibike table is stored in datasets that have different names in the source and target systems. Once the file is updated and saved, the following command runs the validation:

data-validation (--verbose or -v) (--log-level or -ll) configs run -c citibike.yaml

You can also specify a directory to read from using the --config-dir (or -cdir) flag. All yaml files in that directory will be run as validations. Support both local and GCS paths. For example:

data-validation configs run -cdir ./my-validations/

OR

data-validation configs run -cdir gs://my-bucket/my-validations/

View the complete YAML file for a Grouped Column validation on the Examples page.

You can view a list of all saved validation YAML files using data-validation configs list, and print a YAML config using data-validation configs get -c citibike.yaml.

Validation Reports

The result handlers tell DVT where to store the results of each validation. The tool can write the results of a validation run to Google BigQuery or print to stdout (default). View the schema of the results table here.

To output to BigQuery, simply include the -bqrh flag during a validation run like so:

data-validation validate column
  -sc bq_conn
  -tc bq_conn
  -tbls bigquery-public-data.new_york_citibike.citibike_trips
  -bqrh project_id.dataset.table
  -sa [email protected]

Ad Hoc SQL Exploration

There are many occasions where you need to explore a data source while running validations. To avoid the need to open and install a new client, the CLI allows you to run ad hoc queries.

data-validation query
  --conn or -c CONN
          The connection name to be queried
  --query or -q QUERY
          The raw query to run against the supplied connection

Building Matched Table Lists

Creating the list of matched tables can be a hassle. We have added a feature which may help you to match all of the tables together between source and target. The find-tables command:

Pulls all tables in the source (applying a supplied allowed-schemas filter)
Pulls all tables from the target
Uses Jaro Similarity algorithm to match tables
Finally, it prints a JSON list of tables which can be a reference for the validation run config.

Note that our default value for the score-cutoff parameter is 1 and it seeks for identical matches. If no matches occur, reduce this value as deemed necessary. By using smaller numbers such as 0.7, 0.65 etc you can get more matches. For reference, we make use of this jaro_similarity method for the string comparison.

data-validation find-tables --source-conn source --target-conn target \
    --allowed-schemas pso_data_validator \
    --score-cutoff 1

Using Beta CLI Features

There may be occasions we want to release a new CLI feature under a Beta flag. Any features under Beta may or may not make their way to production. However, if there is a Beta feature you wish to use than it can be accessed using the following.

data-validation beta --help

[Beta] Deploy Data Validation as a Local Service

If you wish to use Data Validation as a Flask service, the following command will help. This same logic is also expected to be used for Cloud Run, Cloud Functions, and other deployment services.

data-validation beta deploy

Validation Logic

Aggregated Fields

Aggregate fields contain the SQL fields that you want to produce an aggregate for. Currently the functions COUNT(), AVG(), SUM(), MIN(), and MAX() are supported.

Here is a sample aggregate config:

validations:
- aggregates:
    - field_alias: count
    source_column: null
    target_column: null
    type: count
    - field_alias: count__tripduration
    source_column: tripduration
    target_column: tripduration
    type: count
    - field_alias: sum__tripduration
    source_column: tripduration
    target_column: tripduration
    type: sum

If you are aggregating columns with large values, you can CAST() before aggregation with calculated fields as shown in this example.

Filters

Filters let you apply a WHERE statement to your validation query (ie. SELECT * FROM table WHERE created_at > 30 days ago AND region_id = 71;). The filter is written in the syntax of the given source.

Note that you are writing the query to execute, which does not have to match between source and target as long as the results can be expected to align. If the target filter is omitted, the source filter will run on both the source and target tables.

Primary Keys

In many cases, validations (e.g. count, min, max etc) produce one row per table. The comparison between the source and target table is to compare the value for each column in the source with the value of the column in the target. grouped-columns validation and validate row produce multiple rows per table. Data Validation Tool needs one or more columns to uniquely identify each row so the source and target can be compared. Data Validation Tool refers to these columns as primary keys. These do not need to be primary keys in the table. The only requirement is that the keys uniquely identify the row in the results.

Grouped Columns

Grouped Columns contain the fields you want your aggregations to be broken out by, e.g. SELECT last_updated::DATE, COUNT(*) FROM my.table will produce a resultset that breaks down the count of rows per calendar date.

Hash, Concat, and Comparison Fields

Row level validations can involve either a hash/checksum, concat, or comparison fields. A hash validation (--hash '*') will first sanitize the data with the following operations on all or selected columns: CAST to string, IFNULL replace with a default replacement string, RSTRIP, and UPPER. Then, it will CONCAT() the results and run a SHA256() hash and compare the source and target results. Since each row will be returned in the result set, it is recommended to utilize the --use-random-row feature to validate a subset of the table.

Please note that SHA256 is not a supported function on Teradata systems. If you wish to perform this comparison on Teradata you will need to deploy a UDF to perform the conversion.

The concat validation (--concat '*') will do everything up until the hash. It will sanitize and concatenate the specified columns, and then value compare the results.

Comparison field validations (--comp-fields column) involve an value comparison of the column values. These values will be compared via a JOIN on their corresponding primary key and will be evaluated for an exact match.

See hash and comparison field validations in the Examples page.

Calculated Fields

Sometimes direct comparisons are not feasible between databases due to differences in how particular data types may be handled. These differences can be resolved by applying functions to columns in the query itself. Examples might include trimming whitespace from a string, converting strings to a single case to compare case insensitivity, or rounding numeric types to a significant figure.

Once a calculated field is defined, it can be referenced by other calculated fields at any "depth" or higher. Depth controls how many subqueries are executed in the resulting query. For example, with the following YAML config...

- calculated_fields:
    - field_alias: rtrim_col_a
      source_calculated_columns: ['col_a']
      target_calculated_columns: ['col_a']
      type: rtrim
      depth: 0 # generated off of a native column
    - field_alias: ltrim_col_a
      source_calculated_columns: ['col_b']
      target_calculated_columns: ['col_b']
      type: ltrim
      depth: 0 # generated off of a native column
    - field_alias: concat_col_a_col_b
      source_calculated_columns: ['rtrim_col_a', 'ltrim_col_b']
      target_calculated_columns: ['rtrim_col_a', 'ltrim_col_b']
      type: concat
      depth: 1 # calculated one query above

is equivalent to the following SQL query...

SELECT
  CONCAT(rtrim_col_a, rtrim_col_b) AS concat_col_a_col_b
FROM (
  SELECT
      RTRIM(col_a) AS rtrim_col_a
    , LTRIM(col_b) AS ltrim_col_b
  FROM my.table
  ) as table_0

If you generate the config file for a row validation, you can see that it uses calculated fields to generate the query. You can also use calculated fields in column level validations to generate the length of a string, or cast a INT field to BIGINT for aggregations.

See the Examples page for a sample cast to NUMERIC.

Custom Calculated Fields

DVT supports certain functions required for row hash validation natively (i.e. CAST() and CONCAT()), which are listed in the CalculatedField() class methods in the QueryBuilder.

You can also specify custom functions (i.e. replace() or truncate()) from the Ibis expression API reference. Keep in mind these will run on both source and target systems. You will need to specify the Ibis API expression and the parameters required, if any, with the 'params' block as shown below:

- calculated_fields:
  - depth: 0
    field_alias: format_start_time
    source_calculated_columns:
    - start_time
    target_calculated_columns:
    - start_time
    type: custom
    ibis_expr: ibis.expr.api.TimestampValue.strftime
    params:
    - format_str: '%m%d%Y'

The above block references the TimestampValue.strftime Ibis API expression. See the Examples page for a sample YAML with a custom calculated field.

Contributing

Contributions are welcome. See the Contributing guide for details.

GoogleCloudPlatform/professional-services-data-validator

GoogleCloudPlatform

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