google-pso-data-validator 4.5.0

A package to enable easy data validation

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, stddev, group by)
• Row validation (Not supported for FileSystem connections)
• Schema validation
• Custom Query validation
• Ad hoc SQL exploration

DVT supports the following connection types:

• AlloyDB
• BigQuery
• DB2
• FileSystem
• Hive
• Impala
• MSSQL
• MySQL
• Oracle
• Postgres
• Redshift
• Spanner
• Teradata
• Snowflake

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 and edit validation configurations in a YAML or JSON 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.

Note that we do not support nested or complex columns for column or row validations.

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). Similarly, you can specify timestamp/date columns for aggregation over the unix_seconds(timestamp_col). Running an aggregation over all columns ('*') will only run over numeric columns, unless the --wildcard-include-string-len or --wildcard-include-timestamp flags are 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
  [--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
  [--std COLUMNS]       Comma separated list of columns for stddev_samp or * for all numeric
  [--exclude-columns or -ec]
                        Flag to indicate the list of columns provided should be excluded and not included.
  [--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)
  [--wildcard-include-timestamp or -wit]
                        If flag is present, include timestamp/date columns in aggregation as unix_seconds(ts_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 and other features.
                        See: *Running DVT with YAML Configuration Files* section
  [--config-file-json or -cj CONFIG_FILE_JSON]
                        JSON Config File Path to be used for storing validations only for application purposes.
  [--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]
                        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 *', which will run in addition to the validations you specify. To remove this default, use YAML configs.

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 not supported for FileSystem connections. 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 or JSON config file 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 and other features.
                        See: *Running DVT with YAML Configuration Files* section
  [--config-file-json or -cj CONFIG_FILE_JSON]
                        JSON Config File Path to be used for storing validations only for application purposes.
  [--labels or -l KEY1=VALUE1,KEY2=VALUE2]
                        Comma-separated key value pair labels for the run.
  [--format or -fmt FORMAT]
                        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, except for date and timestamp 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.

Note: An exclamation point before a data type (!string) signifies the column is non-nullable or required.

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 and other features.
                        See: *Running DVT with YAML Configuration Files* section
  [--config-file-json or -cj CONFIG_FILE_JSON]
                        JSON Config File Path to be used for storing validations only for application purposes.
  [--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: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
  [--std COLUMNS]       Comma separated list of columns for stddev_samp or * for all numeric
  [--exclude-columns or -ec]   
                        Flag to indicate the list of columns provided should be excluded and not included.
  [--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 and other features.
                        See: *Running DVT with YAML Configuration Files* section
  [--config-file-json or -cj CONFIG_FILE_JSON]
                        JSON Config File Path to be used for storing validations only for application purposes.
  [--labels or -l KEY1=VALUE1,KEY2=VALUE2]
                        Comma-separated key value pair labels for the run.
  [--format or -fmt FORMAT]
                        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 not supported for FileSystem connections. 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.
  [--config-file or -c CONFIG_FILE]
                        YAML Config File Path to be used for storing validations and other features.
                        See: *Running DVT with YAML Configuration Files* section
  [--config-file-json or -cj CONFIG_FILE_JSON]
                        JSON Config File Path to be used for storing validations only for application purposes.
  [--labels or -l KEY1=VALUE1,KEY2=VALUE2]
                        Comma-separated key value pair labels for the run.
  [--format or -fmt FORMAT]
                        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 ..."
}

Running DVT with YAML Configuration Files

Running DVT with YAML configuration files is the recommended approach if:

• you want to customize the configuration for any given validation OR
• you want to run DVT at scale (i.e. row validations across many partitions)

We recommend generating YAML configs with the --config-file <file-name> flag when running a validation command. The validation 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 use the data-validation configs command to run and view YAMLs.

data-validation (--verbose or -v) (--log-level or -ll) configs run
  [--config-file or -c CONFIG_FILE]
                        Path to YAML config file to run. Supports local and GCS paths.
  [--config-dir or -cdir CONFIG_DIR]
                        Directory path containing YAML configs to be run sequentially. Supports local and GCS paths.
  [--dry-run or -dr]    If this flag is present, prints the source and target SQL generated in lieu of running the validation.
  [--kube-completions or -kc]
                        Flag to indicate usage in Kubernetes index completion mode.
                        See *Scaling DVT to run 10's to 1000's of validations concurrently* section

data-validation configs list
  [--config-dir or -cdir CONFIG_DIR]
                        GCS or local directory from which to list validation YAML configs. Defaults to current local directory.

data-validation configs get
  [--config-file or -c CONFIG_FILE] GCS or local path of validation YAML to print.

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

Scaling DVT to run 10's to 1000's of validations concurrently

As described above, you can run multiple validation configs sequentially with the --config-dir flag. To optimize the validation speed for large tables, you can run DVT concurrently using GKE Jobs (Google Kubernetes Jobs) or Cloud Run Jobs. If you are not familiar with Kubernetes or Cloud Run Jobs, see Scaling DVT with Kubernetes for a detailed overview.

In order to validate partitions concurrently, both the --kube-completions and --config-dir flags are required. The --kube-completions flag specifies that the validation is being run in indexed completion mode in Kubernetes or as multiple independent tasks in Cloud Run. The --config-dir flag will specify the directory with the YAML files to be executed in parallel. If you used generate-table-partitions to generate the YAMLs, this would be the directory where the partition files numbered 0000.yaml to <partition_num - 1>.yaml are stored i.e (my_config_dir/source_schema.source_table/)

In order to run DVT in a container, you have to build a Docker image - see instructions here. You will also need to set the PSO_DV_CONFIG_HOME environment variable to point to a GCS prefix where the connection configuration files are stored. In Cloud Run you can use the option --set-env-vars or --update-env-vars to pass the environment variable. We recommend that you use the bq-result-handler to save your validation results to BigQuery.

The Cloud Run and Kubernetes pods must run in a network with access to the database servers. Every Cloud Run job is associated with a service account. You need to ensure that this service account has access to Google Cloud Storage (to read connection configuration and YAML files) and BigQuery (to publish results). If you are using Kubernetes, you will need to use a service account with the same privileges as mentioned for Cloud Run. In Kubernetes, you will need to set up workload identity so the DVT container can impersonate the service account.

In Cloud Run, the job must be run as multiple, independent tasks with the task count set to the number of partitions generated. In Kubernetes, set the number of completions to the number of partitions generated - see Kubernetes Parallel Jobs. The option --kube-completions or -kc tells DVT that many DVT containers are running in a Kubernetes cluster. Each DVT container only validates the specific partition YAML (based on the index assigned by Kubernetes control plane). If the -kc option is used and you are not running in indexed mode, you will receive a warning and the container will process all the validations sequentially. If the -kc option is used and a config directory is not provided (a --config-file is provided instead), a warning is issued.

By default, each partition validation is retried up to 3 times if there is an error. In Kubernetes and Cloud Run, you can set the parallelism to the number you want. Keep in mind that if you are validating 1000's of partitions in parallel, you may find that setting the parallelism too high (say 100) may result in timeouts and slow down the validation.

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 [e-mail]

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(), MAX(), and STDDEV_SAMP() 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.types.TemporalValue.strftime
    params:
    - format_str: '%m%d%Y'

The above block references the TemporalValue.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.