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Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match
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GoogleCloudPlatform/professional-services-data-validator
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The Data Validation Tool is an open sourced Python CLI tool based on theIbis frameworkthat compares heterogeneous data source tables with multi-leveled validationfunctions.
Data validation is a critical step in a data warehouse, database, or data lakemigration project where data from both the source and the target tables arecompared 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 toperform 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:
TheConnections page provides details about how to createand list connections for the validation tool.
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(seeCONTRIBUTING.md for details).
TheInstallation page describes the prerequisites andsetup steps needed to install and use the Data Validation Tool.
Before using this tool, you will need to create connections to the source andtarget tables. Once the connections are created, you can run validations onthose tables. Validation results can be printed to stdout (default) or outputtedto BigQuery (recommended). DVT also allows you to save and edit validationconfigurations in a YAML or JSON file. This is useful for running common validations orupdating the configuration.
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 theConnections page.
The CLI is the main interface to use this tool and it has several differentcommands which can be used to create and run validations. DVT is designed to run inan environment connected to GCP services, specifically, BigQuery, GCS and Secret manager.If DVT is being run on-premises or in an environment with restricted access to GCP services, seerunning DVT at on-prem. 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 theValidation Logic sectionto 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.
Below is the command syntax for column validations. To run a grouped columnvalidation, simply specify the--grouped-columns flag.
You can specify a list of string columns for aggregations in order to calculatean aggregation over thelength(string_col). Similarly, you can specify timestamp/datecolumns for aggregation over theunix_seconds(timestamp_col). Running an aggregationover 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 ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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. Or shorthand schema.* for all tables. 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. Please note that not all supported SQL engines give results from STDDV_SAMP (or engine specific equivalent) that are comparable across all other supported SQL engines. This option may produce unreliable results. [--exclude-columns or -ec] Flag to indicate the list of columns provided should be excluded and not included. [--result-handler or -rh CONNECTION_NAME.SCHEMA.TABLE or BQ_PROJECT_ID.DATASET.TABLE] Specify a BigQuery or PostgreSQL connection name as destination for validation results. Also supports legacy BigQuery format BQ_PROJECT_ID.DATASET.TABLE. See: *Validation Reports* section [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE or CONNECTION_NAME.DATASET.TABLE] This option has been deprecated and will be removed in a future release. [--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. Supports GCS and local paths. 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,useYAML configs.
TheExamples page provides many examples of how a tool can be used to run powerful validations without writing any queries.
(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 todeploy a UDF to perform the conversion.)
Below is the command syntax for row validations. In order to run row level validations we requireunique columns to join row sets, which are either inferred from the source/target table or providedvia the--primary-keys flag, and either the--hash,--concat or--comparison-fields flags.SeePrimary Keys section.
The--comparison-fields flag specifies the values (e.g. columns) whose raw values will be comparedbased on the primary key join. The--hash flag will run a checksum across specified columns inthe table. This will include casting to string, sanitizing the data (ifnull, rtrim, upper), concatenating,and finally hashing the row.
Under the hood, row validation usesCalculated Fields toapply 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 ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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' --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) --max-concat-columns INT, -mcc INT Maximum number of columns used in one --hash or --concat validation. When there are more columns in the validation, the validation will be split into multiple validations. There are engine specific defaults, so most users do not need to use this option unless they encounter errors. [--primary-keys PRIMARY_KEYS, -pk PRIMARY_KEYS] Comma separated list of primary key columns, when not specified the value will be inferred from the source or target table if available. See *Primary Keys* section [--exclude-columns or -ec] Flag to indicate the list of columns provided should be excluded from hash or concat instead of included. [--result-handler or -rh CONNECTION_NAME.SCHEMA.TABLE or BQ_PROJECT_ID.DATASET.TABLE] Specify a BigQuery or PostgreSQL connection name as destination for validation results. Also supports legacy BigQuery format BQ_PROJECT_ID.DATASET.TABLE. See: *Validation Reports* section [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE or CONNECTION_NAME.DATASET.TABLE] This option has been deprecated and will be removed in a future release. [--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. Supports GCS and local paths. 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. [--case-insensitive-match, -cim] Performs a case insensitive match by adding an UPPER() before comparison.When performing row validations, Data Validation Tool brings each row into memory and can run into MemoryError. Below is the command syntax for generating partitions in order to perform row validations on large dataset (table or custom-query) 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. SeePrimary Keys section
The command generates and stores multiple YAML validations each representing a chunk of the large dataset using filters (WHERE primary_key(s) >= X AND primary_key(s) < Y) in YAML files. The parameter parts-per-file, specifies the number of validations in one YAML file. Each yaml file will have parts-per-file validations in it - except the last one which will contain the remaining partitions (i.e. parts-per-file may not divide partition-num evenly). You can then run the validations in the directory serially (or in parallel in multiple containers, VMs) with thedata-validation configs run --config-dir PATH command as describedhere.
The command takes the same parameters as required forRow Validationplus 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. You can specify tables that are being validated or the source and target custom query. A parameter used in earlier versions,partition-key is no longer supported.
data-validation [--verbose or -v ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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' Either --tables-list or --source-query (or file) and --target-query (or file) must be provided --source-query SOURCE_QUERY, -sq SOURCE_QUERY Source sql query Either --tables-list or --source-query (or file) and --target-query (or file) must be provided --source-query-file SOURCE_QUERY_FILE, -sqf SOURCE_QUERY_FILE File containing the source sql command. Supports GCS and local paths. --target-query TARGET_QUERY, -tq TARGET_QUERY Target sql query Either --tables-list or --source-query (or file) and --target-query (or file) must be provided --target-query-file TARGET_QUERY_FILE, -tqf TARGET_QUERY_FILE File containing the target sql command. Supports GCS and local paths. --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` For custom queries, the yaml files are stored in CONFIG_DIR and, for tables the yamls are stored in a directory named <source_schema>.<table> within CONFIG_DIR. --partition-num INT, -pn INT Number of partitions into which the table should be split, e.g. 1000 or 10000 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) [--primary-keys PRIMARY_KEYS, -pk PRIMARY_KEYS] Comma separated list of primary key columns, when not specified the value will be inferred from the source or target table if available. See *Primary Keys* section [--result-handler or -rh CONNECTION_NAME.SCHEMA.TABLE or BQ_PROJECT_ID.DATASET.TABLE] Specify a BigQuery or PostgreSQL connection name as destination for validation results. Also supports legacy BigQuery format BQ_PROJECT_ID.DATASET.TABLE. See: *Validation Reports* section [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE or CONNECTION_NAME.DATASET.TABLE] This option has been deprecated and will be removed in a future release. [--service-account or -sa PATH_TO_SA_KEY] Service account to use for BigQuery result handler output. [--parts-per-file INT], [-ppf INT] Number of partitions in a yaml file, default value 1. [--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 [--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. [--case-insensitive-match, -cim] Performs a case insensitive match by adding an UPPER() before comparison.Below is the syntax for schema validations. These can be used to compare case insensitive column names andtypes 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 ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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. Or shorthand schema.* for all tables. Target schema name and table name are optional. e.g.: 'bigquery-public-data.new_york_citibike.citibike_trips' [--result-handler or -rh CONNECTION_NAME.SCHEMA.TABLE or BQ_PROJECT_ID.DATASET.TABLE] Specify a BigQuery or PostgreSQL connection name as destination for validation results. Also supports legacy BigQuery format BQ_PROJECT_ID.DATASET.TABLE. See: *Validation Reports* section [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE or CONNECTION_NAME.DATASET.TABLE] This option has been deprecated and will be removed in a future release. [--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. Supports GCS and local paths. 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/.Below is the command syntax for custom query column validations.
data-validation [--verbose or -v ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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 command. Supports GCS and local paths. --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 command. Supports GCS and local paths. [--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. Please note that not all supported SQL engines give results from STDDV_SAMP (or engine specific equivalent) that are comparable across all other supported SQL engines. This option may produce unreliable results. [--exclude-columns or -ec] Flag to indicate the list of columns provided should be excluded and not included. [--result-handler or -rh CONNECTION_NAME.SCHEMA.TABLE or BQ_PROJECT_ID.DATASET.TABLE] Specify a BigQuery or PostgreSQL connection name as destination for validation results. Also supports legacy BigQuery format BQ_PROJECT_ID.DATASET.TABLE. See: *Validation Reports* section [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE or CONNECTION_NAME.DATASET.TABLE] This option has been deprecated and will be removed in a future release. [--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. Supports GCS and local paths. 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.
TheExamplespage provides few examples of how this tool can be used to run custom query 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 levelvalidations you need to pass--hash flag which will specify the fieldsof the custom query result that will be concatenated and hashed. The primary key should be includedin the SELECT statement of both source_query.sql and target_query.sql. SeePrimary Keys section
Below is the command syntax for custom query row validations.
data-validation [--verbose or -v ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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 command. Supports GCS and local paths. --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 command. Supports GCS and local paths. --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) --max-concat-columns INT, -mcc INT Maximum number of columns used in one --hash or --concat validation. When there are more columns in the validation, the validation will be split into multiple validations. There are engine specific defaults, so most users do not need to use this option unless they encounter errors. [--primary-keys PRIMARY_KEYS, -pk PRIMARY_KEYS] Common column between source and target queries for join [--exclude-columns or -ec] Flag to indicate the list of columns provided should be excluded from hash or concat instead of included. [--result-handler or -rh CONNECTION_NAME.SCHEMA.TABLE or BQ_PROJECT_ID.DATASET.TABLE] Specify a BigQuery or PostgreSQL connection name as destination for validation results. Also supports legacy BigQuery format BQ_PROJECT_ID.DATASET.TABLE. See: *Validation Reports* section [--bq-result-handler or -bqrh PROJECT_ID.DATASET.TABLE or CONNECTION_NAME.DATASET.TABLE] This option has been deprecated and will be removed in a future release. [--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. Supports GCS and local paths. 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. [--case-insensitive-match, -cim] Performs a case insensitive match by adding an UPPER() before comparison.TheExamplespage provides few examples of how this tool can be used to run custom query row validations.
Thevalidate command takes a--dry-run command line flag that prints sourceand target SQL to stdout as JSON in lieu of performing a validation:
data-validation [--verbose or -v ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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 ..."}
On-premises environments can have limited access to GCP services. DVT supports using BigQuery for storing validation results, GCS for storage andthe Secret Manager for secrets. You may also use BigQuery and Spanner as a source or target for validation. ServiceAPIs (i.e. bigquery.googleapis.com) may not always be accessible due to firewall restrictions. Work with your networkadminstrator to identify the way to access these services. They may set up aPrivate Service Connect Endpoint. DVT supports accessing source and target tables in Spanner and BigQuery via endpoints set up in your network. Connection Parameters forSpanner andBigQuery outline regarding how to specify endpoints.
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. run multiple validations sequentially or in parallel)
We recommend generating YAML configs with the--config-file <file-name> flag when running a validation command, which supportsGCS and local paths.
You can use thedata-validation configs command to run and view YAMLs.
data-validation [--verbose or -v ] Verbose logging [--log-level or -ll] Log Level to be assigned. Supported levels are (DEBUG,INFO,WARNING,ERROR,CRITICAL). Defaults to INFO. 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* sectiondata-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 theExamples page.
You can scale DVT for large validations by running the tool in a distributed manner. To optimize the validation speed for large tables, you can use GKE Jobs (Google Kubernetes Jobs) orCloud Run Jobs. If you are not familiar with Kubernetes or Cloud Run Jobs, seeScaling DVT with Distributed Jobs for a detailed overview.
We recommend first generating partitions with thegenerate-table-partitions command for your large datasets (tables or queries). Then, use Cloud Run or GKE to distribute the validation of each chunk in parallel. See theCloud Run Jobs Quickstart sample to get started.
When running DVT in a distributed fashion, 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. 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 (i.e. a--config-file is provided instead), a warning is issued.
The--config-dir flag will specify the directory with the YAML files to be executed in parallel. If you usedgenerate-table-partitions to generate the YAMLs, this would be the directory where the partition files numbered0000.yaml to<partition_num - 1>.yaml are stored i.e (gs://my_config_dir/source_schema.source_table/). When creating your Cloud Run Job, set the number of tasks equal to the number of table partitions so the task index matches the YAML file to be validated. When executed, each Cloud Run task will validate a partition in parallel.
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, PostgreSQL or print to stdout (default). Seeresult handler setup for pre-requisites. View the schema of the BigQuery results tablehere.
To output to BigQuery or PostgreSQL, simply include the-rh flag during a validation run includingthe schema and table name for the results.
BigQuery example by connection name:
data-validation validate column \ -sc bq_conn \ -tc bq_conn \ -tbls bigquery-public-data.new_york_citibike.citibike_trips \ -rh bq_conn.dataset.results_table \ -sa'service-acct@project.iam.gserviceaccount.com'BigQuery example by project name:
data-validation validate column \ -sc bq_conn \ -tc bq_conn \ -tbls bigquery-public-data.new_york_citibike.citibike_trips \ -rh bq-project-id.dataset.results_table \ -sa'service-acct@project.iam.gserviceaccount.com'PostgreSQL example:
data-validation validate column \ -sc ora_conn \ -tc pg_conn1 \ -tbls my_schema.some_table \ -rh pg_conn2.dvt_schema.results_table
There are many occasions where you need to explore a data source while runningvalidations. To avoid the need to open and install a new client, the CLI allowsyou 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 [--format or -f {minimal,python}] Format for query output (default: python)Creating the list of matched tables can be a hassle. We have added a featurewhich may help you to match all of the tables together between source andtarget. Thefind-tables command:
- Pulls all tables in the source (applying a supplied
allowed-schemasfilter) - 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 thevalidation run config.
Note that our default value for thescore-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 ofthis jaro_similarity method for the string comparison.
data-validation find-tables --source-connsource --target-conn target \ --allowed-schemas pso_data_validator \ --score-cutoff 1There 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, ifthere is a Beta feature you wish to use than it can be accessed using thefollowing.
data-validation beta --helpIf you wish to use Data Validation as a Flask service, the following commandwill help. This same logic is also expected to be used for Cloud Run, CloudFunctions, and other deployment services.
data-validation beta deploy
Aggregate fields contain the SQL fields that you want to produce an aggregatefor. Currently the functionsCOUNT(),AVG(),SUM(),MIN(),MAX(),andSTDDEV_SAMP() are supported.
Here is a sample aggregate config:
validations:-aggregates: -field_alias:countsource_column:nulltarget_column:nulltype:count -field_alias:count__tripdurationsource_column:tripdurationtarget_column:tripdurationtype:count -field_alias:sum__tripdurationsource_column:tripdurationtarget_column:tripdurationtype:sum
If you are aggregating columns with large values, you can CAST() before aggregationwith calculated fields as shown inthis example.
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 iswritten in the syntax of the given source and must reference columns in theunderlying table, not projected DVT expressions.
Note that you are writing the query to execute, which does not have to matchbetween source and target as long as the results can be expected to align. Ifthe target filter is omitted, the source filter will run on both the source andtarget tables.
In many cases, validations (e.g. count, min, max etc) produce one row per table. The comparison between the sourceand 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 andvalidate 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.
These columns are inferred, where possible, from the source/target table or can be provided via the--primary-keys flag.
Grouped Columns contain the fields you want your aggregations to be broken outby, e.g.SELECT last_updated::DATE, COUNT(*) FROM my.table will produce aresultset that breaks down the count of rows per calendar date.
Row level validations can involve either a hash/checksum, concat, or comparison fields.A hash validation (--hash '*') will first sanitize the data with the followingoperations on all or selected columns: CAST to string, IFNULL replace with a defaultreplacement string and RSTRIP. Then, it will CONCAT() the resultsand run a SHA256() hash and compare the source and target results.
When there are data type mismatches for columns, for example dates compared to timestamps andbooleans compared with numeric columns, you may see other expressions in SQL statements whichensure that consistent values are used to build comparison values.
Since each row will be returned in the result set if is recommended recommended to validate asubset of the table. The--filters and--use-random-row options can be used for this purpose.
Please note that SHA256 is not a supported function on Teradata systems. If you wish to performthis comparison on Teradata you will need todeploy a UDF to perform the conversion.
The concat validation (--concat '*') will do everything up until the hash. It will sanitizeand concatenate the specified columns, and then value compare the results.
Comparison field validations (--comp-fields column) involve an value comparison of thecolumn values. These values will be compared via a JOIN on their corresponding primarykey and will be evaluated for an exact match.
See hash and comparison field validations in theExamples page.
Sometimes direct comparisons are not feasible between databases due todifferences in how particular data types may be handled. These differences canbe resolved by applying functions to columns in the query itself.Examples might include trimming whitespace from a string, converting strings toa single case to compare case insensitivity, or rounding numeric types to asignificant figure.
Once a calculated field is defined, it can be referenced by other calculatedfields at any "depth" or higher. Depth controls how many subqueries are executedin the resulting query. For example, with the following YAML config:
-calculated_fields: -field_alias:rtrim_col_asource_calculated_columns:['col_a']target_calculated_columns:['col_a']type:rtrimdepth:0# generated off of a native column -field_alias:ltrim_col_asource_calculated_columns:['col_b']target_calculated_columns:['col_b']type:ltrimdepth:0# generated off of a native column -field_alias:concat_col_a_col_bsource_calculated_columns:['rtrim_col_a', 'ltrim_col_b']target_calculated_columns:['rtrim_col_a', 'ltrim_col_b']type:concatdepth: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_bFROM (SELECT RTRIM(col_a)AS rtrim_col_a , LTRIM(col_b)AS ltrim_col_bFROMmy.table )as table_0
If you generate the config file for a row validation, you can see that it usescalculated fields to generate the query. You can also use calculated fieldsin column level validations to generate the length of a string, or casta INT field to BIGINT for aggregations.
See theExamples page for a samplecast to NUMERIC.
DVT supports certain functions required for row hash validation natively (i.e. CAST() and CONCAT()),which are listed in the CalculatedField() class methods in theQueryBuilder.
You can also specify custom functions (i.e. replace() or truncate()) from the Ibis expressionAPI reference. Keep in mind these will runon both source and target systems. You will need to specify the Ibis API expression and the parametersrequired, if any, with the 'params' block as shown below:
-calculated_fields: -depth:0field_alias:format_start_timesource_calculated_columns: -start_timetarget_calculated_columns: -start_timetype:customibis_expr:ibis.expr.types.TemporalValue.strftimeparams: -format_str:'%m%d%Y'
The above block references theTemporalValue.strftime Ibis API expression.See theExamples pagefor a sample YAML with a custom calculated field.
Contributions are welcome. See theContributing guide for details.
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Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match
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