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https://github.com/GoogleCloudPlatform/professional-services-data-validator

Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match
https://github.com/GoogleCloudPlatform/professional-services-data-validator

Last synced: about 1 month ago
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Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match

Host: GitHub
URL: https://github.com/GoogleCloudPlatform/professional-services-data-validator
Owner: GoogleCloudPlatform
License: apache-2.0
Created: 2020-04-10T21:18:19.000Z (over 4 years ago)
Default Branch: develop
Last Pushed: 2024-10-28T12:20:55.000Z (about 1 month ago)
Last Synced: 2024-10-29T15:48:10.551Z (about 1 month ago)
Language: Python
Homepage:
Size: 2.22 MB
Stars: 404
Watchers: 37
Forks: 117
Open Issues: 91
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: CODEOWNERS

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my-awesome-github-stars - GoogleCloudPlatform/professional-services-data-validator - Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match (Python)

README

# Data Validation Tool

The Data Validation Tool is an open sourced Python CLI tool based on the
[Ibis framework](https://ibis-project.org/docs/)
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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#alloydb)
* [BigQuery](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#google-bigquery)
* [DB2](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#db2)
* [FileSystem](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#filesystem)
* [Hive](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#hive)
* [Impala](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#impala)
* [MSSQL](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#mssql-server)
* [MySQL](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#mysql)
* [Oracle](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#oracle)
* [Postgres](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#postgres)
* [Redshift](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#redshift)
* [Spanner](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#google-spanner)
* [Teradata](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#teradata)
* [Snowflake](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md#snowflake)

The [Connections](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md) 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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/CONTRIBUTING.md) for details).

## Installation

The [Installation](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/installation.md) 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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/connections.md) 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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/tree/develop/samples)). See the [Validation Logic](https://github.com/GoogleCloudPlatform/professional-services-data-validator#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 ]
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
[--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. 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,
use [YAML configs](https://github.com/GoogleCloudPlatform/professional-services-data-validator/tree/develop#running-dvt-with-yaml-configuration-files).

The [Examples](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/examples.md) 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](https://github.com/akuroda/teradata-udf-sha2/blob/master/src/sha256.c).)

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](https://github.com/GoogleCloudPlatform/professional-services-data-validator#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 ]
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'
--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)
[--exclude-columns or -ec]
Flag to indicate the list of columns provided should be excluded from hash or concat instead of 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.
[--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.
[--trim-string-pks, -tsp]
Trims string based primary key values, intended for use when one engine uses padded string semantics (e.g. CHAR(n)) and the other does not (e.g. VARCHAR(n)).
[--case-insensitive-match, -cim]
Performs a case insensitive match by adding an UPPER() before comparison.
```
#### Generate Partitions for Large 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 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. See *Primary 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 the `data-validation configs run --config-dir PATH` command as described [here](https://github.com/GoogleCloudPlatform/professional-services-data-validator#yaml-configuration-files).

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. 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.
--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:///partitions_dir`
Local: Provide a relative path of the target directory. Eg: `partitions_dir`
If invoked with -tbls parameter, the validations are stored in a directory named ., otherwise the directory is named `custom.`
--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)
[--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.
[--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.
[--trim-string-pks, -tsp]
Trims string based primary key values, intended for use when one engine uses padded string semantics (e.g. CHAR(n)) and the other does not (e.g. VARCHAR(n)).
[--case-insensitive-match, -cim]
Performs a case insensitive match by adding an UPPER() before comparison.
```
#### 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.

#### Custom Query Column Validations

Below is the command syntax for custom query column validations.

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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/examples.md)
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 ]
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)
--primary-key or -pk JOIN_KEY
Common column between source and target tables for join
[--exclude-columns or -ec]
Flag to indicate the list of columns provided should be excluded from hash or concat instead of 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. 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.
[--trim-string-pks, -tsp]
Trims string based primary key values, intended for use when one engine uses padded string semantics (e.g. CHAR(n)) and the other does not (e.g. VARCHAR(n)).
[--case-insensitive-match, -cim]
Performs a case insensitive match by adding an UPPER() before comparison.
```

#### 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:

For example, this flag can be used as follows:

```shell
> 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. run multiple validations sequentially or in parallel)

We recommend generating YAML configs with the `--config-file ` flag when running a validation command, which supports
GCS and local paths.

You can use the `data-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* 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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/examples.md#sample-yaml-config-grouped-column-validation) page.

### Scaling DVT

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](https://cloud.google.com/kubernetes-engine/docs/how-to/deploying-workloads-overview#batch_jobs)) or [Cloud Run Jobs](https://cloud.google.com/run/docs/create-jobs). If you are not familiar with Kubernetes or Cloud Run Jobs, see [Scaling DVT with Distributed Jobs](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/internal/distributed_jobs.md) for a detailed overview.

We recommend first generating partitions with the `generate-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 the [Cloud Run Jobs Quickstart sample](https://github.com/GoogleCloudPlatform/professional-services-data-validator/tree/develop/samples/cloud_run_jobs) 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 used `generate-table-partitions` to generate the YAMLs, this would be the directory where the partition files numbered `0000.yaml` to `.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.

### 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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/terraform/results_schema.json).

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](https://jamesturk.github.io/jellyfish/functions/#jaro-similarity) 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:
```yaml
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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/examples.md#sample-yaml-with-calc-fields-cast-to-numeric-before-aggregation).

### 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 and must reference columns in the
underlying table, not projected DVT expressions.

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 and RSTRIP. Then, it will CONCAT() the results
and 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 and
booleans compared with numeric columns, you may see other expressions in SQL statements which
ensure 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 a
subset 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 perform
this comparison on Teradata you will need to [deploy a UDF to perform the conversion](https://github.com/akuroda/teradata-udf-sha2/blob/master/src/sha256.c).

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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/examples.md#run-a-row-hash-validation-for-all-rows) 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:

```yaml
- 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:

```sql
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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/examples.md#sample-yaml-with-calc-fields-cast-to-numeric-before-aggregation) 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](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/data_validation/query_builder/query_builder.py).

You can also specify custom functions (i.e. replace() or truncate()) from the Ibis expression
[API reference](https://ibis-project.org/reference/expressions/generic/). 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:

```yaml
- 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](https://ibis-project.org/reference/expressions/timestamps/#ibis.expr.types.temporal.TemporalValue.strftime) Ibis API expression.
See the [Examples page](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/docs/examples.md#sample-row-validation-yaml-with-custom-calc-field)
for a sample YAML with a custom calculated field.

## Contributing

Contributions are welcome. See the [Contributing guide](https://github.com/GoogleCloudPlatform/professional-services-data-validator/blob/develop/CONTRIBUTING.md) for details.