Data Validation with Constraints

The TDDA library provides support for constraint generation, data validation (verification) and anomaly detection for datasets, including .csv and other flat files, Parquet files, and Pandas DataFrames. (Support for Polars dataframes is planned; most of the rest of tdda already supports Polars)

The module includes:

• The tdda command-line tool for discovering constraints in data, and for verifying data against those constraints, using the TDDA JSON file format (.tdda files).

• A Python tdda.constraints library containing classes that implement constraint discovery and validation, for use from within other Python programs.

• Python implementations of constraint discovery, data validation (verification), and anomaly detection for a number of data sources:

  • .csv and other flat files

  • Pandas and R DataFrames saved as .parquet files

  • PostgreSQL database tables (postgres:)

  • MySQL database tables (mysql:)

  • SQLite database tables (sqlite:)

  • MongoDB document collections (mongodb:; partial support)

Note

To use databases, you may need to install extra optional packages. See Optional Installations for using Databases.

For a much more detailed tutorial introduction on using TDDA for data validation, read chapter 1-7 of the book.

The tdda Command-line Tool

The tdda command-line utility provides a tool for discovering constraints in data and saving them as a .tdda file in the TDDA JSON file format, and also for verifying data against constraints stored in a .tdda file.

It also provides some other functionality to help with using the tool. The following command forms are supported for data validation:

• tdda discover — perform constraint discovery.

• tdda verify — verify data against constraints.

• tdda detect — detect anomalies in data by checking constraints.

• tdda examples — generate example data and code.

See Examples for more detail on the code and data examples that are included as part of the tdda package.

See Tests for more detail on the tdda package’s own tests, used to test that the package is installed and configured correctly.

tdda discover

The tdda discover command can generate constraints for data, and save the generated constraints as a TDDA JSON file format file (.tdda).

Usage:

tdda discover [FLAGS] input [constraints.tdda]

• input is one of:

  • a .csv file or other flat file (which can have associated metadata)

  • a -, meaning that a .csv file should be read from standard input

  • a parquet file containing a DataFrame, with extension .parquet

  • a database table

• constraints.tdda, if provided, specifies the name of a file to which the generated constraints will be written.

If no constraints output file is provided, or if - is used, the constraints are written to standard output (stdout).

Optional flags include:

• -r or --rex, to include regular expression generation

• -R or --norex, to exclude regular expression generation

See Constraints for CSV Files and Pandas DataFrames for details of how a .csv file is read.

See Constraints for Databases for details of how database tables are accessed.

See the tdda discover man page for more details on options.

tdda verify

The tdda verify command is used to validate data from various sources, against constraints from a TDDA JSON file format constraints file.

Usage:

tdda verify [FLAGS] input [constraints.tdda]

• input is one of:

  • a flat file (e.g. .csv), which can have associated metadata

  • a -, meaning it will read a flat file from standard input

  • a parquet file containing a DataFrame, with extension .parquet

  • a database table

• constraints.tdda, if provided, is a JSON .tdda file containing constraints.

If no constraints file is provided and the input is a flat file, a constraints file with the same path as the input file, but with a .tdda extension, will be used.

For database tables, the constraints file parameter is mandatory.

Optional flags include:

• -a, --all
  Report all fields, even if there are no failures

• -f, --fields
  Report only fields with failures

• -7, --ascii
  Report in ASCII form, without using special characters.

• --epsilon E
  Use this value of epsilon for fuzziness in comparing numeric values.

• --type_checking strict|sloppy
  By default, type checking is sloppy, meaning that when checking type constraints, all numeric types are considered to be equivalent. With strict typing, int is considered different from real.

See Constraints for CSV Files and Pandas DataFrames for details of how a flat file is read.

See Constraints for Databases for details of how database tables are accessed.

See the tdda verify man page for more details on options.

tdda detect

The tdda detect command is used to detect anomalies on data, by checking against constraints from a TDDA JSON file format constraints file.

Usage:

tdda detect [FLAGS] input constraints.tdda output

• input is one of:

  • a flat file (e.g. .csv), which can have associated metadata

  • a -, meaning it will read a flat file from standard input

  • a parquet file containing a DataFrame, with extension .parquet

  • a database table

• constraints.tdda, is a JSON .tdda file containing constraints.

• output is one of:

  • a .csv file to be created containing failing records

  • a -, meaning it will write the .csv file containing failing records to standard output

  • a parquet file with extension .parquet, to be created containing a DataFrame of failing records

If no constraints file is provided and the input is a flat file, a constraints file with the same path as the input file, but with a .tdda extension, will be used.

Optional flags include:

• -a, --all
  Report all fields, even if there are no failures

• -f, --fields
  Report only fields with failures

• -7, --ascii
  Report in ASCII form, without using special characters.

• --epsilon E
  Use this value of epsilon for fuzziness in comparing numeric values.

• --type_checking strict|sloppy
  By default, type-checking is sloppy, meaning that when checking type constraints, all numeric types are considered to be equivalent. With strict typing, int is considered different from real.

• --write-all
  Include passing records in the output.

• --per-constraint
  Write one column per failing constraint, as well as the n_failures total column for each row.

• --output-fields FIELD1 FIELD2 ...
  Specify original columns to write out. If used with no field names, all original columns will be included.

• --index
  Include a row-number index in the output file. The row number is automatically included if no output fields are specified. Rows are usually numbered from 1, unless the (parquet) input file already has an index.

If no records fail any of the constraints, then no output file is created (and if the output file already exists, it is deleted).

See Constraints for CSV Files and Pandas DataFrames for details of how a flat file is read.

See Constraints for Databases for details of how database tables are accessed.

See the tdda detect man page for more details on options.

Constraints for CSV Files and Pandas DataFrames

If a flat file (.csv or other) is used with the tdda command-line tool, it will by default be read using tdda.serial.csv_to_pandas, which calls pandas.read_csv with modified default settings and performs date inference itself.

The tdda.serial colon format can be used to supply metadata describing the flat file, allowing more accurate reading (types, separators, encodings, and so on).

Constraints for Databases

When a database table is used with any tdda command-line tool, the table name (including an optional schema) can be preceded by DBTYPE chosen from postgres, mysql, sqlite or mongodb:

DBTYPE:[schema.]tablename

The following example will use the file .tdda_db_conn_postgres from your home directory (see Database Connection Files), providing all of the default parameters for the database connection.

tdda discover postgres:mytable
tdda discover postgres:myschema.mytable

For MongoDB, document collections are used instead of database tables, and a document can be referred to at any level in the collection structure. Only scalar properties are used for constraint discovery and verification (and any deeper nested structure is ignored). For example:

tdda discover mongodb:mydocument
tdda discover mongodb:subcollection.mysubdocument

Parameters can also be provided using the following flags (which override the values in the .tdda_db_conn_DBTYPE file, if provided):

• --conn FILE
  Database connection file (see Database Connection Files)

• --dbtype DBTYPE
  Type of database

• --db DATABASE
  Name of database to connect to

• --host HOSTNAME
  Name of server to connect to

• --port PORTNUMBER
  IP port number to connect to

• --user USERNAME
  Username to connect as

• --password PASSWORD
  Password to authenticate with

If --conn is provided, then none of the other options are required, and the database connection details are read from the specified file.

If the database type is specified (with the --dbtype option, or by prefixing the table name, such as postgres:mytable), then a default connection file .tdda_db_conn_DBTYPE (in your home directory) is used, if present (where DBTYPE is the name of the kind of database server).

To use constraints for databases, you must have an appropriate DB-API (PEP-0249) driver library installed within your Python environment.

These are:

• For PostgreSQL: pygresql or PyGreSQL

• For MySQL: mysql-connector-python or mysqlclient

• For SQLite: sqlite3

• For MongoDB: pymongo

Database Connection Files

To use a database source, you can either specify the database type using the --dbtype DBTYPE option, or you can prefix the table name with an appropriate DBTYPE: (one of the supported kinds of database server, such as postgres).

You can provide default values for all of the other database options in a database connection file .tdda_db_conn_DBTYPE, in your home directory.

Any database-related options passed in on the command line will override the default settings from the connection file.

A tdda_db_conn_DBTYPE file is a JSON file of the form:

{
    "dbtype": DBTYPE,
    "db": DATABASE,
    "host": HOSTNAME,
    "port": PORTNUMBER,
    "user": USERNAME,
    "password": PASSWORD,
    "schema": SCHEMA,
}

Some additional notes:

• All the entries are optional.

• If a password is provided, then care should be taken to ensure that the file has appropriate filesystem permissions so that it cannot be read by other users.

• If a schema is provided, then it will be used as the default schema, when constraints are discovered or verified on a table name with no schema specified.

• For MySQL (in a .tdda_db_conn_mysql file), the schema parameter must be specified, as there is no built-in default for it to use.

• For Microsoft Windows, the connector file should have the same name as for Unix, beginning with a dot, even though this form of filename is not otherwise commonly used on Windows.

TDDA JSON file format

A .tdda file is a JSON file containing a single JSON object of the form:

{
    "fields": {
        field-name: field-constraints,
        ...
    }
}

Each field-constraints item is a JSON object containing a property for each included constraint:

{
    "type": one of int, real, bool, string or date
    "min": minimum allowed value,
    "max": maximum allowed value,
    "min_length": minimum allowed string length (for string fields),
    "max_length": maximum allowed string length (for string fields),
    "max_nulls": maximum number of null values allowed,
    "sign": one of positive, negative, non-positive, non-negative,
    "no_duplicates": true if the field values must be unique,
    "values": list of distinct allowed values,
    "rex": list of regular expressions, to cover all cases
}

It may also include a dataset section with allowed_fields or required_fields, or both. By default, this looks like this:

"dataset": {
    "allowed_fields": [],
    "required_fields": [
        "*"
    ]
}

The allowed_fields section is a list of fields that are allowed to be present in the dataset to be validated, in addition to those listed in the fields section. (It makes no sense not to allow fields with constraints in data, so those are implicitly allowed.) The wildcards * (for any substring) and ? (for any single character) are allowed, so it would be possible to use

"allowed_fields": ["checksum", "sha*"]

to allow checksum and any field starting sha in the validation data, or "allowed_fields": "*" (or ["*"]) to allow any extra fields.

The required_fields section specifies fields that must be present in the data that is checked. It can also use wildcards, but these now operate only over the fields listed. The default value ["*"] means that all listed fields are required. If only a subset are required, they can be listed explicitly or using wildcards. So

"required_fields": ["checksum", "sha*"]

would mean that the checksum field (which should be among those in the fields section) and any fields in the fields section starting sha are required in data when it is validated.

Constraints Examples

The tdda.constraints module includes a set of examples.

To copy these constraints examples, run the command:

tdda examples constraints

A directory constraints_examples will be created (or overwritten) in the current directory.

Alternatively, you can copy all examples using the following command:

tdda examples

which will create a number of separate subdirectories.