%global _empty_manifest_terminate_build 0 Name: python-pynonymizer Version: 1.25.0 Release: 1 Summary: An anonymization tool for production databases License: MIT URL: https://github.com/rwnx/pynonymizer Source0: https://mirrors.nju.edu.cn/pypi/web/packages/92/15/8c7c4691c6a0bb7d10b735bd687863bf1caa74999f68f41f60eacf8bb3b5/pynonymizer-1.25.0.tar.gz BuildArch: noarch Requires: python3-pyyaml Requires: python3-tqdm Requires: python3-faker Requires: python3-dotenv Requires: python3-pyodbc %description # `pynonymizer` [![pynonymizer on PyPI](https://img.shields.io/pypi/v/pynonymizer)](https://pypi.org/project/pynonymizer/) [![Downloads](https://pepy.tech/badge/pynonymizer)](https://pepy.tech/project/pynonymizer) ![License](https://img.shields.io/pypi/l/pynonymizer) pynonymizer is a universal tool for translating sensitive production database dumps into anonymized copies. This can help you support GDPR/Data Protection in your organization without compromizing on quality testing data. ## Why are anonymized databases important? The primary source of information on how your database is used is in _your production database_. In most situations, the production dataset is usually significantly larger than any development copy, and would contain a wider range of data. From time to time, it is prudent to run a new feature or stage a test against this dataset, rather than one that is artificially created by developers or by testing frameworks. Anonymized databases allow us to use the structures present in production, while stripping them of any personally identifiable data that would consitute a breach of privacy for end-users and subsequently a breach of GDPR. With Anonymized databases, copies can be processed regularly, and distributed easily, leaving your developers and testers with a rich source of information on the volume and general makeup of the system in production. It can be used to run better staging environments, integration tests, and even simulate database migrations. below is an excerpt from an anonymized database: | id |salutation | firstname | surname | email | dob | | - | - | - | - | - | - | | 1 | Dr. | Bernard | Gough | `tnelson@powell.com` | 2000-07-03 | | 2 | Mr. | Molly | Bennett | `clarkeharriet@price-fry.com` | 2014-05-19 | | 3 | Mrs. | Chelsea | Reid | `adamsamber@clayton.com` | 1974-09-08 | | 4 | Dr. | Grace | Armstrong | `tracy36@wilson-matthews.com` | 1963-12-15 | | 5 | Dr. | Stanley | James | `christine15@stewart.net` | 1976-09-16 | | 6 | Dr. | Mark | Walsh | `dgardner@ward.biz` | 2004-08-28 | | 7 | Mrs. | Josephine | Chambers | `hperry@allen.com` | 1916-04-04 | | 8 | Dr. | Stephen | Thomas | `thompsonheather@smith-stevens.com` | 1995-04-17 | | 9 | Ms. | Damian | Thompson | `yjones@cox.biz` | 2016-10-02 | | 10 | Miss | Geraldine | Harris | `porteralice@francis-patel.com` | 1910-09-28 | | 11 | Ms. | Gemma | Jones | `mandylewis@patel-thomas.net` | 1990-06-03 | | 12 | Dr. | Glenn | Carr | `garnervalerie@farrell-parsons.biz` | 1998-04-19 | ## How does it work? `pynonymizer` replaces personally identifiable data in your database with **realistic** pseudorandom data, from the `Faker` library or from other functions. There are a wide variety of data types available which should suit the column in question, for example: * `unique_email` * `company` * `file_path` * `[...]` Pynonymizer's main data replacement mechanism `fake_update` is a random selection from a small pool of data (`--seed-rows` controls the available Faker data). This process is chosen for compatibility and speed of operation, but does not guarantee uniqueness. This may or may not suit your exact use-case. For a full list of data generation strategies, see the docs on [strategyfiles](https://github.com/rwnx/pynonymizer/blob/master/doc/strategyfiles.md) ### Examples You can see strategyfile examples for existing database, such as wordpress or adventureworks sample database, in the the [examples folder](https://github.com/rwnx/pynonymizer/blob/master/examples). ### Process outline 1. Restore from dumpfile to temporary database. 1. Anonymize temporary database with strategy. 1. Dump resulting data to file. 1. Drop temporary database. If this workflow doesnt work for you, see [process control](https://github.com/rwnx/pynonymizer/blob/master/doc/process-control.md) to see if it can be adjusted to suit your needs. ## Requirements * Python >= 3.6 ### mysql * `mysql`/`mysqldump` Must be in $PATH * Local or remote mysql >= 5.5 * Supported Inputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * Supported Outputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * LZMA-compressed SQL file `.xz` ### mssql * Requires extra dependencies: install package `pynonymizer[mssql]` * MSSQL >= 2008 * For `RESTORE_DB`/`DUMP_DB` operations, the database server *must* be running locally with pynonymizer. This is because MSSQL `RESTORE` and `BACKUP` instructions are received by the database, so piping a local backup to a remote server is not possible. * The anonymize process can be performed on remote servers, but you are responsible for creating/managing the target database. * Supported Inputs: * Local backup file * Supported Outputs: * Local backup file ### postgres * `psql`/`pg_dump` Must be in $PATH * Local or remote postgres server * Supported Inputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * Supported Outputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * LZMA-compressed SQL file `.xz` # Getting Started ## Usage ### CLI 1. Write a [strategyfile](https://github.com/rwnx/pynonymizer/blob/master/doc/strategyfiles.md) for your database 1. Start Anonymizing! ``` usage: pynonymizer [-h] [--input INPUT] [--strategy STRATEGYFILE] [--output OUTPUT] [--db-type DB_TYPE] [--db-host DB_HOST] [--db-port DB_PORT] [--db-name DB_NAME] [--db-user DB_USER] [--db-password DB_PASSWORD] [--fake-locale FAKE_LOCALE] [--start-at STEP] [--only-step STEP] [--skip-steps STEP [STEP ...]] [--stop-at STEP] [--seed-rows SEED_ROWS] [--mssql-driver MSSQL_DRIVER] [--mssql-backup-compression] [--mysql-cmd-opts MYSQL_CMD_OPTS] [--mysql-dump-opts MYSQL_DUMP_OPTS] [--postgres-cmd-opts POSTGRES_CMD_OPTS] [--postgres-dump-opts POSTGRES_DUMP_OPTS] [-v] [--verbose] [--dry-run] [--ignore-anonymization-errors] A tool for writing better anonymization strategies for your production databases. optional arguments: -h, --help show this help message and exit --input INPUT, -i INPUT The source dump filepath to read from. Use `-` for stdin. [$PYNONYMIZER_INPUT] --strategy STRATEGYFILE, -s STRATEGYFILE A strategyfile to use during anonymization. [$PYNONYMIZER_STRATEGY] --output OUTPUT, -o OUTPUT The destination filepath to write the dumped output to. Use `-` for stdout. [$PYNONYMIZER_OUTPUT] --db-type DB_TYPE, -t DB_TYPE Type of database to interact with. More databases will be supported in future versions. default: mysql [$PYNONYMIZER_DB_TYPE] --db-host DB_HOST, -d DB_HOST Database hostname or IP address. [$PYNONYMIZER_DB_HOST] --db-port DB_PORT, -P DB_PORT Database port. Defaults to provider default. [$PYNONYMIZER_DB_PORT] --db-name DB_NAME, -n DB_NAME Name of database to restore and anonymize in. If not provided, a unique name will be generated from the strategy name. This will be dropped at the end of the run. [$PYNONYMIZER_DB_NAME] --db-user DB_USER, -u DB_USER Database credentials: username. [$PYNONYMIZER_DB_USER] --db-password DB_PASSWORD, -p DB_PASSWORD Database credentials: password. [$PYNONYMIZER_DB_PASSWORD] --fake-locale FAKE_LOCALE, -l FAKE_LOCALE Locale setting to initialize fake data generation. Affects Names, addresses, formats, etc. [$PYNONYMIZER_FAKE_LOCALE] --start-at STEP Choose a step to begin the process (inclusive). [$PYNONYMIZER_START_AT] --only-step STEP Choose one step to perform. [$PYNONYMIZER_ONLY_STEP] --skip-steps STEP [STEP ...] Choose one or more steps to skip. [$PYNONYMIZER_SKIP_STEPS] --stop-at STEP Choose a step to stop at (inclusive). [$PYNONYMIZER_STOP_AT] --seed-rows SEED_ROWS Specify a number of rows to populate the fake data table used during anonymization. Defaults to 150. [$PYNONYMIZER_SEED_ROWS] --mssql-driver MSSQL_DRIVER [MSSQL] ODBC driver to use for database connection [$PYNONYMIZER_MSSQL_DRIVER] --mssql-backup-compression [MSSQL] Use compression when backing up the database. [$PYNONYMIZER_MSSQL_BACKUP_COMPRESSION] --mysql-cmd-opts MYSQL_CMD_OPTS [MYSQL] pass additional arguments to the restore process (advanced use only!). [$PYNONYMIZER_MYSQL_CMD_OPTS] --mysql-dump-opts MYSQL_DUMP_OPTS [MYSQL] pass additional arguments to the dump process (advanced use only!). [$PYNONYMIZER_MYSQL_DUMP_OPTS] --postgres-cmd-opts POSTGRES_CMD_OPTS [POSTGRES] pass additional arguments to the restore process (advanced use only!). [$PYNONYMIZER_POSTGRES_CMD_OPTS] --postgres-dump-opts POSTGRES_DUMP_OPTS [POSTGRES] pass additional arguments to the dump process (advanced use only!). [$PYNONYMIZER_POSTGRES_DUMP_OPTS] -v, --version show program's version number and exit --verbose Increases the verbosity of the logging feature, to help when troubleshooting issues. [$PYNONYMIZER_VERBOSE] --dry-run Instruct pynonymizer to skip all process steps. Useful for testing safely. [$PYNONYMIZER_DRY_RUN] --ignore-anonymization-errors Instruct pynonymizer to ignore errors during the anonymization process and continue as normal. [$PYNONYMIZER_IGNORE_ANONYMIZATION_ERRORS] ``` ### Package Pynonymizer can also be invoked programmatically / from other python code. See the module entrypoint [pynonymizer](pynonymizer/__init__.py) or [pynonymizer/pynonymize.py](pynonymizer/pynonymize.py) ```python import pynonymizer pynonymizer.run(input_path="./backup.sql", strategyfile_path="./strategy.yml" [...] ) ``` %package -n python3-pynonymizer Summary: An anonymization tool for production databases Provides: python-pynonymizer BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pynonymizer # `pynonymizer` [![pynonymizer on PyPI](https://img.shields.io/pypi/v/pynonymizer)](https://pypi.org/project/pynonymizer/) [![Downloads](https://pepy.tech/badge/pynonymizer)](https://pepy.tech/project/pynonymizer) ![License](https://img.shields.io/pypi/l/pynonymizer) pynonymizer is a universal tool for translating sensitive production database dumps into anonymized copies. This can help you support GDPR/Data Protection in your organization without compromizing on quality testing data. ## Why are anonymized databases important? The primary source of information on how your database is used is in _your production database_. In most situations, the production dataset is usually significantly larger than any development copy, and would contain a wider range of data. From time to time, it is prudent to run a new feature or stage a test against this dataset, rather than one that is artificially created by developers or by testing frameworks. Anonymized databases allow us to use the structures present in production, while stripping them of any personally identifiable data that would consitute a breach of privacy for end-users and subsequently a breach of GDPR. With Anonymized databases, copies can be processed regularly, and distributed easily, leaving your developers and testers with a rich source of information on the volume and general makeup of the system in production. It can be used to run better staging environments, integration tests, and even simulate database migrations. below is an excerpt from an anonymized database: | id |salutation | firstname | surname | email | dob | | - | - | - | - | - | - | | 1 | Dr. | Bernard | Gough | `tnelson@powell.com` | 2000-07-03 | | 2 | Mr. | Molly | Bennett | `clarkeharriet@price-fry.com` | 2014-05-19 | | 3 | Mrs. | Chelsea | Reid | `adamsamber@clayton.com` | 1974-09-08 | | 4 | Dr. | Grace | Armstrong | `tracy36@wilson-matthews.com` | 1963-12-15 | | 5 | Dr. | Stanley | James | `christine15@stewart.net` | 1976-09-16 | | 6 | Dr. | Mark | Walsh | `dgardner@ward.biz` | 2004-08-28 | | 7 | Mrs. | Josephine | Chambers | `hperry@allen.com` | 1916-04-04 | | 8 | Dr. | Stephen | Thomas | `thompsonheather@smith-stevens.com` | 1995-04-17 | | 9 | Ms. | Damian | Thompson | `yjones@cox.biz` | 2016-10-02 | | 10 | Miss | Geraldine | Harris | `porteralice@francis-patel.com` | 1910-09-28 | | 11 | Ms. | Gemma | Jones | `mandylewis@patel-thomas.net` | 1990-06-03 | | 12 | Dr. | Glenn | Carr | `garnervalerie@farrell-parsons.biz` | 1998-04-19 | ## How does it work? `pynonymizer` replaces personally identifiable data in your database with **realistic** pseudorandom data, from the `Faker` library or from other functions. There are a wide variety of data types available which should suit the column in question, for example: * `unique_email` * `company` * `file_path` * `[...]` Pynonymizer's main data replacement mechanism `fake_update` is a random selection from a small pool of data (`--seed-rows` controls the available Faker data). This process is chosen for compatibility and speed of operation, but does not guarantee uniqueness. This may or may not suit your exact use-case. For a full list of data generation strategies, see the docs on [strategyfiles](https://github.com/rwnx/pynonymizer/blob/master/doc/strategyfiles.md) ### Examples You can see strategyfile examples for existing database, such as wordpress or adventureworks sample database, in the the [examples folder](https://github.com/rwnx/pynonymizer/blob/master/examples). ### Process outline 1. Restore from dumpfile to temporary database. 1. Anonymize temporary database with strategy. 1. Dump resulting data to file. 1. Drop temporary database. If this workflow doesnt work for you, see [process control](https://github.com/rwnx/pynonymizer/blob/master/doc/process-control.md) to see if it can be adjusted to suit your needs. ## Requirements * Python >= 3.6 ### mysql * `mysql`/`mysqldump` Must be in $PATH * Local or remote mysql >= 5.5 * Supported Inputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * Supported Outputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * LZMA-compressed SQL file `.xz` ### mssql * Requires extra dependencies: install package `pynonymizer[mssql]` * MSSQL >= 2008 * For `RESTORE_DB`/`DUMP_DB` operations, the database server *must* be running locally with pynonymizer. This is because MSSQL `RESTORE` and `BACKUP` instructions are received by the database, so piping a local backup to a remote server is not possible. * The anonymize process can be performed on remote servers, but you are responsible for creating/managing the target database. * Supported Inputs: * Local backup file * Supported Outputs: * Local backup file ### postgres * `psql`/`pg_dump` Must be in $PATH * Local or remote postgres server * Supported Inputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * Supported Outputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * LZMA-compressed SQL file `.xz` # Getting Started ## Usage ### CLI 1. Write a [strategyfile](https://github.com/rwnx/pynonymizer/blob/master/doc/strategyfiles.md) for your database 1. Start Anonymizing! ``` usage: pynonymizer [-h] [--input INPUT] [--strategy STRATEGYFILE] [--output OUTPUT] [--db-type DB_TYPE] [--db-host DB_HOST] [--db-port DB_PORT] [--db-name DB_NAME] [--db-user DB_USER] [--db-password DB_PASSWORD] [--fake-locale FAKE_LOCALE] [--start-at STEP] [--only-step STEP] [--skip-steps STEP [STEP ...]] [--stop-at STEP] [--seed-rows SEED_ROWS] [--mssql-driver MSSQL_DRIVER] [--mssql-backup-compression] [--mysql-cmd-opts MYSQL_CMD_OPTS] [--mysql-dump-opts MYSQL_DUMP_OPTS] [--postgres-cmd-opts POSTGRES_CMD_OPTS] [--postgres-dump-opts POSTGRES_DUMP_OPTS] [-v] [--verbose] [--dry-run] [--ignore-anonymization-errors] A tool for writing better anonymization strategies for your production databases. optional arguments: -h, --help show this help message and exit --input INPUT, -i INPUT The source dump filepath to read from. Use `-` for stdin. [$PYNONYMIZER_INPUT] --strategy STRATEGYFILE, -s STRATEGYFILE A strategyfile to use during anonymization. [$PYNONYMIZER_STRATEGY] --output OUTPUT, -o OUTPUT The destination filepath to write the dumped output to. Use `-` for stdout. [$PYNONYMIZER_OUTPUT] --db-type DB_TYPE, -t DB_TYPE Type of database to interact with. More databases will be supported in future versions. default: mysql [$PYNONYMIZER_DB_TYPE] --db-host DB_HOST, -d DB_HOST Database hostname or IP address. [$PYNONYMIZER_DB_HOST] --db-port DB_PORT, -P DB_PORT Database port. Defaults to provider default. [$PYNONYMIZER_DB_PORT] --db-name DB_NAME, -n DB_NAME Name of database to restore and anonymize in. If not provided, a unique name will be generated from the strategy name. This will be dropped at the end of the run. [$PYNONYMIZER_DB_NAME] --db-user DB_USER, -u DB_USER Database credentials: username. [$PYNONYMIZER_DB_USER] --db-password DB_PASSWORD, -p DB_PASSWORD Database credentials: password. [$PYNONYMIZER_DB_PASSWORD] --fake-locale FAKE_LOCALE, -l FAKE_LOCALE Locale setting to initialize fake data generation. Affects Names, addresses, formats, etc. [$PYNONYMIZER_FAKE_LOCALE] --start-at STEP Choose a step to begin the process (inclusive). [$PYNONYMIZER_START_AT] --only-step STEP Choose one step to perform. [$PYNONYMIZER_ONLY_STEP] --skip-steps STEP [STEP ...] Choose one or more steps to skip. [$PYNONYMIZER_SKIP_STEPS] --stop-at STEP Choose a step to stop at (inclusive). [$PYNONYMIZER_STOP_AT] --seed-rows SEED_ROWS Specify a number of rows to populate the fake data table used during anonymization. Defaults to 150. [$PYNONYMIZER_SEED_ROWS] --mssql-driver MSSQL_DRIVER [MSSQL] ODBC driver to use for database connection [$PYNONYMIZER_MSSQL_DRIVER] --mssql-backup-compression [MSSQL] Use compression when backing up the database. [$PYNONYMIZER_MSSQL_BACKUP_COMPRESSION] --mysql-cmd-opts MYSQL_CMD_OPTS [MYSQL] pass additional arguments to the restore process (advanced use only!). [$PYNONYMIZER_MYSQL_CMD_OPTS] --mysql-dump-opts MYSQL_DUMP_OPTS [MYSQL] pass additional arguments to the dump process (advanced use only!). [$PYNONYMIZER_MYSQL_DUMP_OPTS] --postgres-cmd-opts POSTGRES_CMD_OPTS [POSTGRES] pass additional arguments to the restore process (advanced use only!). [$PYNONYMIZER_POSTGRES_CMD_OPTS] --postgres-dump-opts POSTGRES_DUMP_OPTS [POSTGRES] pass additional arguments to the dump process (advanced use only!). [$PYNONYMIZER_POSTGRES_DUMP_OPTS] -v, --version show program's version number and exit --verbose Increases the verbosity of the logging feature, to help when troubleshooting issues. [$PYNONYMIZER_VERBOSE] --dry-run Instruct pynonymizer to skip all process steps. Useful for testing safely. [$PYNONYMIZER_DRY_RUN] --ignore-anonymization-errors Instruct pynonymizer to ignore errors during the anonymization process and continue as normal. [$PYNONYMIZER_IGNORE_ANONYMIZATION_ERRORS] ``` ### Package Pynonymizer can also be invoked programmatically / from other python code. See the module entrypoint [pynonymizer](pynonymizer/__init__.py) or [pynonymizer/pynonymize.py](pynonymizer/pynonymize.py) ```python import pynonymizer pynonymizer.run(input_path="./backup.sql", strategyfile_path="./strategy.yml" [...] ) ``` %package help Summary: Development documents and examples for pynonymizer Provides: python3-pynonymizer-doc %description help # `pynonymizer` [![pynonymizer on PyPI](https://img.shields.io/pypi/v/pynonymizer)](https://pypi.org/project/pynonymizer/) [![Downloads](https://pepy.tech/badge/pynonymizer)](https://pepy.tech/project/pynonymizer) ![License](https://img.shields.io/pypi/l/pynonymizer) pynonymizer is a universal tool for translating sensitive production database dumps into anonymized copies. This can help you support GDPR/Data Protection in your organization without compromizing on quality testing data. ## Why are anonymized databases important? The primary source of information on how your database is used is in _your production database_. In most situations, the production dataset is usually significantly larger than any development copy, and would contain a wider range of data. From time to time, it is prudent to run a new feature or stage a test against this dataset, rather than one that is artificially created by developers or by testing frameworks. Anonymized databases allow us to use the structures present in production, while stripping them of any personally identifiable data that would consitute a breach of privacy for end-users and subsequently a breach of GDPR. With Anonymized databases, copies can be processed regularly, and distributed easily, leaving your developers and testers with a rich source of information on the volume and general makeup of the system in production. It can be used to run better staging environments, integration tests, and even simulate database migrations. below is an excerpt from an anonymized database: | id |salutation | firstname | surname | email | dob | | - | - | - | - | - | - | | 1 | Dr. | Bernard | Gough | `tnelson@powell.com` | 2000-07-03 | | 2 | Mr. | Molly | Bennett | `clarkeharriet@price-fry.com` | 2014-05-19 | | 3 | Mrs. | Chelsea | Reid | `adamsamber@clayton.com` | 1974-09-08 | | 4 | Dr. | Grace | Armstrong | `tracy36@wilson-matthews.com` | 1963-12-15 | | 5 | Dr. | Stanley | James | `christine15@stewart.net` | 1976-09-16 | | 6 | Dr. | Mark | Walsh | `dgardner@ward.biz` | 2004-08-28 | | 7 | Mrs. | Josephine | Chambers | `hperry@allen.com` | 1916-04-04 | | 8 | Dr. | Stephen | Thomas | `thompsonheather@smith-stevens.com` | 1995-04-17 | | 9 | Ms. | Damian | Thompson | `yjones@cox.biz` | 2016-10-02 | | 10 | Miss | Geraldine | Harris | `porteralice@francis-patel.com` | 1910-09-28 | | 11 | Ms. | Gemma | Jones | `mandylewis@patel-thomas.net` | 1990-06-03 | | 12 | Dr. | Glenn | Carr | `garnervalerie@farrell-parsons.biz` | 1998-04-19 | ## How does it work? `pynonymizer` replaces personally identifiable data in your database with **realistic** pseudorandom data, from the `Faker` library or from other functions. There are a wide variety of data types available which should suit the column in question, for example: * `unique_email` * `company` * `file_path` * `[...]` Pynonymizer's main data replacement mechanism `fake_update` is a random selection from a small pool of data (`--seed-rows` controls the available Faker data). This process is chosen for compatibility and speed of operation, but does not guarantee uniqueness. This may or may not suit your exact use-case. For a full list of data generation strategies, see the docs on [strategyfiles](https://github.com/rwnx/pynonymizer/blob/master/doc/strategyfiles.md) ### Examples You can see strategyfile examples for existing database, such as wordpress or adventureworks sample database, in the the [examples folder](https://github.com/rwnx/pynonymizer/blob/master/examples). ### Process outline 1. Restore from dumpfile to temporary database. 1. Anonymize temporary database with strategy. 1. Dump resulting data to file. 1. Drop temporary database. If this workflow doesnt work for you, see [process control](https://github.com/rwnx/pynonymizer/blob/master/doc/process-control.md) to see if it can be adjusted to suit your needs. ## Requirements * Python >= 3.6 ### mysql * `mysql`/`mysqldump` Must be in $PATH * Local or remote mysql >= 5.5 * Supported Inputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * Supported Outputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * LZMA-compressed SQL file `.xz` ### mssql * Requires extra dependencies: install package `pynonymizer[mssql]` * MSSQL >= 2008 * For `RESTORE_DB`/`DUMP_DB` operations, the database server *must* be running locally with pynonymizer. This is because MSSQL `RESTORE` and `BACKUP` instructions are received by the database, so piping a local backup to a remote server is not possible. * The anonymize process can be performed on remote servers, but you are responsible for creating/managing the target database. * Supported Inputs: * Local backup file * Supported Outputs: * Local backup file ### postgres * `psql`/`pg_dump` Must be in $PATH * Local or remote postgres server * Supported Inputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * Supported Outputs: * Plain SQL over stdout * Plain SQL file `.sql` * GZip-compressed SQL file `.gz` * LZMA-compressed SQL file `.xz` # Getting Started ## Usage ### CLI 1. Write a [strategyfile](https://github.com/rwnx/pynonymizer/blob/master/doc/strategyfiles.md) for your database 1. Start Anonymizing! ``` usage: pynonymizer [-h] [--input INPUT] [--strategy STRATEGYFILE] [--output OUTPUT] [--db-type DB_TYPE] [--db-host DB_HOST] [--db-port DB_PORT] [--db-name DB_NAME] [--db-user DB_USER] [--db-password DB_PASSWORD] [--fake-locale FAKE_LOCALE] [--start-at STEP] [--only-step STEP] [--skip-steps STEP [STEP ...]] [--stop-at STEP] [--seed-rows SEED_ROWS] [--mssql-driver MSSQL_DRIVER] [--mssql-backup-compression] [--mysql-cmd-opts MYSQL_CMD_OPTS] [--mysql-dump-opts MYSQL_DUMP_OPTS] [--postgres-cmd-opts POSTGRES_CMD_OPTS] [--postgres-dump-opts POSTGRES_DUMP_OPTS] [-v] [--verbose] [--dry-run] [--ignore-anonymization-errors] A tool for writing better anonymization strategies for your production databases. optional arguments: -h, --help show this help message and exit --input INPUT, -i INPUT The source dump filepath to read from. Use `-` for stdin. [$PYNONYMIZER_INPUT] --strategy STRATEGYFILE, -s STRATEGYFILE A strategyfile to use during anonymization. [$PYNONYMIZER_STRATEGY] --output OUTPUT, -o OUTPUT The destination filepath to write the dumped output to. Use `-` for stdout. [$PYNONYMIZER_OUTPUT] --db-type DB_TYPE, -t DB_TYPE Type of database to interact with. More databases will be supported in future versions. default: mysql [$PYNONYMIZER_DB_TYPE] --db-host DB_HOST, -d DB_HOST Database hostname or IP address. [$PYNONYMIZER_DB_HOST] --db-port DB_PORT, -P DB_PORT Database port. Defaults to provider default. [$PYNONYMIZER_DB_PORT] --db-name DB_NAME, -n DB_NAME Name of database to restore and anonymize in. If not provided, a unique name will be generated from the strategy name. This will be dropped at the end of the run. [$PYNONYMIZER_DB_NAME] --db-user DB_USER, -u DB_USER Database credentials: username. [$PYNONYMIZER_DB_USER] --db-password DB_PASSWORD, -p DB_PASSWORD Database credentials: password. [$PYNONYMIZER_DB_PASSWORD] --fake-locale FAKE_LOCALE, -l FAKE_LOCALE Locale setting to initialize fake data generation. Affects Names, addresses, formats, etc. [$PYNONYMIZER_FAKE_LOCALE] --start-at STEP Choose a step to begin the process (inclusive). [$PYNONYMIZER_START_AT] --only-step STEP Choose one step to perform. [$PYNONYMIZER_ONLY_STEP] --skip-steps STEP [STEP ...] Choose one or more steps to skip. [$PYNONYMIZER_SKIP_STEPS] --stop-at STEP Choose a step to stop at (inclusive). [$PYNONYMIZER_STOP_AT] --seed-rows SEED_ROWS Specify a number of rows to populate the fake data table used during anonymization. Defaults to 150. [$PYNONYMIZER_SEED_ROWS] --mssql-driver MSSQL_DRIVER [MSSQL] ODBC driver to use for database connection [$PYNONYMIZER_MSSQL_DRIVER] --mssql-backup-compression [MSSQL] Use compression when backing up the database. [$PYNONYMIZER_MSSQL_BACKUP_COMPRESSION] --mysql-cmd-opts MYSQL_CMD_OPTS [MYSQL] pass additional arguments to the restore process (advanced use only!). [$PYNONYMIZER_MYSQL_CMD_OPTS] --mysql-dump-opts MYSQL_DUMP_OPTS [MYSQL] pass additional arguments to the dump process (advanced use only!). [$PYNONYMIZER_MYSQL_DUMP_OPTS] --postgres-cmd-opts POSTGRES_CMD_OPTS [POSTGRES] pass additional arguments to the restore process (advanced use only!). [$PYNONYMIZER_POSTGRES_CMD_OPTS] --postgres-dump-opts POSTGRES_DUMP_OPTS [POSTGRES] pass additional arguments to the dump process (advanced use only!). [$PYNONYMIZER_POSTGRES_DUMP_OPTS] -v, --version show program's version number and exit --verbose Increases the verbosity of the logging feature, to help when troubleshooting issues. [$PYNONYMIZER_VERBOSE] --dry-run Instruct pynonymizer to skip all process steps. Useful for testing safely. [$PYNONYMIZER_DRY_RUN] --ignore-anonymization-errors Instruct pynonymizer to ignore errors during the anonymization process and continue as normal. [$PYNONYMIZER_IGNORE_ANONYMIZATION_ERRORS] ``` ### Package Pynonymizer can also be invoked programmatically / from other python code. See the module entrypoint [pynonymizer](pynonymizer/__init__.py) or [pynonymizer/pynonymize.py](pynonymizer/pynonymize.py) ```python import pynonymizer pynonymizer.run(input_path="./backup.sql", strategyfile_path="./strategy.yml" [...] ) ``` %prep %autosetup -n pynonymizer-1.25.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pynonymizer -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.25.0-1 - Package Spec generated