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|
%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` [](https://pypi.org/project/pynonymizer/) [](https://pepy.tech/project/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` [](https://pypi.org/project/pynonymizer/) [](https://pepy.tech/project/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` [](https://pypi.org/project/pynonymizer/) [](https://pepy.tech/project/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 <Python_Bot@openeuler.org> - 1.25.0-1
- Package Spec generated
|
