Easily create database dumps with support for redacting data (And replacing that data with valid random values).

Supported databases

• MySQL
• PostgreSQL

More coming soon...

Installation

To install redactdump, run the following command:

pip install redactdump

Usage

Usage: redactdump [OPTIONS]

Create a redacted database dump.

Options:
  -c, --config TEXT      Path to dump configuration. [required]
  -u, --user TEXT        Connection username.
  -p, --password TEXT    Connection password.
  --max-workers INTEGER  Max number of tables dumped concurrently. [default: 4]
  -d, --debug            Enable debug mode.
  --help                 Show this message and exit.

Configuration

To create a dump you currently must use a configuration file, however in the future you might be able to do it all via CLI.

Supported replacement values.

redactdump uses faker to generate random data.

replacement can therefore be any function from the following providers: https://faker.readthedocs.io/en/stable/providers.html

NOTE: redactdump is currently NOT tested with all providers, some might trigger bugs

Example configuration:

connection:
  type: pgsql
  host: 127.0.0.1
  port: 5432
  database: postgres

redact:
  patterns:
    column:
      - pattern: '^[a-zA-Z]+_name'
        replacement: name
    data:
      - pattern: '192.168.0.1'
        replacement: ipv4
      - pattern: 'John Doe'
        replacement: name

output:
  type: multi_file
  naming: 'dump-[table_name]-[timestamp]' # Default: [table_name]-[timestamp]
  location: './output/'

Output types

output.type controls how dumps are written:

• multi_file: one .sql file per table inside location (a directory).
• file: every table is written into a single file. Without naming the file is {location}.sql; with naming the templated name (with [table_name] dropped) is placed in the directory of location. The file is recreated on each run.

Configuration Schema

The configuration schema can be found here

Example

Configuration

connection:
  type: pgsql
  host: 127.0.0.1
  port: 5432
  database: postgres

redact:
  patterns:
    column:
      - pattern: '^new_'
        replacement: name
    data:
      - pattern: '6'
        replacement: random_int

output:
  type: multi_file
  naming: 'dump-[table_name]-[timestamp]' # Default: [table_name]-[timestamp]
  location: './output/'

Original data

(column_1, new_column)

6,"""John Doe"""
6,"John Doe"
6,"John Doe"
6,John Doe
1,\John Doe
1,--John Doe
12312, John Doe
99,!John Doe
99,(John Doe)

Output

INSERT INTO table_name VALUES (890, 'Yolanda Mcdonald');
INSERT INTO table_name VALUES (1982, 'Stephen Lewis');
INSERT INTO table_name VALUES (2952, 'Janet Woodward');
INSERT INTO table_name VALUES (9307, 'Joshua Price');
INSERT INTO table_name VALUES (1, 'Tina Morrison');
INSERT INTO table_name VALUES (1, 'Juan Mejia');
INSERT INTO table_name VALUES (12312, 'Michael Thornton');
INSERT INTO table_name VALUES (99, 'Adrian White');
INSERT INTO table_name VALUES (99, 'Robin Jefferson');

Data types

PostgreSQL-specific types (inet, cidr, macaddr, macaddr8, interval, point, line, lseg, box, circle, polygon, tsvector, tsquery, pg_lsn, pg_snapshot, txid_snapshot) are exported with an explicit ::type cast, and bytea is exported as a hex literal. Redacting one of these columns requires a replacement that produces a value valid for the type.

Performance

redactdump runs on an asyncio pipeline. Tables are dumped concurrently (bounded by --max-workers) using async database drivers (psycopg for PostgreSQL, aiomysql for MySQL) and async file writes via aiofiles.

Benchmark

benchmarks/benchmark_dump.py seeds a live PostgreSQL database, runs the full dump pipeline and reports throughput in rows per second. Connection settings come from BENCH_DB_* environment variables and default to the docker-compose Postgres service.

docker compose up -d
uv run python benchmarks/benchmark_dump.py --rows 40000 --tables 4 --iterations 3

CI runs the same benchmark on every pull request via .github/workflows/benchmark.yaml, tracks the result over time and fails the build if throughput regresses significantly against the main baseline.