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App framework agnostic pure SQL incremental database migrations tool modeled after play-evolutions

Project description


The evolutions package provides a facility similar to play-evolutions (source) for use with Python. It differs from other similar tools in its simple, lightweight philosophy and its pure SQL orientation. It enforces a linear history in schema development with no branching or dependencies. Migrations are contained in SQL files and run automatically as needed, with no code embedding or hand-tuned invocations needed. Evolutions plays well with source control as well as differences between development and production environments. Finally, it's not tied to any application framework so can be used anywhere that Python 3 and the target database are available, regardless of whether the using application is itself implemented in Python.


The basic idea is that, as the database schema is developed incrementally, the changes are placed in ups files named 1.sql (the original schema), 2.sql, 3.sql, etc.. When the tool is invoked (usually just before the app starts up, see Usage), a table is checked in the database to see which of these scripts have been run, and any missing are run in sequence. The scripts should contain both schema changes and data migration. This way, the app and the schema can be developed together, without worrying about manual database updates, legacy data support, or schema-code synchronization.


An application is deployed with its schema in a file 1.sql, containing a lot of CREATE TABLE statements. Some development is done and 2.sql with some ALTER TABLE statements is written. When this is deployed and the evolutions tool run before application startup, it detects that 1.sql has already been run, but 2.sql has not, so it runs that. Later on, 3.sql and 4.sql are developed but reach deployment together. When the evolutions tool is run, it runs first 3.sql then 4.sql in sequence.


In addition to the ups, a set of corresponding downs files 1-downs.sql, 2-downs.sql, 3-downs.sql are created during development, which undo the effects of the corresponding ups files. If the tool detects that a change has been made to an ups file, then it will rerun it. However, its former version, as well as any later ups that have been run "on top" of it, must be undone first. So the tool first runs these in reverse, using versions cached in the database itself in case those have changed as well, then runs back up the sequence starting from the first modified file. This process allows tweaking changes during development, without needing to manually adjust the database each time. It also enables collaboration, so that, for example, a single #.sql file can be used by everyone for an entire sprint (merging in changes to a shared git branch).


The application in the first example continues being developed. In addition to the ups files mentioned earlier, 1-downs.sql (a bunch of DROP TABLE statements), 2-downs.sql (ALTER TABLE statements undoing the alterations in 2.sql), 3-downs.sql, and 4-downs.sql were all written. At one point, someone realizes there was a mistake in 3.sql and it should be fixed. They modify this file, as well as 3-downs.sql, then deploy the application.

When the evolutions tool runs, it detects that 3.sql has changed. It then runs 4-downs.sql and 3-downs.sql (in that order) to get to a point where it can correctly apply the new version of 3.sql. Then it runs that script, and finally 4.sql to bring the database fully up to date.


In general, although running downs will ensure a consistent database schema, some data loss is often unavoidable, simply because elements of the schema are lost through downgrade, meaning the data cannot be preserved. For this reason, you should not run downs in production. Ever. Once you deploy an evolution stage to production, you should freeze it, so evolutions will not run it again. In addition, you can and should enable a safety check by adding the --prod switch when invoking (see Usage). If the tool encounters an ups script change when this switch is active, it will abort rather than run any downs, so you can address the situation manually.

In this case, you will end up in a situation where the database is out of sync with the evolutions scripts. This can also occur if evolutions is only taken into use. This can be remedied by using the --skip=<indices> switch to the tool, which will cause the tool to assume the scripts with the given comma-separated indices have already been run, and mark them so in the database, without actually running them.


The deployment mentioned in the preceding examples as assumed to be on a development environment, where it's OK to lose data in the case downs scripts need to be run. However, in the production deployment, the evolutions tool is invoked with the --prod argument. Hence, in the situation described in the previous example where 3.sql has changed, it will abort and return an error code. The user will then need to address the situation manually. Possibly they will apply the new change needed from 3.sql manually, taking care to preserve data.

Then they can invoke the tool using --skip=3. This will cause it to mark 3.sql has having been "run" in its new modified version, without actually running it. Then, after noting that 4.sql has not changed, the database is now considered to be up to date. (Subsequent invocations do not need the --skip argument and will not trigger an abort, unless there has been a new, different change of an already-run script.)

Database Support

MySQL, PostgreSQL, and Sqlite are supported. The database is accessed through a combination of the command line clients and DB-API2, using mysql-connector-python, PsycoPg2, and sqlite3. (These are not listed as dependencies of this package, so you should install the one needed for your own case yourself.)


The evolutions scripts themselves are run by invoking the command line client for the database being used (e.g., psql for Postgres). Transactionality is therefore under control of the script itself.

When using MySQL or PostgreSQL (but not Sqlite), the evolutions table is locked when the script starts, so any parallel instances of the script started up will wait until the first one completes, and then will find the evolutions already run and do nothing.


pip3 install evolutions

-or- to get development version:

pip3 install


The tool is invoked via a Python 3 command line script, and should be called just before or as part of starting the application, or, in an auto-deploy environment, whenever the schema files have been changed.

./ <db_url> <db_user> <db_pass> <evolutions_dir> [--skip=<stages>] [--prod]
    --skip=<stages> = comma-separated indices to assume already run
    --prod          = abort if downs need to be run (for production)
  • db_url: e.g.: mysql://localhost:3306/dbname, postgresql://localhost:5432/dbname, sqlite:/absolute/path/to/database.db
  • db_user, db_pass: db_pass is ignored for Postgres (must use .pgpass file), and both db_user and db_pass are ignored for Sqlite; pass empty strings ("")
  • evolutions_dir: directory containing the #.sql/#-downs.sql files; can be relative or absolute path
  • --skip: comma-separated list of stage indices to skip running if you have a database which has already had one or more of the #.sql files run on it; will insert rows to the evolutions table to make it up to date, but will not actually run the scripts
  • --prod: if given, tool will abort immediately if it determines any downs would need to be run; database is not touched


The evolutions tool operates by collecting the SHA1 hash of each ups and downs script in the evolutions directory, and storing these values, together with the script contents themselves, in a dedicated table (named evolutions) in the database. Decisions on which ups and downs scripts to run are made by comparing the database record and the scripts found in the directory, and updates are made according to the runs.


On GitHub.

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