Updated remote vs local seeding method and fixture generation.
diff --git a/GSoC2010DataSeeder.wiki b/GSoC2010DataSeeder.wiki index 5b30aa5..579faca 100644 --- a/GSoC2010DataSeeder.wiki +++ b/GSoC2010DataSeeder.wiki
@@ -178,26 +178,11 @@ Python fixtures are used to easily set up and tear down a database for test purposes. The fixture module supports easy addition of static predefined data to the datastore and it's very well integrated with unit tests. The data seeder's main purpose however is to load arbitrary random data. -A local script could be used to generate a fixture dataset file from a configuration sheet which will then be used for unit testing. This will make it easy to provide lots of generated data to tests, and in the same time ensure that the data used for tests will not change because of randomness. +A local script could be used to generate a fixture dataset file from a populated database which will then be used for unit testing. This will make it easy to provide lots of generated data to tests, and in the same time ensure that the data used for tests will not change because of randomness. == Remote vs local == -While it is necessary to use the GAE Task Queue API for seeding a live instance, it's too much of an overhead for seeding locally, especially for automated tests. - -Therefore, I find the need for two scripts that will handle local data generation and seeding, by using a configuration sheet, provided by the web interface. The scripts should be minimal, reusing most of the implementation provided in the SoC data seeder module implementation. - - # *Python fixture generator* -{{{ -This script generates Python fixture datasets for use with unit tests, based on a data seeder JSON configuration file. - -Usage: ./gen_fixture.py [-o output.py] <configuration.json> -}}} - # *Local data seeder* -{{{ -This script seeds the local datastore based on a data seeder JSON configuration file. - -Usage: ./data_seeder.py <configraution.json> -}}} +Remote and local data seeding will be done in the same manner, using GAE's Task Queue API. However, a different solution must be implemented for generating Python fixtures for unit testing. Fixtures will be generated by making a dump of the datastore after it has been seeded using the the web interface. This way, any configuration sheet can be turned into a fixture file by seeding and dumping, but in the same time allows a user to do some manual tweaking of the data before dumping, if necessary. == Web interface workflow ==