issue_comments

Documentation: https://sqlite-utils.readthedocs.io/en/latest/python-api.html#transforming-a-table

The reason I'm working on this now is that I'd like to support many more options for data cleanup in the Datasette ecosystem - so being able to do things like convert the type of existing columns becomes increasingly important.

I think the fiddliest part of the implementation here is code that takes the existing columns_dict of the table and the incoming columns= and drop= and rename= parameters and produces the columns dictionary for the new table, ready to be fed to .create_table().

This logic probably also needs to return a structure that can be used to build the INSERT INTO ... SELECT ... FROM query.

A test that confirms that this mechanism can turn a rowid into a non-rowid table would be good too.

Does it make sense to support the pk= argument for changing the primary key?

If the user requests a primary key that doesn't make sense I think an integrity error will be raised when the SQL is being executed, which should hopefully cancel the transaction and raise an error. Need to check that this is what happens.

Since I have a column_order=None argument already, maybe I can ignore the order of the columns in that first argument and use that instead?

The columns= argument is optional - so you can do just a rename operation like so: table.transform(rename={"age": "dog_age"})

If you want to both change the type of a column AND rename it in the same operation, how would you do that? I think like this: python table.transform({"age": int}, rename={"age": "dog_age"}) So any rename logic is applied at the end, after the type transformation or re-ordering logic.

To expand on what that first argument - the columns argument - does. Say you have a table like this: id integer name text age text Any columns omitted from the columns= argument are left alone - they have to be explicitly dropped using drop= if you want to drop them.

Any new columns are added (at the end of the table):

table.tranform({"size": float})

Any columns that have their type changed will have their type changed:

table.tranform({"age": int})

Should I also re-order columns if the order doesn't match? I think so. Open question as to what happens to columns that aren't mentioned at all in the dictionary though - what order should they go in?

I may need to do something special for rowid tables to ensure that the rowid values in the transformed table match those from the old table.

For FTS tables associated with the table that is being transformed, should I automatically drop the old FTS table and recreate it against the new one or will it just magically continue to work after the table is renamed?

I'm going to sketch out a prototype of this new API design in that branch.

I'm rethinking the API design now. Maybe it could look like this:

To change the type of the author_id column from text to int: python books.transform({"author_id": int}) This would leave the existing columns alone, but would change the type of this column.

To rename author_id to author_identifier: python books.transform(rename={"author_id": "author_identifier"}) To drop a column: python books.transform(drop=["author_id"]) Since the parameters all operate on columns they don't need to be called drop_column and rename_column.

I've decided to call this table.transform() - I was over-thinking whether people would remember that .transform() actually transforms the table, but that's what documentation is for.

Work in progress in transform branch here: https://github.com/simonw/sqlite-utils/tree/transform

I'm going to add a second method .transform_table_sql(...) - which returns the SQL that would have been executed but does NOT execute it.

Advanced callers can use this to include their own additional steps in the same transaction - e.g. recreating views or triggers.

More importantly it gives me a useful hook for writing some unit tests against the generated SQL.

According to https://www.sqlite.org/lang_altertable.html#making_other_kinds_of_table_schema_changes the hardest bits to consider are how to deal with existing foreign key relationships, triggers and views.

I'm OK leaving views as an exercise for the caller - many of these transformations may not need any view changes at all.

Foreign key relationships are important: it should handle these automatically as effectively as possible.

Likewise trigger changes: need to think about what this means.

Work in progress: not quite right yet, I need smarter logic for how renamed columns are reflected in the generated INSERT INTO ... SELECT ... query: python def transform_table( self, columns=None, rename=None, change_type=None, pk=None, foreign_keys=None, column_order=None, not_null=None, defaults=None, hash_id=None, extracts=None, ): assert self.exists(), "Cannot transform a table that doesn't exist yet" columns = columns or self.columns_dict if rename is not None or change_type is not None: columns = {rename.get(key, key): change_type.get(key, value) for key, value in columns.items()} new_table_name = "{}_new_{}".format(self.name, os.urandom(6).hex()) previous_columns = set(self.columns_dict.keys()) with self.db.conn: columns = {name: value for (name, value) in columns.items()} new_table = self.db.create_table( new_table_name, columns, pk=pk, foreign_keys=foreign_keys, column_order=column_order, not_null=not_null, defaults=defaults, hash_id=hash_id, extracts=extracts, ) # Copy across data - but only for columns that exist in both new_columns = set(columns.keys()) columns_to_copy = new_columns.intersection(previous_columns) copy_sql = "INSERT INTO [{new_table}] ({new_cols}) SELECT {old_cols} FROM [{old_table}]".format( new_table=new_table_name, old_table=self.name, old_cols=", ".join("[{}]".format(col) for col in columns_to_copy), new_cols=", ".join("[{}]".format(rename.get(col, col)) for col in columns_to_copy), ) self.db.conn.execute(copy_sql) # Drop the old table self.db.conn.execute("DROP TABLE [{}]".format(self.name)) # Rename the new one self.db.conn.execute( "ALTER TABLE [{}] RENAME TO [{}]".format(new_table_name, self.name) ) return self

I can have a convenient change_type={...} parameter for changing column types too.

Thinking about the method signature: python def transform_table( self, columns, pk=None, foreign_keys=None, column_order=None, not_null=None, defaults=None, hash_id=None, extracts=None, ): This requires the caller to provide the exact set of columns for the new table.

It would be useful if this was optional - if you could omit the columns and have it automatically use the previous columns. This would let you change things like the primary key or the column order using the other arguments.

Even better: allow column renaming using an optional rename={...} argument:

python db["dogs"].transform_table(rename={"name": "dog_name"})

Don't forget this step:

If foreign key constraints are enabled, disable them using PRAGMA foreign_keys=OFF.

Since neither the term "transform" or "migrate" are used in the codebase at the moment, I think I'll go with .transform_table() - that leaves the term "migrate" available for any future database migrations system (similar to Django's).

Alternative possible names: - .transform_table() - .migrate() - .transform()

I'm torn between .migrate_table() and .transform_table().

I'm not so keen on that chained API - it's pretty complicated.

Here's an idea for a much simpler interface. Essentially it lets you say "take table X and migrate its contents to a new table with this structure - then atomically rename the tables to switch them": python db["mytable"].migrate_table({"id": int, "name": str"}, pk="id") The migrate_table() method would take the same exact signature as the table.create() method: https://github.com/simonw/sqlite-utils/blob/a236a6bc771a5a6a9d7e814f1986d461afc422d2/sqlite_utils/db.py#L615-L625

Ideally this would all happen in a single transaction, such that other processes talking to the database would not see any inconsistent state while the table copy was taking place. Need to confirm that this is possible. Also refs transactions thoughts in #121.

Idea: use a chained API to define a complex transition and then execute it all at once. For example: python db["mytable"].transform().rename("col1", "col_1") \ .change_type("col1", float) \ .execute()