Autogeneration

Note

this section discusses the internal API of Alembic as regards the autogeneration feature of the alembic revision command. This section is only useful for developers who wish to extend the capabilities of Alembic. For general documentation on the autogenerate feature, please see Auto Generating Migrations.

The autogeneration system has a wide degree of public API, including the following areas:

1. The ability to do a “diff” of a MetaData object against a database, and receive a data structure back. This structure is available either as a rudimentary list of changes, or as a MigrateOperation structure.

2. The ability to alter how the alembic revision command generates revision scripts, including support for multiple revision scripts generated in one pass.

3. The ability to add new operation directives to autogeneration, including custom schema/model comparison functions and revision script rendering.

Getting Diffs

The simplest API autogenerate provides is the “schema comparison” API; these are simple functions that will run all registered “comparison” functions between a MetaData object and a database backend to produce a structure showing how they differ. The two functions provided are compare_metadata(), which is more of the “legacy” function that produces diff tuples, and produce_migrations(), which produces a structure consisting of operation directives detailed in Operation Directives.

alembic.autogenerate.compare_metadata(context: MigrationContext, metadata: MetaData) → Any

Compare a database schema to that given in a MetaData instance.

The database connection is presented in the context of a MigrationContext object, which provides database connectivity as well as optional comparison functions to use for datatypes and server defaults - see the “autogenerate” arguments at EnvironmentContext.configure() for details on these.

The return format is a list of “diff” directives, each representing individual differences:

from alembic.migration import MigrationContext
from alembic.autogenerate import compare_metadata
from sqlalchemy.schema import SchemaItem
from sqlalchemy.types import TypeEngine
from sqlalchemy import (create_engine, MetaData, Column,
        Integer, String, Table, text)
import pprint

engine = create_engine("sqlite://")

with engine.begin() as conn:
    conn.execute(text('''
        create table foo (
            id integer not null primary key,
            old_data varchar,
            x integer
        )'''))

    conn.execute(text('''
        create table bar (
            data varchar
        )'''))

metadata = MetaData()
Table('foo', metadata,
    Column('id', Integer, primary_key=True),
    Column('data', Integer),
    Column('x', Integer, nullable=False)
)
Table('bat', metadata,
    Column('info', String)
)

mc = MigrationContext.configure(engine.connect())

diff = compare_metadata(mc, metadata)
pprint.pprint(diff, indent=2, width=20)

Output:

[ ( 'add_table',
    Table('bat', MetaData(bind=None),
        Column('info', String(), table=<bat>), schema=None)),
  ( 'remove_table',
    Table(u'bar', MetaData(bind=None),
        Column(u'data', VARCHAR(), table=<bar>), schema=None)),
  ( 'add_column',
    None,
    'foo',
    Column('data', Integer(), table=<foo>)),
  ( 'remove_column',
    None,
    'foo',
    Column(u'old_data', VARCHAR(), table=None)),
  [ ( 'modify_nullable',
      None,
      'foo',
      u'x',
      { 'existing_server_default': None,
        'existing_type': INTEGER()},
      True,
      False)]]

Parameters:

• context – a MigrationContext instance.

• metadata – a MetaData instance.

See also

produce_migrations() - produces a MigrationScript structure based on metadata comparison.

alembic.autogenerate.produce_migrations(context: MigrationContext, metadata: MetaData) → MigrationScript

Produce a MigrationScript structure based on schema comparison.

This function does essentially what compare_metadata() does, but then runs the resulting list of diffs to produce the full MigrationScript object. For an example of what this looks like, see the example in Customizing Revision Generation.

See also

compare_metadata() - returns more fundamental “diff” data from comparing a schema.

Customizing Revision Generation

The alembic revision command, also available programmatically via command.revision(), essentially produces a single migration script after being run. Whether or not the --autogenerate option was specified basically determines if this script is a blank revision script with empty upgrade() and downgrade() functions, or was produced with alembic operation directives as the result of autogenerate.

In either case, the system creates a full plan of what is to be done in the form of a MigrateOperation structure, which is then used to produce the script.

For example, suppose we ran alembic revision --autogenerate, and the end result was that it produced a new revision 'eced083f5df' with the following contents:

"""create the organization table."""

# revision identifiers, used by Alembic.
revision = 'eced083f5df'
down_revision = 'beafc7d709f'

from alembic import op
import sqlalchemy as sa


def upgrade():
    op.create_table(
        'organization',
        sa.Column('id', sa.Integer(), primary_key=True),
        sa.Column('name', sa.String(50), nullable=False)
    )
    op.add_column(
        'user',
        sa.Column('organization_id', sa.Integer())
    )
    op.create_foreign_key(
        'org_fk', 'user', 'organization', ['organization_id'], ['id']
    )

def downgrade():
    op.drop_constraint('org_fk', 'user')
    op.drop_column('user', 'organization_id')
    op.drop_table('organization')

The above script is generated by a MigrateOperation structure that looks like this:

from alembic.operations import ops
import sqlalchemy as sa

migration_script = ops.MigrationScript(
    'eced083f5df',
    ops.UpgradeOps(
        ops=[
            ops.CreateTableOp(
                'organization',
                [
                    sa.Column('id', sa.Integer(), primary_key=True),
                    sa.Column('name', sa.String(50), nullable=False)
                ]
            ),
            ops.ModifyTableOps(
                'user',
                ops=[
                    ops.AddColumnOp(
                        'user',
                        sa.Column('organization_id', sa.Integer())
                    ),
                    ops.CreateForeignKeyOp(
                        'org_fk', 'user', 'organization',
                        ['organization_id'], ['id']
                    )
                ]
            )
        ]
    ),
    ops.DowngradeOps(
        ops=[
            ops.ModifyTableOps(
                'user',
                ops=[
                    ops.DropConstraintOp('org_fk', 'user'),
                    ops.DropColumnOp('user', 'organization_id')
                ]
            ),
            ops.DropTableOp('organization')
        ]
    ),
    message='create the organization table.'
)

When we deal with a MigrationScript structure, we can render the upgrade/downgrade sections into strings for debugging purposes using the render_python_code() helper function:

from alembic.autogenerate import render_python_code
print(render_python_code(migration_script.upgrade_ops))

Renders:

### commands auto generated by Alembic - please adjust! ###
    op.create_table('organization',
    sa.Column('id', sa.Integer(), nullable=False),
    sa.Column('name', sa.String(length=50), nullable=False),
    sa.PrimaryKeyConstraint('id')
    )
    op.add_column('user', sa.Column('organization_id', sa.Integer(), nullable=True))
    op.create_foreign_key('org_fk', 'user', 'organization', ['organization_id'], ['id'])
    ### end Alembic commands ###

Given that structures like the above are used to generate new revision files, and that we’d like to be able to alter these as they are created, we then need a system to access this structure when the command.revision() command is used. The EnvironmentContext.configure.process_revision_directives parameter gives us a way to alter this. This is a function that is passed the above structure as generated by Alembic, giving us a chance to alter it. For example, if we wanted to put all the “upgrade” operations into a certain branch, and we wanted our script to not have any “downgrade” operations at all, we could build an extension as follows, illustrated within an env.py script:

def process_revision_directives(context, revision, directives):
    script = directives[0]

    # set specific branch
    script.head = "mybranch@head"

    # erase downgrade operations
    script.downgrade_ops.ops[:] = []

# ...

def run_migrations_online():

    # ...
    with engine.connect() as connection:

        context.configure(
            connection=connection,
            target_metadata=target_metadata,
            process_revision_directives=process_revision_directives)

        with context.begin_transaction():
            context.run_migrations()

Above, the directives argument is a Python list. We may alter the given structure within this list in-place, or replace it with a new structure consisting of zero or more MigrationScript directives. The command.revision() command will then produce scripts corresponding to whatever is in this list.

See also

More examples of using EnvironmentContext.configure.process_revision_directives

Don’t Generate Empty Migrations with Autogenerate

Don’t emit DROP INDEX when the table is to be dropped as well

Apply Custom Sorting to Table Columns within CREATE TABLE

alembic.autogenerate.render_python_code(up_or_down_op: UpgradeOps, sqlalchemy_module_prefix: str = 'sa.', alembic_module_prefix: str = 'op.', render_as_batch: bool = False, imports: Tuple[str, ...] = (), render_item: None = None, migration_context: MigrationContext | None = None) → str

Render Python code given an UpgradeOps or DowngradeOps object.

This is a convenience function that can be used to test the autogenerate output of a user-defined MigrationScript structure.

Fine-Grained Autogenerate Generation with Rewriters

The preceding example illustrated how we can make a simple change to the structure of the operation directives to produce new autogenerate output. For the case where we want to affect very specific parts of the autogenerate stream, we can make a function for EnvironmentContext.configure.process_revision_directives which traverses through the whole MigrationScript structure, locates the elements we care about and modifies them in-place as needed. However, to reduce the boilerplate associated with this task, we can use the Rewriter object to make this easier. Rewriter gives us an object that we can pass directly to EnvironmentContext.configure.process_revision_directives which we can also attach handler functions onto, keyed to specific types of constructs.

Below is an example where we rewrite ops.AddColumnOp directives; based on whether or not the new column is “nullable”, we either return the existing directive, or we return the existing directive with the nullable flag changed, inside of a list with a second directive to alter the nullable flag in a second step:

# ... fragmented env.py script ....

from alembic.autogenerate import rewriter
from alembic.operations import ops

writer = rewriter.Rewriter()

@writer.rewrites(ops.AddColumnOp)
def add_column(context, revision, op):
    if op.column.nullable:
        return op
    else:
        op.column.nullable = True
        return [
            op,
            ops.AlterColumnOp(
                op.table_name,
                op.column.name,
                modify_nullable=False,
                existing_type=op.column.type,
            )
        ]

# ... later ...

def run_migrations_online():
    # ...

    with connectable.connect() as connection:
        context.configure(
            connection=connection,
            target_metadata=target_metadata,
            process_revision_directives=writer
        )

        with context.begin_transaction():
            context.run_migrations()

Above, in a full ops.MigrationScript structure, the AddColumn directives would be present within the paths MigrationScript->UpgradeOps->ModifyTableOps and MigrationScript->DowngradeOps->ModifyTableOps. The Rewriter handles traversing into these structures as well as rewriting them as needed so that we only need to code for the specific object we care about.

class alembic.autogenerate.rewriter.Rewriter

A helper object that allows easy ‘rewriting’ of ops streams.

The Rewriter object is intended to be passed along to the EnvironmentContext.configure.process_revision_directives parameter in an env.py script. Once constructed, any number of “rewrites” functions can be associated with it, which will be given the opportunity to modify the structure without having to have explicit knowledge of the overall structure.

The function is passed the MigrationContext object and revision tuple that are passed to the Environment Context.configure.process_revision_directives function normally, and the third argument is an individual directive of the type noted in the decorator. The function has the choice of returning a single op directive, which normally can be the directive that was actually passed, or a new directive to replace it, or a list of zero or more directives to replace it.

See also

Fine-Grained Autogenerate Generation with Rewriters - usage example

chain(other: Rewriter) → Rewriter

Produce a “chain” of this Rewriter to another.

This allows two rewriters to operate serially on a stream, e.g.:

writer1 = autogenerate.Rewriter()
writer2 = autogenerate.Rewriter()

@writer1.rewrites(ops.AddColumnOp)
def add_column_nullable(context, revision, op):
    op.column.nullable = True
    return op

@writer2.rewrites(ops.AddColumnOp)
def add_column_idx(context, revision, op):
    idx_op = ops.CreateIndexOp(
        'ixc', op.table_name, [op.column.name])
    return [
        op,
        idx_op
    ]

writer = writer1.chain(writer2)

Parameters:

other – a Rewriter instance

Returns:

a new Rewriter that will run the operations of this writer, then the “other” writer, in succession.

rewrites(operator: Type[AddColumnOp] | Type[MigrateOperation] | Type[AlterColumnOp] | Type[CreateTableOp] | Type[ModifyTableOps]) → Callable

Register a function as rewriter for a given type.

The function should receive three arguments, which are the MigrationContext, a revision tuple, and an op directive of the type indicated. E.g.:

@writer1.rewrites(ops.AddColumnOp)
def add_column_nullable(context, revision, op):
    op.column.nullable = True
    return op

Revision Generation with Multiple Engines / run_migrations() calls

A lesser-used technique which allows autogenerated migrations to run against multiple database backends at once, generating changes into a single migration script, is illustrated in the provided multidb template. This template features a special env.py which iterates through multiple Engine instances and calls upon MigrationContext.run_migrations() for each:

for name, rec in engines.items():
    logger.info("Migrating database %s" % name)
    context.configure(
        connection=rec['connection'],
        upgrade_token="%s_upgrades" % name,
        downgrade_token="%s_downgrades" % name,
        target_metadata=target_metadata.get(name)
    )
    context.run_migrations(engine_name=name)

Above, MigrationContext.run_migrations() is run multiple times, once for each engine. Within the context of autogeneration, each time the method is called the upgrade_token and downgrade_token parameters are changed, so that the collection of template variables gains distinct entries for each engine, which are then referred to explicitly within script.py.mako.

In terms of the EnvironmentContext.configure.process_revision_directives hook, the behavior here is that the process_revision_directives hook is invoked multiple times, once for each call to context.run_migrations(). This means that if a multi-run_migrations() approach is to be combined with the process_revision_directives hook, care must be taken to use the hook appropriately.

The first point to note is that when a second call to run_migrations() occurs, the .upgrade_ops and .downgrade_ops attributes are converted into Python lists, and new UpgradeOps and DowngradeOps objects are appended to these lists. Each UpgradeOps and DowngradeOps object maintains an .upgrade_token and a .downgrade_token attribute respectively, which serves to render their contents into the appropriate template token.

For example, a multi-engine run that has the engine names engine1 and engine2 will generate tokens of engine1_upgrades, engine1_downgrades, engine2_upgrades and engine2_downgrades as it runs. The resulting migration structure would look like this:

from alembic.operations import ops
import sqlalchemy as sa

migration_script = ops.MigrationScript(
    'eced083f5df',
    [
        ops.UpgradeOps(
            ops=[
                # upgrade operations for "engine1"
            ],
            upgrade_token="engine1_upgrades"
        ),
        ops.UpgradeOps(
            ops=[
                # upgrade operations for "engine2"
            ],
            upgrade_token="engine2_upgrades"
        ),
    ],
    [
        ops.DowngradeOps(
            ops=[
                # downgrade operations for "engine1"
            ],
            downgrade_token="engine1_downgrades"
        ),
        ops.DowngradeOps(
            ops=[
                # downgrade operations for "engine2"
            ],
            downgrade_token="engine2_downgrades"
        )
    ],
    message='migration message'
)

Given the above, the following guidelines should be considered when the env.py script calls upon MigrationContext.run_migrations() multiple times when running autogenerate:

• If the process_revision_directives hook aims to add elements based on inspection of the current database / connection, it should do its operation on each iteration. This is so that each time the hook runs, the database is available.

• Alternatively, if the process_revision_directives hook aims to modify the list of migration directives in place, this should be called only on the last iteration. This is so that the hook isn’t being given an ever-growing structure each time which it has already modified previously.

• The Rewriter object, if used, should be called only on the last iteration, because it will always deliver all directives every time, so again to avoid double/triple/etc. processing of directives it should be called only when the structure is complete.

• The MigrationScript.upgrade_ops_list and MigrationScript.downgrade_ops_list attributes should be consulted when referring to the collection of UpgradeOps and DowngradeOps objects.

Autogenerating Custom Operation Directives

In the section Operation Plugins, we talked about adding new subclasses of MigrateOperation in order to add new op. directives. In the preceding section Customizing Revision Generation, we also learned that these same MigrateOperation structures are at the base of how the autogenerate system knows what Python code to render. Using this knowledge, we can create additional functions that plug into the autogenerate system so that our new operations can be generated into migration scripts when alembic revision --autogenerate is run.

The following sections will detail an example of this using the the CreateSequenceOp and DropSequenceOp directives we created in Operation Plugins, which correspond to the SQLAlchemy Sequence construct.

Tracking our Object with the Model

The basic job of an autogenerate comparison function is to inspect a series of objects in the database and compare them against a series of objects defined in our model. By “in our model”, we mean anything defined in Python code that we want to track, however most commonly we’re talking about a series of Table objects present in a MetaData collection.

Let’s propose a simple way of seeing what Sequence objects we want to ensure exist in the database when autogenerate runs. While these objects do have some integrations with Table and MetaData already, let’s assume they don’t, as the example here intends to illustrate how we would do this for most any kind of custom construct. We associate the object with the info collection of MetaData, which is a dictionary we can use for anything, which we also know will be passed to the autogenerate process:

from sqlalchemy.schema import Sequence

def add_sequence_to_model(sequence, metadata):
    metadata.info.setdefault("sequences", set()).add(
        (sequence.schema, sequence.name)
    )

my_seq = Sequence("my_sequence")
add_sequence_to_model(my_seq, model_metadata)

The info dictionary is a good place to put things that we want our autogeneration routines to be able to locate, which can include any object such as custom DDL objects representing views, triggers, special constraints, or anything else we want to support.

Registering a Comparison Function

We now need to register a comparison hook, which will be used to compare the database to our model and produce CreateSequenceOp and DropSequenceOp directives to be included in our migration script. Note that we are assuming a Postgresql backend:

from alembic.autogenerate import comparators

@comparators.dispatch_for("schema")
def compare_sequences(autogen_context, upgrade_ops, schemas):
    all_conn_sequences = set()

    for sch in schemas:

        all_conn_sequences.update([
            (sch, row[0]) for row in
            autogen_context.connection.execute(
                "SELECT relname FROM pg_class c join "
                "pg_namespace n on n.oid=c.relnamespace where "
                "relkind='S' and n.nspname=%(nspname)s",

                # note that we consider a schema of 'None' in our
                # model to be the "default" name in the PG database;
                # this usually is the name 'public'
                nspname=autogen_context.dialect.default_schema_name
                if sch is None else sch
            )
        ])

    # get the collection of Sequence objects we're storing with
    # our MetaData
    metadata_sequences = autogen_context.metadata.info.setdefault(
        "sequences", set())

    # for new names, produce CreateSequenceOp directives
    for sch, name in metadata_sequences.difference(all_conn_sequences):
        upgrade_ops.ops.append(
            CreateSequenceOp(name, schema=sch)
        )

    # for names that are going away, produce DropSequenceOp
    # directives
    for sch, name in all_conn_sequences.difference(metadata_sequences):
        upgrade_ops.ops.append(
            DropSequenceOp(name, schema=sch)
        )

Above, we’ve built a new function compare_sequences() and registered it as a “schema” level comparison function with autogenerate. The job that it performs is that it compares the list of sequence names present in each database schema with that of a list of sequence names that we are maintaining in our MetaData object.

When autogenerate completes, it will have a series of CreateSequenceOp and DropSequenceOp directives in the list of “upgrade” operations; the list of “downgrade” operations is generated directly from these using the CreateSequenceOp.reverse() and DropSequenceOp.reverse() methods that we’ve implemented on these objects.

The registration of our function at the scope of “schema” means our autogenerate comparison function is called outside of the context of any specific table or column. The three available scopes are “schema”, “table”, and “column”, summarized as follows:

• Schema level - these hooks are passed a AutogenContext, an UpgradeOps collection, and a collection of string schema names to be operated upon. If the UpgradeOps collection contains changes after all hooks are run, it is included in the migration script:

  @comparators.dispatch_for("schema")
  def compare_schema_level(autogen_context, upgrade_ops, schemas):
      pass
  

• Table level - these hooks are passed a AutogenContext, a ModifyTableOps collection, a schema name, table name, a Table reflected from the database if any or None, and a Table present in the local MetaData. If the ModifyTableOps collection contains changes after all hooks are run, it is included in the migration script:

  @comparators.dispatch_for("table")
  def compare_table_level(autogen_context, modify_ops,
      schemaname, tablename, conn_table, metadata_table):
      pass
  

• Column level - these hooks are passed a AutogenContext, an AlterColumnOp object, a schema name, table name, column name, a Column reflected from the database and a Column present in the local table. If the AlterColumnOp contains changes after all hooks are run, it is included in the migration script; a “change” is considered to be present if any of the modify_ attributes are set to a non-default value, or there are any keys in the .kw collection with the prefix "modify_":

  @comparators.dispatch_for("column")
  def compare_column_level(autogen_context, alter_column_op,
      schemaname, tname, cname, conn_col, metadata_col):
      pass
  

The AutogenContext passed to these hooks is documented below.

class alembic.autogenerate.api.AutogenContext(migration_context: MigrationContext, metadata: MetaData | None = None, opts: dict | None = None, autogenerate: bool = True)

Maintains configuration and state that’s specific to an autogenerate operation.

connection: Connection | None = None

The Connection object currently connected to the database backend being compared.

This is obtained from the MigrationContext.bind and is ultimately set up in the env.py script.

dialect: Dialect | None = None

The Dialect object currently in use.

This is normally obtained from the dialect attribute.

imports: Set[str] = None

A set() which contains string Python import directives.

The directives are to be rendered into the ${imports} section of a script template. The set is normally empty and can be modified within hooks such as the EnvironmentContext.configure.render_item hook.

See also

Affecting the Rendering of Types Themselves

metadata: MetaData | None = None

The MetaData object representing the destination.

This object is the one that is passed within env.py to the EnvironmentContext.configure.target_metadata parameter. It represents the structure of Table and other objects as stated in the current database model, and represents the destination structure for the database being examined.

While the MetaData object is primarily known as a collection of Table objects, it also has an info dictionary that may be used by end-user schemes to store additional schema-level objects that are to be compared in custom autogeneration schemes.

migration_context: MigrationContext = None

The MigrationContext established by the env.py script.

run_filters(object_: Table | Index | Column | UniqueConstraint | ForeignKeyConstraint, name: str | None, type_: str, reflected: bool, compare_to: Table | Index | Column | UniqueConstraint | None) → bool

Run the context’s object filters and return True if the targets should be part of the autogenerate operation.

This method should be run for every kind of object encountered within an autogenerate operation, giving the environment the chance to filter what objects should be included in the comparison. The filters here are produced directly via the EnvironmentContext.configure.include_object parameter.

run_name_filters(name: str | None, type_: str, parent_names: Dict[str, str | None]) → bool

Run the context’s name filters and return True if the targets should be part of the autogenerate operation.

This method should be run for every kind of name encountered within the reflection side of an autogenerate operation, giving the environment the chance to filter what names should be reflected as database objects. The filters here are produced directly via the EnvironmentContext.configure.include_name parameter.

run_object_filters(object_: Table | Index | Column | UniqueConstraint | ForeignKeyConstraint, name: str | None, type_: str, reflected: bool, compare_to: Table | Index | Column | UniqueConstraint | None) → bool

Run the context’s object filters and return True if the targets should be part of the autogenerate operation.

This method should be run for every kind of object encountered within an autogenerate operation, giving the environment the chance to filter what objects should be included in the comparison. The filters here are produced directly via the EnvironmentContext.configure.include_object parameter.

sorted_tables

Return an aggregate of the MetaData.sorted_tables collection(s).

For a sequence of MetaData objects, this concatenates the MetaData.sorted_tables collection for each individual MetaData in the order of the sequence. It does not collate the sorted tables collections.

table_key_to_table

Return an aggregate of the MetaData.tables dictionaries.

The MetaData.tables collection is a dictionary of table key to Table; this method aggregates the dictionary across multiple MetaData objects into one dictionary.

Duplicate table keys are not supported; if two MetaData objects contain the same table key, an exception is raised.

Creating a Render Function

The second autogenerate integration hook is to provide a “render” function; since the autogenerate system renders Python code, we need to build a function that renders the correct “op” instructions for our directive:

from alembic.autogenerate import renderers

@renderers.dispatch_for(CreateSequenceOp)
def render_create_sequence(autogen_context, op):
    return "op.create_sequence(%r, **%r)" % (
        op.sequence_name,
        {"schema": op.schema}
    )


@renderers.dispatch_for(DropSequenceOp)
def render_drop_sequence(autogen_context, op):
    return "op.drop_sequence(%r, **%r)" % (
        op.sequence_name,
        {"schema": op.schema}
    )

The above functions will render Python code corresponding to the presence of CreateSequenceOp and DropSequenceOp instructions in the list that our comparison function generates.

Running It

All the above code can be organized however the developer sees fit; the only thing that needs to make it work is that when the Alembic environment env.py is invoked, it either imports modules which contain all the above routines, or they are locally present, or some combination thereof.

If we then have code in our model (which of course also needs to be invoked when env.py runs!) like this:

from sqlalchemy.schema import Sequence

my_seq_1 = Sequence("my_sequence_1")
add_sequence_to_model(my_seq_1, target_metadata)

When we first run alembic revision --autogenerate, we’ll see this in our migration file:

def upgrade():
    ### commands auto generated by Alembic - please adjust! ###
    op.create_sequence('my_sequence_1', **{'schema': None})
    ### end Alembic commands ###


def downgrade():
    ### commands auto generated by Alembic - please adjust! ###
    op.drop_sequence('my_sequence_1', **{'schema': None})
    ### end Alembic commands ###

These are our custom directives that will invoke when alembic upgrade or alembic downgrade is run.