Auto Generating Migrations¶
Alembic can view the status of the database and compare against the table metadata
in the application, generating the “obvious” migrations based on a comparison. This
is achieved using the --autogenerate
option to the alembic revision
command,
which places so-called candidate migrations into our new migrations file. We
review and modify these by hand as needed, then proceed normally.
To use autogenerate, we first need to modify our env.py
so that it gets access
to a table metadata object that contains the target. Suppose our application
has a declarative base
in myapp.mymodel
. This base contains a MetaData
object which
contains Table
objects defining our database. We make sure this
is loaded in env.py
and then passed to EnvironmentContext.configure()
via the
target_metadata
argument. The env.py
sample script used in the
generic template already has a
variable declaration near the top for our convenience, where we replace None
with our MetaData
. Starting with:
# add your model's MetaData object here
# for 'autogenerate' support
# from myapp import mymodel
# target_metadata = mymodel.Base.metadata
target_metadata = None
we change to:
from myapp.mymodel import Base
target_metadata = Base.metadata
Note
The above example refers to the generic alembic env.py template, e.g.
the one created by default when calling upon alembic init
, and not
the special-use templates such as multidb
. Please consult the source
code and comments within the env.py
script directly for specific
guidance on where and how the autogenerate metadata is established.
If we look later in the script, down in run_migrations_online()
,
we can see the directive passed to EnvironmentContext.configure()
:
def run_migrations_online():
engine = engine_from_config(
config.get_section(config.config_ini_section), prefix='sqlalchemy.')
with engine.connect() as connection:
context.configure(
connection=connection,
target_metadata=target_metadata
)
with context.begin_transaction():
context.run_migrations()
We can then use the alembic revision
command in conjunction with the
--autogenerate
option. Suppose
our MetaData
contained a definition for the account
table,
and the database did not. We’d get output like:
$ alembic revision --autogenerate -m "Added account table"
INFO [alembic.context] Detected added table 'account'
Generating /path/to/foo/alembic/versions/27c6a30d7c24.py...done
We can then view our file 27c6a30d7c24.py
and see that a rudimentary migration
is already present:
"""empty message
Revision ID: 27c6a30d7c24
Revises: None
Create Date: 2011-11-08 11:40:27.089406
"""
# revision identifiers, used by Alembic.
revision = '27c6a30d7c24'
down_revision = None
from alembic import op
import sqlalchemy as sa
def upgrade():
### commands auto generated by Alembic - please adjust! ###
op.create_table(
'account',
sa.Column('id', sa.Integer()),
sa.Column('name', sa.String(length=50), nullable=False),
sa.Column('description', sa.VARCHAR(200)),
sa.Column('last_transaction_date', sa.DateTime()),
sa.PrimaryKeyConstraint('id')
)
### end Alembic commands ###
def downgrade():
### commands auto generated by Alembic - please adjust! ###
op.drop_table("account")
### end Alembic commands ###
The migration hasn’t actually run yet, of course. We do that via the usual upgrade
command. We should also go into our migration file and alter it as needed, including
adjustments to the directives as well as the addition of other directives which these may
be dependent on - specifically data changes in between creates/alters/drops.
What does Autogenerate Detect (and what does it not detect?)¶
The vast majority of user issues with Alembic centers on the topic of what kinds of changes autogenerate can and cannot detect reliably, as well as how it renders Python code for what it does detect. It is critical to note that autogenerate is not intended to be perfect. It is always necessary to manually review and correct the candidate migrations that autogenerate produces. The feature is getting more and more comprehensive and error-free as releases continue, but one should take note of the current limitations.
Autogenerate will detect:
Table additions, removals.
Column additions, removals.
Change of nullable status on columns.
Basic changes in indexes and explicitly-named unique constraints
Basic changes in foreign key constraints
Autogenerate can optionally detect:
Change of column type. This will occur by default unless the parameter
EnvironmentContext.configure.compare_type
is set toFalse
. The default implementation will reliably detect major changes, such as betweenNumeric
andString
, as well as accommodate for the types generated by SQLAlchemy’s “generic” types such asBoolean
. Arguments that are shared between both types, such as length and precision values, will also be compared. If either the metadata type or database type has additional arguments beyond that of the other type, these are not compared, such as if one numeric type featured a “scale” and other type did not, this would be seen as the backing database not supporting the value, or reporting on a default that the metadata did not specify.The type comparison logic is fully extensible as well; see Comparing Types for details.
Change of server default. This will occur if you set the
EnvironmentContext.configure.compare_server_default
parameter toTrue
, or to a custom callable function. This feature works well for simple cases but cannot always produce accurate results. The Postgresql backend will actually invoke the “detected” and “metadata” values against the database to determine equivalence. The feature is off by default so that it can be tested on the target schema first. Like type comparison, it can also be customized by passing a callable; see the function’s documentation for details.
Autogenerate can not detect:
Changes of table name. These will come out as an add/drop of two different tables, and should be hand-edited into a name change instead.
Changes of column name. Like table name changes, these are detected as a column add/drop pair, which is not at all the same as a name change.
Anonymously named constraints. Give your constraints a name, e.g.
UniqueConstraint('col1', 'col2', name="my_name")
. See the section The Importance of Naming Constraints for background on how to configure automatic naming schemes for constraints.Special SQLAlchemy types such as
Enum
when generated on a backend which doesn’t support ENUM directly - this because the representation of such a type in the non-supporting database, i.e. a CHAR+ CHECK constraint, could be any kind of CHAR+CHECK. For SQLAlchemy to determine that this is actually an ENUM would only be a guess, something that’s generally a bad idea. To implement your own “guessing” function here, use thesqlalchemy.events.DDLEvents.column_reflect()
event to detect when a CHAR (or whatever the target type is) is reflected, and change it to an ENUM (or whatever type is desired) if it is known that that’s the intent of the type. Thesqlalchemy.events.DDLEvents.after_parent_attach()
can be used within the autogenerate process to intercept and un-attach unwanted CHECK constraints.
Autogenerate can’t currently, but will eventually detect:
Some free-standing constraint additions and removals may not be supported, including PRIMARY KEY, EXCLUDE, CHECK; these are not necessarily implemented within the autogenerate detection system and also may not be supported by the supporting SQLAlchemy dialect.
Sequence additions, removals - not yet implemented.
Notable 3-rd party libraries that extend the built-in Alembic autogenerate functionality¶
alembic-utils A library that adds autogenerate support PostgreSQL functions, views, triggers, etc.
alembic-postgresql-enum A library that adds autogenerate support for creation, alteration and deletion of Enums in PostgreSQL.
Autogenerating Multiple MetaData collections¶
The target_metadata
collection may also be defined as a sequence
if an application has multiple MetaData
collections involved:
from myapp.mymodel1 import Model1Base
from myapp.mymodel2 import Model2Base
target_metadata = [Model1Base.metadata, Model2Base.metadata]
The sequence of MetaData
collections will be
consulted in order during the autogenerate process. Note that each
MetaData
must contain unique table keys
(e.g. the “key” is the combination of the table’s name and schema);
if two MetaData
objects contain a table
with the same schema/name combination, an error is raised.
Controlling What to be Autogenerated¶
The autogenerate process scans across all table objects within the database that is referred towards by the current database connection in use.
The list of objects that are scanned in the target database connection include:
The “default” schema currently referred towards by the database connection.
If the
EnvironmentContext.configure.include_schemas
is set toTrue
, all non-default “schemas”, which are those names returned by theget_schema_names()
method ofInspector
. The SQLAlchemy document Specifying the Schema Name discusses the concept of a “schema” in detail.Within each “schema”, all tables present are scanned using the
get_table_names()
method ofInspector
.Within each “table”, most sub-objects of the each
Table
construct are scanned, including columns and some forms of constraints. This process ultimately involves the use of methods onInspector
includingget_columns()
,get_indexes()
,get_unique_constraints()
,get_foreign_keys()
(as of this writing, CHECK constraints and primary key constraints are not yet included).
Omitting Schema Names from the Autogenerate Process¶
As the above set of database objects are typically to be compared to the contents of
a single MetaData
object, particularly when the
EnvironmentContext.configure.include_schemas
flag is enabled
there is an important need to filter out unwanted “schemas”, which for some
database backends might be the list of all the databases present. This
filtering is best performed using the EnvironmentContext.configure.include_name
hook, which provides for a callable that may return a boolean true/false
indicating if a particular schema name should be included:
def include_name(name, type_, parent_names):
if type_ == "schema":
# note this will not include the default schema
return name in ["schema_one", "schema_two"]
else:
return True
context.configure(
# ...
include_schemas = True,
include_name = include_name
)
Above, when the list of schema names is first retrieved, the names will be
filtered through the above include_name
function so that only schemas
named "schema_one"
and "schema_two"
will be considered by the
autogenerate process.
In order to include the default schema, that is, the schema that is
referred towards by the database connection without any explicit
schema being specified, the name passed to the hook is None
. To alter
our above example to also include the default schema, we compare to
None
as well:
def include_name(name, type_, parent_names):
if type_ == "schema":
# this **will* include the default schema
return name in [None, "schema_one", "schema_two"]
else:
return True
context.configure(
# ...
include_schemas = True,
include_name = include_name
)
Omitting Table Names from the Autogenerate Process¶
The EnvironmentContext.configure.include_name
hook is also
most appropriate to limit the names of tables in the target database
to be considered. If a target database has many tables that are not
part of the MetaData
, the autogenerate process
will normally assume these are extraneous tables in the database to be
dropped, and it will generate a Operations.drop_table()
operation
for each. To prevent this, the EnvironmentContext.configure.include_name
hook may be used to search for each name within the
tables
collection of the
MetaData
object and ensure names
which aren’t present are not included:
target_metadata = MyModel.metadata
def include_name(name, type_, parent_names):
if type_ == "table":
return name in target_metadata.tables
else:
return True
context.configure(
# ...
target_metadata = target_metadata,
include_name = include_name,
include_schemas = False
)
The above example is limited to table names present in the default schema only.
In order to search within a MetaData
collection for
schema-qualified table names as well, a table present in the non
default schema will be present under a name of the form
<schemaname>.<tablename>
. The
EnvironmentContext.configure.include_name
hook will present
this schema name on a per-tablename basis in the parent_names
dictionary,
using the key "schema_name"
that refers to the name of the
schema currently being considered, or None
if the schema is the default
schema of the database connection:
# example fragment
if parent_names["schema_name"] is None:
return name in target_metadata.tables
else:
# build out schema-qualified name explicitly...
return (
"%s.%s" % (parent_names["schema_name"], name) in
target_metadata.tables
)
However more simply, the parent_names
dictionary will also include
the dot-concatenated name already constructed under the key
"schema_qualified_table_name"
, which will also be suitably formatted
for tables in the default schema as well with the dot omitted. So the
full example of omitting tables with schema support may look like:
target_metadata = MyModel.metadata
def include_name(name, type_, parent_names):
if type_ == "schema":
return name in [None, "schema_one", "schema_two"]
elif type_ == "table":
# use schema_qualified_table_name directly
return (
parent_names["schema_qualified_table_name"] in
target_metadata.tables
)
else:
return True
context.configure(
# ...
target_metadata = target_metadata,
include_name = include_name,
include_schemas = True
)
The parent_names
dictionary will also include the key "table_name"
when the name being considered is that of a column or constraint object
local to a particular table.
The EnvironmentContext.configure.include_name
hook only refers
to reflected objects, and not those located within the target
MetaData
collection. For more fine-grained
rules that include both MetaData
and reflected
object, the EnvironmentContext.configure.include_object
hook
discussed in the next section is more appropriate.
Omitting Based on Object¶
The EnvironmentContext.configure.include_object
hook provides
for object-level inclusion/exclusion rules based on the Table
object being reflected as well as the elements within it. This hook can
be used to limit objects both from the local MetaData
collection as well as from the target database. The limitation is that when
it reports on objects in the database, it will have fully reflected that object,
which can be expensive if a large number of objects will be omitted. The
example below refers to a fine-grained rule that will skip changes on
Column
objects that have a user-defined flag
skip_autogenerate
placed into the info
dictionary:
def include_object(object, name, type_, reflected, compare_to):
if (type_ == "column" and
not reflected and
object.info.get("skip_autogenerate", False)):
return False
else:
return True
context.configure(
# ...
include_object = include_object
)
Comparing and Rendering Types¶
The area of autogenerate’s behavior of comparing and rendering Python-based type objects in migration scripts presents a challenge, in that there’s a very wide variety of types to be rendered in scripts, including those part of SQLAlchemy as well as user-defined types. A few options are given to help out with this task.
Controlling the Module Prefix¶
When types are rendered, they are generated with a module prefix, so
that they are available based on a relatively small number of imports.
The rules for what the prefix is is based on the kind of datatype as well
as configurational settings. For example, when Alembic renders SQLAlchemy
types, it will by default prefix the type name with the prefix sa.
:
Column("my_column", sa.Integer())
The use of the sa.
prefix is controllable by altering the value
of EnvironmentContext.configure.sqlalchemy_module_prefix
:
def run_migrations_online():
# ...
context.configure(
connection=connection,
target_metadata=target_metadata,
sqlalchemy_module_prefix="sqla.",
# ...
)
# ...
In either case, the sa.
prefix, or whatever prefix is desired, should
also be included in the imports section of script.py.mako
; it also
defaults to import sqlalchemy as sa
.
For user-defined types, that is, any custom type that
is not within the sqlalchemy.
module namespace, by default Alembic will
use the value of __module__ for the custom type:
Column("my_column", myapp.models.utils.types.MyCustomType())
The imports for the above type again must be made present within the migration,
either manually, or by adding it to script.py.mako
.
The above custom type has a long and cumbersome name based on the use
of __module__
directly, which also implies that lots of imports would
be needed in order to accommodate lots of types. For this reason, it is
recommended that user-defined types used in migration scripts be made
available from a single module. Suppose we call it myapp.migration_types
:
# myapp/migration_types.py
from myapp.models.utils.types import MyCustomType
We can first add an import for migration_types
to our script.py.mako
:
from alembic import op
import sqlalchemy as sa
import myapp.migration_types
${imports if imports else ""}
We then override Alembic’s use of __module__
by providing a fixed
prefix, using the EnvironmentContext.configure.user_module_prefix
option:
def run_migrations_online():
# ...
context.configure(
connection=connection,
target_metadata=target_metadata,
user_module_prefix="myapp.migration_types.",
# ...
)
# ...
Above, we now would get a migration like:
Column("my_column", myapp.migration_types.MyCustomType())
Now, when we inevitably refactor our application to move MyCustomType
somewhere else, we only need modify the myapp.migration_types
module,
instead of searching and replacing all instances within our migration scripts.
Affecting the Rendering of Types Themselves¶
The methodology Alembic uses to generate SQLAlchemy and user-defined type constructs
as Python code is plain old __repr__()
. SQLAlchemy’s built-in types
for the most part have a __repr__()
that faithfully renders a
Python-compatible constructor call, but there are some exceptions, particularly
in those cases when a constructor accepts arguments that aren’t compatible
with __repr__()
, such as a pickling function.
When building a custom type that will be rendered into a migration script,
it is often necessary to explicitly give the type a __repr__()
that will
faithfully reproduce the constructor for that type. This, in combination
with EnvironmentContext.configure.user_module_prefix
, is usually
enough. However, if additional behaviors are needed, a more comprehensive
hook is the EnvironmentContext.configure.render_item
option.
This hook allows one to provide a callable function within env.py
that will fully take
over how a type is rendered, including its module prefix:
def render_item(type_, obj, autogen_context):
"""Apply custom rendering for selected items."""
if type_ == 'type' and isinstance(obj, MySpecialType):
return "mypackage.%r" % obj
# default rendering for other objects
return False
def run_migrations_online():
# ...
context.configure(
connection=connection,
target_metadata=target_metadata,
render_item=render_item,
# ...
)
# ...
In the above example, we’d ensure our MySpecialType
includes an appropriate
__repr__()
method, which is invoked when we call it against "%r"
.
The callable we use for EnvironmentContext.configure.render_item
can also add imports to our migration script. The AutogenContext
passed in
contains a datamember called AutogenContext.imports
, which is a Python
set()
for which we can add new imports. For example, if MySpecialType
were in a module called mymodel.types
, we can add the import for it
as we encounter the type:
def render_item(type_, obj, autogen_context):
"""Apply custom rendering for selected items."""
if type_ == 'type' and isinstance(obj, MySpecialType):
# add import for this type
autogen_context.imports.add("from mymodel import types")
return "types.%r" % obj
# default rendering for other objects
return False
The finished migration script will include our imports where the
${imports}
expression is used, producing output such as:
from alembic import op
import sqlalchemy as sa
from mymodel import types
def upgrade():
op.add_column('sometable', Column('mycolumn', types.MySpecialType()))
Comparing Types¶
The default type comparison logic will work for SQLAlchemy built in types as
well as basic user defined types. This logic is enabled by default.
It can be disabled by setting the
EnvironmentContext.configure.compare_type
to False
:
context.configure(
# ...
compare_type = False
)
Changed in version 1.12.0: The default value of
EnvironmentContext.configure.compare_type
has been changed
to True
.
Note
The default type comparison logic (which is end-user extensible) currently (as of Alembic version 1.4.0) works by comparing the generated SQL for a column. It does this in two steps-
First, it compares the outer type of each column such as
VARCHAR
orTEXT
. Dialect implementations can have synonyms that are considered equivalent, this is because some databases support types by converting them to another type. For example, NUMERIC and DECIMAL are considered equivalent on all backends, while on the Oracle backend the additional synonyms BIGINT, INTEGER, NUMBER, SMALLINT are added to this list of equivalentsNext, the arguments within the type, such as the lengths of strings, precision values for numerics, the elements inside of an enumeration are compared. If BOTH columns have arguments AND they are different, a change will be detected. If one column is just set to the default and the other has arguments, Alembic will pass on attempting to compare these. The rationale is that it is difficult to detect what a database backend sets as a default value without generating false positives.
Alternatively, the EnvironmentContext.configure.compare_type
parameter accepts a callable function which may be used to implement custom type
comparison logic, for cases such as where special user defined types
are being used:
def my_compare_type(context, inspected_column,
metadata_column, inspected_type, metadata_type):
# return False if the metadata_type is the same as the inspected_type
# or None to allow the default implementation to compare these
# types. a return value of True means the two types do not
# match and should result in a type change operation.
return None
context.configure(
# ...
compare_type = my_compare_type
)
Above, inspected_column
is a sqlalchemy.schema.Column
as
returned by
sqlalchemy.engine.reflection.Inspector.reflect_table()
, whereas
metadata_column
is a sqlalchemy.schema.Column
from the
local model environment. A return value of None
indicates that default
type comparison to proceed.
Applying Post Processing and Python Code Formatters to Generated Revisions¶
Revision scripts generated by the alembic revision
command can optionally
be piped through a series of post-production functions which may analyze or
rewrite Python source code generated by Alembic, within the scope of running
the revision
command. The primary intended use of this feature is to run
code-formatting tools such as Black or
autopep8, as well as custom-written
formatting and linter functions, on revision files as Alembic generates them.
Any number of hooks can be configured and they will be run in series, given the
path to the newly generated file as well as configuration options.
The post write hooks, when configured, run against generated revision files regardless of whether or not the autogenerate feature was used.
Note
Alembic’s post write system is partially inspired by the pre-commit tool, which configures git hooks that reformat source files as they are committed to a git repository. Pre-commit can serve this role for Alembic revision files as well, applying code formatters to them as they are committed. Alembic’s post write hooks are useful only in that they can format the files immediately upon generation, rather than at commit time, and also can be useful for projects that prefer not to use pre-commit.
Basic Post Processor Configuration¶
The alembic.ini
samples now include commented-out configuration
illustrating how to configure code-formatting tools, or other tools like linters
to run against the newly generated file path. Example:
[post_write_hooks]
# format using "black"
hooks=black
black.type = console_scripts
black.entrypoint = black
black.options = -l 79
Above, we configure hooks
to be a single post write hook labeled
"black"
. Note that this label is arbitrary. We then define the
configuration for the "black"
post write hook, which includes:
type
- this is the type of hook we are running. Alembic includes two hook runners:"console_scripts"
, which is specifically a Python function that usessubprocess.run()
to invoke a separate Python script against the revision file; and"exec"
, which usessubprocess.run()
to execute an arbitrary binary. For a custom-written hook function, this configuration variable would refer to the name under which the custom hook was registered; see the next section for an example.
New in version 1.12: added new exec
runner
The following configuration option is specific to the "console_scripts"
hook runner:
entrypoint
- the name of the setuptools entrypoint that is used to define the console script. Within the scope of standard Python console scripts, this name will match the name of the shell command that is usually run for the code formatting tool, in this caseblack
.
The following configuration option is specific to the "exec"
hook runner:
executable
- the name of the executable to invoke. Can be either a
bare executable name which will be searched in $PATH
, or a full pathname
to avoid potential issues with path interception.
The following options are supported by both "console_scripts"
and "exec"
:
options
- a line of command-line options that will be passed to the code formatting tool. In this case, we want to run the commandblack /path/to/revision.py -l 79
. By default, the revision path is positioned as the first argument. In order specify a different position, we can use theREVISION_SCRIPT_FILENAME
token as illustrated by the subsequent examples.Note
Make sure options for the script are provided such that it will rewrite the input file in place. For example, when running
autopep8
, the--in-place
option should be provided:[post_write_hooks] hooks = autopep8 autopep8.type = console_scripts autopep8.entrypoint = autopep8 autopep8.options = --in-place REVISION_SCRIPT_FILENAME
cwd
- optional working directory from which the code processing tool is run.
When running alembic revision -m "rev1"
, we will now see the black
tool’s output as well:
$ alembic revision -m "rev1"
Generating /path/to/project/versions/481b13bc369a_rev1.py ... done
Running post write hook "black" ...
reformatted /path/to/project/versions/481b13bc369a_rev1.py
All done! ✨ 🍰 ✨
1 file reformatted.
done
Hooks may also be specified as a list of names, which correspond to hook
runners that will run sequentially. As an example, we can also run the
zimports import rewriting tool (written
by Alembic’s author) subsequent to running the black
tool, using a
configuration as follows:
[post_write_hooks]
# format using "black", then "zimports"
hooks=black, zimports
black.type = console_scripts
black.entrypoint = black
black.options = -l 79 REVISION_SCRIPT_FILENAME
zimports.type = console_scripts
zimports.entrypoint = zimports
zimports.options = --style google REVISION_SCRIPT_FILENAME
When using the above configuration, a newly generated revision file will be processed first by the “black” tool, then by the “zimports” tool.
Alternatively, one can run pre-commit itself as follows:
[post_write_hooks]
hooks = pre-commit
pre-commit.type = console_scripts
pre-commit.entrypoint = pre-commit
pre-commit.options = run --files REVISION_SCRIPT_FILENAME
pre-commit.cwd = %(here)s
(The last line helps to ensure that the .pre-commit-config.yaml
file
will always be found, regardless of from where the hook was called.)
Writing Custom Hooks as Python Functions¶
The previous section illustrated how to run command-line code formatters,
through the use of a post write hook provided by Alembic known as
console_scripts
. This hook is in fact a Python function that is registered
under that name using a registration function that may be used to register
other types of hooks as well.
To illustrate, we will use the example of a short Python function that wants
to rewrite the generated code to use tabs instead of four spaces. For simplicity,
we will illustrate how this function can be present directly in the env.py
file. The function is declared and registered using the write_hooks.register()
decorator:
from alembic.script import write_hooks
import re
@write_hooks.register("spaces_to_tabs")
def convert_spaces_to_tabs(filename, options):
lines = []
with open(filename) as file_:
for line in file_:
lines.append(
re.sub(
r"^( )+",
lambda m: "\t" * (len(m.group(1)) // 4),
line
)
)
with open(filename, "w") as to_write:
to_write.write("".join(lines))
Our new "spaces_to_tabs"
hook can be configured in alembic.ini as follows:
[alembic]
# ...
# ensure the revision command loads env.py
revision_environment = true
[post_write_hooks]
hooks = spaces_to_tabs
spaces_to_tabs.type = spaces_to_tabs
When alembic revision
is run, the env.py
file will be loaded in all
cases, the custom “spaces_to_tabs” function will be registered and it will then
be run against the newly generated file path:
$ alembic revision -m "rev1"
Generating /path/to/project/versions/481b13bc369a_rev1.py ... done
Running post write hook "spaces_to_tabs" ...
done
Running Alembic Check to test for new upgrade operations¶
When developing code it’s useful to know if a set of code changes has made any
net change to the database model, such that new revisions would need to be
generated. To automate this, Alembic provides the alembic check
command.
This command will run through the same process as
alembic revision --autogenerate
, up until the point where revision files
would be generated, however does not generate any new files. Instead, it
returns an error code plus a message if it is detected that new operations
would be rendered into a new revision, or if not, returns a success code plus a
message. When alembic check
returns a success code, this is an indication
that the alembic revision --autogenerate
command would produce only empty
migrations, and does not need to be run.
alembic check
can be worked into CI systems and on-commit schemes to ensure
that incoming code does not warrant new revisions to be generated. In
the example below, a check that detects new operations is illustrated:
$ alembic check
FAILED: New upgrade operations detected: [
('add_column', None, 'my_table', Column('data', String(), table=<my_table>)),
('add_column', None, 'my_table', Column('newcol', Integer(), table=<my_table>))]
by contrast, when no new operations are detected:
$ alembic check
No new upgrade operations detected.
New in version 1.9.0.
Note
The alembic check
command uses the same model comparison process
as the alembic revision --autogenerate
process. This means parameters
such as EnvironmentContext.configure.compare_type
and EnvironmentContext.configure.compare_server_default
are in play as usual, as well as that limitations in autogenerate
detection are the same when running alembic check
.