versionedobj

Contents:

• versionedobj
  • versionedobj package

Object serialization & versioning framework for python 3x

versionedobj is an object serialization framework that allows you to create complex python objects that can be serialized/deserialized to and from strings, or dicts, or JSON files.

versionedobj also provides a versioning mechanism, to track changes in object structure across time, and to migrate between different object versions.

See API documentation

Installing

Install versionedobj using pip:

pip install versionedobj

Getting started

Object definition

Define objects by creating a new class that inherits from VersionedObject, and set class attributes to define your object attributes:

from versionedobj import VersionedObjbect

class UserConfig(VersionedObject):
    version = "v1.0.0"
    username = "john smith"
    friend_list = ["user1", "user2", "user3"]

You can also nest VersionedObjects by simply assigning another VersionedObject class or instance object to a class attribute:

from versionedobj import VersionedObject

class DisplayConfig(VersionedObject):
    display_mode = "windowed"
    resolution = "1920x1080"
    volume = 0.66

# Populate class attributes to build your object
class UserConfig(VersionedObject):
    version = "v1.0.0"
    username = "john smith"
    friend_list = ["user1", "user2", "user3"]
    display_config = DisplayConfig() # VersionedObjects can be nested

    # Nested VersionedObjects can be a class object, or an instance of the
    # class, either way will behave the same

    # display_config = DisplayConfig

Creating object instances and accessing object attributes

The values you set on the class attributes of a VersionedObject serve as the default values for that object. When you create an instance of your VersionedObject class, instance attributes will automatically be created to match the class attributes, and the values of the class attributes will be copied over to the instance attributes:

obj = UserConfig()

print(obj.friend_list)
# Output looks like this: ["user1", "user2", "user3"]

print(obj.display_config.display_mode)
# Output looks like this: "windowed"

As well as regular dot notation, you can also treat an object instance like a dict, and access individual attributes using their full dot name as the key:

print(obj['friend_list'])
# Output looks like this: ["user1", "user2", "user3"]

print(obj['display_config.display_mode'])
# Output looks like this: "windowed"

# Change the value of an instance attribute
obj['display_config.display_mode'] = "fullscreen"

print(obj['display_config.display_mode'])
# Output looks like this: "fullscreen"

You can also treat a VersionedObjbect instance as an iterable, to iterate over all object attribute names, as you would with keys in a dict:

for attr_name in obj:
    print(f"{attr_name}: {obj[attr_name]}")

# Output looks like this:
#
# version: v1.0.0
# username: john smith
# friend_list: ["user1", "user2", "user3"]
# display_config.display_mode: windowed
# display_config.resolution: 1920x1080
# display_config.volume: 0.66

Serializing and de-serializing

Create an instance of the versionedobj.Serializer class, and use the to_file and from_file methods to serialize/deserialize data to/from a JSON file:

from versionedobj import VersionedObject, Serializer

class DisplayConfig(VersionedObject):
    display_mode = "windowed"
    resolution = "1920x1080"
    volume = 0.66

class UserConfig(VersionedObject):
    version = "v1.0.0"
    username = "john smith"
    friend_list = ["user1", "user2", "user3"]
    display_config = DisplayConfig() # VersionedObjects can be nested

# Create an instance of our VersionedObject
obj = UserConfig()

# Create a serializer instance
serializer = Serializer(obj)

# Save object instance to JSON file
serializer.to_file('user_config.json', indent=4)

# Load JSON file and populate the same object instance
serializer.from_file('user_config.json')

You can also save/load object data as a JSON string:

# Save object instance to JSON string
obj_as_json = serializer.to_json(indent=4)

# Load object instance from JSON string
serializer.from_json(obj_as_json)

Or, as a dict:

# Save object instance to dict
obj_as_dict = serializer.to_dict()

# Load object instance from dict
serializer.from_dict(obj_as_dict)

Using one Serializer instance with multiple object types

For convenience, you can pass an object instance when you create a versionedobj.Serializer, and this object will be used for all future serialization/deserialization operations, so that you don’t have to pass in the object instance every time (as shown in previous examples).

However, this is not required, and you can optionally provide an object instance for all serialization/deserialization methods, if you want to (for example) use a single versionedobj.Serializer instance for multiple object types:

from versionedobj import VersionedObject, Serializer

class ObjectA(VersionedObject):
    name = "john"
    age = 44

class ObjectB(VersionedObject):
    last_login_time = 12345678
    enabled = False

# Create an instance of each object
a = ObjectA()
b = ObjectB()
serializer = Serializer()

# Serialize both objects using the same serializer
a_jsonstr = serializer.to_json(a)
b_jsonstr = serializer.to_json(b)

# De-serialize both objects using the same serializer
serializer.from_json(a_jsonstr, a)
serializer.from_json(b_jsonstr, b)

Filtering serialization/deserialization output

Whitelisting by field name

When serializing, if you only want to output certain fields, you can use the ‘only’ parameter to specify which fields should be output (effectively a whitelist by field name):

serializer.to_file('user_config.json', only=['version', 'username', 'display_config.resolution'])

# Output looks like this:
#
# {
#     "version": "v1.0.0",
#     "username": "jane doe",
#     "display_config": {
#         "resolution": "1920x1080",
#     }
# }

The same parameter can be used for de-serializing:

serializer.from_file('user_config.json', only=['display_config.display_mode'])

# Only the 'display_config.display_mode' field is loaded from the file

Blacklisting by field name

When serializing, if you don’t want to output certain fields, you can use the ‘ignore’ parameter to specify which fields should be excluded from output (effectively a blacklist by field name):

serializer.to_file('user_config.json', ignore=['friend_list', 'display_config.volume'])

# Output looks like this:
#
# {
#     "version": "v1.0.0",
#     "username": "jane doe",
#     "display_config": {
#         "display_mode": "windowed",
#         "resolution": "1920x1080"
#     }
# }

The same parameter can be used for de-serializing:

serializer.from_file('user_config.json', ignore=['friend_list'])

# Every field except for the 'friend_list' field is loaded from the file

versionedobj.ListField: store a sequence of objects in a single field

versionedobj.ListField is a list class that behaves exactly like a regular python list, except for the following 2 differences:

• Only instances of a class which is a subclass of the VersionedObject may be added to lists (ValueError is raised otherwise)

• Only instances of the same class may be added to a single list (ValueError is raised otherwise)

You can assign a versionedobj.ListField instance as the value for a field in your versioned object class definition, and that field can then hold a sequence of multiple versioned objects. This is useful if you need to store a variably-sized collection of objects that are created a runtime.

from versionedobj import VersionedObject, Serializer, ListField

# The list will contain objects of this type only
class UserData(VersionedObject):
    name = "john"
    age = 30

# This object will contain a list of multiple users
class AllUserData(VersionedObject):
    # a List may only contain instances of the same class
    users = ListField(UserData)

all_user_data = AllUserData()

# Add some users to the list
all_user_data.users.append(UserData(initial_values={'name': 'sam', 'age': 66}))
all_user_data.users.append(UserData(initial_values={'name': 'sally', 'age': 28}))

# Serialize object and print out JSON data
print(Serializer(all_user_data).to_json(indent=4))

# Output looks like this:
#
# {
#     "users": [
#         {
#             "name": "sam",
#             "age": 66
#         },
#         {
#             "name": "sally",
#             "age": 28
#         }
#     ]
# }

Context manager for loading & editing saved object data

If you want to load object data from a JSON file, make some changes to the data, and save it back to the same JSON file, then you can use the FileLoader context manager, which will load/create the file for you on entry, return a deserialized object for you to modify, and then serializes your modified object back to the same file on exit. This may be useful if you are worried about forgetting to re-serialize the object when you are done.

from versionedobj import VersionedObject, FileLoader

class Recipe(VersionedObject):
    ingredient_1 = "onions"
    ingredient_2 = "tomatoes"
    ingredient_3 = "garlic"

# Creates a new instance of the object, and loads data from
# "recipe.json" if the file already exists
with FileLoader(Recipe, "recipe.json") as obj:
    # Change something
    obj.ingredient_3 = "celery"

# recipe.json now looks like this:
#
# {
#     "ingredient_1": "onions",
#     "ingredient_2": "tomatoes",
#     "ingredient_3": "celery",
# }

Migrations: making use of the version number

A VersionedObject object can have a version attribute, which can be any object, although it is typically a string (e.g. "v1.2.3"). This version attribute can be used to support migrations for older objects, in the event that you need to change the format of your object.

Example scenario, part 1: you have created a beautiful versioned object

Let’s take the same config file definition from the previous example:

from versionedobj import VersionedObject

# Nested config object
class DisplayConfig(VersionedObject):
    display_mode = "windowed"
    resolution = "1920x1080"
    volume = 0.66

# Top-level config object with another nested config object
class UserConfig(VersionedObject):
    version = "v1.0.0"
    username = "john smith"
    friend_list = ["user1", "user2", "user3"]
    display_config = DisplayConfig()

Imagine you’ve already released this code out into the world. People are already using it, and they have JSON files generated by your UserConfig class sitting on their computers.

Example scenario, part 2: you update your software, modifying the versioned object

Now, imagine you are making a new release of your software, and some new features require you to make the following changes to your versioned object:

• remove the the DisplayConfig.resolution field entirely

• change the name of DisplayConfig.volume to DisplayConfig.volumes

• change the value of DisplayConfig.volumes from a float to a list

from versionedobj import VersionedObject

# Nested config object
class DisplayConfig(VersionedObject):
    display_mode = "windowed"
    # 'resolution' field is deleted
    volumes = [0.66, 0.1] # 'volume' is now called 'volumes', and is a list

# Top-level config object with another nested config object
class UserConfig(VersionedObject):
    version = "v1.0.0"
    username = "john smith"
    friend_list = ["user1", "user2", "user3"]
    display_config = DisplayConfig()

Uh-oh, you have a problem…

Right now, if you send this updated UserConfig class to your existing users, it will fail to load their existing JSON files with version v1.0.0, since those files will contain the DisplayConfig.resolution field that we deleted in v1.0.1, and DisplayConfig.volume will similarly be gone, having been replaced with DisplayConfig.volumes. This situation is what migrations are for.

Solution– migrations!

The solution is to:

1. Change the version number to something new, e.g. v1.0.0 becomes v1.0.1

2. Write a migration function to transform v1.0.0 object data into v1.0.1 object data

3. Use the versionedobj.migration decorator to register your migration function

from versionedobj import VersionedObject, migration

# Nested config object
class DisplayConfig(VersionedObject):
    display_mode = "windowed"
    # 'resolution' field is deleted
    volumes = [0.66, 0.1] # 'volume' is now called 'volumes', and is a list

# Top-level config object with another nested config object
class UserConfig(VersionedObject):
    version = "v1.0.1" # Version has been updated to 1.0.1
    username = "john smith"
    friend_list = ["user1", "user2", "user3"]
    display_config = DisplayConfig()

# Create the migration function for v1.0.0 to v1.0.1
@migration(UserConfig, "v1.0.0", "v1.0.1")
def migrate_100_to_101(attrs):
    del attrs['display_config']['resolution']        # Delete resolution field
    del attrs['display_config']['volume']            # Delete volume field
    attrs['display_config']['volumes'] = [0.66, 0.1] # Add defaults for new volume values
    return attrs                                     # Return modified data (important!)

after you add the migration function and update the version to v1.0.1, JSON files that are loaded and contain the version v1.0.0 will be automatically migrated to version v1.0.1 using the migration function you added.

The downside to this approach, is that you have to manually udpate the version number, and write a new migration function, anytime the structure of your config data changes.

The upside, of course, is that you can relatively easily support migrating any older version of your config file to the current version.

If you don’t need the versioning/migration functionality, just never change your version number, or don’t create a version attribute on your VersionedObject classes.

Migrations: migrating an unversioned object

You may run into a situation where you release an unversioned object, but then later you need to make changes, and migrate an unversioned object to a versioned object.

This can be handled simply by passing “None” to the “add_migration()” method, for the “from_version” parameter. For example:

from versionedobj import VersionedObj, migration

class UserConfig(VersionedObject):
    version = "v1.0.0"
    username = ""
    friend_list = []

@migration(UserConfig, None, "v1.0.0")
def migrate_none_to_100(attrs);
    attrs['friend_list'] = [] # Add new 'friend_list' field
    return attrs

Validating input data without deserializing

You may want to validate some serialized object data without actually deserializing and loading the object values. You can use the Serializer.validate_dict method for this.

from versionedobj import VersionedObject, Serializer

class Recipe(VersionedObject):
    ingredient_1 = "onions"
    ingredient_2 = "tomatoes"
    ingredient_3 = "garlic"

rcp = Recipe()
serializer = Serializer(rcp)

serializer.validate_dict({"ingredient_1": "celery", "ingredient_2": "carrots"})
# Raises versionedobj.exceptions.InputValidationError because 'ingredient_3' is missing

serializer.validate_dict({"ingredient_1": "celery", "ingredient_2": "carrots", "ingredient_12": "cumin"})
# Raises versionedobj.exceptions.InputValidationError because 'ingredient_12' is not a valid attribute

Resetting object instance to default values

You can use the Serializer.reset_to_defaults method to set all instance attributes to the default values defined in the matching class attributes.

from versionedobj import VersionedObject, Serializer

class Recipe(VersionedObject):
    ingredient_1 = "onions"
    ingredient_2 = "tomatoes"
    ingredient_3 = "garlic"

rcp = Recipe()
serializer = Serializer(rcp)

# Change a value
rcp.ingredient_1 = "celery"

print(serializer.to_dict())
# {"ingredient_1": "celery", "ingredient_2": "tomatoes", "ingredient_3": "garlic"}

# Reset object instance to defaults
serializer.reset_to_defaults()

print(serializer.to_dict())
# {"ingredient_1": "onions", "ingredient_2": "tomatoes", "ingredient_3": "garlic"}

Testing object instance equality

You can test whether two VersionedObject instances are equal in both structure and values, the same way in which you would check equality of any other two objects:

from versionedobj import VersionedObject

class Recipe(VersionedObject):
    ingredient_1 = "onions"
    ingredient_2 = "tomatoes"
    ingredient_3 = "garlic"

rcp1 = Recipe()
rcp2 = Recipe()

print(rcp1 == rcp2)
# True

rcp1.ingredient_3 = "ginger"

print(rcp1 == rcp2)
# False

In order for two VersionedObject instances to be considered equal, the following conditions must be true:

• Both objects are instances of the same class

• Both objects contain matching attribute names and values

Object instance hashing

Objects can be uniquely hashed based on their instance attribute values, using the builtin hash() function. This means, for example, that you can use object instances as dict keys:

from versionedobj import VersionedObject

class Person(VersionedObject):
    name = "sam"
    age = 31

p1 = Person()
p2 = Person()

# Change 1 value on p2 so the hash value is different
p2.age = 32

d = {p1: "a", p2: "b"}
print(d)
# { Person({"name": "sam", "age": 31}): "a", Person({"name": "sam", "age": 32}): "b" }

Testing whether object instances contain specific values

You can check whether an object instance contains a particular attribute value using the in keyword:

from versionedobj import VersionedObject

class Person(VersionedObject):
    name = "sam"
    age = 31

p = Person()

print("sam" in p)
# True

p.name = "sally"

print("sam" in p)
# False

print("sally" in p)
# True

Performance/stress test visualization

The following image is generated by the tests/performance_tests/big_class_performance_test.py script, which creates and serializes/deserializes multiple versioned objects of an incrementally increasing size, and simultaneously having an increasing depth of contained nested objects.

Each data point in the graph represents measurements taken for an object of a particular size. The time taken to serialize the object to a dict, and also to deserialize the object data from a dict, and also to create an instance of the object, is measured for each object size. It is worth mentioning that measuring the from/to_json and from/to_file methods is not very useful in this case, since that would only be measuring to/from_dict with additional JSON parser or file I/O overhead. That is why this graph only measures to/from_dict.

This test was executed on a system with an Intel Core-i7 running Debian GNU/Linux 10 (buster) with Linux debian 4.19.0-21-amd64.

Contributions

Contributions are welcome, please open a pull request at https://github.com/eriknyquist/versionedobj and ensure that:

1. All existing unit tests pass (run tests via python setup.py test)

2. New unit tests are added to cover any modified/new functionality (run python code_coverage.py to ensure that coverage is above 98%)

You will need to install packages required for development, these are listed in dev_requirements.txt:

pip install -r dev_requirements.txt

If you have any questions about / need help with contributions or unit tests, please contact Erik at eknyquist@gmail.com.

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