Skip to main content

Package for simplify data structures migrations

Project description

Codecov Test Code Style Documentation Status PyPI version Anaconda version

This support package simplifies data persistence between user sessions and software version updates.

The main idea of this package is simplify data migration between versions, and allow to define migration information next to data structure definition.

Basic usage (data serialization)

If You only need to serialize data, then you could use only JSON hooks

import json

from pydantic import BaseModel
from nme import NMEEncoder, nme_object_hook


class SampleModel(BaseModel):
    field1: int
    field2: str


data = SampleModel(field1=4, field2="abc")

with open("sample.json", "w") as f_p:
    json.dump(data, f_p, cls=NMEEncoder)

with open("sample.json") as f_p:
    data2 = json.load(f_p, object_hook=nme_object_hook)

assert data == data2

Migrations

To register this information there is register_class decorator. It has 4 parameters:

  • version - version of data structure

  • migration_list - list of tuple (version. migration_function).

  • old_paths - list of fully qualified python paths to previous class definitions. This is to allow move class during code refactoring.

  • use_parent_migrations - if True, then parent class migrations will be used.

Lets imagine that we have such code

from nme import NMEEncoder, nme_object_hook

class SampleModel(BaseModel):
    field1: int
    field_ca_1: str
    field_ca_2: float

with open("sample.json", "w") as f_p:
    json.dump(data, f_p, cls=NMEEncoder)

But there is decision to move both ca field to sub structure:

class CaModel(BaseModel):
    field_1: str
    field_2: float

class SampleModel(BaseModel):
    field1: int
    field_ca: CaModel

Then with nme code may look:

from nme import nme_object_hook, register_class

class CaModel(BaseModel):
    field_1: str
    field_2: float

def ca_migration_function(dkt):
    dkt["field_ca"] = CaModel(field1=dkt.pop("field_ca_1"),
                              field2=dkt.pop("field_ca_2"))
    return dkt

@register_class("0.0.1", [("0.0.1", ca_migration_function)])
class SampleModel(BaseModel):
    field1: int
    field_ca: CaModel

with open("sample.json") as f_p:
    data = json.load(f_p, object_hook=nme_object_hook)

Assume that there is decision to rename field1 to id. Then code may look:

from nme import nme_object_hook, register_class, rename_key

class CaModel(BaseModel):
    field_1: str
    field_2: float

def ca_migration_function(dkt):
    dkt["field_ca"] = CaModel(field1=dkt.pop("field_ca_1"),
                              field2=dkt.pop("field_ca_2"))
    return dkt

@register_class("0.0.2", [("0.0.1", ca_migration_function), ("0.0.2", rename_key("field1", "id"))])
class SampleModel(BaseModel):
    id: int
    field_ca: CaModel

with open("sample.json") as f_p:
    data = json.load(f_p, object_hook=nme_object_hook)

More examples could be found in examples section of documentation

Additional functions

  • rename_key(from_key: str, to_key: str, optional=False) -> Callable[[Dict], Dict] - helper function for rename field migrations.

  • update_argument(argument_name:str)(func: Callable) -> Callable - decorator to keep backward compatibility by converting dict argument to some class base on function type annotation

Additional notes

This package is extracted from PartSeg project for simplify reuse it in another projects.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nme-0.1.4.tar.gz (22.2 kB view details)

Uploaded Source

Built Distribution

nme-0.1.4-py3-none-any.whl (15.0 kB view details)

Uploaded Python 3

File details

Details for the file nme-0.1.4.tar.gz.

File metadata

  • Download URL: nme-0.1.4.tar.gz
  • Upload date:
  • Size: 22.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for nme-0.1.4.tar.gz
Algorithm Hash digest
SHA256 7dbda75678da6ab724091f21ab15d605b1bcfc96b8a681bc8999f465f0ef12b2
MD5 62babe1e7733e952eacd081971b0fb06
BLAKE2b-256 9164cc819e330d0454a800e6734dafebf81c43bf8e31466ec326afe26eb37b9c

See more details on using hashes here.

File details

Details for the file nme-0.1.4-py3-none-any.whl.

File metadata

  • Download URL: nme-0.1.4-py3-none-any.whl
  • Upload date:
  • Size: 15.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for nme-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 48ede3304afd0154d458ca29f653930a3861c9c70f2c69d6e385a89a19531b37
MD5 19d2325acbc881ae293288258d1aca59
BLAKE2b-256 6e68130c5b7c81f2a21a9fdcb8fd57b5fc03ed26734d303cb326492eb20bf2e9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page