Skip to main content

Package for simplify data structures migrations

Project description

Codecov Test Code Style Documentation Status PyPI version

This is support package for simplify data serialization and persistance data between sessions and versions.

Basic usage

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

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

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 mov 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)

CBOR support

Also cbor2 encoder (nme_object_encoder) and object hook (nme_cbor_decoder) are available.

import cbor2
from pydantic import BaseModel
from nme import nme_cbor_encoder, nme_cbor_decoder


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


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

with open("sample.cbor", "wb") as f_p:
    cbor2.dump(data, f_p, default=nme_cbor_encoder)

with open("sample.cbor", "rb") as f_p:
    data2 = cbor2.load(f_p, object_hook=nme_cbor_decoder)

assert data == data2

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-0.1.2.tar.gz (20.4 kB view details)

Uploaded Source

Built Distribution

nme-0.1.2-py3-none-any.whl (14.3 kB view details)

Uploaded Python 3

File details

Details for the file nme-0.1.2.tar.gz.

File metadata

  • Download URL: nme-0.1.2.tar.gz
  • Upload date:
  • Size: 20.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.2

File hashes

Hashes for nme-0.1.2.tar.gz
Algorithm Hash digest
SHA256 cacd36062f5f4f2893e76ff94345ffaf5e22adbbd9c34116cd4c836518ec66ae
MD5 d6b217b7978b5c6ab24759606cd23a73
BLAKE2b-256 faea51f7393d136bea5a03f9b0804e2ab4baf3a619bb835389789cc8870df2ad

See more details on using hashes here.

File details

Details for the file nme-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: nme-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 14.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.2

File hashes

Hashes for nme-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 ffb75fedebd10922d20fb4d0bfecb8f4838fe1019161f9f5411cd73910003f7c
MD5 65707deb5f0f341bde7af81e3aa35946
BLAKE2b-256 7d85596a959c1af266dad1be9fd2fb17d35a12eb3d5463b9ab807c71dd9367ef

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