hopeit.engine 0.25.4

Release Notes

Version 0.25.4

---

• Engine:

  • Removed typing_inspect dependency in favor of typing built-in module

• Plugins:

  • DataFrames:
    • Experimental: DataBlocks API, to allow manipulating and saving multiple datasets at once

hopeit.engine 0.25.3

Version 0.25.3

---

• Engine:

  • Web:
    • Allow to specify cors_routes_prefix to allow cors_origin to be applied
      to custom routes names

hopeit.engine 0.25.2

Version 0.25.2

---

• Plugins:

  • DataFrames:
    • Support for bool fields
    • Dataframes.to_dataobjects: option to convert null values (nan, NaT, etc) to None

hopeit.engine 0.25.1

Version 0.25.1

---

• Engine

  • API: Support for type | None syntax, as well as Optional[type] annotation for optional query args

• Plugins:

  • DataFrames, feature to allow schema evolution:
    • Allowing optional and default values in @dataframe objects
    • Saving json schema for serialized Datasets
    • Handling load errors with DatasetLoadException
    • Allowing load dataset with deprecated fields (removed fields)
    • Allowing load dataset with new fields in schema using a default value

hopeit.engine 0.25.0

Version 0.25.0

---

• Engine:

  • This release adopts pydantic as a the library for dataclasses and schema validation

  • StreamManager: add support for username and password in StreamsConfig

  • Fix: support for empty values in environment variables used in config files

  BREAKING CHANGES

  • Dropped support for Python 3.8

  • Python dataclasses.dataclass is no longer supported in @dataobjects annotated classes.
    Pydantic @dataclasses (and in the future BaseModel should be used).

  • The recommended way is to import from dataobjects module:

    from hopeit.dataobjects import dataclass, dataobject, field
    

    where dataclass and field are aliases of pydantic.dataclasses.dataclass and pydantic.Field

  • In most of the cases replacing the import clause in applications should suffice,
    but could be features of dataclasses (like i.e. metadata) that should be replaced by its
    equivalent in pydantic.

  • API validation: now payload validation is not done in api module and is deferred and done by pydantic when the
    payload is being parsed. Response in case of validation error will return BadRequest (400) but with
    different error message than in previous versions.

• Plugins:

  • Dataframes

    • Made it compatible with pydantic dataclasses
    • Removed @dataframeobject annotation in favor of Dataset[T] generic type
    • Introduced .DataObject companion type for @dataframe conversion to DataObject
    • Fixed type coercion for string fields
    • Datetime fields are converted to UTC when dataframe object is initialized

  • redis-streams: update StreamsConfig usage to support new username and password fields

  • redis-storage: add support for username and password in the connect method

• Development (internal)

  • Simplified and accelerated linting using ruff. Dropping pylint and flake
  • Reformatted code files
  • Moved linting and test logic to Makefile

hopeit.engine 0.25.0b9

Version 0.25.0

---

• Engine:

  • This release adopts pydantic as a the library for dataclasses and schema validation

  • StreamManager: add support for username and password in StreamsConfig

  • Fix: support for empty values in environment variables used in config files

  BREAKING CHANGES

  • Dropped support for Python 3.8

  • Python dataclasses.dataclass is no longer supported in @dataobjects annotated classes.
    Pydantic @dataclasses (and in the future BaseModel should be used).

  • The recommended way is to import from dataobjects module:

    from hopeit.dataobjects import dataclass, dataobject, field
    

    where dataclass and field are aliases of pydantic.dataclasses.dataclass and pydantic.Field

  • In most of the cases replacing the import clause in applications should suffice,
    but could be features of dataclasses (like i.e. metadata) that should be replaced by its
    equivalent in pydantic.

  • API validation: now payload validation is not done in api module and is deferred and done by pydantic when the
    payload is being parsed. Response in case of validation error will return BadRequest (400) but with
    different error message than in previous versions.

• Plugins:

  • Dataframes

    • Made it compatible with pydantic dataclasses
    • Removed @dataframeobject annotation in favor of Dataset[T] generic type
    • Introduced .DataObject companion type for @dataframe conversion to DataObject
    • Fixed type coercion for string fields
    • Datetime fields are converted to UTC when dataframe object is initialized

  • redis-streams: update StreamsConfig usage to support new username and password fields

  • redis-storage: add support for username and password in the connect method

• Development (internal)

  • Simplified and accelerated linting using ruff. Dropping pylint and flake
  • Reformatted code files
  • Moved linting and test logic to Makefile

hopeit.engine 0.25.0b7

Version 0.25.0

---

• Engine:

  • This release adopts pydantic as a the library for dataclasses and schema validation

  • StreamManager: add support for username and password in StreamsConfig

  • Fix: support for empty values in environment variables used in config files

  BREAKING CHANGES

  • Python dataclasses.dataclass is no longer supported in @dataobjects annotated classes.
    Pydantic @dataclasses (and in the future BaseModel should be used).

  • The recommended way is to import from dataobjects module:

    from hopeit.dataobjects import dataclass, dataobject, field
    

    where dataclass and field are aliases of pydantic.dataclasses.dataclass and pydantic.Field

  • In most of the cases replacing the import clause in applications should suffice,
    but could be features of dataclasses (like i.e. metadata) that should be replaced by its
    equivalent in pydantic.

  • API validation: now payload validation not done in api module and is deferred and done by pydantic when the
    payload is being parsed. Response in case of validation error will return BadRequest (400) but with
    different error message than in previous versions.

• Plugins:

  • Dataframes

    • Made it compatible with pydantic dataclasses
    • Removed @dataframeobject annotation in favor of Dataset[T] generic type
    • Introduced .DataObject companion type for @dataframe conversion to DataObject
    • Fixed type coercion for string fields
    • Datetime fields are converted to UTC when dataframe object is initialized

  • redis-streams: update StreamsConfig usage to support new username and password fields

  • redis-storage: add support for username and password in the connect method

hopeit.engine 0.25.0b6

Version 0.25.0

---

• Engine:

  • This release adopts pydantic as a the library for dataclasses and schema validation

  • StreamManager: add support for username and password in StreamsConfig

  • Fix: support for empty values in environment variables used in config files

  BREAKING CHANGES

  • Python dataclasses.dataclass is no longer supported in @dataobjects annotated classes.
    Pydantic @dataclasses (and in the future BaseModel should be used).

  • The recommended way is to import from dataobjects module:

    from hopeit.dataobjects import dataclass, dataobject, field
    

    where dataclass and field are aliases of pydantic.dataclasses.dataclass and pydantic.Field

  • In most of the cases replacing the import clause in applications should suffice,
    but could be features of dataclasses (like i.e. metadata) that should be replaced by its
    equivalent in pydantic.

• Plugins:

  • Dataframes

    • Made it compatible with pydantic dataclasses
    • Removed @dataframeobject annotation in favor of Dataset[T] generic type
    • Introduced .DataObject companion type for @dataframe conversion to DataObject
    • Fixed type coercion for string fields

  • redis-streams: update StreamsConfig usage to support new username and password fields

  • redis-storage: add support for username and password in the connect method

hopeit.engine 0.25.0b5

Version 0.25.0

---

• Engine:

  • This release adopts pydantic as a the library for dataclasses and schema validation

  • StreamManager: add support for username and password in StreamsConfig

  • Fix: support for empty values in environment variables used in config files

  BREAKING CHANGES

  • Python dataclasses.dataclass is no longer supported in @dataobjects annotated classes.
    Pydantic @dataclasses (and in the future BaseModel should be used).

  • The recommended way is to import from dataobjects module:

    from hopeit.dataobjects import dataclass, dataobject, field
    

    where dataclass and field are aliases of pydantic.dataclasses.dataclass and pydantic.Field

  • In most of the cases replacing the import clause in applications should suffice,
    but could be features of dataclasses (like i.e. metadata) that should be replaced by its
    equivalent in pydantic.

• Plugins:

  • Dataframes

    • Made it compatible with pydantic dataclasses
    • Removed @dataframeobject annotation in favor of Dataset[T] generic type
    • Introduced .DataObject companion type for @dataframe conversion to DataObject

  • redis-streams: update StreamsConfig usage to support new username and password fields

  • redis-storage: add support for username and password in the connect method

hopeit.engine 0.25.0b4

Version 0.25.0

---

• Engine:

  • This release adopts pydantic as a the library for dataclasses and schema validation

  • StreamManager: add support for username and password in StreamsConfig

  BREAKING CHANGES

  • Python dataclasses.dataclass is no longer supported in @dataobjects annotated classes.
    Pydantic @dataclasses (and in the future BaseModel should be used).

  • The recommended way is to import from dataobjects module:

    from hopeit.dataobjects import dataclass, dataobject, field
    

    where dataclass and field are aliases of pydantic.dataclasses.dataclass and pydantic.Field

  • In most of the cases replacing the import clause in applications should suffice,
    but could be features of dataclasses that should be replaced by its
    equivalent in pydantic.

  • For cases where you store data in the field property metadata, you can access it as follows:

    from hopeit.dataobjects import dataclass, dataobject, field
    
    @dataobject
    @dataclass
    class User:
        name: str = field(metadata={"key": "value"})
    
    metadata = User.name.json_schema_extra["metadata"]
    
    

• Plugins:

  • Dataframes

    • Made it compatible with pydantic dataclasses
    • Removed @dataframeobject annotation in favor of Dataset[T] generic type
    • Introduced .DataObject companion type for @dataframe conversion to DataObject

  • redis-streams: update StreamsConfig usage to support new username and password fields

  • redis-storage: add support for username and password in the connect method