richfile 0.4.5

A library for reading and writing hierarchical data files

richfile

A more natural approach to saving hierarchical data structures.

richfile saves any Python object using directory structures on disk, and loads them back again into the same Python objects.

richfile can save any atomic Python object, including custom classes, so long as you can write a function to save and load it. It is intended as a replacement for things like: pickle, json, yaml, HDF5, Parquet, netCDF, zarr, numpy, etc. when you want to save a complex data structure in a human-readable and editable format. We find the richfile format ideal to use when you are building a data processing pipeline and you want to contain intermediate results in a format that allows for custom data types, is insensitive to version changes (pickling issues), allows for easy debugging, and is human readable.

It is easy to use, the code is simple and pure python, and the operations follow ACID principles.

Installation

pip install richfile

Examples

Try out the examples in the demo_notebook.ipynb file.

Usage

Saving and loading data is simple:

## Given some complex data structure
data = {
    "name": "John Doe",
    "age": 25,
    "address": {
        "street": "1234 Elm St",
        "zip": None
    },
    "siblings": [
        "Jane",
        "Jim"
    ],
    "data": [1,2,3],
    (1,2,3): "complex key",
}

## Save it
import as rf
r = rf.RichFile("path/to/data.richfile").save(data)

## Load it back
data = rf.RichFile("path/to/data.richfile").load()

You can also load just a part of the data:

r = rf.RichFile("path/to/data.richfile")
first_sibling = r["siblings"][0]  ## Lazily load a single item using pythonic indexing
print(f"First sibling: {first_sibling}")

>>> First sibling: Jane

View the contents of a richfile directory without loading it:

r.view_directory_structure()

Output:

Directory structure:
Viewing tree structure of richfile at path: ~/path/data.richfile (dict)
├── name.dict_item (dict_item)
|   ├── key.json (str)
|   ├── value.json (str)
|   
├── age.dict_item (dict_item)
|   ├── key.json (str)
|   ├── value.json (int)
|   
├── address.dict_item (dict_item)
|   ├── key.json (str)
|   ├── value.dict (dict)
|   |   ├── street.dict_item (dict_item)
|   |   |   ├── key.json (str)
|   |   |   ├── value.json (str)
|   |   |   
|   |   ├── zip.dict_item (dict_item)
|   |   |   ├── key.json (str)
|   |   |   ├── value.json (None)
|   |   |   
|   |   
|   
├── siblings.dict_item (dict_item)
|   ├── key.json (str)
|   ├── value.list (list)
|   |   ├── 0.json (str)
|   |   ├── 1.json (str)
|   |   
|   
├── data.dict_item (dict_item)
|   ├── key.json (str)
|   ├── value.list (list)
|   |   ├── 0.json (int)
|   |   ├── 1.json (int)
|   |   ├── 2.json (int)
|   |   
|   
├── 5.dict_item (dict_item)
|   ├── key.tuple (tuple)
|   |   ├── 0.json (int)
|   |   ├── 1.json (int)
|   |   ├── 2.json (int)
|   |   
|   ├── value.json (str)
|   

You can also add new data types easily:

## Add type to a RichFile object
r = rf.RichFile("path/to/data.richfile")
r.register_type(
    type_name='numpy_array',
    function_load=lambda path: np.load(path),
    function_save=lambda path, obj: np.save(path, obj),
    object_class=np.ndarray,
    library='numpy',
    suffix='npy',
)

## OR
## Add type to environment so that all new RichFile objects can use it
rf.functions.register_type(
    type_name='numpy_array',
    function_load=lambda path: np.load(path),
    function_save=lambda path, obj: np.save(path, obj),
    object_class=np.ndarray,
    library='numpy',
    suffix='npy',
)

Installation from source

git clone https://github.com/RichieHakim/richfile
cd richfile
pip install -e .

Considerations and Limitations

• Inversibility: When creating custom data types, it is important to consider whether the saving and loading operations are exactly reversible.
• ACID principles are reasonably followed via the use of temporary files, file locks, and atomic operations. However, the library is not a database, and therefore cannot guarantee the same level of ACID compliance as a database. In addition, atomic replacements of existing non-empty directories require two operations, which reduces atomicity.
• Performance: Data structures with many branches will require many files and operations, which may become slow. Consider packaging highly branched data structures into a single file that supports hierarchical data, such as JSON, HDF5, Parquet, netCDF, zarr, numpy, etc. and making a custom data type for it.

TODO:

• Tests
• Documentation
• Examples
• Readme
• License
• PyPi
• Hashing
• Item assignment (safely)
• Custom saving/loading functions
• Put the library imports in the function calls
• Add handling for data without a known type
• Change name of library to something more descriptive
• Test out memmap stuff
• Make it a .zip type