spoonbill-framework 0.0.1a6

A lightweight, universal interface for Key-Values data stores

Spoonbill

What is Spoonbill? Inspired by ibis, Spoonbill is a Python library that provides a lightweight, universal interface for Key-Values data stores.
Write once, run anywhere.

For fast prototyping, testing, and simplification of data pipelines.

Docs

Features

1. A unified interface for all key-value data stores.
2. A simple, intuitive API.
3. A lightweight, fast, and flexible library.
4. Extra features like Search, batch inserts and retrieval on (almost) all backends.

Installation

pip install spoonbill-framework

Operations map

Operation	InMemoryStore	FilesystemStore	RedisStore	LmdbStore	PysosStore	ShelveStore	DynamoDBStore	FireStoreStore	CosmosDBStore	MongoDBStore	SafetensorsStore	ModalStore	UnQLite
backend	python dict	fsspec (S3/gs,az,local,ftp, etc)	Redis	Lmdb	Pysos	Shelve	AWS DynamoDB	GCP Firestore	Azure Cosmos DB	MongoDB	safetensors	modal	unqlite-python
set	+	+	+	+	+	+	+	+	+	+	-	+	+
get	+	+	+	+	+	+	+	+	+	+	+	+	x
pop	+	+	+	+	+	+	+	+	+	+	-	+	x
delete	+	+	+	+	+	+	+	+	+	+	-	+	x
len	+	+	+	+	+	+	+	+	+	+	+	+	x
eq	+	+	+	+	+	+	+	+	+	+	-	+	x
keys	+	+	+	+	+	+	+	+	+	+	+	+	x
values	+	+	+	+	+	+	+	+	+	+	+	+	x
items	+	+	+	+	+	+	+	+	+	+	+	+	x
iter	+	+	+	+	+	+	+	+	+	+	+	+	x
contains	+	+	+	+	+	+	+	+	+	+	+	+	x
update	+	+	+	+	+	+	+	+	+	+	-	+	x
persistence	-	+	+	+	+	+	+	+	+	+	+	-	x
save/load	+	Auto	Save (experimental)	+	+	+	Serverless	Serverless	Serverless	+ (strict)	+	-	x
key type (Not strict/strict)	Any	Any(local) / String(cloud)	Any/String	Any	Any	Any	String	Any/String	String	String	String	Any	Any
value_type	Any	Any(local) / String(cloud)	Any/String	Any	Any	Any	Jsonable	Any	Any	Any/Dict[str,Any]	Tensors	Any	Any

• A strict=False mode is available to allow for more flexible data types - anything which is cloudpickle-able will work including classes and functions.

Usage

All the classes have the same interface, so you can use them interchangeably.

• The strict argument is used to control if to encode the keys and values with cloudpickle or keep original backend behavior. if strict is False, any key and value can be used, otherwise it depends on the backend.

APIs

from spoonbill.datastores import InMemoryStore

store = InMemoryStore()
store["key"] = "value"
store["key"] = {"feature": "value"}
store["key"] == "value"
del store['key']
store.set("key", "value")
store.get("key", None)
store.delete("key")
store.pop('key', None)
store.popitem()
store.keys()
store.items()
store.values()
'key' in store  # contains
len(store)
for key in store: pass  # iterate
store.update({'key': 'value'})
store.save('path')
store.load('path')

When using strict=True we can use some advanced features of the backend. specifically for searches.

from spoonbill.datastores import InMemoryStore

store = InMemoryStore()
store.keys(pattern="*", limit=10)  # scan keys to a pattern
store.values(keys=['key1', 'key2'])  # retrieve a batch of values efficiently 
store.items(conditions={'a': '1+', 'b': 1}, limit=10)  # filter based on match conditions

How to choose a backend?

For fastest performance, use the InMemoryStore. It is a simple dict that is not persisted to disk.
If you need local persistence, I prefer the LmdbStore, but PysosStore and ShelveStore should work too.

If speed is not important, but you want cheap persistence in the cloud, use FilesystemStore with S3,GCP, or Azure.

If you are using it to load tensors for embedding or deep learning weights, use SafetensorsStore

If you need persistence in the cloud with realtime search, use one of the Providers key-values store:

• CosmosDB (Azure)
• Firestore (GCP)
• DynamoDB (AWS)
• MongoDB (Wherever it is deployed)

If you need very fast realtime, then the RedisStore is the best choice.

Backends

InMemoryStore

This object is to have a common interface for all the key-value stores. It is great for testing and for the average use case, to have a common interface which includes the search operations.

• Save/load are implemented to save/load the whole dict to/from a file, locally or on the cloud using fsspec.

from spoonbill.datastores import InMemoryStore

store = InMemoryStore()  # InMemoryDict.open() or InMemoryDict.open('path/to/file') from file

# Also works with any dict-like object
from collections import defaultdict, OrderedDict, Counter

store = InMemoryStore(defaultdict)
store = InMemoryStore(OrderedDict)
store = InMemoryStore(Counter)

LmdbStore

An LMDB key-value store based on lmdb-python-dbm. This is ideal for lists or datastores which either need persistence, are too big to fit in memory or both.
This is a Python DBM interface style wrapper around LMDB (Lightning Memory-Mapped Database)

Details

Requirements:
pip install lmdbm

from spoonbill.datastores import LmdbStore

store = LmdbStore.open('tmp.db')

PysosStore

This is ideal for lists or dictionaries which either need persistence, are too big to fit in memory or both.

There are existing alternatives like shelve, which are very good too. There main difference with pysos is that:

• only the index is kept in memory, not the values (so you can hold more data than what would fit in memory)
• it provides both persistent dicts and lists
• objects must be json "dumpable" (no cyclic references, etc.)
• it's fast (much faster than shelve on windows, but slightly slower than native dbms on linux)
• it's unbuffered by design: when the function returns, you are sure it has been written on disk
• it's safe: even if the machine crashes in the middle of a big write, data will not be corrupted
• it is platform independent, unlike shelve which relies on an underlying dbm implementation, which may vary from system to system the data is stored in a plain text format

Requirements:
pip install pysos

from spoonbill.datastores import PysosStore

store = PysosStore.open('tmp.db')

Shelve

The difference with “dbm” databases is that the values (not the keys!) in a shelf can be essentially arbitrary Python objects — anything that the pickle module can handle. This includes most class instances, recursive data types, and objects containing lots of shared sub-objects. The keys are ordinary strings.

from spoonbill.datastores import ShelveStore

store = ShelveStore.open('tmp.db')

Safetensors

This is ideal whe you want to work with tensors from disc, but it is a frozen store - no set or update.

Requirements:
pip install safetensors

• if you use tensorflow, torch, numpy or flax, youll need to install them too... duh.

from spoonbill.datastores import SafetensorsStore
import numpy as np

data = {'weight1': np.array([1, 2, 3]), 'weight2': np.array([4, 5, 6])}
SafetensorsStore.export_safetensors(data, 'tmp.db', framework=SafetensorsStore.NUMPY)
store = SafetensorsStore.open('tmp.db', framework=SafetensorsStore.NUMPY, device='cpu')

store['weight1']  # returns a numpy array
store['weight1'] = 1  # raises an error

If you must be able to have a mutable store, you can use the SafetensorsInMemoryStore.

from spoonbill.datastores import SafetensorsInMemoryStore, SafetensorsStore
import numpy as np

store = SafetensorsInMemoryStore(framework=SafetensorsStore.NUMPY)
store['weight'] = np.array([1, 2, 3])  # backed by an InMemoryStore
safetensors_store = store.export_safetensors("path")

In you want a mutable and persisted safetensors, we got you cover with the SafetensorsLmdbStore backed by the LmdbStore backend

• pip install lmdbm

from spoonbill.datastores import SafetensorsLmdbStore, SafetensorsStore
import numpy as np

store = SafetensorsLmdbStore(path='tmp.db', framework=SafetensorsStore.NUMPY)
store['weight'] = np.array([1, 2, 3])  # backed by a LmdbStore
safetensors_store = store.export_safetensors("path")

FilesystemStore

This dict is implemented as key-value files locally or on a cloud provider. It is slow, but good for as a cheap persisted key-value store. It is a wrapepr around fsspec key-value feature. Therefor it supports all the filesystems supported by fsspec (s3, gs, az, local, ftp, http, etc).

• It supports caching
• It can be exported to a local directory or other clouds (s3, gs, az, etc)

For faster applications with cloud persistence, you can use InMemoryStore/LmdbStore and save/load to the cloud after updates.

from spoonbill.datastores import FilesystemStore

# set strict to True to use redis with its default behaviour which turns keys and values to strings
store = FilesystemStore.open("s3://bucket/path/to/store")
store.save("local_dir_path")

Redis

Probably the fastest solution for key-value stores not only in python, but in general. It is a great solution.

• When strict=False any key-value can be used, otherwise only string keys and values can be used.
• When using keys with patterns -> the pattern is passed to redis keys function, so the behaviour is what you would expect from redis.
• Redis doesn't have any search for values.

Requirements:
pip install redis

from spoonbill.datastores import RedisStore

# set strict to True to use redis with its default behaviour which turns keys and values to strings
store = RedisStore.open("redis://localhost:6379/1")
store[1] = 1
assert store[1] == store["1"] == "1"

assert list(store.keys('1*')) == ['111', '1', '11']  # redis turn every key to string
assert list(store.scan('1*')) == ['111', '1', '11']  # slower but non-blocking

store = RedisStore.open("redis://localhost:6379/1", strict=False)
store[1] = lambda x: x + 1  # anything goes using cloudpickle
assert store[1](1) == 2

Serverless stores

• Recommended to use values as dict values, as they are more efficient to scan.
  • Good Example: store['key'] = {'a': 1, 'b': 2}
  • Bad Example: store['key'] = "a value which is not a dict"

Recommended using with strict=True to enjoy all the benefits of backends including searches.

Searches API Example:

from spoonbill.datastores import MongoDBStore

store = MongoDBStore()
store.keys(pattern="*", limit=10)  # scan keys to a pattern
store.values(keys=['key1', 'key2'])  # retrieve a batch of values efficiently 
store.items(conditions={'a': '1+', 'b': 1}, limit=10)  # filter based on match conditions

MongoDB

• Save/load is only implemented for strict=True.

Requirements: pip install pymongo

from spoonbill.datastores import MongoDBStore

store = MongoDBStore.open(uri='mongodb://localhost:27017/')

DynamoDB

Notes:

• It is always recommended to set values which are a dict {attribute_name: value} to enjoy all the dynamodb features.
• Keys are defined per table as either strings ('S'), numbers ('N') or bytes ('B').
• If you set a primitive number value, it will return as float (:
• cerealbox is required for retrieving multiple values with values(["key1", "key2"]):
  • pip install cerealbox

Requirements:

pip install boto3 

Firestore

Notes:

• It is recommended use dict-values {attribute_name: value} + strict=True to enjoy all the firestore features.
  • Example: store['key'] = {'feature': 'value'}
    Prerequisites:

1. Create a project in Google Cloud Platform
2. Enable Firestore API
3. Create a service account and download the json file
4. Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the path of the json file
5. Create a database in Firestore
6. Create a collection in the database
7. Install google-cloud-firestore with

pip install --upgrade google-cloud-firestore 

from spoonbill.datastores import Firestore

# this rest of the credentials are picked up from the file in the GOOGLE_APPLICATION_CREDENTIALS environment variable
store = Firestore.open(table_name="my-collection")

Azure CosmosDB

Notes:

• It is recommended use dict-values {attribute_name: value} + strict=True to enjoy all the CosmosDB features.
  • Example: store['key'] = {'feature': 'value'}
• The scans are implemented with SQL and LIKE (Regex is not implemented on Cosmos). So it is not possible to do store.keys('a*') but store.keys('a%') works.

Prerequisites: Quickstart

Requirements:

pip install azure-cosmos

from spoonbill.datastores import CosmosDBStore

store = CosmosDBStore.open(database='db',
                           container='container',
                           endpoint='endpoint',
                           credential='credential')

Modal.com

Requirements:

pip install modal-client
modal token new

The modal Dict has a different context than the other stores. For it to work, we must give it the stub at creation time and the app at runtime.

Currently, modal implemented only contains, put, get, update, len, and pop.
For the sake of consistency, we implemented the keys, values, items and the search APIs naively with another metadata modal.Dict, which makes them slow.

It is recommended to use it mostly as a cache.

The ModalStore is initiated with a stub outside the runtime with data optionally and cannot be updated outside the app by design.

Within the runtime, the context app is passed to the store to be able to update the data.

Within a function, only the name of the dict is needed.

import modal
from spoonbill.datastores import ModalStore

image = modal.Image.debian_slim().pip_install("spoonbill-framework")

name = "data" 
stub = modal.Stub("app name", **kwargs)
# with stub
store = ModalStore.open(name=name, stub=stub, data={"key": "value"}) # data is optional

@stub.function(image=image)
def foo():
  # in function
  store = ModalStore.open(name=name) 

if __name__ == "__main__":
    with stub.run() as app:
      # in stub.run context
      store = ModalStore.open(name=name, app=app) 

[UnQlite] (https://unqlite.org)([python-bindings](https://github.com/coleifer/unqlite-python))

Read the issue tracker for this database before considering using it. UnQLite has not seen any meaningful development since 2014. It is strongly recommended that you use Sqlite. Sqlite has robust support for json and is actively developed and maintained.

UnQLite is a in-process software library which implements a self-contained, serverless, zero-configuration, transactional NoSQL database engine.

Requirements:
pip install unqlite

from spoonbill.datastores import UnQLiteStore

store = UnQLiteStore.open('tmp.db') # leave empty for in-memory

Use cases

Mock data on local dictionary and cloud store in dev or production.

from spoonbill.datastores import DynamoDBStore, InMemoryStore
import os

environment = os.getenv("environment", "test")

if environment == "test":
    store = InMemoryStore.open("mock data")
elif environment == "dev":
    store = DynamoDBStore.open("dev table")
else:
    store = DynamoDBStore.open("prod table")

Real-time feature engineering with any backend

from spoonbill.datastores import RedisStore
import pandas as pd

df = pd.DataFrame({'user': [1, 2, 3]})
feature_store = RedisStore.open("features table")  # {1: {"age":20:, "sex":female",...}}


def get_user_details(x):
    default = {"age": 25, "sex": "female"}
    return pd.Series(feature_store.get(x['user'], default).values())


df[['age', 'sex']] = df.apply(get_user_details, axis=1)
df
"""
   user  age     sex
0     1   20    male
1     2   30  female
2     3   25  female
"""