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Python library for managing and persisting named Python Queues with a variety of backend choices

Project description

dq

A python library for managing and persisting named Python Queues

Installation

pip install pydq

Some backends may require extra packages:

pip install pydq[aws]

Basic Usage

with dq_provider('name') as dq:
    # Query the last 10 entries with qid='item_id'
    dq(qid='item_id', start_time=datetime(1, 1, 1), end_time=datetime.utcnow(), limit=10)
    item = dq.get()
    # queue items should be dictionaries with 3 keys: qid, ts, and val
    dq.put({'qid': 'item_id', 'ts': '2019-09-11T03:23:54Z', 'val': 'foo'})

note: specifying qid in the query is optional, if omitted all items between the start and end time are returned

Supported Backends:

Local Storage

  • SQLite:
from dq.local import SQLite as dq_provider

Cloud Storage

  • Buffered DynamoDB
from dq.cloud import BufferedDDB as dq_provider

note: cloud storage is created lazily; the first call to one of these providers will create the resources in the cloud.

Additional API

Some queue methods accept a forget argument that will tell the queue object whether the operation should be persisted. This always defaults to False.

dq.get(forget=True)  # the returned item will be deleted from persistence
dq.put(item, forget=True)    # item will not be persisted
dq.get_all(forget=True)  # Easy way to delete all items in a queue
dq.put_all(items, forget=False)
dq.get_to_dataframe()    # install with pandas extra. Returned items are not deleted
dq.put_from_dataframe(df)    # items will be persisted
dq.clear(forget=True)    # another way to delete all items in a queue

Configuration queues

dq is great for managing simple JSON configuration too. And we get change tracking for free!

from dq.helpers import DqConfig
from dq.local import SQLite
with DqConfig(name='ApplicationConfig', dq_provider=SQLite) as config:
    app_id = config['app_id']
    last_start = config['last_start']
    config['app_status'] = '#1best'

Project details


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