pgmq-sqlalchemy 0.1.2

More flexible PGMQ Postgres extension Python client that using sqlalchemy ORM, supporting both async and sync engines, sessionmakers or built from dsn.

pgmq-sqlalchemy

More flexible PGMQ Postgres extension Python client that using sqlalchemy ORM, supporting both async and sync engines, sessionmakers or built from dsn.

Table of Contents

• pgmq-sqlalchemy
  • Features
  • Installation
  • Getting Started
    • Postgres Setup
    • Usage
  • Issue/ Contributing / Development
  • TODO

Features

• Supports async and sync engines and sessionmakers, or built from dsn.
• Automatically creates pgmq (or pg_partman) extension on the database if not exists.
• Supports all postgres DBAPIs supported by sqlalchemy.

  e.g. psycopg, psycopg2, asyncpg ..
  See SQLAlchemy Postgresql Dialects

Installation

Install with pip:

pip install pgmq-sqlalchemy

Install with additional DBAPIs packages:

pip install "pgmq-sqlalchemy[asyncpg]"
pip install "pgmq-sqlalchemy[psycopg2-binary]"
# pip install "pgmq-sqlalchemy[postgres-python-driver]"

Getting Started

Postgres Setup

Prerequisites: Postgres with PGMQ extension installed.
For quick setup:

docker run -d --name postgres -e POSTGRES_PASSWORD=postgres -p 5432:5432 quay.io/tembo/pg16-pgmq:latest

For more information, see PGMQ

Usage

[!NOTE]
Check pgmq-sqlalchemy Document for more examples and detailed usage.

For dispatcher.py:

from typing import List
from pgmq_sqlalchemy import PGMQueue

postgres_dsn = 'postgresql://postgres:postgres@localhost:5432/postgres'

pgmq = PGMQueue(dsn=postgres_dsn)
pgmq.create_queue('my_queue')

msg = {'key': 'value', 'key2': 'value2'}
msg_id:int = pgmq.send('my_queue', msg)

# could also send a list of messages
msg_ids:List[int] = pgmq.send_batch('my_queue', [msg, msg])

For consumer.py:

from pgmq_sqlalchemy import PGMQueue
from pgmq_sqlalchemy.schema import Message

postgres_dsn = 'postgresql://postgres:postgres@localhost:5432/postgres'

pgmq = PGMQueue(dsn=postgres_dsn)

# read a single message
msg:Message = pgmq.read('my_queue')

# read a batch of messages
msgs:List[Message] = pgmq.read_batch('my_queue', 10)

For monitor.py:

from pgmq_sqlalchemy import PGMQueue
from pgmq_sqlalchemy.schema import QueueMetrics

postgres_dsn = 'postgresql://postgres:postgres@localhost:5432/postgres'

pgmq = PGMQueue(dsn=postgres_dsn)

# get queue metrics
metrics:QueueMetrics = pgmq.metrics('my_queue')
print(metrics.queue_length)
print(metrics.total_messages)

Issue/ Contributing / Development

Welcome to open an issue or pull request !
See Development on Online Document or CONTRIBUTING.md for more information.

TODO

• Add time-based partition option and validation to create_partitioned_queue method.
• Read(single/batch) Archive Table ( read_archive method )
• Detach Archive Table ( detach_archive method )
• Add set_vt utils method.