Build a Shared-Nothing Distributed Queue with SQLite and Python

Need a lightweight job queue that runs across multiple machines without Redis, RabbitMQ, or cloud services? In this post, we’ll build a surprisingly capable distributed queue using SQLite, Python, and a little file-locking magic. This pattern works best when you need a shared queue across local or networked disks — think cron clusters, render farms, or batch processors — without spinning up infrastructure. Why Use SQLite as a Queue? No external services to maintain Great for jobs with infrequent updates Works well on shared network storage (NFS, SMB) Step 1: Create the Queue Table We’ll use a simple table with a claimed flag and a timestamp: import sqlite3 def init_db(): conn = sqlite3.connect("queue.db") conn.execute(""" CREATE TABLE IF NOT EXISTS jobs ( id INTEGER PRIMARY KEY, task TEXT, claimed_by TEXT, claimed_at DATETIME ) """) conn.commit() conn.close() Step 2: Enqueue Jobs Add a job by inserting a row: def enqueue(task): conn = sqlite3.connect("queue.db") conn.execute("INSERT INTO jobs (task) VALUES (?)", (task,)) conn.commit() conn.close() Step 3: Claim a Job with Locking To safely claim jobs across machines, we’ll use a conditional update: import datetime, socket def claim_job(): conn = sqlite3.connect("queue.db", isolation_level="IMMEDIATE") conn.row_factory = sqlite3.Row hostname = socket.gethostname() now = datetime.datetime.utcnow().isoformat() cur = conn.execute(""" UPDATE jobs SET claimed_by = ?, claimed_at = ? WHERE id = ( SELECT id FROM jobs WHERE claimed_by IS NULL LIMIT 1 ) RETURNING * """, (hostname, now)) job = cur.fetchone() conn.commit() conn.close() return job Step 4: Process and Delete the Job def process(job): print(f"Processing: {job['task']}") conn = sqlite3.connect("queue.db") conn.execute("DELETE FROM jobs WHERE id = ?", (job["id"],)) conn.commit() conn.close() Step 5: Worker Loop This can run in a cron or systemd timer on multiple machines: if name == "main": init_db() job = claim_job() if job: process(job) Pros and Cons ✅ Pros Zero external dependencies Easy to inspect/debug Works across NFS or shared volumes ⚠️ Cons Not suitable for high-throughput workloads Concurrent writes are serialized SQLite file locking can behave differently per OS

import sqlite3

def init_db():

  conn = sqlite3.connect("queue.db")

  conn.execute("""

    CREATE TABLE IF NOT EXISTS jobs (

      id INTEGER PRIMARY KEY,

      task TEXT,

      claimed_by TEXT,

      claimed_at DATETIME

    )

  """)

  conn.commit()

  conn.close()

def enqueue(task):

  conn = sqlite3.connect("queue.db")

  conn.execute("INSERT INTO jobs (task) VALUES (?)", (task,))

  conn.commit()

  conn.close()

import datetime, socket

def claim_job():

  conn = sqlite3.connect("queue.db", isolation_level="IMMEDIATE")

  conn.row_factory = sqlite3.Row

  hostname = socket.gethostname()

  now = datetime.datetime.utcnow().isoformat()

cur = conn.execute("""

    UPDATE jobs

    SET claimed_by = ?, claimed_at = ?

    WHERE id = (

      SELECT id FROM jobs WHERE claimed_by IS NULL LIMIT 1

    )

    RETURNING *

  """, (hostname, now))

job = cur.fetchone()

  conn.commit()

  conn.close()

  return job

def process(job):

  print(f"Processing: {job['task']}")

conn = sqlite3.connect("queue.db")

  conn.execute("DELETE FROM jobs WHERE id = ?", (job["id"],))

  conn.commit()

  conn.close()

if name == "main":

  init_db()

  job = claim_job()

  if job:

    process(job)