Injecting Real-Time WebSocket Streams into SQLite Using Python and WAL Hooking

SQLite isn't the first database that comes to mind for real-time streaming — but with Python and a little creativity, we can tap into a WebSocket stream and pipe the data directly into an active SQLite instance. This article explores how to combine WebSockets, Python’s sqlite3 module, and SQLite’s Write-Ahead Logging (WAL) mode to build a fast, lightweight real-time ingestion pipeline — no cloud required. Why Stream Into SQLite? Logging IoT or edge device data Storing real-time events in offline-first apps Building dashboards without a full database server Step 1: Enable WAL Mode WAL improves concurrency and smooths out frequent writes: import sqlite3 def init_db(): conn = sqlite3.connect("live_stream.db") conn.execute("PRAGMA journal_mode=WAL;") conn.execute(""" CREATE TABLE IF NOT EXISTS stream_data ( id INTEGER PRIMARY KEY, timestamp TEXT, payload TEXT ) """) conn.commit() conn.close() Step 2: Connect to WebSocket We’ll use the websockets package to receive and log each message: import asyncio, datetime, websockets async def stream_to_sqlite(): uri = "wss://echo.websocket.org" async with websockets.connect(uri) as ws: while True: msg = await ws.recv() save(datetime.datetime.utcnow().isoformat(), msg) def save(ts, payload): conn = sqlite3.connect("live_stream.db") conn.execute("INSERT INTO stream_data (timestamp, payload) VALUES (?, ?)", (ts, payload)) conn.commit() conn.close() Step 3: Run the Pipeline if name == "main": init_db() asyncio.run(stream_to_sqlite()) Pros and Cons ✅ Pros No external services or infra Fast local writes with WAL Portable and file-based ⚠️ Cons Not distributed or cloud-native Limited concurrency under load WebSocket disconnections need handling

Apr 23, 2025 - 00:38

Injecting Real-Time WebSocket Streams into SQLite Using Python and WAL Hooking

SQLite isn't the first database that comes to mind for real-time streaming — but with Python and a little creativity, we can tap into a WebSocket stream and pipe the data directly into an active SQLite instance. This article explores how to combine WebSockets, Python’s sqlite3 module, and SQLite’s Write-Ahead Logging (WAL) mode to build a fast, lightweight real-time ingestion pipeline — no cloud required.

Why Stream Into SQLite?

Logging IoT or edge device data
Storing real-time events in offline-first apps
Building dashboards without a full database server

Step 1: Enable WAL Mode

WAL improves concurrency and smooths out frequent writes:

import sqlite3

def init_db():

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

  conn.execute("PRAGMA journal_mode=WAL;")

  conn.execute("""

    CREATE TABLE IF NOT EXISTS stream_data (

      id INTEGER PRIMARY KEY,

      timestamp TEXT,

      payload TEXT

    )

  """)

  conn.commit()

  conn.close()

Step 2: Connect to WebSocket

We’ll use the websockets package to receive and log each message:

import asyncio, datetime, websockets

async def stream_to_sqlite():

  uri = "wss://echo.websocket.org"

  async with websockets.connect(uri) as ws:

    while True:

      msg = await ws.recv()

      save(datetime.datetime.utcnow().isoformat(), msg)

def save(ts, payload):

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

  conn.execute("INSERT INTO stream_data (timestamp, payload) VALUES (?, ?)", (ts, payload))

  conn.commit()

  conn.close()

Step 3: Run the Pipeline

if name == "main":

  init_db()

  asyncio.run(stream_to_sqlite())