simplebroker 2.3.1

A lightweight message queue backed by SQLite

SimpleBroker

A lightweight message queue backed by SQLite. No setup required, just works.

$ pipx install simplebroker
$ broker write tasks "ship it 🚀"
$ broker read tasks
ship it 🚀

SimpleBroker is a zero-configuration message queue that runs anywhere Python runs. It's designed to be simple enough to understand in an afternoon, yet powerful enough for real work.

Table of Contents

• Features
• Use Cases
• Installation
• Quick Start
• Command Reference
  • Global Options
  • Commands
  • Command Options
  • Exit Codes
• Critical Safety Notes
  • Potential Data Loss with watch
  • Safe Message Handling
• Core Concepts
  • Timestamps as Message IDs
  • JSON for Safe Processing
  • Checkpoint-based Processing
• Common Patterns
• Real-time Queue Watching
• Python API
• Performance & Tuning
• Architecture & Technical Details
• Development & Contributing
• License

Features

• Zero configuration - No servers, daemons, or complex setup
• SQLite-backed - Rock-solid reliability with true ACID guarantees
• Concurrent safe - Multiple processes can read/write simultaneously
• Simple CLI - Intuitive commands that work with pipes and scripts
• Portable - Each directory gets its own isolated .broker.db
• Fast - 1000+ messages/second throughput
• Lightweight - ~2500 lines of code, no external dependencies
• Real-time - Built-in watcher for event-driven workflows

Use Cases

• Shell Scripting: Decouple stages of a complex script
• Background Jobs: Manage tasks for cron jobs or systemd services
• Development: Simple message queue for local development without Docker
• Data Pipelines: Pass file paths or data chunks between processing steps
• CI/CD Pipelines: Coordinate build stages without external dependencies
• Log Processing: Buffer logs before aggregation or analysis
• Simple IPC: Communication between processes on the same machine

Good for: Scripts, cron jobs, small services, development
Not for: Distributed systems, pub/sub, high-frequency trading

Installation

# Use pipx for global installation (recommended)
pipx install simplebroker

# Or install with uv to use as a library
uv add simplebroker

# Or with pip
pip install simplebroker

The CLI is available as both broker and simplebroker.

Requirements:

• Python 3.8+
• SQLite 3.35+ (released March 2021) - required for DELETE...RETURNING support

Quick Start

# Write a message
$ broker write myqueue "Hello, World!"

# Read the message (removes it)
$ broker read myqueue
Hello, World!

# Write from stdin
$ echo "another message" | broker write myqueue -

# Read all messages at once
$ broker read myqueue --all

# Peek without removing
$ broker peek myqueue

# Move messages between queues
$ broker move myqueue processed
$ broker move errors retry --all

# List all queues
$ broker list
myqueue: 3
processed: 1

# Broadcast to all queues
$ broker broadcast "System maintenance at 5pm"

# Clean up when done
$ broker --cleanup

Command Reference

Global Options

• -d, --dir PATH - Use PATH instead of current directory
• -f, --file NAME - Database filename or absolute path (default: .broker.db)
  • If an absolute path is provided, the directory is extracted automatically
  • Cannot be used with -d if the directories don't match
• -q, --quiet - Suppress non-error output
• --cleanup - Delete the database file and exit
• --vacuum - Remove claimed messages and exit
• --version - Show version information
• --help - Show help message

Commands

Command	Description
write <queue> <message|->	Add message to queue (use - for stdin)
read <queue> [options]	Remove and return message(s)
peek <queue> [options]	Return message(s) without removing
move <source> <dest> [options]	Atomically transfer messages between queues
list [--stats]	Show queues and message counts
delete <queue> [-m <id>]	Delete queue or specific message (marks for removal; use --vacuum to reclaim space)
delete --all	Delete all queues (marks for removal; use --vacuum to reclaim space)
broadcast <message|->	Send message to all existing queues
watch <queue> [options]	Watch queue for new messages

Command Options

Common options for read/peek/move:

• --all - Process all messages
• --json - Output as line-delimited JSON (includes timestamps)
• -t, --timestamps - Include timestamps in output
• -m <id> - Target specific message by its 19-digit timestamp ID
• --since <timestamp> - Process messages newer than timestamp

Watch options:

• --peek - Monitor without consuming
• --move <dest> - Continuously drain to destination queue
• --quiet - Suppress startup message

Timestamp formats for --since:

• ISO 8601: 2024-01-15T14:30:00Z or 2024-01-15 (midnight UTC)
• Unix seconds: 1705329000 or 1705329000s
• Unix milliseconds: 1705329000000ms
• Unix nanoseconds/Native hybrid: 1837025672140161024 or 1837025672140161024ns

Best practice: Heuristics are used to distinguish between different values for interactive use, but explicit suffixes (s/ms/ns) are recommended for clarity if referring to particular times.

Exit Codes

• 0 - Success (returns 0 even when no messages match filters like --since)
• 1 - General error (e.g., database access error, invalid arguments)
• 2 - Queue empty, no matching messages, or invalid message ID format (only when queue is actually empty, no messages match the criteria, or the provided message ID has an invalid format)

Note: The delete command marks messages as "claimed" for performance. Use --vacuum to permanently remove them.

Critical Safety Notes

Safe Message Handling

Messages can contain any characters including newlines, control characters, and shell metacharacters:

• Shell injection risks - When piping output to shell commands, malicious message content could execute unintended commands
• Special characters - Messages containing newlines or other special characters can break shell pipelines that expect single-line output
• Queue names - Limited to alphanumeric + underscore/hyphen/period (cannot start with hyphen or period)
• Message size - Limited to 10MB

Always use --json for safe handling - see examples below.

Robust message handling with watch

When using watch in its default consuming mode, messages are permanently removed from the queue before your script or handler processes them. If your script fails or crashes, the message is lost. For critical data, you must use a safe processing pattern (move or peek-then-delete) that ensures that your data is not removed until you can acknowledge receipt. Example:

#!/bin/bash
# safe-worker.sh - A robust worker using the peek-and-acknowledge pattern

# Watch in peek mode, which does not remove messages
broker watch tasks --peek --json | while IFS= read -r line; do
    message=$(echo "$line" | jq -r '.message')
    timestamp=$(echo "$line" | jq -r '.timestamp')
    
    echo "Processing message ID: $timestamp"
    if process_task "$message"; then
        # Success: remove the specific message by its unique ID
        broker delete tasks -m "$timestamp"
    else
        echo "Failed to process, message remains in queue for retry." >&2
        # Optional: move to a dead-letter queue
        # echo "$message" | broker write failed_tasks -
    fi
done

Core Concepts

Timestamps as Message IDs

Every message receives a unique 64-bit number that serves dual purposes as a timestamp and unique message ID. Timestamps are always included in JSON output. Timestamps can be included in regular output by passing the -t/--timestamps flag.

Timestamps are:

• Unique - No collisions even with concurrent writers (enforced by database constraint)
• Time-ordered - Natural chronological sorting
• Efficient - 64-bit integers, not UUIDs
• Meaningful - Can extract creation time from the ID

The format:

• High 52 bits: microseconds since Unix epoch
• Low 12 bits: logical counter for sub-microsecond ordering
• Similar to Twitter's Snowflake IDs or UUID7

JSON for Safe Processing

Messages with newlines or special characters can break shell pipelines. Use --json to avoid shell issues:

# Problem: newlines break line counting
$ broker write alerts "ERROR: Database connection failed\nRetrying in 5 seconds..."
$ broker read alerts | wc -l
2  # Wrong! One message counted as two

# Solution: JSON output (line-delimited)
$ broker write alerts "ERROR: Database connection failed\nRetrying in 5 seconds..."
$ broker read alerts --json
{"message": "ERROR: Database connection failed\nRetrying in 5 seconds...", "timestamp": 1837025672140161024}

# Parse safely with jq
$ broker read alerts --json | jq -r '.message'
ERROR: Database connection failed
Retrying in 5 seconds...

Checkpoint-based Processing

Use --since for resumable processing:

# Save checkpoint after processing
$ result=$(broker read tasks --json)
$ checkpoint=$(echo "$result" | jq '.timestamp')

# Resume from checkpoint
$ broker read tasks --all --since "$checkpoint"

# Or use human-readable timestamps
$ broker read tasks --all --since "2024-01-15T14:30:00Z"

Common Patterns

Basic Worker Loop

while msg=$(broker read work 2>/dev/null); do
    echo "Processing: $msg"
    # do work...
done

Multiple Queues

# Different queues for different purposes
$ broker write emails "send welcome to [e-mail]"
$ broker write logs "2023-12-01 system started"
$ broker write metrics "cpu_usage:0.75"

$ broker list
emails: 1
logs: 1
metrics: 1

Fan-out with Broadcast

# Send to all queues at once
$ broker broadcast "shutdown signal"

# Each worker reads from its own queue
$ broker read worker1  # -> "shutdown signal"
$ broker read worker2  # -> "shutdown signal"

Note: Broadcast sends to all existing queues at execution time. There's a small race window for queues created during broadcast.

Unix Tool Integration

# Store command output
$ df -h | broker write monitoring -

# Process files through a queue
$ find . -name "*.log" | while read f; do
    broker write logfiles "$f"
done

# Parallel processing with xargs
$ broker read logfiles --all | xargs -P 4 -I {} process_log {}

# Remote queue via SSH
$ echo "remote task" | ssh server "cd /app && broker write tasks -"
$ ssh server "cd /app && broker read tasks"


### Integration with Unix Tools

```bash
# Pipe to queue
$ df -h | broker write monitoring -

# Store command output
$ df -h | broker write monitoring -
$ broker peek monitoring

# Process files through a queue
$ find . -name "*.log" | while read f; do
    broker write logfiles "$f"
done

# Parallel processing with xargs
$ broker read logfiles --all | xargs -P 4 -I {} process_log {}

# Use absolute paths for databases in specific locations
$ broker -f /var/lib/myapp/queue.db write tasks "backup database"
$ broker -f /var/lib/myapp/queue.db read tasks

# Reserving work using move
$ msg_json=$(broker move todo in-process --json 2>/dev/null)
  if [ -n "$msg_json" ]; then
      msg_id=$(echo "$msg_json" | jq -r '.[0].id')
      msg_data=$(echo "$msg_json" | jq -r '.[0].data')

      echo "Processing message $msg_id: $msg_data"

      # Process the message here
      # ...

      # Delete after successful processing
      broker delete in-process -m "$msg_id"
  else
      echo "No messages to process"
  fi

Dead Letter Queue Pattern

# Process messages, moving failures to DLQ
while msg=$(broker read tasks); do
    if ! process_task "$msg"; then
        echo "$msg" | broker write dlq -
    fi
done

# Retry failed messages
broker move dlq tasks --all

Resilient Worker with Checkpointing

#!/bin/bash
# resilient-worker.sh - Process messages with checkpoint recovery

QUEUE="events"
CHECKPOINT_FILE="/var/lib/myapp/checkpoint"
BATCH_SIZE=100

# Load last checkpoint (default to 0 if first run)
last_checkpoint=$(cat "$CHECKPOINT_FILE" 2>/dev/null || echo 0)
echo "Starting from checkpoint: $last_checkpoint"

while true; do
    # Check if there are messages newer than our checkpoint
    if ! broker peek "$QUEUE" --json --since "$last_checkpoint" >/dev/null 2>&1; then
        echo "No new messages, sleeping..."
        sleep 5
        continue
    fi
    
    echo "Processing new messages..."
    
    # Process messages one at a time to avoid data loss
    processed=0
    while [ $processed -lt $BATCH_SIZE ]; do
        # Read exactly one message newer than checkpoint
        message_data=$(broker read "$QUEUE" --json --since "$last_checkpoint" 2>/dev/null)
        
        # Check if we got a message
        if [ -z "$message_data" ]; then
            echo "No more messages to process"
            break
        fi
        
        # Extract message and timestamp
        message=$(echo "$message_data" | jq -r '.message')
        timestamp=$(echo "$message_data" | jq -r '.timestamp')
        
        # Process the message
        echo "Processing: $message"
        if ! process_event "$message"; then
            echo "Error processing message, will retry on next run"
            # Exit without updating checkpoint - failed message will be reprocessed
            exit 1
        fi
        
        # Atomically update checkpoint ONLY after successful processing
        echo "$timestamp" > "$CHECKPOINT_FILE.tmp"
        mv "$CHECKPOINT_FILE.tmp" "$CHECKPOINT_FILE"
        
        # Update our local variable for next iteration
        last_checkpoint="$timestamp"
        processed=$((processed + 1))
    done
    
    if [ $processed -eq 0 ]; then
        echo "No messages processed, sleeping..."
        sleep 5
    else
        echo "Batch complete, processed $processed messages"
    fi
done

Key features:

• No data loss from pipe buffering - Reads messages one at a time
• Atomic checkpoint updates - Uses temp file + rename for crash safety
• Per-message checkpointing - Updates checkpoint after each successful message
• Batch processing - Processes up to BATCH_SIZE messages at a time for efficiency
• Failure recovery - On error, exits without updating checkpoint so failed message is retried

Real-time Queue Watching

The watch command provides three modes for monitoring queues:

1. Consume (default): Process and remove messages from the queue
2. Peek (--peek): Monitor messages without removing them
3. Move (--move DEST): Drain ALL messages to another queue

# Start watching a queue (consumes messages)
$ broker watch tasks

# Watch without consuming (peek mode)
$ broker watch tasks --peek

# Watch with JSON output (timestamps always included)
$ broker watch tasks --json
{"message": "task 1", "timestamp": 1837025672140161024}

# Continuously drain one queue to another
$ broker watch source_queue --move destination_queue

The watcher uses an efficient polling strategy:

• Burst mode: First 100 checks with zero delay for immediate message pickup
• Smart backoff: Gradually increases polling interval to 0.1s maximum
• Low overhead: Uses SQLite's data_version to detect changes without querying
• Graceful shutdown: Handles Ctrl-C (SIGINT) cleanly

Move Mode (--move)

The --move option provides continuous queue-to-queue message migration:

# Like: tail -f /var/log/app.log | tee -a /var/log/processed.log
$ broker watch source_queue --move dest_queue

Key characteristics:

• Drains entire queue: Moves ALL messages from source to destination
• Atomic operation: Each message is atomically moved before being displayed
• No filtering: Incompatible with --since (would leave messages stranded)
• Concurrent safe: Multiple move watchers can run safely without data loss

Python API

SimpleBroker also provides a Python API for more advanced use cases:

from simplebroker import Queue, QueueWatcher
import logging

# Basic usage
with Queue("tasks") as q:
    q.write("process order 123")
    message = q.read()  # Returns: "process order 123"

# Safe peek-and-acknowledge pattern (recommended for critical data)
def process_message(message: str, timestamp: int):
    """Process message and acknowledge only on success."""
    logging.info(f"Processing: {message}")
    
    # Simulate processing that might fail
    if "error" in message:
        raise ValueError("Simulated processing failure")
    
    # If we get here, processing succeeded
    # Now explicitly acknowledge by deleting the message
    with Queue("tasks") as q:
        q.delete(message_id=timestamp)
    logging.info(f"Message {timestamp} acknowledged")

def handle_error(exception: Exception, message: str, timestamp: int) -> bool:
    """Log error and optionally move to dead-letter queue."""
    logging.error(f"Failed to process message {timestamp}: {exception}")
    # Message remains in queue for retry since we're using peek=True
    
    # Optional: After N retries, move to dead-letter queue
    # Queue("errors").write(f"{timestamp}:{message}:{exception}")
    
    return True  # Continue watching

# Use peek=True for safe mode - messages aren't removed until explicitly acknowledged
watcher = QueueWatcher(
    queue=Queue("tasks"),
    handler=process_message,
    error_handler=handle_error,
    peek=True  # True = safe mode - just observe, don't consume
)

# Start watching (blocks until stopped)
try:
    watcher.watch()
except KeyboardInterrupt:
    print("Watcher stopped by user")

Thread-Based Background Processing

Use run_in_thread() to run watchers in background threads:

from pathlib import Path
from simplebroker import QueueWatcher

def handle_message(msg: str, ts: int):
    print(f"Processing: {msg}")

# Create watcher with database path (recommended for thread safety)
watcher = QueueWatcher(
    Path("my.db"),
    "orders",
    handle_message
)

# Start in background thread
thread = watcher.run_in_thread()

# Do other work...

# Stop when done
watcher.stop()
thread.join()

Context Manager Support

For cleaner resource management, watchers can be used as context managers which automatically start the thread and ensure proper cleanup:

import time
from simplebroker import QueueWatcher

def handle_message(msg: str, ts: int):
    print(f"Received: {msg}")

# Automatic thread management with context manager
with QueueWatcher("my.db", "notifications", handle_message) as watcher:
    # Thread is started automatically
    # Do other work while watcher processes messages
    time.sleep(10)
    
# Thread is automatically stopped and joined when exiting the context
# Ensures proper cleanup even if an exception occurs

Async Integration Patterns

SimpleBroker is synchronous by design for simplicity, but can be easily integrated with async applications:

import asyncio
import concurrent.futures
from simplebroker import Queue

class AsyncQueue:
    """Async wrapper for SimpleBroker Queue using thread pool executor."""
    
    def __init__(self, queue_name: str, db_path: str = ".broker.db"):
        self.queue_name = queue_name
        self.db_path = db_path
        self._executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
        
    async def write(self, message: str) -> None:
        """Write message asynchronously."""
        loop = asyncio.get_event_loop()
        def _write():
            with Queue(self.queue_name, self.db_path) as q:
                q.write(message)
        await loop.run_in_executor(self._executor, _write)
    
    async def read(self) -> str | None:
        """Read message asynchronously."""
        loop = asyncio.get_event_loop()
        def _read():
            with Queue(self.queue_name, self.db_path) as q:
                return q.read()
        return await loop.run_in_executor(self._executor, _read)

# Usage
async def main():
    tasks_queue = AsyncQueue("tasks")
    
    # Write messages concurrently
    await asyncio.gather(
        tasks_queue.write("Task 1"),
        tasks_queue.write("Task 2"),
        tasks_queue.write("Task 3")
    )
    
    # Read messages
    while msg := await tasks_queue.read():
        print(f"Got: {msg}")

For advanced use cases requiring direct database access, you can use the DBConnection context manager:

from simplebroker.db import DBConnection

# Safe database connection management for cross-queue operations
with DBConnection("myapp.db") as conn:
    db = conn.get_connection()
    
    # Perform cross-queue operations
    queues = db.get_queue_stats()
    for queue_name, stats in queues.items():
        print(f"{queue_name}: {stats['pending']} pending")
    
    # Broadcast to all queues
    db.broadcast("System maintenance at 5pm")

Key async integration strategies:

1. Use Queue API: Prefer the high-level Queue class for single-queue operations
2. Thread Pool Executor: Run SimpleBroker's sync methods in threads
3. One Queue Per Operation: Create fresh Queue instances for thread safety
4. DBConnection for Advanced Use: Use DBConnection context manager for cross-queue operations

See examples/async_wrapper.py for a complete async wrapper implementation including:

• Async context manager for proper cleanup
• Background watcher with asyncio coordination
• Streaming message consumption
• Concurrent queue operations

Advanced: Custom Extensions

Note: This is an advanced example showing how to extend SimpleBroker's internals. Most users should use the standard Queue API.

from simplebroker.db import BrokerDB, DBConnection
from simplebroker import Queue

class PriorityQueueSystem:
    """Example: Priority queue system using multiple standard queues."""
    
    def __init__(self, db_path: str = ".broker.db"):
        self.db_path = db_path
    
    def write_with_priority(self, base_queue: str, message: str, priority: int = 0):
        """Write message with priority (higher = more important)."""
        queue_name = f"{base_queue}_p{priority}"
        with Queue(queue_name, self.db_path) as q:
            q.write(message)
    
    def read_highest_priority(self, base_queue: str) -> str | None:
        """Read from highest priority queue first."""
        # Check queues in priority order
        for priority in range(9, -1, -1):
            queue_name = f"{base_queue}_p{priority}"
            with Queue(queue_name, self.db_path) as q:
                msg = q.read()
                if msg:
                    return msg
        return None

# For even more advanced use requiring database subclassing:
class CustomBroker(BrokerDB):
    """Example of extending BrokerDB directly (advanced users only)."""
    
    def custom_operation(self):
        # Access internal database methods
        with self._lock:
            # Your custom SQL operations here
            pass

See examples/ directory for more patterns including async processing and custom runners.

Performance & Tuning

• Throughput: 1000+ messages/second on typical hardware
• Latency: <10ms for write, <10ms for read
• Scalability: Tested with 100k+ messages per queue
• Optimization: Use --all for bulk operations

Environment Variables

Click to see all configuration options

Core Settings:

• BROKER_BUSY_TIMEOUT - SQLite busy timeout in milliseconds (default: 5000)
• BROKER_CACHE_MB - SQLite page cache size in megabytes (default: 10)
  • Larger cache improves performance for repeated queries and large scans
  • Recommended: 10-50 MB for typical workloads, 100+ MB for heavy use
• BROKER_SYNC_MODE - SQLite synchronous mode: FULL, NORMAL, or OFF (default: FULL)
  • FULL: Maximum durability, safe against power loss (default)
  • NORMAL: ~25% faster writes, safe against app crashes, small risk on power loss
• BROKER_WAL_AUTOCHECKPOINT - WAL auto-checkpoint threshold in pages (default: 1000)
  • Controls when SQLite automatically moves WAL data to the main database
  • Default of 1000 pages ≈ 1MB (with 1KB page size)
  • Increase for high-traffic scenarios to reduce checkpoint frequency
  • Set to 0 to disable automatic checkpoints (manual control only)
  • OFF: Fastest but unsafe - only for testing or non-critical data

Read Performance:

• BROKER_READ_COMMIT_INTERVAL - Number of messages to read before committing in --all mode (default: 1)
  • Default of 1 provides exactly-once delivery guarantee
  • Increase for better performance with at-least-once delivery guarantee

Vacuum Settings:

• BROKER_AUTO_VACUUM - Enable automatic vacuum of claimed messages (default: true)
• BROKER_VACUUM_THRESHOLD - Number of claimed messages before auto-vacuum triggers (default: 10000)
• BROKER_VACUUM_BATCH_SIZE - Number of messages to delete per vacuum batch (default: 1000)
• BROKER_VACUUM_LOCK_TIMEOUT - Seconds before a vacuum lock is considered stale (default: 300)

Watcher Tuning:

• SIMPLEBROKER_INITIAL_CHECKS - Number of checks with zero delay (default: 100)
• SIMPLEBROKER_MAX_INTERVAL - Maximum polling interval in seconds (default: 0.1)

Example configurations:

# High-throughput configuration
export BROKER_SYNC_MODE=NORMAL
export BROKER_READ_COMMIT_INTERVAL=100
export SIMPLEBROKER_INITIAL_CHECKS=1000

# Low-latency configuration  
export SIMPLEBROKER_MAX_INTERVAL=0.01
export BROKER_CACHE_MB=50

# Power-saving configuration
export SIMPLEBROKER_INITIAL_CHECKS=50
export SIMPLEBROKER_MAX_INTERVAL=0.5

Architecture & Technical Details

Database Schema and Internals

SimpleBroker uses a single SQLite database with Write-Ahead Logging (WAL) enabled:

CREATE TABLE messages (
    id INTEGER PRIMARY KEY AUTOINCREMENT,  -- Ensures strict FIFO ordering
    queue TEXT NOT NULL,
    body TEXT NOT NULL,
    ts INTEGER NOT NULL UNIQUE,            -- Unique hybrid timestamp serves as message ID
    claimed INTEGER DEFAULT 0              -- For read optimization
);

Key design decisions:

• The id column guarantees global FIFO ordering across all processes
• The ts column serves as the public message identifier with uniqueness enforced
• WAL mode enables concurrent readers and writers
• Claim-based deletion enables ~3x faster reads

Concurrency and Delivery Guarantees

Exactly-Once Delivery: Read and move operations use atomic DELETE...RETURNING operations. A message is delivered exactly once to a consumer by default.

FIFO Ordering: Messages are always read in the exact order they were written to the database, regardless of which process wrote them. This is guaranteed by SQLite's autoincrement and row-level locking.

Message Lifecycle:

1. Write Phase: Message inserted with unique timestamp
2. Claim Phase: Read marks message as "claimed" (fast, logical delete)
3. Vacuum Phase: Background process permanently removes claimed messages

This optimization is transparent - messages are still delivered exactly once.

Security Considerations

• Queue names: Validated (alphanumeric + underscore + hyphen + period only)
• Message size: Limited to 10MB
• Database files: Created with 0600 permissions (user-only)
• SQL injection: Prevented via parameterized queries
• Message content: Not validated - can contain any text including shell metacharacters

Development & Contributing

SimpleBroker uses uv for package management and ruff for linting.

# Clone the repository
git clone [e-mail]:VanL/simplebroker.git
cd simplebroker

# Install development environment
uv sync --all-extras

# Run tests
uv run pytest              # Fast tests only
uv run pytest -m ""        # All tests including slow ones

# Lint and format
uv run ruff check --fix simplebroker tests
uv run ruff format simplebroker tests
uv run mypy simplebroker

Contributing guidelines:

1. Keep it simple - the entire codebase should stay understandable
2. Maintain backward compatibility
3. Add tests for new features
4. Update documentation
5. Run linting and tests before submitting PRs

License

MIT © 2025 Van Lindberg

Acknowledgments

Built with Python, SQLite, and the Unix philosophy.