What Are Lazy Queues?

• Lazy Queues are designed to store messages primarily on disk rather than in memory.
• They are optimized for use cases involving large message backlogs where minimizing memory usage is critical.

Key Characteristics

1. Disk-Based Storage – Messages are stored on disk immediately upon arrival, rather than being held in memory.
2. Low Memory Usage – Only minimal metadata for messages is kept in memory.
3. Scalability – Can handle millions of messages without consuming significant memory.
4. Message Retrieval – Retrieving messages is slower because messages are fetched from disk.
5. Durability – Messages persist on disk, reducing the risk of data loss during RabbitMQ restarts.

Trade-offs

• Latency: Fetching messages from disk is slower than retrieving them from memory.
• Throughput: Not suitable for high-throughput, low-latency applications.

Choose Lazy Queues if

• You need to handle very large backlogs of messages.
• Memory is a constraint in your system.Latency and throughput are less critical.

Implementation

Pre-requisites

1. Install and run RabbitMQ on your local machine.

docker run -it --rm --name rabbitmq -p 5672:5672 -p 15672:15672 rabbitmq:4.0-management

2. Install the pika library

pip install pika

Producer (producer.py)

This script sends a persistent message to a Lazy Queue.

import pika

# RabbitMQ connection parameters for localhost
connection_params = pika.ConnectionParameters(host="localhost")

# Connect to RabbitMQ
connection = pika.BlockingConnection(connection_params)
channel = connection.channel()

# Custom Exchange and Routing Key
exchange_name = "custom_exchange"
routing_key = "custom_routing_key"
queue_name = "lazy_queue_example"

# Declare the custom exchange
channel.exchange_declare(
    exchange=exchange_name,
    exchange_type="direct",  # Direct exchange routes messages based on the routing key
    durable=True
)

# Declare a Lazy Queue
channel.queue_declare(
    queue=queue_name,
    durable=True,
    arguments={"x-queue-mode": "lazy"}  # Configure the queue as lazy
)

# Bind the queue to the custom exchange with the routing key
channel.queue_bind(
    exchange=exchange_name,
    queue=queue_name,
    routing_key=routing_key
)

# Publish a message
message = "Hello from the Producer via Custom Exchange!"
channel.basic_publish(
    exchange=exchange_name,
    routing_key=routing_key,
    body=message,
    properties=pika.BasicProperties(delivery_mode=2)  # Persistent message
)

print(f"Message sent to Lazy Queue via Exchange: {message}")

# Close the connection
connection.close()

Consumer (consumer.py)

import pika

# RabbitMQ connection parameters for localhost
connection_params = pika.ConnectionParameters(host="localhost")

# Connect to RabbitMQ
connection = pika.BlockingConnection(connection_params)
channel = connection.channel()

# Custom Exchange and Routing Key
exchange_name = "custom_exchange"
routing_key = "custom_routing_key"
queue_name = "lazy_queue_example"

# Declare the custom exchange
channel.exchange_declare(
    exchange=exchange_name,
    exchange_type="direct",  # Direct exchange routes messages based on the routing key
    durable=True
)

# Declare the Lazy Queue
channel.queue_declare(
    queue=queue_name,
    durable=True,
    arguments={"x-queue-mode": "lazy"}  # Configure the queue as lazy
)

# Bind the queue to the custom exchange with the routing key
channel.queue_bind(
    exchange=exchange_name,
    queue=queue_name,
    routing_key=routing_key
)

# Callback function to process messages
def callback(ch, method, properties, body):
    print(f"Received message: {body.decode()}")
    ch.basic_ack(delivery_tag=method.delivery_tag)  # Acknowledge the message

# Start consuming messages
channel.basic_consume(queue=queue_name, on_message_callback=callback, auto_ack=False)

print("Waiting for messages. To exit, press CTRL+C")
try:
    channel.start_consuming()
except KeyboardInterrupt:
    print("Stopped consuming.")

# Close the connection
connection.close()

Explanation

1. Producer
   • Defines a custom exchange (custom_exchange) of type direct.
   • Declares a Lazy Queue (lazy_queue_example).
   • Binds the queue to the exchange using a routing key (custom_routing_key).
   • Publishes a persistent message via the custom exchange and routing key.
2. Consumer
   • Declares the same exchange and Lazy Queue to ensure they exist.
   • Consumes messages routed to the queue through the custom exchange and routing key.
3. Custom Exchange and Binding
   • The direct exchange type routes messages based on an exact match of the routing key.
   • Binding ensures the queue receives messages published to the exchange with the specified key.
4. Lazy Queue Behavior
   • Messages are stored directly on disk to minimize memory usage.

What Are Quorum Queues?

• Quorum Queues are distributed queues built on the Raft consensus algorithm.
• They are designed for high availability, durability, and data safety by replicating messages across multiple nodes in a RabbitMQ cluster.
• Its a replacement of Mirrored Queues.

Key Characteristics

1. Replication:
   • Messages are replicated across a quorum (a majority of nodes).
   • A quorum consists of an odd number of replicas (e.g., 3, 5, 7) to ensure a majority can elect a leader during failovers.
2. Leader-Follower Architecture:
   • Each Quorum Queue has one leader and multiple followers.
   • The leader handles all write and read operations, while followers replicate messages and provide redundancy.
3. Durability:
   • Messages are written to disk on all quorum nodes, ensuring persistence even if nodes fail.
4. High Availability:
   • If the leader node fails, RabbitMQ elects a new leader from the remaining quorum, ensuring continued operation.
5. Consistency:
   • Quorum Queues prioritize consistency over availability.
   • Messages are acknowledged only after replication is successful on a majority of nodes.
6. Message Ordering:
   • Message ordering is preserved during normal operations but may be disrupted during leader failovers.

Use Cases

• Mission-Critical Applications – Systems where message loss is unacceptable (e.g., financial transactions, order processing).
• Distributed Systems – Environments requiring high availability and fault tolerance.
• Data Safety – Applications prioritizing consistency over throughput (e.g., event logs, audit trails).

Setups

Using rabbitmqctl

rabbitmqctl add_queue quorum_queue --type quorum

Using python

channel.queue_declare(queue="quorum_queue", arguments={"x-queue-type": "quorum"})

References:

1. https://www.rabbitmq.com/docs/quorum-queues