🔧 The Kafka Conundrum: Choosing Between Consumer Groups and Partitions for Efficient Message Consumption

In this Article will mostly talk about the challenges that I faced when i have a application which was using kafka (i.e AWS MSK).

I will discuss the use case here first,

Use Case: Leveraging Kafka in Modern Applications

In today's fast-paced digital landscape, many modern applications rely on Kafka as a messaging system to process and store records. These records can take various formats, including JSON, Avro, or others.

The Challenge: Consuming Messages from Kafka

When implementing an API that interacts with Kafka, one of the primary challenges arises when writing a consumer API to retrieve messages from Kafka. To illustrate this, let's consider a Python code example for a Kafka consumer:

Consumer.py

from confluent_kafka import Consumer

c = Consumer({
    'bootstrap.servers': 'mybroker',
    'group.id': 'mygroup',
    'auto.offset.reset': 'earliest'
})

c.subscribe(['mytopic'])

while True:
    msg = c.poll(1.0)

    if msg is None:
        continue
    if msg.error():
        print("Consumer error: {}".format(msg.error()))
        continue

    print('Received message: {}'.format(msg.value().decode('utf-8')))

c.close()

Producer.py

from confluent_kafka import Producer

p = Producer({'bootstrap.servers': 'mybroker1,mybroker2'})

def delivery_report(err, msg):
    """ Called once for each message produced to indicate delivery result.
        Triggered by poll() or flush(). """
    if err is not None:
        print('Message delivery failed: {}'.format(err))
    else:
        print('Message delivered to {} [{}]'.format(msg.topic(), msg.partition()))

for data in some_data_source:
    # Trigger any available delivery report callbacks from previous produce() calls
    p.poll(0)

    # Asynchronously produce a message. The delivery report callback will
    # be triggered from the call to poll() above, or flush() below, when the
    # message has been successfully delivered or failed permanently.
    p.produce('mytopic', data.encode('utf-8'), callback=delivery_report)

# Wait for any outstanding messages to be delivered and delivery report
# callbacks to be triggered.
p.flush()

Use AdminClient:

create a topic, list and other operations.

from confluent_kafka.admin import AdminClient, NewTopic

a = AdminClient({'bootstrap.servers': 'mybroker'})

new_topics = [NewTopic(topic, num_partitions=3, replication_factor=1) for topic in ["topic1", "topic2"]]
# Note: In a multi-cluster production scenario, it is more typical to use a replication_factor of 3 for durability.

# Call create_topics to asynchronously create topics. A dict
# of <topic,future> is returned.
fs = a.create_topics(new_topics)

# Wait for each operation to finish.
for topic, f in fs.items():
    try:
        f.result()  # The result itself is None
        print("Topic {} created".format(topic))
    except Exception as e:
        print("Failed to create topic {}: {}".format(topic, e))

you can install this package:
$ pip install confluent-kafka

The Challenges of Kafka Consumer Groups and Partitions

When building an application that leverages Kafka as a messaging system, one of the critical components is the consumer API. This API is responsible for retrieving messages from Kafka, and its implementation can significantly impact the overall performance and reliability of the application. In this article, we'll delve into the challenges of using Kafka consumer groups and partitions, highlighting their pros and cons.

Consumer Groups: Balancing Convenience and Flexibility

Kafka consumer groups offer a convenient way to manage message consumption, as they automatically maintain the offset of the last consumed message. This approach provides several benefits:

• Offset management: With consumer groups, you don't need to worry about explicitly managing offsets, as Kafka handles this task for you. #### Scalability: Consumer groups can scale with the number of partitions in your Kafka topic, ensuring that message consumption is not affected by partition growth.
• Flexibility: You can create multiple consumer groups, each consuming records from all available partitions.

However, consumer groups also have some limitations:

• Limited offset control: With consumer groups, you cannot start reading from a specific offset unless you have previously committed to that offset.
• Commit-based consumption: If you want to read earlier messages before your last commit, it's not possible with consumer groups.

Partitions: Customizable but Challenging

In contrast, using partitions provides more control over message consumption, but also introduces additional complexity:

• Customizable offset: With partitions, you can start consuming records from a specific offset, providing more flexibility in your message processing.
• Latest and earliest behavior: Partitions allow you to consume records from the latest or earliest offset, or from a specific offset.

However, partitions also come with some drawbacks:

• Offset maintenance: You are responsible for maintaining the offset, which can be stored at the client or server end.
• No commit option: Partitions do not provide a commit option, which means you cannot mark a specific point in the message stream as consumed.

In conclusion, when implementing a Kafka-based application, it's essential to carefully consider the trade-offs between using consumer groups and partitions. While consumer groups offer convenience and scalability, they limit offset control. Partitions, on the other hand, provide more flexibility but require manual offset maintenance. By understanding these challenges, you can design a more effective and efficient message consumption strategy for your application.