Kafka vs. SQS: Retention & Ordering

In the intricate landscape of distributed systems, the strategies you choose for message retention and ordering can make or break the efficiency of your application. Having already covered the architecture, scalability, and performance aspects of Kafka and Amazon SQS, it's time to turn our focus to how these systems manage the lifecycle of your messages.

The ability to replay past events, maintain a strict order of operations, or simply ensure that messages remain accessible for a required duration can significantly impact system reliability. The choice between Kafka and SQS is more than just a matter of technology—it’s about aligning with the unique demands of your application. In this post, we’ll dissect how Kafka’s log-based design and SQS’s queueing approach handle these critical factors, helping you make the best choice for your needs.

Ready to explore the nuances of message retention and ordering? Let’s dive in.

Message Retention

Kafka's Retention Model

Kafka’s approach to message retention is deeply rooted in its log-based architecture. When a message is produced to a Kafka topic, it is appended to a log file on disk within the appropriate partition. These messages are retained according to configurable policies, which allow you to control how long and under what conditions data should be kept.

Time-Based Retention: You can set a retention period that dictates how long Kafka should retain messages before they are eligible for deletion. For example, a topic might be configured to retain messages for 7 days.
Size-Based Retention: Kafka also allows retention based on the total size of the logs. When the log reaches a specified size limit, older messages are purged to make room for new ones.
Log Compaction: Kafka offers log compaction as a way to retain the latest version of messages with the same key. This ensures that you always have the most recent update, which is particularly useful in scenarios where only the latest state is relevant.

These flexible retention options make Kafka a powerful tool for use cases requiring long-term data storage, reprocessing, or recovery.

SQS's Retention Model

Amazon SQS handles message retention differently due to its queue-based architecture. When a message is sent to an SQS queue, it remains there until one of the following occurs:

Default Retention: SQS retains messages for a default period of 4 days.
Maximum Retention: This retention period can be extended up to a maximum of 14 days, after which the message is automatically deleted if not consumed.

Unlike Kafka, SQS does not allow for fine-grained control over retention based on size or specific criteria like compaction. This more straightforward approach is well-suited to scenarios where messages are expected to be consumed quickly and do not need to be retained for long periods.

Consequences of Retention Choices

The differences in retention models between Kafka and SQS have significant implications:

Kafka: The ability to retain messages for extended periods and replay them as needed makes Kafka ideal for scenarios where historical data needs to be reprocessed or audited. For example, event sourcing architectures, where the complete history of events is stored, can leverage Kafka’s flexible retention policies.
SQS: SQS’s retention model is better suited for transient messaging where messages are processed shortly after being produced. The 14-day limit imposes a constraint that might not be suitable for applications requiring long-term message storage or replay capabilities. If your use case involves brief interactions, such as triggering asynchronous tasks or simple request-response patterns, SQS’s simpler retention model will likely meet your needs.

Ordering Guarantees

Kafka Ordering Guarantees

Kafka provides strong ordering guarantees within individual partitions. Each partition in a Kafka topic is an ordered, immutable sequence of messages that are appended to in a strict order. Consumers process messages in the exact order they are stored in the partition. This design is particularly advantageous when you need to maintain the sequence of events, such as in transaction logs or event sourcing architectures.

However, while Kafka ensures order within a partition, it does not guarantee order across different partitions. If you need to maintain global ordering across all messages in a topic, you might face challenges in Kafka, especially as you scale out by increasing the number of partitions. The trade-off between scalability and strict global ordering is a key consideration.

Example: Consider a financial application processing transactions. Each account’s transactions are sent to a specific partition based on the account ID, ensuring that transactions for the same account are processed in order. However, transactions across different accounts may be processed out of order if they are spread across multiple partitions.

SQS Ordering Guarantees

SQS offers two types of queues: Standard and FIFO (First-In-First-Out).

Standard Queues: These do not guarantee ordering. Messages might be delivered out of order, and duplicates are possible. This is due to the distributed nature of SQS and its focus on high throughput and availability. Standard queues are suitable for applications where ordering isn’t critical, like task queues or processing independent jobs.
FIFO Queues: These ensure that messages are processed in the exact order they are sent. FIFO queues also prevent duplicates, making them ideal for use cases where message order and uniqueness are crucial. However, FIFO queues have lower throughput compared to Standard queues, which can be a limitation for high-volume applications.

Example: In a social media application, where posts are displayed in the order they are created, using a FIFO queue ensures that the timeline appears correctly. If a Standard queue were used, posts could appear out of order, leading to a confusing user experience.

Consequences of Differences

The differences in ordering guarantees between Kafka and SQS can significantly impact application design:

Kafka: Its partition-based ordering is ideal for scenarios requiring ordered processing within a subset of data (e.g., per user or account), but it can complicate global ordering. If your application requires global ordering across a large dataset, Kafka may need complex workarounds or sacrifice some level of performance or scalability.
SQS: The choice between Standard and FIFO queues allows for flexibility. However, the trade-off between ordering and throughput in SQS means that if your application requires high message throughput and ordering, you might need to use multiple FIFO queues or design around the limitations, potentially increasing complexity.

When evaluating message retention and ordering in Kafka vs. SQS, it’s clear that each system offers distinct advantages tailored to different use cases. Kafka’s strong ordering guarantees and configurable retention make it ideal for scenarios requiring precise event processing and historical data access, such as event sourcing and stream processing. On the other hand, SQS’s simplicity and automatic handling of message retention and deletion provide a more straightforward approach, suitable for decoupling services, managing job queues, or handling background tasks.

Choosing between Kafka and SQS ultimately comes down to the specific needs of your application. For complex, data-intensive applications that demand high-throughput, long-term storage, and strong ordering guarantees, Kafka is the superior choice. However, for simpler, scalable systems where ease of use, integration, and reliable message delivery are more important than fine-grained control, SQS shines.

Both tools have their place in the modern developer’s toolkit, and understanding their differences in retention and ordering is key to making the right choice for your project’s needs.

Choosing the right message queue: Message retention and ordering in Kafka vs. SQS