Kafka vs. Redis: Decoding and comparing 2 famous message queues!

In the world of software development and backend systems, you’ve probably heard of Apache Kafka and Redis. These are two extremely popular names that often appear in discussions about performance, scalability, and data processing. But do you really understand what they are, how they differ, and when to use each?

Many beginners or even experienced people sometimes still get confused or do not see clearly the core difference between Kafka and Redis. Some people think they are direct competitors, others see them solving completely different problems. Where does the truth lie?

This article will be a deep dive into the world of Kafka and Redis. We will dissect each tool, compare them on many important aspects, and ultimately help you make an informed decision when choosing a technology for your project. Grab a cup of coffee ☕ and let’s start exploring!

Part 1: Exploring Apache Kafka

1.1 What Is Kafka? - More Than A Message Queue!

Many people who first come across Kafka often think that it is simply a "message queue" like RabbitMQ or ActiveMQ. This is not wrong, but it is not enough!

Think of Kafka as a distributed, fault-tolerant, and highly scalable logging system . Instead of being a temporary queue, Kafka stores messages/events in “topics” in a durable manner for a configurable period of time (days, weeks, or forever!). Data is written as a continuous stream of events .

• Easy analogy: Imagine Kafka as a giant, immutable ledger. Whenever an event occurs (e.g. a user places an order, updates a profile, clicks on an ad), a new line is written to this ledger (topic). Whoever needs to know what information (e.g. an order processing system, a product recommendation system, a behavior analysis system) will read from that ledger. Importantly, the data in the ledger is not lost immediately after reading.

1.2 Kafka Core Concepts:

• Event/Record: The basic unit of data in Kafka. Usually contains information about an event that occurred (eg: {"user_id": 123, "action": "click", "item_id": "abc"}).
• Topic: Like a category or a table in a database, used to categorize events. For example: orders, user_activities, payments.
• Partition: Each topic is divided into one or more partitions. This is how Kafka achieves scalability and parallelism. Data in a partition is guaranteed to be written in the same order.
• Producer: An application or service that publishes events to topics in Kafka.
• Consumer: An application or service that reads (subscribes) events from topics in Kafka. Consumers are often grouped into Consumer Groups to read data in parallel from different partitions of a topic.
• Broker: A Kafka server. A Kafka cluster typically consists of multiple brokers for fault tolerance and storage.
• Zookeeper/KRaft: (Note: KRaft is gradually replacing Zookeeper) Used to manage cluster metadata (information about brokers, topics, partitions, consumer offsets...).

1.3. Outstanding Features of Kafka:

• High Throughput: Kafka is designed to handle millions of events per second. It is optimized for sequential writes and reads from disk.
• Scalability: You can easily add brokers to the cluster to increase processing and storage capacity without stopping the system. Topic can also increase the number of partitions.
• Durability & Fault Tolerance: Data is replicated across multiple brokers. If one broker dies, data is still safe on other brokers. Events are persistently stored on disk.
• Decoupling: Producers and Consumers do not need to know about each other. Kafka acts as a mediator, allowing systems to communicate flexibly.
• Real-time & Batch Processing: Kafka supports both real-time streaming and batch processing.
• Ordering Guarantee: Kafka guarantees the ordering of events within a partition.

1.4. When to Use Kafka?

Kafka shines in the following cases:

• Event Sourcing: Records every change in system state as a sequence of events.
• Log Aggregation: Collect logs from different services into one central place.
• Real-time Data Pipelines: Build real-time data processing flows (e.g. ETL, data enrichment).
• Stream Processing: Analyze data as it moves (e.g. fraud detection, real-time analytics).
• Microservices Communication: Used as a central event bus for microservices to communicate asynchronously.
• Commit Log: Used as a state storage platform for other distributed systems.

Part 2: Exploring Redis

2.1. What is Redis? - Super Fast In-Memory Data Store!

Redis (REmote DIctionary Server) is essentially an in-memory data structure store . Think of it as an extremely fast key-value NoSQL database, but much more powerful with support for a wide variety of complex data structures.

Because it operates primarily in RAM, Redis's data access speed is extremely fast (often sub-millisecond). Despite being in-memory, Redis also provides mechanisms to store data on disk (persistence) to avoid loss during restarts.

• Easy Analogy: Think of Redis as a whiteboard or a super-fast notebook right on your desk (RAM). You can write, delete, and read information on it extremely quickly. It can not only store simple name: value pairs, but can also organize information into lists, sets, hash tables... very flexibly.

2.2. Core Concepts of Redis:

• Key-Value Store: The most basic model. Each data is stored as a pair of key (unique) and value.
• Data Structures: The real power of Redis lies here:
  • Strings: Store text or binary data (can be used for simple caching).
  • Lists: List of strings, sorted by insertion order (used for simple queues, timelines...).
  • Sets: A collection of non-duplicated, unordered strings (used to store tags, check membership...).
  • Sorted Sets: Same as Sets but each member has an additional "score" to sort (used for leaderboards, rate limiting...).
  • Hashes: Store field-value pairs (similar to object/map, used to store user profile information...).
  • Bitmaps & HyperLogLogs: For bitwise operations and memory efficient unique element count estimation.
  • Streams: A log-like data structure, quite similar to Kafka topics but on a smaller scale and often used in Redis (will compare in more detail later).
• Persistence: How Redis persists data to disk:
  • RDB (Snapshotting): Save snapshots of the entire dataset to disk periodically.
  • AOF (Append Only File): Records all write commands that change data to a log file.
• Replication & Sentinel/Cluster: Mechanism for replicating data to other nodes (replicas) and cluster management to increase availability and read/write scalability.

2.3. Outstanding Features of Redis:

• Extreme Speed/Low Latency: Because it operates mainly on RAM, access speed is extremely fast.
• Versatility: Supports multiple data structures, allowing you to solve different problems with one tool.
• Atomicity: Single Redis commands are typically atomic (either execute completely, or not at all). Transaction (MULTI/EXEC) support is available for multiple commands.
• Simplicity: Easier to install, use, and understand than many other complex systems. Intuitive API.
• Extensibility: Supports Lua scripting, Modules to extend functionality.

2.4. When to Use Redis?

Redis is a great choice for:

• Caching: The most common use case! Reduce load on the main database by caching frequently accessed data.
• Session Management: Stores user login session information.
• Rate Limiting: Limit the number of requests from a user/IP within a period of time.
• Real-time Leaderboards/Counters: Use Sorted Sets to rank or count real-time metrics.
• Pub/Sub (Simple Messaging): Provides a fast, basic publish/subscribe mechanism for messages that do not require high durability.
• Distributed Locking: Manages access to shared resources in a distributed system.
• Geospatial Indexing: Store and query data based on geographic location.
• Simple Queue: Use Lists as a simple job queue (but be careful about reliability).

Part 3: Putting It On The Scale - Kafka vs. Redis

Now, let's compare Kafka and Redis directly based on important criteria:

Kafka vs. Redis Pub/Sub Comparison in More Detail:

This is the most confusing point:

• Redis Pub/Sub: Very fast, simple. When a publisher sends a message, Redis only forwards it to subscribers who are connected at that time. If a subscriber loses connection, it misses the message. No message storage, no replays. Good for non-critical real-time notifications (e.g. "new user online").
• Kafka: Messages are stored persistently in topics/partitions. Consumers can replay messages from any offset. Consumers can go offline and still read missed messages when they come back online. Suitable for critical data streams, reliable asynchronous processing.
• Redis Streams: A recent addition to Redis, it provides a log-like data structure that is more persistent than basic Pub/Sub. It allows consumers to read from a certain point, with consumer groups. However, it still operates primarily in RAM (despite persistence) and is generally not designed for the massive throughput and storage capabilities of Kafka. It is like a “mini Kafka” in Redis, suitable for smaller event stream use cases that need tight integration with Redis caching/data store.

Part 4: So, Which One to Choose? Kafka or Redis

The answer is NOT which is better, but which is better suited for your SPECIFIC problem.

Choose Apache Kafka if you need:

• ✅ Handle high throughput data streams reliably.
• ✅ High data durability , long-term message storage.
• ✅ Ability to replay messages .
• ✅ Ensure message order (within a partition).
• ✅ Build complex data pipelines, event sourcing, log aggregation .
• ✅ Decoupling large systems, large-scale asynchronous communication.

Choose Redis if you need:

• ✅ Extremely fast speed (low latency) for reading/writing data.
• ✅ Effective caching to reduce database load.
• ✅ Store sessions, temporary information .
• ✅ Flexible data structures (leaderboards, counters, sets...).
• ✅ Simple, fast Pub/Sub messaging (no high durability required).
• ✅ Rate limiting, distributed lock .
• ✅ A simpler, easier to deploy solution.

The perfect combination:

In fact, Kafka and Redis are often not competitors but partners , complementing each other in many modern architectures:

• Use Redis to cache data from the database that Kafka consumers need to access quickly.
• Use Redis to quickly store the processing state (offset) of Kafka consumers.
• Use Kafka to bring data into the system, then use Redis to serve real-time queries (eg dashboard).
• Use Redis Streams for small work queues within a service, while Kafka handles the main event flow between services.

Kafka and Redis are two extremely powerful tools in the backend ecosystem. Kafka is the king of large-scale, persistent data stream processing, while Redis is the champion of in-memory data access speed and data structure flexibility.

Understanding the core differences and use cases of each will help you design more efficient, reliable, and scalable systems. Don’t think of them as mutually exclusive competitors, but rather as important pieces in the modern software engineer’s toolkit.

Hope this article has helped you have a clearer view of Kafka and Redis!