Difference Between Redis and Kafka

In this post, we shall look at the top differences and performance between Redis vs Kafka.

Redis: Redis is an in-memory, key-value data store which is also open source. It is extremely fast one can use it for caching session management, high-performance database and a message broker. In terms of storage and multiple functionalities, Redis is a bit different from Kafka.

Apache Kafka: Apache Kafka is a messaging system that allows you to publish and subscribe to streams of messages that are based on topics and partition. In this way, it is similar to products such as ActiveMQ, RabbitMQ. But even with these similarities, Kafka has a range of fundamental differences from traditional messaging systems that make it different completely.

What is Redis?

Redis is an acronym for the Remote Dictionary Server. It is a key-value store, this key-value store can be used as a repository for reading and writing data. Redis is a No-SQL database, which means that unlike MySQL, or Oracle database, it does not have structures such as table/row/column/functions/ procedures, etc. and also it does not support commands like SELECT, INSERT, DELETE and UPDATE. Strings, hashes, lists, sets and sorted sets are the different data structures provided by Redis.

There are two main processes in Redis architecture: Redis client and Redis server

Redis clients and servers can be present on the same computer or on two different computers. Use a client/server interface to communicate with Redis.

Redis Server: Redis server is responsible for storing data in memory. This manages all manner of operations and forms the architecture’s main part.
Redis Client: Redis client may be the Redis console interface or the Redis API of any other programming language which sends data to the Redis server.

What is Kafka?

Kafka is a publish-subscribe messaging system. With streaming technologies such as Kafka, you can actually process new data as it is generated in your cluster, you might save it to HDFS, or you can save it to HBase or some other database, so you can actually process it in real-time as it comes in, you can do all that with streaming.

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Producers: Producer publishes a message to a topic.
Broker: Broker is a cluster made up of one or more servers in Kafka. The broker receives messages from the producer, assigns them to offset and commits the message.
Topics: Messages are divided into topics. Topics are broken down into a number of partitions where they index and store messages that receive an incremental Id named offset.
Consumers: Consumers subscribe to various topics and read data from brokers. They read data in consumer groups. The consumer always keeps track of which messages it has consumed by keeping track of the offset of messages.

Head to Head comparison between Redis and Kafka (Infographics)

Below are the top 7 differences between Redis vs Kafka

Key Differences Between Redis and Kafka

Let us discuss some of the major key differences between Redis vs Kafka

1. Origin

Redis: It was developed by Salvatore Sanfilippo for developing a real-time weblog
Apache Kafka: Kafka is an open-source software platform developed by LinkedIn.

2. Subscription

Redis: Redis supports push-based delivery of messages that means messages published to Redis will be delivered automatically to subscribers immediately.
Apache Kafka: Kafka supports pull-based delivery of messages, meaning that messages published in Kafka are never distributed directly to consumers, consumers subscribe to topics and ask for messages when consumers are ready to deal with them.

3. Parallelism

Redis: Redis does not support the concept of parallelism.
Apache Kafka: Kafka supports parallelism due to the log partitioning of data where multiple consumers consume in consumer groups at the same.

4. Message Retention

Redis: Redis sends the messages to the consumer all at once and later the message is removed. Thus, no one knows where the data is held.
Apache Kafka: Because Kafka is a log, there are always messages; you can monitor this by setting a retention policy for messages. E.g. 7 days retention

5. Speed

Redis: If speed is of a concern than use Redis as messages are not persisted by Redis, so it delivers them more quickly.
Apache Kafka: Even after it delivers the message, Kafka persists the messages, so it is quite slow compared to Redis.

6. Amount of Data

Redis: Redis is an in-memory store. It ensures that it uses its primary memory for storage and processing which makes it much faster than the disk-based Kafka. The only issue with Redis ‘ in-memory storage is that we cannot store large amounts of data for a long time. Because the primary in-memory is smaller than a disk
Apache Kafka: Kafka is meant to handle large amounts of data. It allows as many servers as required to be used. It uses a disk to for its storage, so it may be slow to load. Nevertheless, it can hold a large amount of data (i.e. in terabytes) for a longer retention period thanks to the disk storage ability.

7. Use cases

Redis: It is used for various use cases such as Session Cache, Full Page Cache (FPC), Leader Boards/Counting, Pub s/ Sub, Queues
Apache Kafka: Kafka has various use cases such as Messaging, Website Activity Tracking, Log Aggregation, Stream Processing, Metrics, Event Sourcing, Commit log.

Redis vs Kafka Comparison Table

Let’s discuss the top comparison between Redis vs Kafka

Comparison Points	Redis	Kafka
Origin	To develop real-time weblog analyzer by Salvatore Sanfilippo	Started at LinkedIn
Parallelism	Doesn’t support parallelism	Supports parallelism due to log partitioning of data
Message Retention	It does not retain data.	Can retain data for a longer period of time
Subscription	Push Based Subscription	Pull Based Subscription
Category	In-Memory Database	Message Queue
Speed	Faster	Not as fast as Redis
Amount of Data	Amount of data is less as compared with Kafka	It is meant to deal with a huge amount of data

Conclusion

Redis is used if you want to deliver messages instantly to the consumer and you can live up with data loss, and the amount of data to deal is less. Kafka can be used when you’re looking for reliability, high throughput, fault-tolerant, and volume of data is huge.