Apache Kafka and Redis are robust technologies that help you process and stream real-time data. As per the StackOverflow survey 2024, Redis is used by 22.8% of developers under the database category. Conversely, 10.9% of developers rely on Kafka as a framework for extensive application development.
Both technologies are essential in modern system design, log aggregation, and instant messaging applications but differ in core functionalities. This article will provide a guide to help you understand the Redis vs Kafka comparison.
Overview of Redis
Redis (REmote DIctionary Server) is an in-memory data structure server that enables you to store entire datasets directly in RAM. By keeping data in the main memory, Redis facilitates exceptionally fast read and write operations. This capability makes Redis a powerful solution for real-time processing and low-latency data access in various industries, such as finance, e-commerce, and IoT applications.
In addition to its in-memory capabilities, Redis is an easy-to-use NoSQL solution that allows you to organize data in key-value pairs. A key is a unique identifier that is useful in performing CRUD operations within the Redis database. Each key is associated with a value that supports varied data types, including strings, lists, sorted sets, and hashes. With this rich set of data structures, Redis helps you model your data for various use cases like real-time analytics, messaging, and caching.
Key Features of Redis
Let’s explore a few key features of Redis:
- Least Frequently Used (LFU) Key Eviction Policy: In Redis, the LFU eviction policy allows you to track the access frequency of data items using a probabilistic counter called a Morris counter. This mechanism helps you identify and evict items that are used less often. With LFU policy eviction, you can easily optimize memory usage by keeping the most relevant keys.
- High Availability with Redis Sentinel: Redis Sentinel, a distributed system with multiple Redis instances, enables you to provide high availability through an automatic failover process. If a primary Redis instance goes down, Sentinel helps you automatically promote one of its replicas containing the latest data to be the new instance.
- Isolated Transactions: All commands within a Redis transaction are serialized and executed sequentially as a single isolated operation. This ensures that no other user requests interfere with your transaction.
- Data Durability: Redis supports a snapshotting strategy to help you save snapshots of the dataset to disk in a binary file named “dump.rdb” by default. You can configure Redis to automatically save the dataset every second if at least one change has occurred.
- Keys with Limited Time-to-Live: In Redis, you can set a timeout for keys that are relevant for a limited time using the EXPIRE command. Once the specified time elapses, the key is automatically deleted, which helps you manage memory efficiently.
Overview of Apache Kafka
Apache Kafka is a distributed event streaming platform that helps you develop data pipelines to handle large-scale data in real-time. To efficiently manage and stream this data, you can set up a Kafka cluster consisting of one or more servers known as brokers. This cluster ensures that data is distributed and replicated across brokers to provide fault tolerance and high availability.
Your application captures real-time data like user activity logs, payment events, and location. The Kafka cluster allows your application to publish each data stream to separate Kafka topics using the Producer API. The Consumer API then enables subscriber applications to read the most up-to-date data from these topics as it arrives.
Key Features of Apache Kafka
Here are some of the key features of Apache Kafka:
- Real-time Processing at Scale: Kafka Streams API, a lightweight library, allows you to process and analyze data streams as soon as they are generated. It also helps you to aggregate data, set up windowing parameters, and even join data within a stream.
- High Throughput: Kafka helps you manage large volumes of messages across a cluster of servers with latencies of less than two milliseconds. This capability is useful for real-time data processing and providing low-latency responses to incoming data streams.
- Semantic Guarantee: Semantic guarantees in Kafka allow you to define how messages are shared between brokers, producers, and consumers. Kafka supports three delivery guarantees: at most once, at least once, and exactly once.
- Permanent Storage: Kafka helps you store streams of records in a durable and fault-tolerant manner, ensuring that records remain accessible even if a server fails.
- End-to-End Batch Compression: Kafka enables you to compress a batch of records using protocols like GZIP, LZ4, ZStandard, and Snappy. You can then write the batch in compressed form to the disk and transmit it to the consumer application. Using appropriate compression techniques, you can improve performance and storage requirements.
Redis vs Kafka: A Tabular Comparison
Let’s quickly look into the Redis vs Kafka differences from the following table:
Factors to Consider When Choosing Kafka vs Redis
Here are certain key factors to be considered when choosing Redis or Kafka:
Redis vs Kafka Performance
Redis is extremely fast, with sub-millisecond response times due to its in-memory data structure, allowing you to handle millions of requests. However, its performance is constrained by memory size, as it relies entirely on RAM; you can manage incoming requests with lower persistence and durability guarantees with Redis. This makes Redis suitable for scenarios prioritizing low latency over long-term data storage.
In contrast, Kafka is a disk-based system and writes data to the disk by default. Even with durable storage, it offers higher throughput than Redis for real-time data processing. However, this disk-based nature introduces slightly higher latency compared to Redis, making it a little slower in responding to individual requests.
Redis vs Kafka Pub/Sub Messaging Workflow
Redis operates on a client-server architecture and uses keys and primary-secondary nodes to manage the exchange of messages between producers and consumers. Keys help you cluster similar messages, and primary-secondary nodes facilitate message replication to ensure data availability. When you send a message to a particular Redis instance as a producer, it will be transmitted to all the connected consumers based on the key. To receive these messages, the consumer must establish a connection with the Redis server.
Contrarily, Kafka allows you to connect producers and consumers through Kafka clusters that include multiple brokers on different servers. In this messaging system, you can publish messages to the broker, which helps you organize similar data into an appropriate topic. The broker then divides these topics into partitions, each holding a subset of data related to the topic. Finally, the consumer applications can connect to the relevant topic and extract data from its corresponding partition.
Redis Vs Kafka Use Cases
You can use Redis as an in-memory cache in web applications to store frequently accessed data such as user profiles, product details, or API responses. When a user requests this data, Redis allows you to retrieve it from memory quickly. In addition to cache management, Redis is well-suited for managing real-time leaderboards to track player scores, achievements, and rankings.
In contrast, besides large-scale messaging systems, Kafka is useful for building event-driven architectures for modern applications or microservices. Such architecture helps the system instantly react to events like login, purchase, or website clicks. Kafka can also be used for monitoring purposes, which helps you keep track of system performance in real-time.
Redis vs Kafka Pricing
Redis offers a range of pricing options, including a free plan, two on-demand pricing plans (Essential and Pro), and an annual subscription plan. The Essential plan provides a single 12 GB database, supporting 10,000 operations per second and up to 10,000 user connections at $5/month. Conversely, Pro allows unlimited databases of varying sizes, along with unlimited support for operations and connections, at $0.881/hour.
Apache Kafka is an open-source platform that is freely available to download and use. To deploy Kafka to the cloud, you can utilize a managed service like Confluent Cloud or CloudKarafka with a pay-as-you-go pricing model. They usually charge for data ingress, egress, storage, throughput, and retention policies.
Simplify Kafka or Redis Integration with Airbyte
After exploring the strengths of Kafka and Redis, you might be able to identify which platform best matches your needs. However, if your data is spread across multiple data sources, it is difficult to integrate into your preferred destination. To ease your task, consider using Airbyte, a data movement and replication platform. It offers over 400 connectors, allowing you to transfer data from any source to your target system within minutes.
Here are some of the key features of Airbyte:
- Varied Options for Data Integration: Airbyte offers various options, including Terraform Provider, PyAirbyte, UI, and API, to help you build data pipelines that streamline the data integration process. These options allow you to choose a flexible and efficient approach for your needs.
- Change Data Capture (CDC): Once you initially load the data, there is no need to reload the entire dataset into the destination. Airbyte’s CDC approach helps you to track every change in the source system and copies it immediately to the target system. This ensures that your data remains up-to-date without the overhead of full reloads.
- Log Monitoring: Airbyte offers connection logging, Airbyte Datadog integration, and Airbyte OpenTelemetry integration. These options help you monitor and provide comprehensive insights into your data pipeline performance and reliability.
Summary
Redis and Apache Kafka are powerful tools that offer unique data management and processing features. Redis is an in-memory data store used as a database, cache, and message broker. Its high-speed performance makes it suitable for applications that require quick data retrieval. On the other hand, Apache Kafka is a distributed platform for event streaming and building real-time data pipelines.
While Redis is beneficial for low-latency applications, Kafka is optimized for handling large volumes of continuous data streams. By understanding the Redis vs Kafka differences, you can choose the right tool that best aligns with your requirements.