Kafka vs Redis – What’s the Difference ? (Pros and Cons). At the outset, we introduce Apache Kafka, which is used for example for data storage. In addition, it is used for real time data streams as well as large data collection or real time analysis or both. The second data store in this article will be Redis. Also known for its rich open source data store. It contains memory that contains the key value. Additionally, due to its high performance, it is a very popular choice of cache and more.
In this article, we’ll take a closer look at what these two data stores are and how they work. I will introduce their benefits and features as well as pros and cons. Finally, I compare them them both.
If you want to learn about the highlights of these two solutions please follow Kafka vs Redis – What’s the Difference ? (Pros and Cons).
What is Kafka?
So the distributed data stream platform is Apache Kafka. It stores, publishes, subscribes and processes so called stream of records in real time. The goal of this platform is to handle data streams from a mass of sources and send them to recipients. In summary, it moves incredibly large amounts of data from A to B. Moreover, from A to Z and elsewhere in real time.
Kafka interesting functions
So what are the Kafka functions? Find below:
- Publish and subscribe to a stream of records.
- Efficiently store logs streams in the order they were created.
- Real time recording process.
- Used to create data flow lines and applications that adapt data flows in real time.
- Combines messaging, storage, and stream processing to enable the storage and analysis of real time and historical data.
How does Kafka work?
In nutshell, it combines two notification models, queuing, publishing and subscribing, to offer consumers the main advantages of each. Queuing helps distribute data processing across many consumer states, making it highly scalable. However, traditional queues are not multi subscribed.
The subscriber publisher approach is multi subscriber, but since each message is sent to each subscriber, it cannot be used to distribute work across multiple worker processes.
Finally, the Kafka model provides reliability, allowing many independent applications to read streams of data to operate independently at their own pace.
Graphical representation of Kafka's activities:
Queuing
Publish-Subscribe
Features of Kafka
- Stream processing – Used with streaming applications such as Spark and Storm.
- Replication – Messages are repeated throughout the group, supporting multiple subscribers.
- Durability – Provides support for persistence of messages to disk, which is further used for batch consumption.
- High throughput – Provides support for hundreds of thousands of messages with modest hardware.
- Zero downtime – Has no downtime requirements for updates. The health of the Kafka mass can be monitored using a myriad of preventive maintenance tools.
- Extensibility – The popularity of Kafka over the past few years has encouraged many other applications to develop integrations with Kafka. This makes it easy to add new features, such as connecting to other apps.
Pros and cons of Kafka
Pros
Variety of use cases – Capable of handling a wide range of use cases that data lakes typically require. For example, collecting logs, monitoring network activity, etc.
Fault tolerant – One of the biggest advantages is fault tolerance. Kafka is resilient to node/machine failures in the cluster.
Reduces the need for multiple integrations – All data written by the producer goes through Kafka. So we just need an integration with Kafka and it will automatically integrate us into all production and consumption systems.
Easily accessible – Since all our data is stored in Kafka, it is easily accessible by anyone.
Cons
Reduces performance – Brokers and consumers degrade Kafka performance by compressing and decompressing data streams. This affects not only your performance, but also your productivity.
Lack some message paradigms – Some messaging models such as peer to peer queues, request/response, etc, in some cases in Kafka are missing.
Not support wildcard topic selection – Mainly the problem is that Kafka only matches exact topic names, which means it doesn’t support wildcards. Because this makes it impossible to manage certain use cases.
Do not have complete set of monitoring tools – Does not contain a complete set of monitoring as well as managing tools. Thus, new startups or enterprises fear to work with Kafka.
Next with Kafka vs Redis – What’s the Difference ? we introduce the other solution, which is Redis.
What is Redis?
We can only say that this platform is an open source NoSQL database. It is known as a very fast storage of key value in memory, cache also as a message broker and queue. Additionally, it is worth mentioning that this project was started by Salvatore Sanfilippo, considered the creator of Redis.
Written in ANSI C and used with most POSIX systems such as BSD, Linux and OS X without any external dependencies.
We can also mention that Linux along with OS X are considered to be the two operating systems on which Redis has been extensively tested and developed. It also runs on Solaris derived systems such as SmartOS.
Additionally is a popular choice for caching, session management, gaming, leaderboards, real time analytics, geospatial, auto sharing, chat/messaging, media streaming, and publish/subscribe applications. Then Redis now delivers sub millisecond response times and supports millions of requests per second for real time applications in gaming, the Internet of Things, social networking, financial services, healthcare, and tech advertising.
How does Redis work?
Developed to have a reliable data storage capable of storing and reading information in a short period of time. What sets this database apart is the aforementioned combination of key value store and memory store.
An in memory database stores data in a computer’s random access memory (RAM) instead of writing it to a hard disk, such as a hard disk or solid state drive. This speeds up the write and read process many times over, but at the cost of security and data. Random access memory (RAM) is usually more expensive than similar hard disk storage and disappears completely when the device is turned off or the system crashes.
Each record in this memory is assigned a unique key that is used to access the data record. Because working memory on a computer is often a finite resource, it should be used with caution. Also includes special data structures that use as little memory as possible.
Redis Data Types
Features of Redis
The functions that Redis offers us are interesting and enrich the existing environment thanks to an unusual possibility. It has many individual functions and features.
- High availability and scalability.
- Simplicity.
- Data persistence.
- In memory datastore.
- Rich data structures.
Pros and cons of Redis
Pros
- Uses its own hash mechanism called Redis Hashing – Redis stores data in the form of keys and maps, i.e. string fields and string values.
- Offers a pub/sub messaging system – Develop high performance messaging applications using Redi’s Pub/sub mechanism in any language you want.
- Redis supports transactions -Transactions are supported, which means that orders can be executed in a queue instead of executing one order at a time. Normally, commands after MULTI are added to the queue, and when EXEC is executed, all commands stored in the queue are executed immediately.
- Redis protocol makes it easy for clients to implement – Client communicates with its server using RESP (Redis Serialization Protocol). This protocol is simple to implement and is human readable.
Cons
- Price is a bit high but over all there are no issues worth mentioning.
- It lacks support for datatypes that are available on other products.
- Only command line application available for free. Difficult to use.
So how do the 2 solutions differ? Let’s find out next with Kafka vs Redis – What’s the Difference ? (Pros and Cons).
Key differences between Kafka vs Redis
Amount of data
- Kafka is designed to handle large amounts of data. It allows the use of as many servers as needed. Uses disk storage, so loading may be slow. However, due to its disk storage capacity, it stores large amounts of data (i.e. terabytes) for long storage periods.
- Redis is an in memory store. Ensures that it uses its main memory for storage and processing, making it much faster than Kafka’s disk based memory. The only problem with storing Redis in memory is that we cannot store large amounts of data for long periods of time. Because main memory is smaller than disk.
Subscription
- Kafka supports cloud based messaging, which means that messages published to Kafka are not delivered directly to consumers, who subscribe to topics and request messages when consumers are ready to process them.
- Redis supports push messages, which means that messages published to Redis are automatically and instantly delivered to subscribers.
Message retention
- Since Kafka is a diary, there are always letters. You can monitor this by setting a message retention policy. For example, store for 7 days
- Redis immediately sends the message to the consumer and the message is deleted. So no one knows where the data is stored.
Use cases
- Kafka has various use cases such as messaging, website activity tracking, log aggregation, stream processing, metrics, event sourcing, commit log.
- Redis is used for different use cases such as session cache, full page cache (FPC), rank/count tables, pub/sub and queues.
Speed
- Kafka keeps messages even after they are delivered, so it is very slow compared to Redis.
- If speed is an issue, use Redis, because Redis doesn’t keep sending messages, so it can deliver them faster.
Parallelism
- Because of data log splitting, Kafka supports parallelism, consuming multiple consumers simultaneously in the consumer pool.
- Redis does not support the concept of parallelism.
Language Written in
- Kafka is written in Java. This has a significant burden in terms of management, disk space and memory usage.
- Redis is written in C with much less overhead in almost every dimension.
Software functions
- I think the Kafka API is a bit difficult to access for non-Java programming languages and environments.
- Redis functions are available from many different programming languages and frameworks, almost all of which are widely used today.
Return on investment
- Kafka makes messages more reliable (because they only have a distributed storage space and messages don’t disappear even after they are consumed).
- Kafka supports larger use cases without putting too much pressure on existing hardware.
- Redis helps us reduce the load on the database. By being able to scale and cache large data, we reduce database load, reduce costs and infrastructure issues.
- Running a Redis node on AWS is expensive, but is often required to scale your business. If you need to get data quickly and your business is already seeing a positive ROI, then Redis is well worth the investment.
Usability
- Apache Kafka is highly recommended for developing loosely coupled real time processing applications. Apache Kafka also provides property based configuration. Producers, consumers and brokers have their own folders owned.
- Setting up to manage user sessions in the backend is very simple. It is easily combined with other products or technologies, such as Spring in Java. If you really need to see the data stored in Redis in your application, it is very difficult to figure out at first, but it is possible.
Thank you for reading Kafka vs Redis – What’s the Difference ? (Pros and Cons). We shall conclude.
Kafka vs Redis – What’s the Difference ? Conclusion
Based on the capabilities of both tools, Redis processes messages in real time, the delay is the smallest, then you should try Redis. However, if the message is large and the data needs to be reused, Kafka should be considered first.
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