Pulsar vs. Kafka
Let’s take a closer look at the Pulsar vs. Kafka distributed messaging solutions. In the world of event streaming and distributed messaging, Apache Pulsar is probably one of the most reliable and popular systems used by many businesses from various industries.
While Apache Kafka shares certain similarities with Pulsar and is renowned as a fast event streaming system, Pulsar has made tremendous strides in terms of reputation and credibility. In fact, Pulsar is recognized as a better solution for many use cases.
Both solutions can be used to build real-time messaging systems, and because of that, Pulsar vs Kafka has been an ongoing battle for quite a long time. Businesses of all sorts widely use both solutions as they can process vast chunks of data daily.
Such effective new-age solutions allow modern businesses to build a secure infrastructure to handle AI, machine learning, and big data. With more and more companies demanding powerful real-time technologies, the adoption of Pulsar saw a serious surge in 2020.
Business organizations developing event-streaming and messaging applications are particularly interested in Pulsar because it is cost-effective, efficient, fast, and reliable. Let’s explain what both Pulsar and Kafka are, how they differ, and the main reasons why Pulsar is a better solution for modern enterprises.
What is Apache Pulsar?
Pulsar can be defined as an open-source distributed pub-sub event streaming and messaging system originally designed to help businesses cater to queuing use cases. Pulsar essentially allows businesses to handle the security, durability, and performance of their messaging needs to allow companies to shift focus to their applications.
It is a perfect distributed messaging solution for achieving unprecedented efficiencies and performance. If a business relies on moving data among microservices for core business processes, it will inevitably need a highly reliable underlying messaging technology, and Apache Pulsar can deliver the expected results.
Solutions built on Apache Pulsar provide a range of benefits for businesses:
- Full support for maintaining your messaging operations while you focus on your core mission;
- Since there is no operational cost with Pandio’s managed service, Apache Pulsar is
- an excellent solution for organizations that are after top, cost-effective performance;
- Apache Pulsar as a service allows businesses to pay only for what they use;
- Pulsar can be easily updated for increased security, scalability, and stability;
- Pulsar achieves unmatched latency, often less than 5 ms, and can be easily deployed into your ongoing applications;
- Since Pulsar is an all-in-one solution, it can help reduce technical stack complexity by allowing you to use one tool to manage your pubsub, queues, streams, and stateful functions;
- Pulsar is a messaging solution that can help an organization achieve unmatched messaging throughput and performance;
Pandio’s Pulsar is AI-powered and designed to run machine learning models, meaning it allows you to harness the power of AI technologies such as machine learning to ensure you achieve your business and messaging goals.
What is Apache Kafka?
While also an open-source distributed event streaming system in its essence, Apache Kafka differs from Pulsar by its design. It is a platform designed as a persistent, distributed, and replicated commit log with the primary goal to power either large-scale stream processing applications or event-driven microservices.
Kafka helps businesses gather, manage, and process enormous amounts of raw data. Like Pulsar, Kafka is an open-source solution that you can tailor to match your specific needs.
Initially, Kafka and Pulsar were designed for two different things. However, their functionalities and features overlap so much that it’s tough to tell the difference between the two. You can’t change the Kafka system due to the nature of its architecture, but you can upgrade and enhance some of its functionalities to better match your specific needs.
Differences between Apache Pulsar and Apache Kafka
One of the very first differences between Pulsar and Kafka is that Pulsar is a newer solution, and unlike Kafka, you can actually change it. Pulsar comes with technical upgrades that are based on Kafka’s existing functionalities. Since Pulsar is designed on Kafka’s API interface, it can be seamlessly upgraded and integrated to fit specific use-case scenarios better.
Pulsar also comes with a built-in datacenter replication system, and unlike Kafka, it can make your data flow more efficient and faster by streamlining it. On the other hand, Kafka differs from Pulsar because it was developed as a streaming data processor.
Pulsar is an all-in-one system
While Kafka is just a one-system solution, Pulsar is both a streaming platform and distributed messaging system. Kafka is an excellent solution for businesses that need to handle high performance and vast amounts of data. However, it requires an abundant source of computing power to handle these things.
Compared to Kafka, Pulsar handles data flows in a multi-layer system and decouples the message-broker connection, making it an excellent and much faster solution for managing large volumes of data.
In addition, it’s also important to mention that Pulsar can be a more cost-effective solution than Kafka as its technical design allows the user to drive higher performance. If we take a look at some of the testing performed by Kubernetes, Pulsar beats Kafka in terms of performance and is more cost-effective because it requires a smaller number of nodes to provide equivalent or better results than Kafka.
Pulsar eliminates the risk of data loss
Kafka wasn’t designed as an enterprise-wide messaging system. Because of that, it isn’t capable of handling such immense amounts of data without exposing the user to the risk of data loss. While Kafka can perform such a function, it does so very poorly.
That’s why many companies stopped using Kafka for their distributed messaging. Kafka can distribute trillions of messages, but it requires constant upgrades and modifications to handle such loads.
Even with all those upgrades, Kafka still had a big problem with data loss, meaning the system wasn’t able to deliver all the necessary data. Fortunately, Pulsar is here to save the day and eliminate such problems. It can do everything that Kafka can, only much better.
It can operate with a far higher workload and doesn’t expose the user to any risks, including data loss. On top of that, Pulsar is much faster than Kafka. Finally, Pulsar comes with much better and more advanced features, such as:
- 99.99% uptime thanks to SLA support for your vital business applications;
- Top-grade enhanced security features such as policies, authentication, authorization, encryption, etc.;
- High availability and deployment;
- Switch to Kafka without changing the code;
- Distributed SQL engine;
- Real-time metrics;
- Advanced messaging and monitoring;
- AI-powered, real-time forecasting;
- Tiered storage.
Pulsar vs. Kafka: Why Apache Pulsar beats Apache Kafka
If you need to build your messaging service infrastructure, we recommend you use Apache Pulsar. Here is why:
It can do more than Kafka
As we already mentioned, Pulsar is a two-in-one system that can easily handle high-rate use cases in real-time. While you can use it as an event-streaming platform, Pulsar is also an excellent solution for distributed messaging. It supports standard message queuing needs, such as easy message distribution, fail-over subscriptions, and competing consumers.
As a messaging system, it uses automation to keep track of every client read position in the specific use case. More importantly, it uses its tiered storage to save that data in its high-end distributed ledger, Apache BookKeeper.
With Pulsar, you’ll have one solution for two different purposes – one for queuing and one for real-time event streaming. In comparison,, Kafka doesn’t offer a traditional queuing system, whereas Pulsar does.
Pulsar is more efficient and much simpler to use than Kafka
If you’ve ever used Kafka before, you’re familiar with partitions. Kafka allows you to divide all topics into separate parts. It’s vital as partitioning increases throughput, enabling you to process more topics from multiple brokers.
It also helps reduce the workload as you can evenly spread it across partitions. However, Pulsar really excels over Kafka when you come across topics that don’t require high processing rates.
In fact, Pulsar offers a few advantages over Kafka – to eliminate management and API complexity and get rid of partitions. Pulsar allows you to handle topics from as many consumers as you want without the need to specify the exact number of partitions.
It will also keep track of every single interaction, while there is also an option to use partitioned topics if you need them.
Pulsar allows you to use distributed ledgers for tiered storage
Kafka uses logs to exchange data in real-time as they allow for quick data extraction on demand, and they are append-only, so the data can be quickly written in a log. While the log abstraction can be an effective method for fast sequential data writing, reading, and extraction, it can also create certain problems.
The first challenge is fitting a log on a single server, but the real problem arises when the log gets full, and your business needs to scale out.
Since copying a log from one server to another takes too long, the solution is to avoid such challenges altogether, and that is where Pulsar excels, once more. Instead of copying the entire logs, Pulsar segments them into smaller parts that are easier to manage.
More importantly, it can support the distribution of these segments across multiple servers while still allowing you to write and store data by relying on Pulsar’s tiered storage, Apache BookKeeper. Instead of risking storing your logs on a single server and facing all the problems that can arise from it, Pulsar solves that issue by simplifying the process of adding another server.
Pulsar allows for stateless message brokers
Any business building cloud-native applications knows how beneficial stateless components can be. They can scale seamlessly and are interchangeable, but their biggest benefit is that you can quickly start them up. In terms of distributed messaging, modern businesses are increasingly looking for stateless message brokers.
In its essence, Kafka doesn’t support stateless brokers as each broker uses the complete log for each of its partitions. If any of those brokers fails and the workload gets too high, the user won’t be able to simply solve this by adding another broker.
Kafka brokers must synchronize state from other brokers that use the same partitions or their replicas, but Pulsar doesn’t have this kind of limitation, as it uses stateless message brokers. It also maintains a state outside of its brokers as Pulsar keeps message brokers separated from its data storage.
Data is always stored in Apache BookKeeper so that Pulsar brokers can send data to consumers and accept data from producers individually. In other words, if the load gets too high, you can simply solve this by adding another broker.
Pulsar is much faster and supports geo-replication
Pulsar is much faster than Kafka, thanks to its capability to deliver higher throughput with more consistent, significantly lower latency. However, the thing that really separates Pulsar from Kafka is one of its top-class features – geo-replication.
Aside from being efficient and effective, configuring this feature is simple, making Pulsar more user-friendly than Kafka.
Quick overview of Pulsar vs. Kafka
Since an increasing number of businesses consistently chooses Apache Pulsar over Kafka, let’s quickly review what exactly makes Pulsar a better choice than Kafka:
Faster benchmarking – when it comes to streaming messages and events and decreasing latency, and providing higher throughput, Pulsar is the clear winner.
Geo-replication – Pulsar is designed with geo-replication in mind; it is its core feature. Unlike Kafka, Pulsar supports native geo-replication with no additional tools needed.
Pubsub, queue, stream – Pulsar is an all-in-one platform that allows the user to combine pubsubs, queues, and streams all in one place.
Tiered/decoupled storage – since Pulsar was built on a multi-layered architecture, it can keep data storage and processing separated. In addition, you won’t need to pay for additional storage or create partitions like with Kafka, as Pulsar comes with tiered storage that allows you to retain message backlogs for as long as you need them.
AI and ML workloads – Pulsar allows you to make more informed, data-driven decisions as it is built for machine learning and artificial intelligence, a perk that makes it a more viable solution for modern businesses that rely on data as a competitive advantage.
Cloud-native – with independent compute and storage levels, you can use Pulsar to scale your messaging and event-streaming traffic according to your exact needs.
Pulsar is simply a more user-friendly, full-featured solution when compared to Kafka, especially when it comes to distributed messaging. With features such as native geo-replication, all-in-one messaging, and zero data loss – Pulsar consistently outperforms Kafka in every field.
On top of all that, Pulsar also comes with multi-tenancy and offers effective access control mechanisms. Pulsar can help you make a successful transition if you need to scale your business, as it allows for both stateless and stateful stream processing.
It doesn’t require a separate server to render messaging, and it can perform windowing, routing, and transformation with the distributed state. If you’re working with AI analytics applications, Pulsar is a more reliable and faster solution for event streaming.
After all, it’s all Apache open source, even though Pulsar is so much more than just a collection of open-source features. Compared to Pulsar, Kafka shows significant limitations, such as structured brokers, broker-tied storage, and the need to rebalance data when you need to scale up your operations.
With all these points in mind, it’s no wonder that Pulsar is a more logical choice for so many modern businesses these days. With newer business needs and next-generation workloads comes a more advanced, innovative solution that can cater to those needs.
While Kafka was the prime solution in the past, Pulsar is a newer option with better features and functions, especially in AI/ML use cases. It offers more scalability and is significantly faster than Kafka and comes with a much lower TCO.
It all comes down to your use-case-specific needs. When it comes to distributed messaging for enterprises, Kafka does not match Pulsar in terms of core capabilities.