Pulsar vs Kafka | Comparing Costs and Value
Pulsar vs Kafka has been a fierce messaging systems competition for a long time. Both are widely used in large enterprises across the globe, both handle very large amounts of data daily, and both are experiencing strong growth as new-age solutions require their infrastructure to handle big data, machine learning, and artificial intelligence.
Just because these two are competing technologies doesn’t mean each one has its specific benefits and drawbacks.
Pulsar and Kafka were developed for two different things, but today, so many of their features overlap that the two are in direct competition. Due to this, many companies have a tough time choosing between Pulsar and Kafka.
In this article, we’ll help you make your own decisions by providing you data on which of these two solutions gives you the best cost to performance ratio, as well as an outline of which one will likely take the cake as technology continues evolving.
Comparing the Performance of Kafka and Pulsar
Kafka and Pulsar are both relatively new. Kafka has been around for longer than Pulsar, and is today the more popular messaging system and streaming data processor worldwide. Companies use it to handle petabytes upon petabytes of data by some of the world’s largest corporations including Citi, Expedia, The Home Depot, etc.
Kafka, like Pulsar, is open source, so it can be adapted to fit specific needs. However, it can’t be fundamentally changed, as the architecture will always remain the same, but you can mold some of its features to fit specific scenarios.
Pulsar is newer than Kafka and offers technical upgrades for many of Kafka’s existing features. For example, Pulsar has a built-in multi-datacenter replication system that streamlines data flow, making it far faster and more efficient.
From our perspective there are many reasons why Pulsar is a better choice than Kafka including its premier support for Kafka. Pulsar has a Kafka API interface, allowing for seamless integration and “upgrading.”
Minor Use Case Differences Between Pulsar and Kafka
Apache Kafka and Apache Pulsar are two different things. Apache Kafka was created as a streaming data processor, whereas Apache Pulsar was created as a distributed messaging system and streaming platform. In essence, the two do similar things in different ways.
Apache Kafka was built as a one-system solution, meaning it couples the message-broker computing to one layer. This particular system makes Kafka ideal for handling large volumes and high performance, but with the tradeoff that it requires significant computing power to execute these things.
Apache Pulsar, on the other hand, doesn’t function in the same way. It works to decouple the message-broker connection, allowing the whole process to flow in a multi-layer system.
In this regard, Apache Pulsar is ideal for handling large quantities of data at a much faster rate than Kafka.
The Cost-Effective Performance of Pulsar
Pulsar’s technical design drives performance. In regards to Kubernetes testing, Pulsar either matches or outperforms Kafka, all while using a smaller number of nodes. Because of that, Pulsar can effectively outperform Kafka in MB/s far faster, all while using less computing power and resources to do so without any risk of data loss.
The performance prices of these systems are primarily down to how many nodes the two use, and since Pulsar uses fewer nodes and works at a far faster rate – overall consumption costs are lower.
The Major Software Architectural Differences between Pulsar and Kafka
The software architecture behind Pulsar is far newer than that of Kafka and is purpose-built with new-age applications in mind.
In the past, workloads themselves were very sophisticated, but they were one-dimensional and usually functioned on a simplistic framework. These days, the data is structured in a far different manner and requires different solutions, which just so happen to be features of Apache Pulsar.
The three premiere features that make Apache Pulsar superior to Apache Kafka are multi-tenancy, geo-replication, and tiered storage.
Multi-Tenancy
The concept of Tenants is present across most major streaming platforms and messaging systems. While single-tenant systems such as Kafka were the industry standard, new developments in technology and new technologies such as artificial intelligence and big data could benefit massively from a multi-tenant system.
Apache Pulsar constructs an entirely different framework where packets become accessible in multiple layers, meaning more than one tenant can access one node. It efficiently spreads tenants across different clusters, allowing for far faster, better, and most importantly, safer operations.
Geo-Replication
The way that the data is stored is a significant part of all streaming platforms and messaging systems. While Apache Kafka operates on a monolithic data structure, Apache Pulsar takes a different route. Apache Pulsar is cloud-native, meaning all of the data it stores is on the cloud.
The data is protected and streamlined through geo-replication, which basically means that persistently stored message data is segmented and spread to different clusters of the same pulsar instance. It makes logic implementation easy, next to no overhead when it’s disabled, and since it works on-switch, you can set it up or shut it down whenever you want.
Tiered Storage
While on the topic of storage, it’s important to note that one significant advantage that Pulsar has over Kafka is that Pulsar is a cloud-native service, making it adaptable to modern-day cloud-native computing. Aside from geo-replication, Apache Pulsar utilizes something known as tiered storage for all of its messaging and data.
The storage system is fully categorized, sorting data by importance. Data that hasn’t been used recently and just needs to be housed for a while is stored separately from the data you access daily so that getting the information you need is far faster.
Managed Services or In-House?
Now, there are two ways you can go about using either Apache Kafka or Apache Pulsar – in-house teams or managed services. These platforms are complex and require significant experience in order to optimize performance. Aside from setup, they require continual maintenance, monitoring, and operating, which mandates a specific team.
Enterprise engineering teams should ask the question, “Is distributed messaging a core competency?” If it is, you likely already have the technical know-how and experience in-house. In that case, scaling the team and operating the clusters in-house is a logical decision. For most companies, this will not be the case.
A managed service for distributed messaging is a recent phenomenon. A managed service provider can typically demand lower rates with the cloud providers, have in-house expertise with Kubernetes, and are able to optimize messaging environments at a fraction of the cost.
If you are unsure, one recommended approach is to select a few use cases and enlist a managed provider to manage. Track the cost carefully and extrapolate across your organization. In our experiences, a managed provider typically can reduce costs 30%+ as opposed to an in-house team.
Why Pandio
Pandio is the only managed SaaS vendor on the market that has built it’s managed Apache Pulsar specifically to be enterprise grade. This means a total rewrite of the Pulsar functions, an easy to use dashboard interface, and the infrastructure support for AI/ML workloads.
It’s becoming increasingly difficult in a tight labor market to identify engineering talent to support the implementation of messaging infrastructures that can support AI / ML initiatives. And as the amount of data continues to grow, it’s a challenge to provision, manage, and optimize messaging environments. Choosing a managed service for these challenges allows the enterprise to focus on core competencies with the assurance of performance and support.
Furthermore, Pandio has a full-stack solution with its AI Orchestration approach. It includes data abstraction through Trino, data movement by handling Apache Pulsar, and their very own PandioML, a system that can rapidly build, train, and deploy intricate machine learning models.
In Conclusion
To summarize, there are compelling reasons why Apache Pulsar is becoming an increasingly popular choice for managing next generation workloads. Apache Pulsar has real benefits when compared against Kafka for AI/ML use cases. It’s faster, offers more scalability, and most importantly, has a much lower TCO compared with Apache Kafka.
As for many comparisons, the choice between Kafka and Pulsar is use-case specific. Talk to the vendors that are offering these solutions and have them demonstrate why one or the other is the right fit. Run a proof of concept to determine the advantages and disadvantages. Then choose a provider and get started!
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