Choosing between Apache Kafka and Apache Pulsar depends on your needs. Here’s a quick summary:
| Feature/Aspect | Kafka | Pulsar |
|---|---|---|
| Architecture | Single-layer, monolithic | Multi-layered (compute + storage separated) |
| Throughput | Higher peak throughput | Consistent performance, lower peak |
| Scaling | Requires cluster rebalancing | Independent scaling (no rebalancing) |
| Multi-tenancy | Limited | Built-in, tenant/namespace hierarchy |
| Geo-replication | Requires additional tools | Built-in |
| Latency | ~5ms at p99 for high throughput | Single-digit latency across workloads |
| Ecosystem | Mature, 17 programming languages supported | Smaller, 6 programming languages supported |
| Use Cases | Real-time event streaming, CDC, IoT | Cloud-native setups, multi-tenant systems |
Bottom line: Kafka is a strong choice for straightforward, high-performance streaming, while Pulsar offers advanced features for flexibility and scalability.
The internal architecture of Kafka and Pulsar plays a big role in how they perform under different conditions, shaping their strengths and limitations in practical use. Let’s break down the key architectural traits of each platform.
Kafka operates on a monolithic, partition-focused architecture. In this setup, brokers are responsible for both message processing and data storage, achieved through a commit log stored on local disks. While this design simplifies the system, scaling a Kafka cluster can be challenging. Adding new broker nodes requires data rebalancing across the cluster, a process that is both time-consuming and complex.
Pulsar, on the other hand, takes a multi-layer approach by separating compute and storage. Brokers in Pulsar manage message routing, while storage duties are offloaded to Apache BookKeeper nodes, also known as bookies. This separation allows for independent scaling of resources, offering greater flexibility. Unlike Kafka, Pulsar doesn’t require data rebalancing when brokers are added, making the scaling process much smoother. Additionally, its three-level topic hierarchy - comprising tenants, namespaces, and topics - provides robust support for multi-tenant environments, which is a significant contrast to Kafka’s simpler flat topic structure.
| Aspect | Kafka | Pulsar |
|---|---|---|
| Design Philosophy | Single-layer, monolithic | Multi-layered, separated |
| Compute & Storage | Combined in brokers | Decoupled (brokers + BookKeeper nodes) |
| Scaling Approach | Requires scaling full brokers | Compute and storage scale independently |
| Data Rebalancing | Necessary | Not needed when adding brokers |
| Multi-tenancy | Limited, flat topic structure | Supports tenant/namespace hierarchy |
| Complexity | Simpler overall | More intricate due to separation |
| Resource Utilization | Fixed broker resources | Flexible allocation of resources |
These architectural distinctions make each platform shine in different scenarios. Kafka’s straightforward design works best for high-throughput tasks with predictable scaling needs, while Pulsar’s flexible and modular setup is ideal for cloud-based environments where multi-tenancy and resource adaptability are key priorities.
Understanding how each platform performs under different workloads is key to making an informed decision. With their unique architectures, Kafka and Pulsar bring distinct strengths to the table when it comes to performance and scalability.
Kafka’s integrated compute and storage design is engineered for maximum raw throughput, making it a powerhouse for high-speed data streaming. A cluster of just three machines can handle up to 2 million writes per second, showcasing its efficiency. Benchmarks reveal Kafka can write data 15 times faster than RabbitMQ and twice as fast as Pulsar. Its polling-based delivery model also contributes to its speed, achieving 5ms latency at the p99 percentile during high-throughput operations.
Configuration tuning can further enhance Kafka's performance. For example, a Confluent study demonstrated that increasing the linger.ms setting from 0 to 5ms significantly improved batching efficiency, reducing the request rate from 2,800 to 1,100.
However, Kafka’s performance can degrade under certain conditions, such as when concurrent producers increase significantly. Careful tuning is essential to maintain optimal latency and throughput.
Pulsar focuses on durability and consistent low-latency performance, even if it doesn’t quite match Kafka’s peak throughput numbers. It delivers single-digit publish latency across a wide range of workloads. Pulsar’s separated storage and compute architecture offers additional strengths, including 3.2 GB/s historical data read throughput, which is 60% faster than Kafka. This advantage comes from Pulsar’s ability to let both leader and follower brokers read directly from the object store, unlike Kafka, which relies on consumers reading from the log tail.
Pulsar’s approach to durability is another standout feature. By default, it flushes every message to disk, ensuring stronger data persistence compared to Kafka’s replication-based model. While this can slightly impact latency, it provides a safety net in failure scenarios, making Pulsar a reliable choice for durability-focused workloads.
| Performance Aspect | Kafka | Pulsar |
|---|---|---|
| Peak Throughput | Up to 2 million writes/sec | High, but lower than Kafka |
| Latency at High Load | 5ms at p99 | Consistent single-digit latency |
| Historical Data Access | Optimized for tail reads | 3.2 GB/s (60% faster than Kafka) |
| Durability Approach | Replication-based | Flushes each message to disk |
| Scaling Bottlenecks | Network-limited in optimized deployments | Storage and compute scale independently |
| Consistency | Variable based on load | More predictable across workloads |
Choosing between Kafka and Pulsar depends heavily on your performance needs. Kafka is ideal for workloads requiring maximum throughput and low latency, especially when infrastructure is optimized to suit its architecture. Pulsar, on the other hand, stands out for its consistent performance, robust durability, and ability to independently scale storage and compute to meet varying demands.
For teams managing infrastructure that needs to handle both real-time data streaming and historical analysis, these performance characteristics play a critical role in decision-making. Companies leveraging Optiblack's Data Infrastructure services often rely on detailed performance evaluations to determine which platform aligns best with their unique requirements.
The features and ecosystem surrounding a platform play a crucial role in determining its success during implementation. While both Kafka and Pulsar excel in messaging capabilities, their ecosystems differ significantly in terms of maturity, community support, and available tools. Let’s take a closer look at what sets each platform apart.
Kafka boasts one of the most established ecosystems in the messaging space. It supports 17 programming languages, has a thriving Slack community with 23,057 members, and over 21,233 questions on Stack Overflow. By comparison, Pulsar supports only six languages, has 134 Stack Overflow questions, and a Slack community of 2,332 members.
The job market reflects Kafka's dominance, with 4,293 job mentions in the USA on Monster.com, compared to just 23 for Pulsar. Kafka also offers robust tools like Kafka Streams for stream processing, and enterprise-grade solutions such as Confluent Platform, Schema Registry, and ksqlDB. Integration is another strong suit, with over 400 open-source connectors available via the Kafka Connect framework.
Pulsar, on the other hand, shines in areas like multi-tenancy and messaging flexibility, catering to specific enterprise needs. Unlike Kafka, Pulsar is designed with native multi-tenancy, offering resource isolation at both the tenant and namespace levels. Yabin Meng from DataStax highlights this capability:
"Pulsar is designed as a true multi-tenancy system with built-in policies to protect data integrity and ensure fair resource utilization".
Pulsar also supports a variety of messaging patterns, including queuing, pub-sub, event streaming, and key-shared subscriptions, all natively. Additionally, it offers built-in geo-replication at both the topic and namespace levels, which bolsters disaster recovery efforts. However, its ecosystem is more limited, with only around 20 connectors compared to Kafka’s extensive library.
| Feature Category | Kafka | Pulsar |
|---|---|---|
| Programming Languages | 17 supported | 6 supported |
| Slack Community | 23,057 members | 2,332 members |
| Stack Overflow Questions | 21,233 questions | 134 questions |
| Job Market (USA) | 4,293 mentions | 23 mentions |
| Connectors Available | 400+ open source | ~20 connectors |
| Multi-tenancy | Limited support | Native, built-in |
| Messaging Patterns | Primarily event streaming | Multiple patterns natively |
| Stream Processing | Kafka Streams (built-in) | Pulsar Functions (basic) |
| Geo-replication | Requires additional tools | Built-in at topic/namespace levels |
These distinctions in features and community support play a critical role in determining which platform is better suited for specific real-time data processing needs.
Choosing between Kafka and Pulsar isn't about picking a "better" platform - it’s about finding the one that aligns with your specific needs. Both platforms excel in different scenarios, and your decision should consider factors like operational requirements, team expertise, and long-term goals.
Kafka is a powerhouse in the world of event streaming, trusted by over 80% of Fortune 100 companies. With a peak throughput of 605 MB/s and a p99 latency of just 5 milliseconds, it’s the ideal choice for applications that demand the ability to process massive amounts of real-time data. Financial trading platforms, fraud detection systems, and large-scale IoT deployments thrive with Kafka’s performance capabilities.
Kafka also shines in real-time communication systems, such as chat applications and IoT management, by ensuring reliable message delivery and persistent storage. Its log-based design allows for reprocessing, making debugging and historical analysis straightforward.
When it comes to Change Data Capture (CDC), Kafka stands out as a tool for building data warehouses, data lakes, and analytics platforms. It simplifies data synchronization across systems while keeping a complete data history. This makes it invaluable for backfilling historical records or reprocessing data for compliance.
Complex event processing (CEP) scenarios also benefit from Kafka’s ability to handle high-volume, high-speed data streams. Whether it’s real-time anomaly detection, pattern recognition, or fraud prevention, Kafka delivers consistent performance with ordering guarantees at the partition level.
Kafka is a great fit if your team already has Kafka expertise, you need maximum throughput, or your retention requirements don’t demand extensive long-term storage. However, if your use case involves flexible messaging or multi-tenancy, Pulsar might be a better fit.
Pulsar’s strength lies in its flexibility and its ability to support multi-tenant environments. Its architecture is particularly well-suited for complex enterprise scenarios.
For organizations serving multiple clients or applications from a single cluster, Pulsar’s native multi-tenancy features are a game-changer. These features provide resource isolation at both tenant and namespace levels, eliminating the need to manage separate Kafka clusters for different business units or customers.
If your application needs both queuing and streaming capabilities, Pulsar’s unified approach simplifies things. It natively supports multiple messaging patterns, allowing traditional message queuing, publish–subscribe, and event streaming to coexist seamlessly.
Pulsar also excels in geo-replication. Its built-in geo-replication works right out of the box without requiring additional tools or extensive configuration. In contrast, Kafka relies on MirrorMaker 2.0, which requires significant tuning to achieve similar functionality.
Another standout feature is Pulsar’s tiered storage, which offloads data to object stores for long-term retention. This is especially useful for industries with strict compliance requirements to retain data over extended periods.
Organizations that anticipate frequent scaling benefit from Pulsar’s stateless brokers. This design makes scaling and maintenance easier compared to Kafka’s broker-heavy architecture. It’s a smart choice for businesses expecting rapid growth or variable workloads.
"Pulsar has legitimately crossed the line where it is better than Kafka for queueing and streaming... Pulsar's design also seems more able to support building features Kafka will struggle to offer like delayed messaging." - keatsshrike
Consider Pulsar if you need decoupled storage and compute, unified queuing and streaming, or robust multi-tenancy. It’s a strong option for organizations with complex scaling and messaging needs.
For those navigating the challenges of data infrastructure, platforms like Optiblack provide specialized services to help implement and fine-tune messaging systems as part of broader data solutions.
When deciding between Kafka and Pulsar, let your technical needs and business priorities guide you. Kafka's proven performance and straightforward scalability have made it a popular choice for many organizations. If your focus is on pure event streaming with top-tier performance and strong community support, Kafka might be the better fit.
On the other hand, Pulsar's decoupled architecture offers more operational flexibility. For organizations that need both queuing and streaming, multi-tenancy, or built-in geo-replication, Pulsar's design could be a strong advantage. These differences in architecture highlight the key factors to consider when evaluating performance and ecosystem support.
While Kafka may still have an edge in peak throughput, Pulsar balances the scales with features like tiered storage and consistent single-digit publish latency. This makes Pulsar particularly appealing for use cases where adaptability is a priority.
Ultimately, your choice should reflect your team's expertise and long-term goals. Kafka excels with its mature ecosystem and ease of scaling, while Pulsar stands out for its modern, flexible architecture and advanced enterprise features. The trade-offs are clear: Kafka brings simplicity and high performance, while Pulsar offers versatility and robust functionality.
Selecting the right platform is vital for optimizing real-time processing systems. For industries like SaaS, eCommerce, Fintech, and Hospitality, where infrastructure decisions are complex, Optiblack can provide the expertise needed to build, maintain, and scale these systems. Their services in Product Acceleration, Data Infrastructure, and AI Initiatives help businesses navigate the challenges of deploying either platform, improving operational efficiency and enabling smarter, data-driven decisions.
The key to success lies in aligning the platform with your specific use cases, team capabilities, and business goals.
The way Kafka and Pulsar are built significantly impacts their scalability and performance. Kafka relies on a monolithic design where storage and compute are tightly linked. This means if you need to scale one, you have to scale the other, which can lead to inefficiencies as demand grows. In contrast, Pulsar uses a more modular approach, separating storage from compute. This separation allows each to scale independently, offering more flexibility. As a result, Pulsar can manage a larger number of partitions and topics without the need for complicated rebalancing, making it highly flexible and capable of handling demanding workloads.
In terms of performance, Kafka generally stands out for its higher throughput, making it a great fit for use cases that require fast data ingestion. Pulsar, however, shines with its ability to deliver lower latency and consistent performance across a variety of workloads. Its design allows it to adapt dynamically, making it a strong option for applications that need steady performance and the ability to adjust to changing demands.
Pulsar's multi-tenancy and geo-replication features bring valuable benefits, particularly for organizations prioritizing resource isolation and scalability. With its built-in multi-tenancy, Pulsar enables multiple teams or projects to share the same infrastructure while keeping resources logically separated. This is achieved through namespaces, quotas, and access controls. The result? Simplified management, reduced complexity, and lower costs - no need to provision separate clusters for every team or project.
When it comes to geo-replication, Pulsar offers native support to replicate data across multiple geographic regions effortlessly. This ensures greater data availability, stronger disaster recovery capabilities, and smoother management of distributed environments. While Kafka also supports geo-replication, it often involves more complex configurations and lacks Pulsar's integrated multi-tenancy. For multi-tenant setups and globally distributed systems, Pulsar stands out as a more streamlined option.
Apache Pulsar stands out in scenarios where complex messaging patterns, robust durability, and multi-tenancy are essential. With built-in geo-replication and adaptable message retention policies, it’s a strong choice for applications that demand high availability and disaster recovery capabilities. It also supports a variety of messaging patterns, like topic-based and key-based routing, which are particularly useful for managing intricate workflows.
What sets Pulsar apart is its decoupled architecture, enabling scalability and high performance, even in environments with unpredictable workloads. This makes it an excellent option for organizations anticipating rapid growth or varying data streaming needs. If your application requires managing multiple tenants or handling dynamic scaling, Pulsar’s design is tailor-made for such requirements.