Kafka and Pulsar are often compared because they are both data platforms known for their ability to handle high-throughput, low-latency data streams. They enable enterprises to build scalable, fault-tolerant data pipelines and real-time processing applications. Their architectures are all built on a producer-consumer model, which means they are suitable for a variety of use cases and integrate seamlessly with modern data ecosystems.
I wrote this article to help you understand the main similarities and differences between the two solutions. We'll compare Kafka and Pulsar, focusing on the following areas:
Architecture. Operational attributes such as scalability, latency, and durability.
Developer experience, community, and ecosystem.
Licensing, deployment options, and managed products.
Finally, I hope you'll have a clearer understanding of each platform's unique features, and which one best suits your organization's needs.
1. Overview of Apache Kafka
Kafka is a distributed event streaming platform designed to handle high-speed, high-volume, and fault-tolerant data streams. It was originally developed by LinkedIn and later donated to the Apache Software Association. Kafka has quickly become a popular choice for building real-time data pipelines, event-driven architectures, and microservices applications.
1.Core Competencies
Publish and subscribe to record streams.
Store the stream of records in a fault-tolerant and durable manner.
Works in tandem with companion services to process record streams (Kafka streams and ksqldb) as they occur.
2.Key features:
Deliver high-throughput, low-latency messaging for real-time data streams.
Scalable architecture that supports data partitioning and replication.
It adopts a distributed fault-tolerant design with a strong durability guarantee.
Implement stream processing capabilities with complementary services from the Kafka ecosystem (Kafka Streams and KSQLDB).
A rich ecosystem of connectors and integrations is available through Kafka Connect.
An active open-source community that supports multiple programming languages.
2. Overview of Apache Pulsar
Pulsar is a distributed messaging system designed to handle high-performance, low-latency messaging and data streams. Originally created by Yahoo and later donated to Apache Software, pulsar is now a strong choice for building real-time data pipelines and event-driven architectures.
1.Core Competencies
Support for publishing sub-messaging and message queues.
Preserve the flow of information in a fault-tolerant and durable manner.
Multi-tenancy is natively supported.
2.Key features:
High-throughput, low-latency information transmission suitable for real-time data streams.
Scalable, multi-tier architecture that separates the storage layer from the service layer.
Ensure data durability by fault-tolerant design, including geographic replication.
Basic built-in streaming capabilities (pulsar functionality).
Deliver a right-sized ecosystem of connectors and integrations with Pulsar IO.
Support for multiple programming languages through official and third-party client libraries.
3. Pulsar vs. Kafka: Architecture Comparison
Now let's review the architectures of Pulsar and Kafka to understand their similarities and differences.
1.Apache Kafka architecture
At a high level, the Kafka architecture consists of three main elements: producers, consumers, and brokers. The producer generates the data and sends it to the broker, while the consumer reads the data ingested by the broker.
The broker runs on a Kafka cluster, while the producer and consumer are completely decoupled from the system. Each broker stores the actual data sent by the producer in a topic (a collection of messages belonging to the same set of categories). These themes can be divided into multiple sections for optimization. Data partitioning has the advantages of fault tolerance, scalability, and parallelism. In addition, each section may contain only part of a topic, and the rest of the sections are allocated to others. This approach helps to balance the workload between brokers. To improve reliability, a Kafka cluster can be configured as replicas of different topics, limiting downtime when it's unavailable.
In the image above, you can also see a zookeeper component, which is responsible for things like:
Store metadata about a Kafka cluster, such as information about topics, partitions, and replicas.
Manage and coordinate Kafka **, including leader elections.
Maintain access control lists (ACLs) for security purposes.
Plan to remove the ZooKeeper dependency entirely. (expected to be released in April 2024) to begin the complete removal of dependencies on Zookeeper. Instead, a new mechanic called kraft (which is actually production-ready). Instead of running a Zookeeper cluster next to each Kafka cluster, Kraft shifts the responsibility for metadata management to Kafka itself. This simplifies architecture, reduces operational complexity, and improves scalability.
There are also plans to introduce a tiered storage approach for Kafka. The local tier will use the local disk on Kafka brokers to store data. It is designed to retain data for short periods of time, such as hours. At the same time, remote storage will use systems such as Hadoop Distributed File System (HDFS) and Amazon S3 to store data for long periods of time (days, months, etc.).
2.Apache Pulsar architecture
Similar to Kafka, Pulsar's architecture includes brokers, producers, and consumers. The broker runs on a Pulsar cluster, while the producer and consumer are completely decoupled from the system. Each broker manages the actual data sent by the producer in the topic. Just like in the case of Kafka, these topics can be divided into many partitions, providing benefits such as fault tolerance, scalability, and parallelism.
Zookeeper is also present in Pulsar's architecture. It is used for a variety of tasks, including configuration management, coordination between nodes, and maintaining metadata for Pulsar clusters. As mentioned earlier, Kafka is moving away from ZooKeeper and replacing it with Kraft. Pulsar doesn't take ZooKeeper out of its architecture, but it does offer alternatives: local memory, rocksdb, and etcd (note that the first two only work with standalone Pulsar or single-node Pulsar clusters).
The biggest difference between Pulsar and Kafka is that it separates the storage layer and the service layer. In Pulsar's architecture, the broker handles message routing and delivery, while the Apache Bookkeeper handles long-term storage. Specifically, every message sent by a producer is written to the Bookkeeper Notepad. This tiered storage approach means that Pulsar's architecture is more complex than Kafka's – there are more components to manage (at least for now; But as mentioned earlier, Kafka will also introduce a tiered storage approach). On the other hand, this decoupling means that you can scale the storage and service tiers independently.
4. Kafka and Pulsar: Running Properties
How do Pulsar and Kafka compare in terms of storage and message consumption patterns, latency, throughput, durability, and scalability?
1.Storage and information consumption
Both solutions in terms of information consumption and storage patternsexistsThere is a big difference
Kafka's log-based storage model uses an append-only log file for each subject partition, and the information is written sequentially and stored on disk. Readings are sequential, starting with offsets (note that consumers are responsible for managing their offsets). Writes are appended to the end of the log. For message consumption, Kafka's pull model involves the consumer polling for a new message from **.
In contrast, Pulsar's tiered storage model divides information into smaller segments and stores it across multiple bookkeeper ledgers (ledgers). It's worth noting that pieces of information can also be offloaded to long-term storage solutions like Amazon S3 or Google Cloud Storage. Information is consumed through a push-based model.
A few comments on these differences:
While Pulsar's tiered architecture increases network utilization and requires information to be written to disk twice, it also allows for data segmentation, efficient management, and in some cases faster retrieval.
Compared to Kafka's simple architecture, Pulsar's layered architecture can increase operational complexity (with more components to manage).
When dealing with lagging consumers, both Kafka and Pulsar models can cause cache flush issues. Pulsar's approach can exacerbate this problem due to additional network redirects and IO operations.
Compared to Kafka, Pulsar's push mode can reduce latency and resource consumption. In Kafka, on the other hand, consumers can pull information and thus manage their own traffic control.
2.Performance
There is no doubt about itKafka and Pulsar are both high-performance distributed streaming and messaging platforms。It's hard (if not impossible) to say which one is better in terms of latency and throughput. Some benchmarks show that Pulsar performs better, while others show Kafka to be superior.
Nonetheless, push-based messaging systems and tiered storage models such as Pulsar do help reduce latency because they facilitate data organization, improve the efficient use of storage resources, and speed up data retrieval.
Instead, Kafka relies on a continuous polling process, where clients repeatedly request data at set intervals. During periods with low message volumes, this can result in higher latency as clients may wait idly between polling intervals.
However, whether the theoretical advantages of Pulsar over Kafka hold up in practice depends on the specific workload and usage patterns. It's still best to do your own benchmarking to determine what this looks like.
3.Scalability and durability
Kafka and Pulsar provide endurance features to ensure high availability and system resiliency. Both solutions allow you to store messages indefinitely, which is critical for recovery and continuity in the event of a failure or disaster. In addition, Kafka and Pulsar support geographic replication (across different data centers and even between regions). Kafka supports topic-level replication; Pulsar, on the other hand, provides replication at the topic and namespace level. It's worth noting that Pulsar requires an additional "global" Zookeeper cluster when geographically replicating data compared to Kafka, which adds to the complexity.
Kafka and Pulsar are both highly scalable platforms. Compared to Kafka, Pulsar's segmented, layered architecture may increase flexibility and scalability to some extent (since Pulsar's data and service tiers scale independently).
By far the biggest bottleneck in Kafka's scalability has been the use of Apache Zookeeper. ZooKeeper stores Kafka's metadata, including information about topics, partitions, replicas, and their configurations. ZooKeeper limits the maximum size of data that can be stored in a znode, a data node in ZooKeeper. This data size limit indirectly limits the number of partitions that Kafka can manage (approximately 500k partitions per cluster).
However, as mentioned earlier, Kafka is removing the dependency on Zookeeper in favor of kraft. In other words, the 500k partition limit imposed by ZooKeeper on each cluster will no longer exist. Kraft also brings other benefits – for example, it enables near-instantaneous failover of controllers and simplifies Kafka's architecture, deployment, and configuration.
With or without kraft, Kafka scales to the vast majority of use cases without difficulty. Pulsar is also great for handling large-scale scenarios. In fact, unless you're dealing with a hyperscale scenario (petabytes of data and trillions of messages per day), you're unlikely to run into serious scalability issues with either of these two tools. Even so, it's impossible not to solve these problems by rearchitecting or optimizing your Kafka or Pulsar deployment.
5. Kafka vs. Pulsar: Ecosystem
So far, we've seen Kafka and Pulsar as high-performance, highly scalable, and durable solutions. However, when choosing a data streaming platform, you can't just consider latency and scale. With that in mind, let's compare the developer experience and ecosystem of Kafka and Pulsar.
1.GitHub Stats, Resources, Community & Documentation, Learning & Training
You'll need some background knowledge to fully understand the differences between Kafka and Pulsar communities and resources.
Kafka became an official Apache Software Conference project in 2012, and Pulsar reached the same milestone four years later in 2016. In addition, Kafka's open-source nature has contributed to its rapid adoption of a surge in demand for a real-time event streaming solution.
This goes a long way to illustrating Kafka's advantages over Pulsar in the following ways. That being said, Pulsar's community is experiencing growth, which is always a good indicator of a project's future growth.
In conclusion, it is undeniable,When it comes to documentation, resources, and community, Kafka is superior to Pulsar. Kafka is also more popular(judging by github stats),It's also easier to learn(Although both kafka and pulsar are difficult to master).
2.CLI and client
Overall, Kafka and Pulsar seem to be on par when it comes to CLI tools. Both provide a CLI that allows you to manage and interact with Kafka Pulsar deployments. Of course, there are some differences in what you can do with these CLIs (some of these differences stem from the fact that Kafka and Pulsar are different platforms with some different features). For example, the Kafka CLI provides better and more detailed commands for managing consumer groups, while Pulsar's CLI tool allows you to manage packages (which Kafka's CLI can't do).
Both Kafka and Pulsar support multiple programming languages through their client libraries. Kafka has a slight edge in the number of languages it supports, mainly due to its longer existence and wider range of applications, which has led to the development of more third-party client libraries. For details, see the next section.
3.Language support
Kafka provides the official J**a and Scala client libraries. Confluent (founded by the creators of Apache Kafka) provides a number of other officially supported clients, for C C++, C.NET, Python, GO, and Nodejs。Similarly, Pulsar also has targets for j**a, c c++, c.NET, Python, GO, and NodeThe official client library for JS. Essentially, Pulsar and Kafka target the same programming language through their official client libraries (the only notable difference is that the official Kafka client supports Scala, while Pulsar does not).
In addition to these official clients, there are:Many third-party Pulsar and Kafka client librariesMost of them are open-source projects。Kafka has a slight advantage because you can find Kafka clients for PowerShell, Perl, and Swift (these languages don't have a Pulsar client library).
Note that Kafka and Pulsar also offer some language-independent clients. For example, Pulsar provides REST and WebSocket clients, while Kafka provides multiple HTTP clients (both official and community). Learn more about the Kafka client and the Pulsar client.
4.Ecosystem
Kafka has a larger ecosystem as compared to Pulsar. The Kafka Connect Framework makes it easy to ingest data from other systems to Kafka and stream data from Kafka topics to different destinations through the Kafka Connect Framework. There are hundreds of connectors for different types of systems, such as databases (such as MongoDB), storage systems (such as Azure Blob Storage), messaging systems (RabbitMQ, JMS), and more.
At the same time, while Pulsar's ecosystem is not as mature as Kafka, it still offers a large and diverse range of connectors and integrates with other systems such as Aerospike, Datadog, and RabbitMQ.
When it comes to built-in stream processing capabilities, Kafka is superior to Pulsar. Their Kafka Streams library allows you to build real-time stream processing applications, with features such as connectivity, aggregation, windowing, and exact-one-time processing. In contrast, Pulsar only provides basic functionality for stream processing through the Pulsar Function interface, which is suitable for simple **. In addition to built-in stream processing capabilities, Kafka and Pulsar integrate with stream processing solutions such as Apache Flink, Apache Storm, and Apache Beam.
6. Kafka vs. Pulsar: Licenses and Deployment Options
This section compares Kafka and Pulsar licensing terms, commercial support options, deployment models, and managed service offerings.
1.Licensing and commercial support
As you can see,There is no difference between Kafka and Pulsar in terms of licensing。Both platforms are open source – they both use the Apache License 20.
Be that as it mayIf you don't want to manage Kafka Pulsar yourself, there are also third-party vendors that offer commercial support。However, it's worth pointing out that Kafka's commercial support is more mature and extensive compared to Pulsar. See the next section for more information on this.
2.Deployment models and managed products
Kafka and Apache can be flexibly deployed in a variety of ways, such as on-premise, in the cloud, using Docker, or Kubernetes. In addition, manyManaged Service ProvidersBoth support Kafka and Pulsar, which simplifies the deployment, scaling, and management of these systems. However, it is important to note thatThe number of Kafka vendors is larger (and more well-known).。This is not surprising, after all, Kafka has a much longer history and was adopted earlier (and more widely) than Pulsar.
7. Kafka vs. Pulsar: Security
Security is often a top priority when choosing a data streaming platform. So how does Pulsar compare to Kafka?
Both Kafka and Pulsar offer robust security features such as encryption and strong authentication and authorization mechanisms.
In some ways,Pulsar has advantagesFor example, it natively supports end-to-end encryption and has built-in audit logs. That's not to say that Pulsar is inherently more secure than Kafka, or that Kafka lacks key security features, but it's worth noting that Pulsar offers some additional security mechanisms that might come in handy.
Conclusion
As we've seen, Kafka and Pulsar are both data streaming platforms with some similar characteristics. They are both high-throughput, low-latency, durable, and highly scalable solutions with good programming language coverage through official and third-party client SDKs.
However, there are also a lot of differences between them. For example, Pulsar has some additional security features (like built-in audit logs). It also has a tiered architecture that separates the storage tier from the service tier. This gives you flexibility as the tiers can scale independently based on your needs.
Kafka, on the other hand, has a less complex architecture and fewer components. In addition, Kafka has a more robust, mature ecosystem of connectors and integrations than Pulsar, and offers richer stream processing capabilities. Kafka also has a much larger community, and it's been more widely tested by giants like Netflix and LinkedIn in its hyperscale practice.
Whether Kafka or Pulsar is the best choice for streaming use cases ultimately depends on your decision.
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