Kafka vs Pulsar – What’s the Difference ? (Pros and Cons)
Kafka vs Pulsar – What’s the Difference ? (Pros and Cons). To start with, Apache Kafka is a message broker whose main feature is a powerful data store optimized for real time data processing. In addition, streaming platforms must manage and process this continuous flow of data sequentially and incrementally.
So what are their differences? In this blog, we will take a closer look at these two message brokers. We will find out what they are and what their usefulness is. Additionally, we will learn about their features and benefits. As well as the pros and cons. However, at the very end I will put them together for the sake of comparison.
Shall we start with Kafka vs Pulsar – What’s the Difference ? (Pros and Cons).
What is a Apache Kafka?
First and foremost Apache Kafka message broker is an open source software that analyses, transmits, reads and stores data. Additionally, this broker is resistant to asynchronous errors of message queues. Secondly it distributes messages and scales well. A website called producer delivers messages to Kafka that are consumed by another consumer (website).
There can be one or more producers sending messages to Kafka. Similarly, there can be one or more consumers consuming Kafka messages.
How does Apache Kafka work?
Moreover Kafka combines two messaging models, queues and publish subscribe. Then it provides consumers with the main advantages of each model. Queuing helps distribute data processing among many consumer states and makes it highly scalable. However, traditional queues are not multi subscription.
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.
Thirdly Kafka stitches these two solutions together using a partitioned log model. A log is an ordered sequence of records divided into segments or partitions corresponding to different subscribers.
This means that there can be multiple subscribers to the same topic, and each subscriber is assigned a partition for greater scalability. Finally, the Kafka model provides replay ability, allowing multiple independent applications reading from the data stream to run independently at their own speed.
Queuing
Publish subscribe
Benefits of using Apache Kafka
Scalable – with Kafka’s split log model allows data to be distributed across multiple servers, making it scalable beyond what a single server can accommodate.
Performance -stable and provides reliable robustness. Also has flexible publish/subscriber queues, good metrics, has strong replication, provides tuneable consistency guarantees for producers, and provides shard level order retention.
Durability – data in Kafka is very fault tolerant in two main ways. First, it protects against server failure by distributing streaming storage across a fault tolerant array. Second, it provides a replica within the cluster where it continues to send messages to disk.
Speed – so by separating the data stream, Apache Kafka is able to deliver messages at network limited rates through a set of servers with very low latency (down to 2ms).
React to customers in real time – a big data technology that allows you to manipulate dynamic data and quickly determine what is working and what is not.
Simple – easy to set up and use, and it’s easy to understand how Kafka works.
Pros and cons of using Apache Kafka
Pros
Low latency – provides low latency values, i.e. up to 10ms. In effect, this separates the message, allowing consumers to consume it at any time.
Fault tolerance – It has the primary function of providing resiliency to node/device failure in the cluster.
High Throughput – Because of the low latency, Kafka is able to process multiple messages in large quantities and at high speed. In nutshell Kafka can process thousands of messages per second. Many companies like Uber use Kafka to ingest large amounts of data.
Batch approach – can also be used as an ETL tool due to its data persistence capabilities.
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.
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.
Do not support wildcard topic selection – Wildcard selection is not supported. Instead, it only matches the exact name of the topic. In fact, the selection of generic attributes makes it impossible to deal with specific use cases.
Message tweaking issues – Kafka brokers send messages to consumers using system calls. If the message needs some tuning, Kafka’s performance will deteriorate dramatically. So it works fine if the message doesn’t have to be changed.
Do not have complete set of monitoring tools – a full suite of monitoring and management tools is not included. So start-ups or new companies are afraid to work with Kafka.
Now it is time with Kafka vs Pulsar to learn about the other tool that is Pulsar.
What is a Apache Pulsar?
The second tool Pulsar also a message broker is high performance, multi tenant solution for sending messages between servers and queuing in a sub publisher model. Moreover Pulsar combines the best features of traditional messaging systems like RabbitMQ and the best features of advertising subsystems like Kafka.
It’s no wonder that Pulsar has grown in popularity since becoming an open source Apache project. Given its strengths, its community will likely continue to grow rapidly.
Why Apache Pulsar?
Scales Horizontally – well Pulsar deployments can be expanded to meet demand. So it’s capable of growing hundreds of nodes, so themes, posts, and storage can grow effortlessly.
Cloud Native – since Pulsar is a cloud system, it offers additional benefits to organizations as many companies maintain most of their infrastructure in the cloud.
Low publish latency – The deployment response time is less than 5ms compared to the well known Apache Kafka with a deployment latency of 5ms or more. In addition Apache Pulsar has low latency performance even with increased productivity.
Benefits of using Apache Pulsar
- Firstly Pulsar functions and IO connectors for easy connections.
- Schema recording to avoid data mismatch.
- Completely open source, forever.
- Easy scaling, up and down.
- Kubernetes and cloud ready architecture.
- Multiple tenancies to support different teams.
- Modernize legacy applications.
- Messaging, streaming and queuing all in one place.
Pros and cons of using Apache Pulsar
Pros
- Streamlined performance.
- Easy to use.
- Low latency.
- Full stack solution.
- Easier to use API.
- Deployment is also easy. A standalone Pulsar will start it’s own local Zookeeper, and I personally found that the configuration easier to understand.
- Scales to millions of topics.
- There are some features that Kafka currently lacks, like seeking to a particular Message Id.
Cons
- Requires more sophisticated hardware.
- Reader cannot easily read last message in the topic need to skim through all the messages to the end.
- latency questionable – there is one extra remote call between Broker node and Bookkeeper (compared to Kafka).
- java client has little to no java doc.
Kafka vs Pulsar - Key difference
Architecture
On tone hand, Kafka only uses Zookeeper which is currently being phased out. The result is a simpler structure that is easier to understand and use. When Zookeeper is completely deprecated and replaced by a new quorum console, all metadata responsibilities will be handled by the Kafka Group itself.
Contrarily Pulsar is not that easy to understand and use, especially since Pulsar, Zookeeper and Bookkeeper are distributed systems. Since Pulsar also offers more configuration parameters than Kafka, which makes getting started a bit tricky.
Message consumption
In the case of Kafka, consumers pull messages from servers. Long polling ensures that new messages are consumed almost instantly.
Oppositely here Pulsar is based on the pub-sub model. Producers post messages to servers and consumers must register to receive them.
Brokers
In Kafka, each medium uses the full record of its sections. These brokers must synchronize data with all other brokers and their replicas on the same partition. Pulsar, on the other hand, stores state outside the medium and completely separates it from the data storage layer.
Consequently Kafka clients contact Kafka brokers to write or read events. Once received, the broker will store events as needed in a robust and fault-tolerant manner. One of Apache Pulsar’s biggest selling points is its stateless proxies. These brokers can be released quickly and in large quantities to meet growing demand.
Multi Data Center Replication
Altogether Kafka has two replication methods: Mirror Maker 2 or Confluent Replicator. If you use the Apache Kafka distribution, you have Mirror Maker 2 which works well, but takes time to set up. If you purchase a Confluent license, the Replicator is available as a standalone application or as a plug-in that runs on Kafka Connect nodes.
For me, Pulsar wins the replication battle, it provides geo replication out of the box. Replication groups can be created in multiple data centers. Applications can be prevented from using the local block until messages are copied and accepted.
Service Discovery
If you’ve worked with Kafka, you’ll be familiar with setting properties and adding bootstrap servers, broker lists, or Zookeeper nodes, depending on what you’re doing. When a new agent is added, the properties must be modified by adding the new address to the configuration.
Certainly Pulsar provides a proxy layer to process groups using unique addresses. This is a huge advantage over Kafka, especially when publishing with frameworks like Kubernetes that don’t have direct access to intermediaries.
Another advantage is that you can run any number of Pulsar agents and have a single access point for them through a load balancer. For cloud based deployments, this makes managing and accessing the pool easier.
Scaling Clusters
Where Kafka uses brokers for storage, Pulsar uses Apache Bookkeeper instead of the brokers themselves. The main difference is that Pulsar caches and replays unacknowledged messages and separates message persistence from the medium.
Besides Pulsar provides the ability to use non persistent elements in memory without writing data to disk. However, note that if a Pulsar broker breaks with the pool, these non-persistent messages and topics will be lost, either stored in the broker or on their way to the consumer.
Community Support
Support for Confluent Slack channels is excellent (if you’re not a member and using Kafka, I highly recommend subscribing). There are a lot of people on the web covering different aspects of the Kafka ecosystem and there is a lot to do.
Unfortunately, the Pulsar community is still small (but growing), so it’s hard to find answers. Kafka community support wins. If it’s going to compete with Kafka in the future, that’s where I think it needs to focus more.
Thank you for reading Kafka vs Pulsar – What’s the Difference ? (Pros and Cons).
Now with Kafka vs Pulsar – so What’s the Difference? Let’s find out.
Kafka vs Pulsar – What’s the Difference ? Conclusion
Summing up Pulsar has clear advantages when you need to separate tenants, store less demanding legacy data on cheaper storage, easily replicate groups in different geolocations, or integrate queuing and streaming capabilities into a single messaging system.
When it comes to trust, configuration, documentation, use cases, and support, Kafka is your choice. It has its quirks: sometimes it can eat your data or introduce a change that disables powerful messaging command guarantees.
But if you learn them, read the release notes carefully, and keep your knowledge up to date, you’ll end up with a platform that will not surprise you.
If you wanna know more about Kafka click here.
