In this episode, we delve into the world of distributed messaging systems, comparing two of the most prominent platforms: Apache Kafka and Apache Pulsar. This overview provides a concise yet comprehensive exploration of their architectural designs, key concepts, internal mechanisms, and the algorithms they employ to achieve high throughput and scalability.
We begin with an architectural overview of both systems, highlighting the unique approaches they take in message storage, delivery, and fault tolerance. You'll gain insights into the core components of each system, such as brokers, topics, and partitions, and how these components interact.
The discussion moves to the key concepts like producers and consumers, exploring how each system handles message production and consumption. We cover how messages are stored, including Kafka’s reliance on the operating system's page cache, and Pulsar's use of Apache BookKeeper for persistent storage.
Next, we examine the internal workings and algorithms that make these systems efficient and reliable. For Kafka, this includes an explanation of offsets, pull requests, and the sendfile API. For Pulsar, we explore its consensus protocol with BookKeeper, load balancing algorithms, and message acknowledgment mechanisms.
The episode also highlights advanced features and use cases for both systems, showcasing their application in real-time data processing and log aggregation. We explore Pulsar’s multi-tenancy support, schema registry, and TableView interface for event-driven applications. Furthermore we discuss topic compaction in Pulsar which optimizes storage and retrieval of messages.
We examine geo-replication and cluster failover, and while Kafka requires external tools like MirrorMaker for cross-datacenter replication, Pulsar offers built-in geo-replication capabilities along with synchronous and asynchronous strategies for disaster recovery.
Finally we touch upon the performance considerations for both systems, highlighting the key differences that make each system suitable for different use cases.
Whether you are an experienced data engineer or new to distributed systems, this episode will provide you with valuable insights into the inner workings of these two powerful technologies.
Key Topics Covered:
Credits:
This episode draws information from the following sources:
Disclaimer:
Please note that parts or all this episode was generated by AI. While the content is intended to be accurate and informative, it is recommended that you consult the original research papers for a comprehensive understanding.
