Apache Pulsar

What is Apache Pulsar?

Apache Pulsar is an open-source distributed pub-sub messaging platform developed by Yahoo and now being maintained under Apache Software Foundation. It's designed to handle high volumes of data with low latency, offering powerful capabilities for real-time data stream processing and analytics. Pulsar's key features include multi-tenancy, high throughput, low latency, and geo-replication.


Apache Pulsar was initially developed by Yahoo! to overcome the challenges in handling large-scale messaging and streaming data. Released under the Apache License 2.0 in 2016, it has since seen numerous updates and enhancements, becoming a reliable choice for many organizations handling big data.

Functionality and Features

Apache Pulsar offers a host of unique features that make it particularly attractive for data-heavy applications:

  • Scalability: Pulsar can handle millions of topics, making it ideal for large-scale data processing applications.
  • Integrated message queuing and streaming: This ensures reliable delivery of messages and allows real-time processing.
  • Multi-tenancy: Pulsar supports authentication, authorization, quotas, and load balancing over several tenants.
  • Geo-replication: Pulsar has built-in geo-replication to ensure data is accessible from multiple geographic locations.


Apache Pulsar follows a two-layer architecture: a serving layer called ‘Broker’ and a storage layer called ‘BookKeeper’. The serving layer is responsible for receiving and transmitting messages, while the storage layer handles the durable storage of messages.

Benefits and Use Cases

Apache Pulsar is beneficial in applications where low latency, high throughput, and scalability are critical. For instance, it's used in real-time analytics, IoT, and machine learning applications. Its ability to seamlessly integrate with frameworks like Apache Flink, Apache Spark, and Apache Storm enhances its utility in data procession and analytics.

Challenges and Limitations

While Apache Pulsar brings considerable benefits, its complex two-layer architecture can lead to operational challenges and higher maintenance. Also, as a more recent addition to the data streaming ecosystem, its community is smaller compared to more established projects like Apache Kafka.

Integration with Data Lakehouse

Apache Pulsar can serve as a key component in data lakehouse architecture, acting as a data ingestion mechanism. By streaming data into the lakehouse, Pulsar enables real-time data processing and analytics, making it a bridge between the real-time and batch processing environments in a lakehouse setup.

Security Aspects

Apache Pulsar supports a range of security measures such as transport encryption, authentication, and authorization that help ensure data protection and systems integrity.


With its low latency and high throughput, Apache Pulsar exhibits impressive performance in real-time data processing. Its architectural separation of serving and storage allows for independent scaling and helps maintain optimal performance.


1. What is the main difference between Apache Pulsar and Apache Kafka? Kafka uses a single-tier architecture while Pulsar uses a two-tier architecture. Pulsar may offer better performance in certain high-scale applications due to its two-tier design, integrated message queuing and streaming capabilities, and advanced features like geo-replication.

2. Can Apache Pulsar be used in a data lakehouse setup? Yes, Pulsar can serve as a data ingest mechanism in a data lakehouse setup, providing real-time streaming for processing and analytics.


Pub-Sub Messaging: A form of asynchronous service-to-service communication used in serverless and microservices architectures

Geo-replication: A feature that allows replicating a database over geographically distant regions to ensure data availability even in case of a regional failure.

Multi-tenancy: A software architecture where a single instance of the software application serves multiple customers, referred to as tenants.

Data Lakehouse: A new data management paradigm that combines the features of data lakes and data warehouses for flexible, efficient data management.

Apache Flink: An open-source stream processing framework for high-throughput, low-latency and scalable applications.

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