What Is Apache Pulsar?
Published on: June 25, 2024
Last updated: January 25, 2026 Read in fullscreen view
Last updated: January 25, 2026 Read in fullscreen view
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What Is Apache Pulsar?
Apache Pulsar is a distributed message streaming system.
Apache Pulsar is an open-source message streaming system built on a distributed messaging architecture and a streaming platform developed by the Apache Software Foundation.
Below are some key features of Apache Pulsar: GitHub - apache/pulsar: Apache Pulsar - distributed pub-sub messaging system
- Message Schema: Apache Pulsar supports various message data formats, including JSON, Avro, Protobuf, and custom schemas. This enables integration of data from multiple sources without requiring prior data transformation.
- Integration with Apache BookKeeper: Apache Pulsar uses Apache BookKeeper as its storage layer for message streams, providing strong consistency and excellent scalability.
- Multi-Tenancy Architecture: Apache Pulsar allows multiple tenants to share the same Pulsar cluster while maintaining isolation of data and resources.
- Publish-Subscribe Architecture: Pulsar is built on the publish-subscribe (pub-sub) model. In this model, producers publish messages to topics; consumers subscribe to those topics, process incoming messages, and send acknowledgements to the broker once processing is complete.
- Reliability and Durability: Pulsar guarantees no message loss and ensures messages are safely stored even in the event of system failures.
- Auto-Scaling Support: Pulsar can automatically scale up or down system components based on the current workload.
- Integration with Apache Flink and Apache Spark: Apache Pulsar integrates with popular data processing frameworks such as Apache Flink and Apache Spark, enabling flexible and efficient stream processing applications.
- Geo-Replication Support: Pulsar provides data replication across multiple geographic regions, improving availability and preventing data loss.
- Multiple API Interfaces: Apache Pulsar supports a wide range of APIs, including C++, Python, Go, and Java, making application development easier and more flexible.
Where Is Apache Pulsar Used?
Below are some common application scenarios where Apache Pulsar is widely used:
- Real-time Data Processing: Apache Pulsar serves as a platform for real-time data processing applications, including stream analytics, real-time event processing, and pattern matching.
- Real-time Messaging Systems: Pulsar can be used as a real-time messaging backbone for applications such as online chat, video communication, weather alerts, and monitoring systems.
- IoT and Sensor Data: Apache Pulsar can be used to collect, process, and store data from IoT devices and sensors. This includes environmental monitoring, smart security systems, and other IoT-related applications.
- Log and Event Management: Pulsar can be used to store and manage system logs and events in production environments, including logistics tracking, application log management, and enterprise event processing.
- Distributed Application Connectivity: Apache Pulsar provides a mechanism for distributed applications to communicate and exchange data. This includes building microservices architectures, cloud services, and other distributed systems.
- Routing and Sensor Processing: Pulsar can be used as a data routing and sensor processing system in complex IoT networks.
- Big Data Analytics: Apache Pulsar can be used as a platform for storing and analyzing large-scale data, including natural language processing, machine learning prediction, and data mining.
Any Other Use Cases?
The operational mechanism of Pulsar clusters in production using an MQTT proxy (MoP) involves connecting a large number of IoT devices to Pulsar via the MQTT protocol. Below is a detailed description of how this system works:
- Connecting IoT Devices to the MQTT Proxy: IoT devices send data via the MQTT protocol to the MQTT proxy, a component of the Pulsar system. The MQTT proxy is responsible for receiving and handling MQTT messages from devices.
- Conversion to Pulsar Message Streams: The MQTT proxy converts incoming MQTT messages into corresponding Pulsar message streams. This includes adding metadata and reformatting data to comply with Pulsar’s structure.
- Distribution to the Pulsar Cluster: The message streams are forwarded to brokers within the Pulsar cluster. Brokers are responsible for receiving, processing, and storing Pulsar message streams.
- Storage and Reliability Guarantees: The Pulsar cluster stores incoming message streams while ensuring reliability by replicating data across multiple nodes and physical locations.
- Data Processing and Analytics: Applications and services use the Pulsar consumer API to read and process message streams from the Pulsar cluster, enabling tasks such as data analysis, storage, and decision-making based on IoT device data.
Comparison with Competitor “Kafka”
Apache Kafka: Kafka provides horizontal scalability through partitioning and replication. Scaling Kafka clusters often requires careful and precise management of partitions and rebalancing mechanisms.
Apache Pulsar: Pulsar excels in elastic scalability, enabling seamless and independent scaling of both the serving layer and the storage layer.
Conclusion
Apache Pulsar is a powerful distributed message streaming system that offers flexibility, high reliability, and excellent scalability, making it a critical tool for real-time data processing and big data analytics applications.
In summary, using an MQTT proxy (MoP) in Pulsar clusters enables seamless connectivity and data transformation from IoT devices into Pulsar message streams, providing a robust platform for storing, processing, and analyzing IoT data in production environments.
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