me :
Emily, talk about your understanding of Pulsar?
ChatGPT：
sure! Apache
Pulsar is a distributed message middleware that supports multiple message modes, including publish/subscribe mode, queue mode and stream mode. In the publish/subscribe mode, the message publisher publishes the message to a topic, and the subscriber can subscribe to the topic and receive all the messages published to the topic. In the queue mode, the message sender sends a message to a queue, and each consumer obtains a message from the queue, and load balancing is achieved among consumers. In streaming mode, Pulsar treats the message stream as a continuous stream, and can process and transform messages in real time.The underlying principle of Pulsar is based on the architecture of distributed log storage. It adopts an idea similar to Kafka, stores messages in a horizontally scalable and durable distributed storage engine, and supports multi-tenancy. There are three core components in Pulsar's architecture: broker, bookkeeper, and Zookeeper. Broker is responsible for receiving and routing messages, Bookkeeper is a high-performance distributed log storage engine for persistent messages, and Zookeeper is responsible for managing and maintaining cluster metadata.
In general, the design of Pulsar's distributed architecture and log storage engine enables it to scale to millions of topics and hundreds of billions of messages, and provide high reliability and high performance messaging services.
me :
How does Pulsar ensure that messages are not lost?
ChatGPT：
Pulsar uses various mechanisms to ensure that messages are not lost. First of all, Pulsar uses persistent storage to keep all messages, even if an unexpected failure occurs, there will be no data loss. Secondly, Pulsar adopts a multi-copy mechanism to automatically copy the messages of each topic to multiple copies to ensure that even if a copy fails, data can be read from other copies. In addition, Pulsar also provides multi-level storage, which can store cold data on low-cost storage media to save costs. Finally, Pulsar also provides a variety of guaranteed delivery mechanisms, such as At-least-once and Exactly-once, to ensure that each message is delivered and processed correctly.
me :
Talk about At-least-once and Exactly-once in detail
ChatGPT：
When we talk about At-least-once and Exactly-once, we usually mean semantic guarantees in message passing. At-least-once guarantees that the message will be delivered at least once, while Exactly-once guarantees that the message will be delivered only once and will not be sent repeatedly.
Under at-least-once semantics, if a message encounters an error during delivery, it may be resent. This guarantees eventual delivery of the message, but may result in duplicate delivery, so the receiver must be able to recognize duplicate messages and not adversely affect them.
In contrast, Exactly-once semantics ensure that messages are not delivered repeatedly. This usually involves a more complex coordination process to ensure consistency of delivery. Exactly-once semantics are more suitable for scenarios that require high data consistency, such as financial transactions.
It should be noted that At-least-once and Exactly-once are not absolute concepts, they depend on the specific implementation and context. In some cases, consistency cannot be fully guaranteed even with Exactly-once.
me :
How does Pulsar deal with the problem of repeated consumption of messages?
ChatGPT：
Pulsar has multiple mechanisms to deal with message duplication consumption. One of these is to use unique identifiers for messages, called Message
IDs, to track the lifecycle of each message in the system. When a consumer receives a message, it can record the Message ID of the message
, and submit an acknowledgment after processing to inform the system that the message has been consumed. If the consumer fails to acknowledge the message for some reason, the system will attempt to redeliver the message to ensure it is consumed. At the same time, Pulsar also supports a message replay mechanism, which can consume messages repeatedly according to requirements. These mechanisms can effectively deal with the problem of repeated consumption of messages and ensure the correct processing of messages in the system.
me :
How does Pulsar solve the message backlog problem?
ChatGPT：
Pulsar addresses the message backlog in several ways. One of these ways is to use
the consumer group functionality provided by Pulsar. By dividing consumers into multiple groups, load balancing and parallel consumption can be achieved, which speeds up message processing and reduces backlogs. In addition, Pulsar
also provides an adaptive message storage function, which can dynamically adjust the size of the storage space according to the message load, so as to better cope with the instantaneous message traffic peak.
me :
What modes does Pulsar have?
ChatGPT：
Pulsar has multiple modes, including durable subscriptions, exclusive subscriptions, shared subscriptions, and failover subscriptions. Durable subscriptions allow multiple consumers to consume the same message.
Exclusive subscriptions allow only one consumer to consume messages. Shared subscriptions allow multiple consumers to share the same message. Failover subscriptions enable high availability of messaging.
me :
Let’s talk about the shared subscription model in detail
ChatGPT：
When talking about a shared subscription model, it usually refers to multiple users sharing a subscription to a service, such as a music streaming service or a video streaming service. This model allows multiple users to share an account and share the cost according to different subscription plans.
A shared subscription model can help users save money because they don't have to subscribe to the service individually for each person. Instead, they can enjoy the service by sharing an account. This is especially handy for families, roommates, or groups of friends.
However, shared subscriptions have their limitations and potential problems. For example, some services may limit the amount of account sharing, or take restrictive action when it detects that multiple users are using an account at the same time. Therefore, users need to understand the service provider's policies and abide by them to avoid account closure or other restrictions.
me :
Pulsar common interview questions
ChatGPT：
Regarding common Pulsar interview questions, here are some possible questions and their answers:
What is the definition of Pulsar?
Pulsar is a distributed, scalable, persistent message queue system. It features high throughput, low latency, and high availability, and can support multiple languages and protocols.What is the difference between Pulsar and Kafka?
Similar to Kafka, Pulsar is a message queuing system, but it has the following differences: Pulsar supports multi-tenant and multi-data centers, which can better manage different businesses and geographical locations; Pulsar's persistent storage uses Segmented Storage, which can be
more Handle data expiration and deletion well; Pulsar supports stream processing and function computing, which can be processed directly in the queue.What are the main components of Pulsar?
The main components of Pulsar include: Broker, used to process message publishing and subscription; ZooKeeper, used to manage cluster metadata and coordinator; BookKeeper, used to persistently store messages and metadata; CLI, used to manage and monitor clusters .How does Pulsar guarantee the reliability of messages?
Pulsar uses multiple copies and asynchronous replication to ensure message reliability. Each message has multiple copies, which can be stored on different nodes to prevent data loss. Asynchronous replication can reduce the impact on performance.How does Pulsar implement stream processing and function computing? Pulsar can use Pulsar Functions and Pulsar
IO to implement stream processing and function computing. Pulsar Functions allows users to write custom functions to process messages, which can be processed directly in the queue, while Pulsar
IO provides a series of input and output adapters that can be integrated with various data sources.
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