On an upcoming client project, our software company needs to implement a real-time comment system as a module on the client's web project.
We were trying to figure out what technologies or services to use so we are not reinventing the wheel, and while Google gives us specific results one has to know what to query for.
So... we thought to ask the "AI". Here's the conversation with it sprinkled in our thought process too.
A real-time comment system can be built using several architectures, but the most popular ones are:
Client-side rendering architecture: In this architecture, the front-end application is responsible for rendering the comments in real-time. Whenever a new comment is added, the front-end application sends a request to the server to retrieve the new comment, and then the comment is rendered on the web page. This architecture requires the use of technologies like WebSockets, AJAX, and JavaScript frameworks like React, Angular, or Vue.
Server-side rendering architecture: In this architecture, the server is responsible for rendering the comments in real-time. Whenever a new comment is added, the server sends the updated HTML to the client, which is then displayed on the web page. This architecture requires the use of technologies like Node.js, Socket.io, and server-side templating engines like Pug or Handlebars.
Hybrid architecture: This architecture is a combination of client-side and server-side rendering. In this architecture, the server is responsible for rendering the comments initially, and then the client takes over the rendering process. Whenever a new comment is added, the server sends the updated HTML to the client, and then the client uses JavaScript to update the DOM in real-time. This architecture requires
This wasn't a very helpful response but it gave ChatGPT a context of what we're looking for.
There are several backend technologies that can be used to build a real-time comment system. Here are some of the most popular ones:
Node.js: Node.js is a popular choice for building real-time web applications because of its event-driven architecture and support for WebSockets. It also has a large and active community that provides numerous libraries and frameworks for building real-time applications.
Ruby on Rails: Ruby on Rails is a popular web application framework that provides built-in support for WebSockets through the Action Cable library. It's easy to use and has a large and supportive community.
Django: Django is a popular Python web application framework that provides support for WebSockets through the Django Channels library. It's a mature and stable framework that's easy to learn and use.
Laravel: Laravel is a popular PHP web application framework that provides support for WebSockets through the Laravel Echo library. It's a modern and elegant framework that's easy to learn and use.
Firebase: Firebase is a backend-as-a-service platform that provides real-time data synchronization and messaging services. It's easy to use and provides a complete set of tools for building real-time applications.
Pusher: Pusher is a hosted service that provides real-time messaging.
This response was more helpful, at least we have a list to choose from, but it's a mixed list like programming languages and messaging services thrown in the same place. So we singled out the "Pusher" service from the list as we felt this was the most relevant from the response.
Prompt: Show me more like what Pusher does.
Sure! Here are some other real-time messaging and synchronization services similar to Pusher:
PubNub: PubNub is a real-time messaging and synchronization platform that provides APIs and infrastructure for building real-time applications. It provides support for WebSockets, MQTT, and other communication protocols.
SocketCluster: SocketCluster is a scalable real-time messaging and synchronization platform that provides support for WebSockets and other communication protocols. It's built on top of Node.js and provides a flexible and customizable architecture.
Ably: Ably is a real-time messaging and synchronization platform that provides APIs and infrastructure for building real-time applications. It provides support for WebSockets, MQTT, and other communication protocols, and has a scalable and reliable architecture.
Syncano: Syncano is a backend-as-a-service platform that provides real-time data synchronization and messaging services. It provides support for WebSockets and other communication protocols, and has a flexible and customizable architecture.
Pushy: Pushy is a real-time messaging and synchronization platform that provides APIs and infrastructure for building real-time applications. It provides support for WebSockets and other communication protocols, and has a scalable and reliable architecture.
Now we're getting somewhere. Let's see if there are open-source techs too!
Prompt: The same list but with open-source technologies.
Sure, here are some open source real-time messaging and synchronization technologies that you can use to build a real-time comment system:
Apache Kafka: Apache Kafka is a distributed streaming platform that provides support for real-time messaging and synchronization. It's a scalable and fault-tolerant technology that can handle high-volume data streams.
Apache Pulsar: Apache Pulsar is a distributed messaging and streaming platform that provides support for real-time messaging and synchronization. It's a scalable and flexible technology that can handle both streaming and batch data.
Mosquitto: Mosquitto is an open source MQTT broker that provides support for real-time messaging and synchronization. It's a lightweight and easy-to-use technology that's suitable for small to medium-sized projects.
SignalR: SignalR is an open source library for building real-time web applications. It provides support for WebSockets and other communication protocols, and has a simple and easy-to-use API.
Pushpin: Pushpin is an open source reverse proxy server that provides support for real-time messaging and synchronization. It's designed to handle high-volume traffic and can be used with any backend technology.
We knew about these technologies but it's nice to see a succinct overview of them, especially an overview in the context of a "comment system" architecture.
Prompt: What's the difference between Kafka and pulsar from the previous list?
Both Apache Kafka and Apache Pulsar are distributed messaging and streaming platforms that provide support for real-time messaging and synchronization. However, there are some differences between the two:
Architecture: Kafka uses a broker-based architecture, where messages are sent to brokers and stored on disk, while Pulsar uses a brokerless architecture, where messages are stored in memory or offloaded to storage systems like Hadoop or S3.
Multi-tenancy: Pulsar provides better support for multi-tenancy, where multiple organizations or applications can share the same messaging system, while Kafka is designed to be used by a single organization or application.
Message retention: Pulsar provides better support for message retention, where messages can be retained for a longer period of time, while Kafka has some limitations on message retention.
Performance: Both Kafka and Pulsar are designed to handle high-volume, real-time data streams, but Pulsar provides better performance in some scenarios, especially when dealing with large-scale data.
Community: Both Kafka and Pulsar have large and active communities, but Kafka has been around longer and has a more established ecosystem of tools and integrations. Pulsar is a newer technology but is gaining popularity rapidly.
Prompt: Give me more comparisons
Sure, here are some more comparisons between Kafka and Pulsar:
Protocol support: Kafka supports only the Kafka protocol, while Pulsar supports multiple protocols, including Kafka, MQTT, and a custom protocol called Pulsar Native.
Security: Both Kafka and Pulsar provide support for security features like SSL and authentication, but Pulsar provides more fine-grained access control and authentication options.
Geo-replication: Pulsar provides built-in support for geo-replication, where data can be replicated across multiple data centers, while Kafka requires external tools and configurations to achieve the same.
Schema management: Pulsar provides built-in support for schema management, where data can be serialized and deserialized using a schema, while Kafka requires external tools for schema management.
Operational ease: Pulsar provides better operational ease, with features like automatic partition reassignment and easier configuration management, while Kafka requires more manual intervention for these tasks.
Commercial support: Both Kafka and Pulsar have commercial support offerings, but Kafka has a more established ecosystem of vendors and service providers. However, Pulsar is gaining popularity rapidly and is being adopted by several large organizations.
Now we tried to use ChatGPT as a "software technology" consultant.
Prompt: Can Pulsar be used with gRPC?
Yes, Apache Pulsar can be used with gRPC (Google Remote Procedure Call), which is a high-performance open-source RPC framework. Pulsar provides a gRPC proxy that can be used to interface with Pulsar using the gRPC protocol.
The Pulsar gRPC proxy acts as a gateway between the gRPC client and the Pulsar broker, translating gRPC requests into Pulsar requests and vice versa. This allows gRPC clients to consume and produce messages from Pulsar topics using the gRPC protocol.
Using gRPC with Pulsar has several benefits, including high performance, low latency, and support for bidirectional streaming. It also provides a type-safe API and supports automatic code generation for client and server stubs.
To use Pulsar with gRPC, you will need to configure the Pulsar gRPC proxy and generate the gRPC client and server stubs using the appropriate tooling. Once this is done, you can use the gRPC API to interact with Pulsar topics and consume or produce messages.
Prompt: What are the downsides of using Pulsar in contrast with Kafka?
While Apache Pulsar is a promising distributed messaging and streaming platform, it also has some downsides compared to Apache Kafka. Here are some of the main downsides to consider:
Maturity: While Pulsar has matured considerably since its first release, it is still a relatively new technology compared to Kafka. As a result, it may not have as many integrations, tools, and community support as Kafka.
Complexity: Pulsar's brokerless architecture and support for multiple protocols make it a more complex system to manage than Kafka, which has a simpler broker-based architecture. This can result in higher operational complexity and maintenance costs.
Learning Curve: Pulsar's advanced features and architecture can make it more challenging to learn and use than Kafka, which has a more straightforward design. This can make it more difficult to onboard new team members or integrate it into existing systems.
Performance: While Pulsar provides better performance in some scenarios, Kafka generally provides better performance overall, especially in high-throughput use cases.
Ecosystem: While Pulsar has a growing ecosystem of integrations and tools, it may not have as many options as Kafka, which has a large and mature ecosystem.
Compatibility: While Pulsar provides support for the Kafka protocol, not all Kafka clients and tools may be fully compatible with Pulsar. This can cause compatibility issues and additional development work to adapt existing Kafka-based systems to use Pulsar.
Ultimately, the choice between Kafka and Pulsar will depend on your specific use case and requirements. While Pulsar may have some downsides compared to Kafka, it also has several unique features and advantages that may make it a better choice for certain use cases. It's important to evaluate both technologies carefully and choose the one that best meets your needs.
Prompt: Are there any Apache Pulsar Go packages?
Yes, Apache Pulsar has official Go packages that provide a client library for interacting with Pulsar. The Go client library is part of the official Pulsar client library and provides a simple and easy-to-use API for consuming and producing messages from Pulsar topics.
The Go client library supports all of the main Pulsar features, including message acknowledgments, batching, compression, and encryption. It also provides support for message filtering, which allows consumers to subscribe only to messages that match specific criteria.
The Go client library is actively maintained and has a growing community of contributors. It's also well-documented and provides comprehensive examples and tutorials to help developers get started quickly.
In addition to the official Go client library, there are also several third-party packages and tools available for working with Pulsar in Go, including libraries for working with the Pulsar proxy, CLI tools for managing Pulsar clusters, and more.
As we're mostly using Go as our backend technology, we asked it.
Certainly, here are some real-time comment systems that have been implemented using Apache Pulsar or Apache Kafka:
Disqus: Disqus is a popular commenting system that uses Apache Kafka to power its real-time messaging and synchronization capabilities. It uses Kafka to process and deliver comment notifications to users in real-time.
Livefyre: Livefyre is a real-time commenting and social engagement platform that uses Apache Kafka for its real-time messaging and synchronization features. It uses Kafka to process and deliver comments, social media mentions, and other user-generated content in real-time.
Stream: Stream is a real-time activity feed and commenting platform that uses Apache Kafka to power its real-time messaging and synchronization capabilities. It uses Kafka to process and deliver user-generated content, including comments, likes, and shares.
Commento: Commento is a real-time commenting system that uses Apache Pulsar to power its real-time messaging and synchronization capabilities. It uses Pulsar to process and deliver comments in real-time and provides a simple and easy-to-use API for integrating with other systems.
Hyperledger Fabric: Hyperledger Fabric is an open-source blockchain platform that uses Apache Kafka for its real-time messaging and synchronization capabilities. It uses Kafka to process and deliver blockchain transaction data in real-time and provide a tamper-proof record of all transactions.
These are just a few examples of real-time comment systems that use Apache Pulsar or Apache Kafka. Both technologies are well-suited to building real-time messaging and synchronization systems and are used by many organizations for a wide range of applications.
We love the history and context that's kept throughout this conversation, it feels more like a brainstorming session than just simply entering search queries.
The architecture of Disqus using Kafka is a typical event-driven architecture, where events are generated and propagated by producers and consumed by consumers. Here's an overview of the architecture:
Producers: Disqus uses Kafka producers to generate events related to comments, such as new comments, replies, likes, or flags. These events are sent to Kafka brokers for processing and delivery to consumers.
Kafka brokers: Kafka brokers are responsible for receiving and storing events produced by the producers. They also manage the distribution of events to the consumers based on their subscription preferences.
Consumers: Disqus uses Kafka consumers to consume events from Kafka brokers and process them in real-time. The consumers are responsible for processing the events and updating the relevant data structures, such as comment counts or user profiles.
Real-time notification system: Disqus uses a real-time notification system to deliver comment notifications to users in real-time. This system is powered by Kafka and is responsible for processing events related to comments and delivering them to users through various channels, such as email, push notifications, or in-app notifications.
Data storage: Disqus uses a combination of databases and caching systems to store comment data and user information. These systems are updated in real-time by the consumers and are used to serve comment threads and user profiles to users.
Overall, the architecture of Disqus using Kafka is designed to provide a scalable and fault-tolerant real-time commenting system that can handle high traffic and deliver notifications to users in real-time.
The architecture of Commento using Apache Pulsar is centered around the Pulsar messaging system and is designed to provide a scalable and fault-tolerant real-time commenting system. Here's an overview of the architecture:
Producers: Commento uses Pulsar producers to generate events related to comments, such as new comments, replies, likes, or flags. These events are sent to Pulsar brokers for processing and delivery to consumers.
Pulsar brokers: Pulsar brokers are responsible for receiving and storing events produced by the producers. They also manage the distribution of events to the consumers based on their subscription preferences.
Consumers: Commento uses Pulsar consumers to consume events from Pulsar brokers and process them in real-time. The consumers are responsible for processing the events and updating the relevant data structures, such as comment counts or user profiles.
Real-time notification system: Commento uses a real-time notification system to deliver comment notifications to users in real-time. This system is powered by Pulsar and is responsible for processing events related to comments and delivering them to users through various channels, such as email, push notifications, or in-app notifications.
Data storage: Commento uses a combination of databases and caching systems to store comment data and user information. These systems are updated in real-time by the consumers and are used to serve comment threads and user profiles to users.
The Pulsar messaging system provides several benefits for Commento, including high availability, scalability, and fault tolerance. It also provides support for multi-tenancy, which allows Commento to serve multiple clients or organizations from a single Pulsar cluster. Overall, the architecture of Commento using Pulsar is designed to provide a modern, scalable, and real-time commenting system that can handle high traffic and deliver notifications to users in real-time.
... and that's about it.
Conclusion
ChatGPT is becoming a very powerful tool, now we know why Google is shook to its core when version 3.5 was released.
We are excited about what this technology will be capable of in the near future and bare in mind that this chat was processed by v3.5 and not version 4, which was recently released.
