Imagine a situation where you are managing a miniaturized version of Uber. By ‘managing’, I mean you are the CXO whose company owns this mini-Uber where you have, say, a hundred drivers listed. You have a super-fast cloud setup that powers the REST APIs exchanging locational and other data of these 100 taxis at any time. Things look pretty stable, right?
Now, imagine the expansion, or in other words, your company decides to launch this ‘Uber-clone’ in 10 more cities with a hefty promotional budget. Overnight, your setup will experience a huge surge as 2000 taxis will now be using the same REST APIs.
Do you think the performance will remain the same? No way! What should you do then? Increase the cloud capacity by 10 times. Is that viable since the bill next month will skyrocket? Looks like, you are in a jam, eh? Worry not. Enters message broker. Your monthly cloud bill may increase, say, by 25%. But performance? Oh, it will remain the same as when you were managing 100 taxis even after adding a couple of thousand taxis more!
What is a message broker?
A message broker service is a software application that enables communication between different applications, systems, and services by translating messages between different messaging protocols. It acts as an intermediary between the sender and the receiver, allowing them to communicate asynchronously and independently. They act as a mediator between different components, allowing them to communicate asynchronously and decoupling them from each other. In other words, applications can continue to send messages even if the receiver is down, and the receiver can process messages at its own pace, without being blocked by the sender.
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How are message brokers used under different scenarios?
Message brokers can be used to manage and distribute events to different components in the system, integrate data from different sources, and enable real-time data processing. They can also be used to handle long-running or resource-intensive tasks asynchronously, distribute workload across multiple instances of an application or service, and enable messaging between different applications.
Message broker in Microservices communication
In a microservices architecture, each service is designed to perform a specific function and communicates with other services using APIs. By using a message broker, services can communicate asynchronously and independently, without being blocked by each other, or more specifically, services can continue to operate even if one or more services are down. Message brokers can help to decouple different services, making it easier to modify or replace individual services without affecting the rest of the system.
Message broker in Event-driven architecture
Message brokers are commonly used in event-driven architecture to manage and distribute events to different components in the system. In an event-driven architecture, events are generated by different components and are broadcast to other components that are interested in them. Message brokers can help to manage and distribute these events, ensuring that they are delivered to the appropriate destination. By using a message broker, components can communicate asynchronously and independently, without being impacted by each other.
In a modern setup, message brokers are a critical tool for managing the complexity of data integration and enabling seamless communication and data exchange between different systems and technologies. By using a message broker, data can be transformed, validated, and routed to the appropriate destination, making it easier to integrate data from different sources. Message brokers can help to manage and distribute data from different sources, making it easier to process and analyze data from a large number of sources.
In a modern distributed system, asynchronous processing in message brokers refers to the ability of message brokers to handle messages in a non-blocking manner, allowing for concurrent processing of messages and improved system performance. In asynchronous processing, messages are sent to a message broker and then processed by one or more consumers in parallel, without the need for the producer to wait for a response from the consumer before sending the next message. Message brokers also provide features such as message buffering, message queuing, and load balancing to further optimize the processing of messages.
Load balancing in message brokers
Load balancing is an important feature of message brokers that helps to distribute incoming messages evenly among different consumers. In a message broker, load balancing can be achieved through various mechanisms such as round-robin, weighted round-robin, or least connections. These mechanisms ensure that messages are distributed evenly among consumers, preventing any single consumer from becoming overloaded. Thus, the reliability of the system is improved, as consumers can be added or removed dynamically without affecting the overall performance of the system.
Messaging between applications
Messaging between applications in message brokers works by enabling communication and data exchange between different applications, systems, and services. Applications send messages to a message broker, which then routes the messages to the appropriate destination(s) based on predefined rules and configurations. The message broker can also transform and validate the messages as needed, ensuring that the messages are in the correct format and contain the required information.
Top 10 Message Broker Services in the market today
The list of champions goes as follows.
Apache Kafka is a distributed streaming platform that is extensively utilized for creating streaming applications and real-time data pipelines. It offers a messaging system that is scalable, fault-tolerant, and high-throughput, enabling communication between different applications and services. Kafka is particularly suitable for managing large volumes of data in real-time, making it a preferred choice for use cases such as log aggregation, real-time analytics, and event-driven architectures.
It provides several features, including data partitioning, replication, and fault tolerance, to ensure reliable and efficient data processing. In Kafka, the publish-subscribe model is used, where messages are published to topics by producers, and consumers subscribe to these topics to receive the messages. Its popularity has surged in recent years, and it is now widely used by organizations across various industries, including finance, healthcare, and e-commerce, to manage their real-time data processing and streaming requirements.
RabbitMQ, an open-source message, is widely used for enabling communication and data exchange between different applications and services. It supports numerous messaging protocols, such as AMQP, MQTT, and STOMP, making it highly flexible and interoperable. RabbitMQ offers a messaging system that is scalable, highly available, and reliable, enabling the decoupling and scaling of distributed systems.
It comes with several features, including message queuing, routing, and delivery guarantees, ensuring efficient and dependable message processing. RabbitMQ follows the exchange and queue model, where producers transmit messages to exchanges, and consumers receive messages from queues.
Amazon Simple Queue Service (SQS)
Amazon Simple Queue Service (SQS) is a message queuing service that is entirely managed, facilitating communication between distributed applications and services in a reliable and scalable manner. SQS offers a messaging system that is highly available, secure, and durable, enabling the decoupling and scaling of distributed systems.
It provides both standard and FIFO queues, with the latter ensuring the strict ordering and elimination of duplicate messages. Additionally, SQS comes with message retention, dead-letter queues, and message filtering features, which enable efficient and reliable message processing. SQS integrates seamlessly with other AWS services such as EC2, Lambda, and SNS, making it an excellent option for creating event-driven and serverless architectures.
Microsoft Azure Service Bus
Azure Service Bus enables the communication between various applications and services through a reliable and scalable messaging system that supports both message queuing and publish-subscribe patterns. Azure Service Bus comes with numerous features that facilitate efficient and dependable message processing, including message filtering, auto-forwarding, and dead-lettering.
It also supports various messaging protocols such as AMQP, MQTT, and HTTP, making it highly interoperable. Azure Service Bus is a popular choice for building distributed systems and event-driven architectures in the cloud.
Google Cloud Pub/Sub
Google Cloud Pub/Sub is a messaging service that supports both message queuing and publish-subscribe patterns, providing a highly scalable, reliable, and durable messaging system. It comes with multiple features, including message filtering, dead-lettering, and message ordering, to ensure efficient and dependable message processing.
Pub/Sub seamlessly integrates with other Google Cloud services, such as Dataflow and Cloud Functions, allowing the creation of serverless and data processing pipelines. Google Cloud Pub/Sub is widely used for building real-time analytics applications, streaming data pipelines, and event-driven architectures.
Redis is a versatile technology that can be used for various purposes including session caching, real-time analytics, message queuing, and pub/sub messaging. Redis is also commonly used for implementing leaderboards, rate limiting, and job queuing1.
Additionally, Redis can be used as a primary database or as a complement to traditional relational databases. Redis’s in-memory architecture makes it highly efficient for handling high read-and-write throughput with low latency, making it suitable for real-time applications and data processing pipelines.
Now, the list of minnows in message broking.
IBM MQ supports both point-to-point and publish-subscribe messaging patterns and includes message queuing, message prioritization, and transaction management. It supports AMQP, MQTT, and JMS, making it highly interoperable.
Apache ActiveMQ is open-source and is equipped with multiple messaging patterns, including point-to-point, publish-subscribe, and request-reply. ActiveMQ comes with message persistence, message filtering, and message transformation and supports AMQP, MQTT, and STOMP.
Apache Pulsar is a distributed pub-sub messaging system designed to handle high message throughput with low latency and provides message deduplication, message expiration, and message filtering. It supports AMQP, MQTT, and Kafka.
NATS: A high-performance messaging system that provides secure and efficient communication between microservices and other distributed applications.
Use of Message Broker in different industries
In the finance industry, message brokers can be used to facilitate secure and reliable communication between financial institutions, as well as to process financial transactions in real-time. This can include sending and receiving trade messages, managing market data feeds, and processing payment transactions.
The healthcare industry can use message brokers to manage patient data and facilitate communication between healthcare providers, as well as to ensure the secure and timely delivery of critical medical information. Message brokers can also be used to manage electronic health records (EHRs) and to integrate different healthcare systems and applications.
Message brokers can be used in e-commerce to manage inventory and order processing, as well as to facilitate communication between customers and vendors. This can include sending order confirmations, tracking information, and managing product catalogs.
In the telecommunications industry, message brokers can be used to manage network traffic and facilitate communication between devices, as well as to provide real-time updates on network performance and status. This can include managing network events, processing billing information, and providing customer support.
Message brokers can be used in the transportation industry to manage logistics and supply chain operations, as well as to facilitate communication between different modes of transportation and logistics providers. This can include managing shipment information, tracking inventory, and processing delivery notifications.
In the gaming industry, message brokers can be used to manage game state and facilitate communication between players, as well as to provide real-time updates on game events and status. This can include managing game lobbies, processing matchmaking requests, and sending game notifications.
Message brokers can be used in social media to manage user data and facilitate communication between users, as well as to provide real-time updates on social media activity and trends. This can include managing user profiles, processing friend requests, and sending notifications for new messages or activities.
Using a message broker can save money on cloud bills in several ways. Message brokers optimize resource utilization by reducing the number of API calls and network requests between different cloud services. By minimizing the amount of data transferred between services, message brokers can reduce network traffic and data transfer costs.
Moreover, message brokers can help improve application performance and reduce latency by allowing services to communicate asynchronously, rather than relying on synchronous communication. Finally, message brokers can help improve the reliability and fault tolerance of cloud applications by providing a buffer between services, which can help prevent cascading failures and reduce downtime.
So, next time you think about scaling up from a few hundred requests to several thousand, include the message broker in your list of services.