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A message broker is a software component that allows an async interchange of messages or pieces of data. These messages could be application logs, app-created events, or user-created events. Message interchange is essential for multiple use cases like establishing data lakes, real-time pipelines, event-driven features, microservices communication, log collection, asynchronous operations, and informing systems of necessary actions.
Message broker operates on the principles of the log data structure, which is similar to a queue. This data structure allows different clients to read messages from the latest or from an offset point in the past.
Message brokers are an essential component of any event-driven or inter-service communication system. They act as a middleman between different parts of a system, routing messages from one end to another in a reliable and secure manner. Message brokers’ core features include message routing, queuing, publish-subscribe functionality, and security measures such as message encryption and hashing. This section provides a more in-depth look at the various features of a traditional message broker.
One of the primary functions of a message broker is to route messages between different clients and services. This is done using a routing key or a set of rules to determine where a message should be sent. This allows for messages to be delivered to the correct recipients without the need for direct connections between the sender and the receiver.
Another critical feature of a traditional message broker is the ability to queue messages. This allows messages to be stored temporarily if the recipient is not currently available to receive them. The message broker will then deliver the messages when the recipient becomes available. This ensures that messages are not lost and that they are delivered in the correct order.
A traditional message broker also supports transactional messaging. This means that the message broker can ensure that messages are delivered reliably and in the correct order. This is done using a two-phase commit protocol to ensure that messages are only delivered if they have been successfully committed to the message broker’s storage.
A traditional message broker also allows for external connections. This means that it can connect to other systems and services that may be running on different platforms or in different locations. This allows for messages to be exchanged between different systems and services in a seamless and efficient manner.
These features of message brokers lend them to be an essential component of large-scale systems. There are different types of message brokers.
Apache Kafka is an open-source distributed event streaming platform that follows a publish-subscribe messaging model. This means that message senders publish their messages without any idea of the receivers. The receivers subscribe to the type of messages they need without knowing the sender. Kafka acts as an intermediary between these publishers and subscribers. The publisher’s messages are sent through topics, which are essentially pipelines for messages.
Kafka implements partitions across different brokers (Kafka servers/nodes) in a distributed manner. One topic can have many partitions and these partitions are stored in different brokers. The quorum system is the Zookeeper which orchestrates the allocation of data to these partitions based on laid-down rules. Take, for example, a system for sending match updates as they happen. You could design the system with a topic called `match-updates` that has partitions for every team playing. These partitions can then be distributed across the message brokers in the system.
Kafka has two main components:
Apache Kafka is a distributed messaging platform that can be used to build a wide variety of applications and systems. One of the key advantages of using Kafka is its ability to handle high volumes of data and support real-time data processing, making it a great choice for big data and streaming applications. This section goes over the different types of applications that can be built using Apache Kafka.
Kafka, though powerful and widely used has some downsides that should be taken into consideration when deciding whether it is the right choice for a particular application or system. Some of the downsides of Kafka include:
Memphis is a next-generation alternative to Apache Kafka.
It enables building modern queue-based applications that require large volumes of streamed and enriched data, modern protocols, zero ops, up to x9 faster development, up to x46 fewer costs, and significantly lower dev time for data-oriented developers and data engineers.
Memphis focuses on four pillars –
One of the biggest advantages of Memphis.dev is its ability to handle high volumes of data with ease. Its unique architecture allows it to handle millions of messages per second with a very small footprint, making it a great choice for applications that require real-time data processing. One of the biggest advantages of Memphis.dev is its native ecosystem and DevEx, which removes most of the heavy lifting from the clients’ side like –
If your use case requires event streaming and processing, you should give Memphis.dev a spin.