How Event-Driven Architectures Benefit from Stream Processing
Suppose you’re an app developer or an ML/AI engineer. In that case, it’s safe to say that some of your top priorities include making your apps faster, more reliable, easily scalable, and decoupled. One of the best ways to achieve that and improve any app’s performance is to create an event-driven architecture that uses event streams.
How can event stream processing improve your event-driven architecture? How are event streams different from message queues? Which are better, and can you use both?
Read on to find out before exploring some key features of Apache Pulsar, one of the best distributed messaging and event streaming platforms that can take your apps to the next level.
What Is an Event-Driven Architecture?
An event-driven architecture is a software design paradigm that enables the communication between back-end systems.
Some of its key components are producers and consumers. Producers or publishers are apps that generate events (state changes), while consumers or subscribers are apps that consume events and perform various tasks.
They communicate via messages, which message brokers distribute based on subscriptions to certain channels or topics, subsets of events in a category. In addition, they can use message queues or event stream processing to transmit messages.
What Are Message Queues?
Message queues are components of early event-driven architectures. Serverless architectures and microservices that don’t use event streams still use queues to send messages to specific consumers.
With message queues, producers are familiar with all their consumers. Therefore, when they send a message to a queue, they target specific consumers, alerting them about a certain event. Consumers then obtain and process the message before performing the necessary task.
Most message brokers that use message queues follow the FIFO (First-In, First-Out) order. Thus, each of them deletes every message once the consumer retrieves it.
What Is Event Stream Processing?
Event stream processing includes processing real-time events in an event-driven architecture to make it asynchronous, decouple its services, and ensure easy scaling. It’s about processing events continuously and storing them for later retrieval.
One of the most popular models for transferring event messages between back-end systems in event-driven architectures is the pub/sub-design pattern. It enables message brokers to receive events from various sources (e.g., IoT devices, sensors, databases, apps, etc.) and send messages to all subscribers within a specific channel.
Thanks to this pattern, producers and consumers don’t need to be familiar with one another. Their services are decoupled, so they perform tasks independently. There’s no specific targeting, and multiple consumers can receive numerous simultaneous messages.
Apart from decoupling services, another critical feature of event stream processing is temporal durability. For example, brokers don’t delete messages in streams after consumers process them, so they can retrieve them anytime from an event history.
Message Queues vs. Event Streams
Both event streams and message queues have their advantages, so what should you use in your event-driven architecture?
Let’s go over the most important benefits of each.
Benefits of message queues
Message queues can work great when a message broker knows a particular routing structure. It can help control high data volumes and streamline batch processing.
When a system uses exactly-once processing semantics to ensure that every message is delivered only once and prevent data duplicates, message queueing is the way to go.
For instance, it’s a perfect choice when you need to connect legacy systems with certain dependencies or when you need to process events in a system that uses different back-end and front-end programming languages.
Ecommerce websites are the most common application of message queues. This is because they have an established routing logic that message brokers are familiar with to ensure decoupling and asynchronous task handling in event-driven architectures.
Some of the most popular open-source, queue-based message brokers are RabbitMQ and ActiveMQ.
Benefits of event streams
As already mentioned, event streams enable sending messages to multiple consumers and storing them for later retrieval, which gives room for more flexibility. In addition, keeping message logs can help consumers track various metrics and perform efficient and accurate data analyses.
Decoupling services is another benefit, as producers and consumers can scale quickly and go through various changes without negatively affecting each other’s performance. If you modify one microservice, you don’t need to modify everything else or worry about the entire system crashing.
A system can track real-time website activity with event stream processing, including all the visits, searches, clicks, and page views. All the subscribed consumers can track and analyze that data in real-time and access it later anytime.
Systems can also use the sliding-window computation model to process and analyze recent data in streams of per-minute or per-second events.
Event streams are perfect for messaging apps and social media platforms as well since they send events and messages chronologically and help drive real-time data insights.
IoT devices are another common application, as they transfer huge volumes of events and messages. Event streams enable publishers to send tons of messages to many IoT consumers without risking poor performance.
Last but not least, stream processing enables event sourcing, that is, updating state changes and publishing events automatically. Instead of saving an object’s current event states, it saves state changes as a series of events, thus persisting data in an event-centric way.
Overall, event streams work better when a system has many complex consumers that can benefit from fully decoupled services.
Can You Use Both in an Event-Driven Architecture?
By implementing event stream processing with the sub/pub design pattern, you create an event-driven architecture. But what if you wanted to add queues between producers and consumers? Could you do it when there are streams in place?
The answer is yes. You can use both event streams and queues and still maintain high performance and low latency.
That’s because there are platforms that support both event streaming and message queueing, such as Apache Pulsar and Apache Kafka. In addition, they have persistent message queues, which means they don’t delete messages immediately after consumers process them, as is the case with most message brokers.
You can specify a certain retention period or a data size limit for storing messages in queues before removing them. That way, you can enable subscribers to consume messages multiple times while preventing backpressure.
However, properly combining queues and streams can be cumbersome and time-consuming, as you need to prevent data duplicates. In some cases, message replays can even lead to a negative user experience.
For instance, an online purchase order is an event that should happen only once. Replaying it could result in multiple same orders from a single customer.
Of course, it can be beneficial in other situations. For instance, if you need to remove a bug in an app, going back to certain previous messages can help you update the app efficiently and deploy the new version seamlessly.
Harness the Power of Apache Pulsar
Apache Pulsar is your way to go if you want to use both stream processing and message queueing within a single subscription model. It offers the best of both worlds, and then some. Here’s why it outperforms Kafka and many other distributed messaging and event streaming platforms.
Apache Pulsar is a cloud-native, multi-tenant platform with a broker architecture and many features and integrated tools.
It comes with free tiered storage, multiple subscription modes, end-to-end encryption, low latency, seamless scalability, a robust SQL engine, and native support for multiple clusters. Thanks to Apache ZooKeeper, its open-source server for cluster coordination, it enables cluster replication across multiple data centers worldwide.
You never have to worry about potential data losses due to power outages or other issues. Your data will always stay secure, and your apps can keep publishing and consuming events, even if one data center fails.
Apache Pulsar’s geo-replication functionality enables seamless event publishing and message consumption in different geographical locations. It allows both synchronous and asynchronous data replication, thus providing greater flexibility. Coupled with data storage in HDD and SSD nodes, it ensures high efficiency with zero data loss.
Apache BookKeeper is another excellent Pulsar feature. It’s a persistent storage service that optimizes workloads in real-time while ensuring low latency, easy scalability, and fault tolerance.
Apache Pulsar uses a pub/sub design pattern, and comes with a powerful REST Admin API and a Client API that supports Java, Python, Go, C++, and C#. Built for ML and AI applications, the enterprise-ready Apache Pulsar is perfect for deploying game-changing ML/AI models.
Other notable features that make Apache Pulsar an obvious choice include decoupled storage, fast benchmarking, the Pulsar proxy for service discovery, Pulsar IO connectors for external system communications, and Kubernetes, and Docker integrations.
Stream processing can improve any event-driven architecture and ensure flawless performance. Depending on your needs, combining streams with message queues might be a fantastic choice, for which you need a platform like Apache Pulsar that supports both.
Pandio can help you make the most of big data, AI, and ML and enjoy low latency, better throughput, and faster scaling. Built on Apache Pulsar, it’s a distributed messaging service that combines pub/sub design, streams, and queues into a single AI-ready and cost-effective solution.
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