Despite the benefits of distributed and complex cloud systems, creating, optimizing, and maintaining them are not without challenges. The complex nature of these systems—the need for high-level coordination and communication between different components and the requirement for robust data handling and processing mechanisms—can cause issues with real-time feedback and alerts, maintaining a shared state across various components, managing service discovery, balancing system load, and handling real-time data processing.
The real-time, asynchronous nature of event-driven architecture makes it a game changer for addressing these problems, though. It's ideally suited for creating real-time alerts and giving immediate feedback about system states or significant events.
This article examines the benefits of event-driven architecture to address the challenges of distributed systems. You'll also see a practical example of how using a rule-based event processing platform like Apperate allows you to create and manage real-time alerts with simplicity and ease.
What Is Event-Driven Architecture?
Event-driven architecture (EDA) is a software architecture paradigm that orchestrates behavior around the production, detection, and consumption of events or significant changes in state. It's been around in various forms for several decades, with roots in complex event processing, message-oriented middleware, and other asynchronous communication paradigms. However, it has gained more attention and prominence with the rise of microservices and cloud-based systems in recent years.
In this context, an event is a change in state that is meaningful within a business domain. For example, the completion of a transaction, a customer clicking a link, or the failure of a system component are all events. These events are emitted by event producers and detected by event listeners, and they lead to actions taken by event consumers.
EDA stands out for its real-time, asynchronous nature. It's a nonblocking architecture that allows for flexible, loosely coupled, and highly scalable system designs. EDA is particularly well-suited to dynamic, distributed environments where systems need to respond swiftly to changes in state.
EDA's ability to respond to events as they happen makes it excellent for real-time alerts. For example, an EDA system can be configured to generate an alert in response to a specific event, such as a system failure or a sudden spike in transaction volume. This capability to respond instantly to events makes EDA an excellent solution for systems that require real-time monitoring and alerts.
Because EDA is about modeling your system around real-world occurrences that trigger responses within your system, it's highly relevant to distributed and cloud-based systems where scalability, flexibility, and real-time responses are paramount.
Some of the many use cases of EDA include the following:
- Data or state transfer: EDA facilitates data or state transfer between different parts of a system or between separate systems, promoting seamless coordination and collaboration.
- Log aggregation: By consolidating logs from different services in a centralized location, EDA simplifies log aggregation.
- Notifications/alerts: EDA enables real-time notifications and alerts, allowing system administrators to respond promptly to significant events.
- Complex business processes: EDA is excellent for implementing complex business processes that involve multiple steps and services because it helps ensure proper orchestration and coordination.
How EDA Addresses the Challenges of Distributed Systems
Let's start by examining the reasons for the challenges you'll encounter most commonly when designing and managiing distributed systems:
- Network issues: Since distributed systems rely heavily on network communication, network latency, bandwidth limitations, and network failures can greatly affect the performance and availability of a distributed system.
- Concurrency: Distributed systems often have multiple nodes performing tasks simultaneously. Managing this concurrency and ensuring data consistency (see the next point) can be challenging.
- Data consistency: Data stored across multiple nodes makes it difficult to maintain consistency, especially when data is updated in one part of the system and needs to be reflected accurately across all nodes.
- Fault tolerance and recovery: In a distributed system, individual components can and will fail. It's difficult to design systems to be resilient to these failures and recover quickly when they occur.
- Scalability: A system should continue to function efficiently even as it grows. It should be able both to handle the increased load (scale up) and to expand the system with more machines or nodes (scale out).
- Complexity: The inherent complexity of distributed systems make them difficult to understand and reason about. It can lead to errors in design and implementation, which, in turn, can be difficult to debug and resolve.
- Service discovery: Services in a distributed system need to find each other. However, the dynamic nature of distributed systems with nodes that can come and go makes service discovery nontrivial. While this particular challenge has many possible solutions, it's still a problem that needs solving, and you need to consider it as part of your project architecture.
- Real-time data processing: Processing data as it arrives can be a challenge, but it's often necessary for providing timely and relevant responses in distributed systems.
- Balancing load: You must ensure that all nodes in a distributed system share the load evenly to prevent any single node from becoming a bottleneck.
Implementing real-time alerts in distributed systems poses its own subset of these challenges.
The latency of distributed systems makes it difficult to trigger and communicate alerts in real time. Since distributed systems can generate much noise and insignificant events, you need effective noise-filtering strategies to ensure only significant events trigger alerts. Managing real-time alerts in a distributed system adds an extra layer of complexity to system design and operation, calling for careful planning and design.
So how does event-driven architecture help address these challenges?
Since EDA is inherently asynchronous—that is, components don't need to wait for a response before moving on to the next task—it can help to mitigate the impact of network latency.
Using events as the single source of truth allows you to maintain a shared state among multiple services and address issues with concurrency and data consistency. You can also enhance fault tolerance and recovery by using events to notify relevant components or services when a failure occurs, enabling them to respond accordingly.
EDA is highly scalable due to its asynchronous nature and the loose coupling of its components, and it simplifies service discovery by broadcasting events about the state and availability of services. The event-driven nature of this architecture also allows it to distribute workload evenly across services and process data in real time.
Overall, the flexibility and scalability of EDA make it an excellent solution for real-time alerts in distributed systems.
How to Create and Manage Event-Driven Systems
So what does creating and managing event-driven systems look like in practice?
Let's look at how you'd use Apperate, a rule-based event processing platform that streams data for finance.
Apperate can help developers build real-time systems by simplifying event processing, real-time data storage, and providing an easy-to-use but powerful interface for managing and creating events. At its core, Apperate does three things:
- Ingest data from a variety of sources, including third-party sources, your own custom sources, and our production-grade financial data.
- Process data in real-time using a variety of techniques, including filtering, aggregation, and transformation.
- Output data to a variety of destinations, including REST endpoints and event-triggered alerts.
Let's walk through how you could use it to create real-time alerts for financial data.
*Note:* If you don't have an Apperate account, you can sign up for a [free trial](https://iexcloud.io/cloud-login#/register) to follow along. Your trial account comes with a predefined data set called IEX Cloud Data, which contains a wealth of financial data, including stock prices, company information, and more. You can use it to replicate this example.
In your IEX Cloud console, pictured below, you'd start by creating a workspace.
In your workspace, navigate to the **Create rules** menu:
Let's say you want to create a simple rule that will trigger when the value of Shopify Inc SHOP stock goes under a specific threshold:
You'll define the action that will be taken when the rule is triggered, such as sending an email to a specific address:
You should get your first alert in a matter of minutes:
Rules can be more complex, of course. Outputting to another data set or posting a webhook allows you to build a complex event-driven system.
Using Apperate's console is simple and straightforward, but for more complex use cases, you could also use the IEX Cloud Financial Data API to create and manage rules, explore and manipulate data sets, create virtual data sets, and schedule rules. You can find more documentation about it in the IEX Cloud API documentation.
For the purpose of this article, let's take a look at how you can use the API to create a similar rule in the previous example. You'd start by adding code like the snippet below into a file:
```javascript
'use strict;
const Request = require('request-promise-native');
function connect() {
Request({
method: 'POST',
url: 'https://api.iex.cloud/v1/rules/create',
json: {
token: 'YOUR_TOKEN',
ruleName: "Shopify Simple Rule",
type: "any",
index: [ "CORE:STOCK:MSFT:latestPrice" ],
conditions: [
["$index[0]","<=",310],
],
outputs: [
{
method: "Email",
to: "my@email.com",
subject: "Time to buy MSFT!",
message: "Microsoft stock price is under threshold."
frequency: 60
}
],
additionalIndex: [
"CORE:STOCK:MSFT:high",
]
},
}).then((body) => {
console.log(body);
}).catch((err) => {
console.log("Error in request", err);
});
}
connect();
```
This code uses the `Request` library to make a `POST` request to the `https://api.iex.cloud/v1/rules/create` endpoint. This endpoint takes a JSON object with the following properties:
- **token:** secret API token with permission to create rules (This token can be retrieved from your IEX cloud dashboard.)
- **ruleName:** arbitrary rule name
- **type:** whether any or all the conditions must be true for the rule to take action
- **index:** facts to use in the conditions
- **conditions:** fact conditions to check
- **outputs:** destinations for outputting the index facts and `additionalIndex` facts
- **additionalIndex:** additional facts to include in the output
Running this code results in the following output:
```bash
{ id: '4bc8c9de-e133-4f66-b529-f750e9bd0932', weight: 1 }
```
You can confirm that the rule was created by going to the Apperate Console and checking the rules page:
Conclusion
Event-driven architecture can be a game changer in solving challenges inherent to implementing and scaling distributed systems.
The real-time, asynchronous nature of event-driven architecture is ideally suited for creating real-time alerts and giving immediate feedback about system states or significant events. This architecture excels at maintaining a shared state among multiple services by using events as the single source of truth. Moreover, it simplifies service discovery by broadcasting events about the state and availability of services. Event-driven architecture's asynchronous nature allows it to distribute the workload evenly across services, and its event-driven design is ideal for processing and responding to data in real time.
You also saw a practical example of how you can use Apperate, a rule-based event processing platform that streams data for finance, to solve the challenge of real-time alerts in a distributed system with simplicity and ease. You can sign up for a free trial to try it out.
