Serverless

AWS EventBridge Essentials. A Guide to Rules and Scheduler

Let’s take a look into AWS EventBridge, a powerful service designed to connect applications using data from our own apps, integrated Software as a Service (SaaS) apps, and AWS services. In particular, we’ll focus on the two main features: EventBridge Rules and the relatively new EventBridge Scheduler. These features overlap in many ways but also offer distinct functionalities that can significantly impact how we manage event-driven applications. Let’s explore what each of these features brings to the table and how to determine which one is right for our needs.

What is AWS EventBridge?

AWS EventBridge is a serverless event bus that makes it easy to connect applications using data from our applications, integrated SaaS applications, and AWS services. EventBridge simplifies the process of building event-driven architectures by routing events from various sources to targets such as AWS Lambda functions, Amazon SQS queues, and more. With EventBridge, we can set up rules to determine how events are routed based on their content.

EventBridge Rules

Overview

EventBridge Rules allow you to define how events are routed to targets based on their content. Rules enable you to match incoming events and send them to the appropriate target. There are two primary types of invocations:

  1. Event Pattern-Based Invocation
  2. Timer-Based Invocation

Event Pattern-Based Invocation

This feature lets us create rules that match specific patterns in event payloads. Events can come from various sources, such as AWS services (e.g., EC2 state changes), partner services (e.g., Datadog), or custom applications. Rules are written in JSON and can match events based on specific attributes.

Example:

Suppose we have an e-commerce application, and we want to trigger a Lambda function whenever an order’s status changes to “pending.” We would set up a rule that matches events where the orderState attribute is pending and routes these events to the Lambda function.

{
  "detail": {
    "orderState": ["pending"]
  }
}

This rule ensures that only events with an orderState of pending invoke the Lambda function, ignoring other states like delivered or shipped.

Timer-Based Invocation

EventBridge Rules also support timer-based invocations, allowing you to trigger events at specific intervals using either rate expressions or cron expressions.

  • Rate Expressions: Trigger events at regular intervals (e.g., every 5 minutes, every hour).
  • Cron Expressions: Provide more flexibility, enabling us to specify exact times for event triggers (e.g., every day at noon).

Example:

To trigger a Lambda function every day at noon, we would use a cron expression like this:

{
 "scheduleExpression": "cron(0 12 * * ? *)"
}

Limitations of EventBridge Rules

  1. Fixed Event Payload: The payload passed to the target is static and cannot be changed dynamically between invocations.
  2. Requires an Event Bus: All rule-based invocations require an event bus, adding an extra layer of configuration.

EventBridge Scheduler

Overview

The EventBridge Scheduler is a recent addition to the AWS arsenal, designed to simplify and enhance the scheduling of events. It supports many of the same scheduling capabilities as EventBridge Rules but adds new features and improvements.

Key Features

  1. Rate and Cron Expressions: Like EventBridge Rules, the Scheduler supports both rate and cron expressions for defining event schedules.
  2. One-Time Events: A unique feature of the Scheduler is the ability to create one-time events that trigger a single event at a specified time.
  3. Flexible Time Windows: Allows us to define a time window within which the event can be triggered, helping to stagger event delivery and avoid spikes in load.
  4. Automatic Retries: We can configure automatic retries for failed event deliveries, specifying the number of retries and the time interval between them.
  5. Dead Letter Queues (DLQs): Events that fail to be delivered even after retries can be sent to a DLQ for further analysis and handling.

Example of One-Time Events

Imagine we want to send a follow-up email to customers 21 days after they place an order. Using the Scheduler, we can create a one-time event scheduled for 21 days from the order date. When the event triggers, it invokes a Lambda function that sends the email, using the context provided when the event was created.

{
 "scheduleExpression": "at(2023-06-01T00:00:00)",
 "target": {
 "arn": "arn:aws:lambda:region:account-id:function:sendFollowUpEmail",
 "input": "{\"customerId\":\"123\",\"email\":\"customer@example.com\"}"
 }
}

Comparing EventBridge Rules and Scheduler

When to Use EventBridge Rules

  • Pattern-Based Event Routing: If we need to route events to different targets based on the event content, EventBridge Rules are ideal. For example, routing different order statuses to different Lambda functions.
  • Complex Event Patterns: When we have complex patterns that require matching against multiple attributes, EventBridge Rules provide the necessary flexibility.

When to Use EventBridge Scheduler

  • Timer-Based Invocations: For any time-based scheduling (rate or cron), the Scheduler is preferred due to its additional features like start and end times, flexible time windows, and automatic retries.
  • One-Time Events: If you need to schedule events to occur at a specific time in the future, the Scheduler’s one-time event capability is invaluable.
  • Simpler Configuration: The Scheduler offers a more straightforward setup for time-based events without the need for an event bus.

AWS Push Towards Scheduler

AWS seems to be steering users towards the Scheduler for timer-based invocations. In the AWS Console, when creating a new scheduled rule, you’ll often see prompts suggesting the use of the EventBridge Scheduler instead. This indicates a shift in focus, suggesting that AWS may continue to invest more heavily in the Scheduler, potentially making some of the timer-based functionalities of EventBridge Rules redundant in the future.

Summing It Up

AWS EventBridge Rules and EventBridge Scheduler are powerful tools for building event-driven architectures. Understanding their capabilities and limitations will help us choose the right tool for our needs. EventBridge Rules excel in dynamic, pattern-based event routing, while EventBridge Scheduler offers enhanced features for time-based scheduling and one-time events. As AWS continues to develop these services, keeping an eye on new features and updates will ensure that we leverage the best tools for our applications.

Simplifying AWS Lambda. Understanding Reserved vs. Provisioned Concurrency

Let’s look at the world of AWS Lambda, a fantastic service from Amazon Web Services (AWS) that lets you run code without provisioning or managing servers. It’s like having a magic box where you put in your code, and AWS takes care of the rest. But, as with all magic boxes, understanding how to best use them can sometimes be a bit of a head-scratcher. Specifically, we’re going to unravel the mystery of Reserved Concurrency versus Provisioned Concurrency in AWS Lambda. Let’s break it down in simple terms.

What is AWS Lambda Concurrency?

Before we explore the differences, let’s understand what concurrency means in the context of AWS Lambda. Imagine you have a function that’s like a clerk at a store. When a customer (or in our case, a request) comes in, the clerk handles it. Concurrency in AWS Lambda is the number of clerks you have available to handle requests. If you have 100 requests and 100 clerks, each request gets its own clerk. If you have more requests than clerks, some requests must wait in line. AWS Lambda automatically scales the number of clerks (or instances of your function) based on the incoming request load, but there are ways to manage this scaling, which is where Reserved and Provisioned Concurrency come into play.

Reserved Concurrency

Reserved Concurrency is like reserving a certain number of clerks exclusively for your store. No matter how busy the mall gets, you are guaranteed that number of clerks. In AWS Lambda terms, it means setting aside a specific number of execution environments for your Lambda function. This ensures that your function has the necessary resources to run whenever it is triggered.

Pros:

  • Guaranteed Availability: Your function is always ready to run up to the reserved limit.
  • Control over Resource Allocation: It helps manage the distribution of concurrency across multiple functions in your account, preventing one function from hogging all the resources.

Cons:

  • Can Limit Scaling: If the demand exceeds the reserved concurrency, additional invocations are throttled.
  • Requires Planning: You need to estimate and set the right amount of reserved concurrency based on your application’s needs.

Provisioned Concurrency

Provisioned Concurrency goes a step further. It’s like not only having a certain number of clerks reserved for your store but also having them come in before the store opens, ready to greet the first customer the moment they walk in. This means that AWS Lambda prepares a specified number of execution environments for your function in advance, so they are ready to immediately respond to invocations. This is effectively putting your Lambda functions in “pre-warm” mode, significantly reducing the cold start latency and ensuring that your functions are ready to execute with minimal delay.

Pros:

  • Instant Scaling: Prepared execution environments mean your function can handle spikes in traffic from the get-go, without the cold start latency.
  • Predictable Performance: Ideal for applications requiring consistent response times, thanks to the “pre-warm” mode.
  • No Cold Start Latency: Functions are always ready to respond quickly, making this ideal for time-sensitive applications.

Cons:

  • Cost: You pay for the provisioned execution environments, whether they are used or not.
  • Management Overhead: Requires tuning and management to ensure cost-effectiveness and optimal performance.

E-Commerce Site During Black Friday Sales

Let’s put this into a real-world context. Imagine you run an e-commerce website that experiences a significant spike in traffic during Black Friday sales. To prepare for this, you might use Provisioned Concurrency for critical functions like checkout, ensuring they have zero cold start latency and can handle the surge in traffic. For less critical functions, like product recommendations, you might set a Reserved Concurrency limit to ensure they always have some capacity to run without affecting the critical checkout function.

This approach ensures that your website can handle the spike in traffic efficiently, providing a smooth experience for your customers and maximizing sales during the critical holiday period.

Key Takeaways

Understanding and managing concurrency in AWS Lambda is crucial for optimizing performance and cost. Reserved Concurrency is about guaranteeing availability, while Provisioned Concurrency, with its “pre-warm” mode, is about ensuring immediate, predictable performance, eliminating cold start latency. Both have their place in a well-architected cloud environment. The key is to use them wisely, balancing cost against performance based on the specific needs of your application.

So, the next time you’re planning how to manage your AWS Lambda functions, think about what’s most important for your application and your users. The goal is to provide a seamless experience, whether you’re running an online store during the busiest shopping day of the year or simply keeping your blog’s contact form running smoothly.

GitOps, The Conductor of Cloud Adoption

Let’s embark on a brief journey through the different “buckets” of technology that define our era.

The “Traditional” bucket harks back to days when deploying applications was a lengthy affair, often taking weeks or months. This was the era of WAR, ZIP, and EAR files, where changes were cumbersome and cautious.

Then comes the “New Wave,” synonymous with cloud-native approaches. Here, containers have revolutionized the scene, turning those weeks into mere minutes or seconds. It’s a realm where agility meets efficiency, unlocking rapid deployment and scaling.

Lastly, we reach “Serverless,” where the cloud truly flexes its muscles. In this space, containers are still key, but the real star is the suite of microservices. These tiny, focused units of functionality allow for an unprecedented focus on the application logic without the weight of infrastructure management.

Understanding these buckets is like mapping the terrain before a journey—it sets the stage for a deeper exploration into how modern software development and deployment are evolving.

GitOps: Streamlining Cloud Transition

As we chart a course through the shifting tides of technology, GitOps emerges as a guiding force. Imagine GitOps as a masterful conductor, orchestrating the principles of Git—such as version control, collaboration, compliance, and CI/CD (Continuous Integration and Continuous Delivery)—to create a symphony of infrastructure automation. This method harmonizes development and operational tasks, using familiar tools to manage and deploy in the cloud-native and serverless domains.

Cloud adoption, often seen as a complex migration, is simplified through GitOps. It presents a transparent, traceable, and efficient route, ensuring that the shift to cloud-native and serverless technologies is not just a leap, but a smooth transition. With GitOps, every iteration is a step forward, reliability becomes a standard, and security is enhanced. These are the cornerstones of a solid cloud adoption strategy, paving the way for a future where changes are swift, and innovation is constant.

Tech’s Transformative Trio: From Legacy to Vanguard

Whilst we chart our course through the shifting seas of technology, let’s adopt the idea that change is the only constant. Envision the technology landscape as a vast mosaic, continually shifting under the pressures of innovation and necessity. Within this expanse, three distinct “buckets” stand out, marking the epochs of our digital saga.

First, there’s the “Traditional” bucket—think of it as the grandparent of technology. Here, deploying software was akin to moving mountains, a process measured in weeks or months, where WAR, ZIP, and EAR files were the currency of the realm.

Enter the “New Wave,” the hip cloud-native generation where containers are the cool kids on the block, turning those grueling weeks into minutes or even seconds. This bucket is where flexibility meets speed, a playground for the agile and the brave.

Finally, we arrive at “Serverless,” the avant-garde, where the infrastructure becomes a magician’s vanishing act, leaving nothing but the pure essence of code—microservices that dance to the tune of demand, untethered by the physical confines of hardware.

This transformation from traditional to modern practices isn’t just a change in technology; it’s a revolution in mindset, a testament to the industry’s relentless pursuit of innovation. Welcome to the evolution of technology practices—a journey from the solid ground of the old to the cloud-kissed peaks of the new.

GitOps: Synchronizing the Pulse of Development and Operations

In the heart of our modern tech odyssey lies GitOps, a philosophy that blends the rigors of software development with the dynamism of operations. It’s a term that sparkles with the promise of enhanced deployment frequency and the rock-solid stability of a seasoned sea captain.

Think of GitOps as the matchmaker of Dev and Ops, uniting them under the banner of Git’s version control mastery. By doing so, it forges a union so seamless that the once-staggered deployments now step to a brisk, rhythmic cadence. This is the dance floor of the New Wave and Serverless scenes, where each deployment is a step, each rollback a twirl, all choreographed with precision and grace.

In this convergence, the benefits are as clear as a starlit sky. With GitOps, the deployments aren’t just frequent; they’re also more predictable, and the stability is something you can set your watch to. It’s a world where “Oops” turns into “Ops,” and errors become lessons learned, not catastrophes endured. Welcome to the era where development and operations don’t just meet—they waltz together.

Catching the Cloud: Why the Sky’s the Limit in Tech

Imagine a world where your tech needs can scale as effortlessly as turning the volume knob on your favorite song, where the resources you tap into for your business can expand and contract like an accordion playing a tune. This is the world of cloud technology.

The cloud offers agility; it’s like having an Olympic gymnast at your beck and call, ready to flip and twist at the slightest nudge of demand. Then there’s scalability, akin to a balloon that inflates as much as you need, only without the fear of popping. And let’s not forget cost-efficiency; it’s like shopping at a buffet where you only pay for the spoonfuls you eat, not the entire spread.

Adopting cloud technologies is not just a smart move; it’s an imperative stride into the future. It’s about making sure your tech can keep pace with your ambition, and that, my friends, is why the cloud is not just an option; it’s a necessity in our fast-moving digital world.

Constructing Clouds with GitOps: A Blueprint for Modern Infrastructure

In the digital construction zone of today’s tech, GitOps is the scaffold that supports the towering ambitions of cloud adoption. It’s a practice that takes the guesswork out of building and managing cloud-based services, a bit like using GPS to navigate through the labyrinth of modern infrastructure.

By using Git as a single source of truth for infrastructure as code (IaC), GitOps grants teams the power to manage complex cloud environments with the same ease as ordering a coffee through an app. Version control becomes the wand that orchestrates entire ecosystems, allowing for replication, troubleshooting, and scaling with a few clicks or commands.

Imagine deploying a network of virtual machines as simply as duplicating a file, or rolling back a faulty environment update with the same ease as undoing a typo in a document. GitOps not only builds the bridge to the cloud but turns it into a conveyor belt of continuous improvement and seamless transition. It’s about making cloud adoption not just achievable, but natural, almost instinctive. Welcome to the construction site of tomorrow’s cloud landscapes, where GitOps lays down the bricks with precision and flair.

Safeguarding the Cloudscape: Mastering Risk Management in a Cloud-Native Realm

Embarking on a cloud-native journey brings its own set of weather patterns, with risks and rewards as variable as the climate. In this vibrant ecosystem, risk management becomes a craft of its own, one that requires finesse and a keen eye for the ever-changing horizon.

GitOps emerges as a lighthouse in this environment, guiding ships safely to port. By integrating version control for infrastructure as code, GitOps ensures that each deployment is not just a launch into the unknown but a calculated step with a clear recovery path.

Consider this: in a cloud-native world, risks are like storms; they’re inevitable. GitOps, however, provides the barometer to anticipate them and the tools to weather them. It’s about creating consistent and recoverable states that turn potential disasters into mere moments of adjustment, ensuring that your cloud-native journey is both adventurous and secure.

Let’s set sail with a tangible example. Imagine a financial services company managing their customer data across several cloud services. They decide to update their data encryption across all services to bolster security. In a pre-GitOps world, this could be a treacherous voyage with manual updates, risking human error, and potential data breaches.

Enter GitOps. The company uses a Git repository to manage their infrastructure code, automating deployments through a CI/CD pipeline. The update is coded once, reviewed, and merged into the main branch. The CI/CD pipeline picks up the change, deploying it across all services systematically. When a flaw in the encryption method is detected, rather than panic, they simply roll back to the previous version of the code in Git, instantly reverting all services to the last secure state.

This isn’t just theory; it’s a practice that keeps the company’s digital fleet agile and secure, navigating the cloud seas with the assurance of GitOps as their compass.

Sailing Ahead: Mastering the Winds of Technological Change

As we draw the curtains on our exploration, let’s anchor our thoughts on embracing GitOps for a future-proof voyage into the realms of cloud-native and serverless technologies. Adopting GitOps is not just about upgrading tools; it’s about cultivating an organizational culture that learns, adapts, and trusts in the power of automation.

It’s akin to teaching an entire crew to sail in unison, navigating through the unknown with confidence and precision. By fostering this mindset, we prepare not just for the technology of today but for the innovations of tomorrow, making each organization a flagship of progress and resilience in the digital sea. Let’s set our sails high and embrace these winds of change with the assurance that GitOps provides, charting a course towards a horizon brimming with possibilities.