The serverless pipeline handled peak moments (say, a flash sale surge) with ease. Meanwhile, Lambdas smoothly scaled out and handled events concurrently, and the event queue (SNS/EventBridge) cushioned any spikes, fending off overload. Crucially, all this was pulled off in a pocket-friendly manner. The company skipped running pricey dedicated servers for the pricing service. They only forked out when Lambda and the messaging service were in play, proving to be cost-effective.
The central event router (Amazon EventBridge in our case) picks up the event. The beauty of using an event bus is that it splits producers and consumers. The inventory system doesn’t need to grasp the pricing logic; it just shoots out an event. The event bus then sieves and directs the message to interested subscribers. In our setup, the subscriber is the Pricing Service, but we could effortlessly add other consumers (e.g., a low-stock alert service) without tweaking the inventory module. This publish-subscribe pattern crafts a flexible, scalable architecture.
While our demo honed in on pricing, this architectural pattern applies to a raft of real-time flows (think inventory alerts, personalized promos, fraud detection, and the like). The clincher emerges in how cloud services like AWS Lambda, SNS, and EventBridge fuel near real-time data transport and processing, amplifying business nimbleness. For outfits eyeing a facelift in their e-commerce landscape, an event-driven approach charts a course to respond swifter and smarter to pivotal events. By sketching pipelines swayed by triggers (like inventory tweaks), you set up your system to match pace with your business, sometimes even with your clientele’s stride.
It also flung open gates for future enhancements. For instance, adding a new subscriber to the inventory event required no tweak to the inventory publisher or the pricing Lambda, underscoring the extensibility of the event-driven path.
This event-driven layout packs several perks. It’s serverless and scalable – AWS Lambda can tackle surges of events sans pre-provisioning servers, scaling the compute layer as events surge. It’s also decoupled – the inventory system, event router, and pricing logic stand independently. This disconnection boosts maintainability and grants each piece the liberty to progress separately. Moreover, by leveraging an event-driven pipeline, we scrapped the need for unceasing polling or batch jobs, slicing the delay in data flow and slashing heft on systems. Layering in a dedicated cache also grants the best of both worlds: rapid dispense of data to users and sync with source-of-truth updates courtesy of the pipeline.
Picture a mad rush in an online flash sale, with thousands of shoppers scrambling to snag a limited-stock product. If the price stays the same while the inventory dips, the retailer could end up selling out too fast and missing out on potential revenue. In the fast-paced world of online retail, dynamic pricing is a game-changer – adjusting prices on the fly based on demand or stock levels. But implementing real-time pricing calls for a flexible backend. This piece dives into a real-life case study of setting up an event-based system for updating prices on the go in an e-commerce setting.
This fine-tuned event-driven pricing pipeline ushered in a host of gains for the e-commerce retailer, ramping up both update frequency and system reactivity. Price updates that previously dragged on for hours (or until the next batch run) now breeze through in near real-time, often within a second or two of an inventory tweak. This translated into the pricing algorithm reacting instantly to spikes in demand or dwindling stock, corralling more revenue from in-demand items and nimbly discounting slow movers. The system shifted from daily or hourly price refreshes to a continuous update tempo, syncing pricing with live business dynamics.
Here’s a snapshot of the prime takeaways from our case study:
With the backend spruced up, the new price disseminates to user-facing systems. For instance, the product details page or search outcomes on the website will fetch the price from Redis (or via an API tapping into the cache/db) and flaunt the latest figure. In some setups, you might also ping updates to the front end in real-time (via WebSockets or server-sent events) if live price updates on the page are wanted. In our case study, even without a nudge to the client, the next regular page load or API call will score the right price from the freshened-up cache.
The inventory system (like a warehouse database or an inventory microservice) churns out an event whenever a product’s stock shifts. In AWS, this can be done via an event bus like Amazon EventBridge or a pub/sub mechanism like Amazon SNS. This event (such as a “Item X stock changed to Y units” message) is the cue for our pipeline. This event-centric approach does away with the old batch jobs or polling, erasing the lag between an inventory change and downstream actions.
Various technical hurdles compounded this issue. The pricing logic was buried within a monolithic application, making frequent updates dicey and resource-intensive. Polling for changes (or running scheduled queries) was inefficient and introduced delays – fresh data might languish for minutes or hours before being picked up by the system. The system heavily cached product data for swift website performance, but this cache became a liability when the data grew stale. We needed a fix to propel price changes in real-time whenever there was an inventory tweak, without revamping the entire platform or sacrificing performance.
In the conventional retail setup, price updates often happen in batches or through manual intervention – like updating prices overnight or using hourly cron jobs. This is too sluggish for today’s dynamic markets. Our e-commerce scenario ran into a critical snag: shifts in inventory weren’t promptly reflected in product prices. When a product’s stock plummeted suddenly (hinting at high demand), the price stayed outdated until the next update. Conversely, overstocked items retained high prices, missing chances to clear inventory with timely discounts. The absence of real-time updates led to missed revenue and subpar inventory management. In a fast-paced, customer-centric landscape, this sluggishness puts the company in a competitive bind.
To address these pain points, the team crafted an event-driven pipeline on AWS that detaches pricing updates from the main app. The core idea is simple: each time an inventory change occurs (e.g., a stock level update), it triggers an event that flows through a pipeline to tweak the price. Here’s the breakdown of how it works:
After computing the new price, the Lambda updates the data repositories. In our case, the price goes into a swift cache (using Amazon ElastiCache for Redis) that the e-commerce site taps into for real-time reads. The update might also be saved in a central database (e.g., an Aurora or DynamoDB table hoarding all prices) for uniformity. The caching layer is vital for performance – the website can fetch prices from an in-memory cache that’s now kept up to the minute by the pipeline. The Lambda’s cache update lands within seconds of the original inventory shift, so the next shopper eyeing that product will see an updated price. This method significantly beats the old model where caches refreshed only every 30 minutes or longer.
From a tech standpoint, the project spotlighted how the right architecture could turbocharge business agility. The team sliced out a pivotal logic piece (pricing) from the monolith and sculpted it into a dexterous microservice that reacts to events. This independence from the core website structure allowed for seamless updates to pricing logic sans prodding the core app, trimming risks, and fast-tracking dev cycles.
I’m Ravi Thutari, a Lead Software Engineer with stints at Hopper, Amazon, and Wayfair. My knack lies in erecting scalable, low-latency systems leveraging distributed architecture and serverless tech. I relish shedding light on real-world engineering insights through scribbles, speaking gigs, and shepherding devs keen on delving into backend and cloud engineering.
The user experience got a shot in the arm too. Shoppers were less likely to bump into outdated details. For example, a shopper no longer encountered mismatched pricing or inventory glitches, as the site’s data stayed up-to-date. Behind the scenes, the infra revamp led to superior performance and scalability.
Our inspiration comes from a design using Google Cloud Run and Pub/Sub, but we’ll showcase it on AWS for wider usability. We swap Cloud Run (GCP’s serverless container service) with AWS counterparts like AWS Lambda (serverless functions) or AWS Fargate (serverless containers), and exchange Pub/Sub (the message broker) with AWS messaging services (e.g., Amazon SNS or EventBridge). The focus isn’t on the pricing model itself, but on the infrastructure design – how the right architecture enables swift price adjustments triggered by inventory updates. In this piece, we’ll tackle the business challenge, the event-driven pipeline architecture, and the impact on update frequency and system responsiveness.
This case study spotlights that setting up real-time price prediction (or more precisely, real-time price updates) is less of a data science puzzle and more of an engineering feat. By harnessing an event-driven pipeline on AWS, an e-commerce player shifted pricing in sync with inventory changes. The perfect blend of inventory update events, a serverless compute layer for pricing, and snap cache updates formed the backbone of a slick pricing engine. The output was a system that swiftly tuned to market hitches, keeping up in the competition, no platform overhaul necessary.
Once the event bus catches an inventory update, it triggers an AWS Lambda function (serverless compute) that encapsulates the pricing logic. This Lambda mirrors a container on Cloud Run – it runs on demand, scales automatically, and bills only when it’s active. The Lambda unloads the necessary data (product details, current inventory, perhaps demand forecasts) and crunches a new price. This could involve a simple rule (e.g., if stock < 10, bump up price by 5%) or a machine learning model for price optimization. The crux is that the logic kicks in pronto in response to the event. AWS Lambda's event-driven invocation and auto-scaling guarantee that even if hundreds of inventory events trigger in quick succession, the pricing function will ramp up to process them concurrently. By automating price calculations on inventory events, the system gets ultra-responsive, wiping out the delay of manual or scheduled updates.
