Best Practices for Managing Traffic Spikes in Ecommerce Without Downtime

Peak season is the crash test of every ecommerce platform. It’s when architecture, infrastructure, and code either perform like a well-rehearsed orchestra or fall apart under pressure, watching a spinning loader. Black Friday, influencer drops, or holiday collections often act like a truth serum. They expose weak queries, misconfigured nodes, and sluggish integrations faster than any audit ever will.

At Expert Soft, we provide application support services to long-term ecommerce clients and often take part in preparing platforms for high-load events. This article is a collection of best practices we actually use before, during, and after traffic spikes to keep storefronts stable, responsive, and profitable under any load.

Quick Tips for Busy People

If you don’t have time to read the whole article, here’s the condensed version: the things that decide whether your platform survives peak ecommerce traffic or falls apart.

• Identify where systems crack under load: databases, order workflows, integrations, and caching layers are the components that tend to fail first, impacting the whole system.
• Strengthen the data layer before scaling out: profiling the database, cleaning up schemas, and removing heavy joins usually deliver far bigger performance gains than blindly throwing more hardware.
• Mix scaling strategies: combine proactive scaling, smart autoscaling, and caching of most used data to ensure the platform stays fast even when traffic behaves differently than expected.
• Keep heavy workflows asynchronous to protect checkout: shift fraud checks, ERP syncs, invoice generation, and stock validation into event-driven queues so customer-facing steps remain quick during peak load.
• Maintain accurate availability under high load: rely on real-time stock alerts, automatic bundle rules, and continuous synchronization with inventory systems so customers never buy items that can’t be fulfilled.
• Test for real behavior, not ideal conditions: simulate full customer journeys, add natural pauses, introduce failures intentionally, and monitor queues and backlogs to reveal issues that clean, stateless load tests never show.
• Let infrastructure automate resilience: define environments with IaC, run automated health checks before releases, and use gradual rollout strategies so the platform scales predictably and recovers safely during peak events.

Keep on reading for a deep dive.

Common Weak Points Under High Load

When traffic spikes, problems rarely start with the UI or business logic. They begin in underlying components and become impossible to ignore when thousands of users hit checkout at once. Based on our experience, these are the areas most likely to fail under heavy load — parts of the system that tend to show stress first.

1. Database

The database often becomes the bottleneck during peak load because design choices that work under normal traffic start showing their limits. High read/write volume, unoptimized queries, or redundant data structures quickly amplify latency.

Our team has dealt with this more than once: massive queries, deep joins, and overloaded indexes dragging performance down. Once we simplified joins, and cleaned up redundant structures, we saw a significant boost in both database scalability and reliability.

For example, in one SAP Commerce Cloud project, we achieved up to a 100× reduction in database queries, cutting product detail page load times from 5 seconds to under 90 milliseconds by restructuring the data model: localized and non-localized attributes were split, query patterns simplified, and frequent configs cached.

2. Configurations

Default SAP Commerce Cloud settings aren’t built for high concurrency or complex data flows. Thread pools, timeout values, cron job execution, and cache regions must be tuned before peak load, otherwise, processes like checkout or order placement start timing out. Even with enough infrastructure, untuned defaults can silently throttle performance. Proper configuration ensures the platform matches real workload demands.

3. Order fulfillment processes

Scaling is usually a problem of timing and architecture. When additional resources are provisioned only after performance starts to drop, the system is already under strain. Order processing and fulfillment workflows tend to hit this limit first: once queues overflow on a single node, delays cascade through dependent services.

In SAP Commerce Cloud systems, the platform supports modular scaling, allowing separate nodes for storefront activity and background business processes. This setup works well only when capacity planning is done in advance and scaling policies are defined before peak load.

4. Integrations

Even if the platform itself is stable, external systems can become the weakest link. Enterprise ecommerce environments typically rely on third-party services, such as payment gateways, ERPs, tax providers, etc., and if any of these slow down or stop responding, order fulfillment processes begin to fail.

Here is an example. We worked on a peak campaign where everything on our side was fully prepared and scaled in advance: plenty of resources, healthy business-process nodes, no unusual spikes. But at one point, we noticed that order validation had stalled.

Monitoring showed the issue wasn’t internal, as the resources were fine. After digging in, we found that the external fraud check system had stopped responding under load and wasn’t returning the data we needed. Without that information, our system couldn’t validate orders, so they weren’t being processed.

It took several hours for the provider to fix their system, plus extra engineering work on our side. Fortunately, customers never felt the impact: orders were still captured, and we later restored all of them using a recovery script. That’s how a single overloaded integration can undermine ecommerce scalability and performance.

5. Caching

Caching mistakes are a common source of performance issues during peak events. The most frequent problems include:

We encountered the last issue during preparation for a flash sale. Five caching layers were in place: CDN, Spartacus front-end cache, Node.js middleware, SAP API cache, and database-level caching, but TTLs weren’t synchronized. Some users saw outdated prices and missing product images. After aligning TTL values and invalidation rules, both consistency and system performance returned to normal.

Best Practices for Managing Traffic Spikes

When traffic surges, systems don’t break evenly. Some parts hold up, others slow down, and one weak component can bring everything else with it. Below are best practices that will help you avoid that.

Start with database tuning

Peak events often expose how much extra work a database is doing. If joins are slow, indexes are missing, or tables have ballooned in size, response times climb quickly as more users hit search and checkout at the same time. When you’re scaling a database to handle growing traffic, these bottlenecks show up first.

That’s why, before any high-load release, our team starts with database profiling rather than provisioning new nodes. A good practice here is bringing in performance engineers. They spin up the solution on one of the environments, run their tooling, and evaluate how efficiently the system interacts with the database. If they spot inefficiencies or slow query paths, we revisit the design and adjust our approach long before traffic arrives.

Pro tips:

Rely on several scaling approaches

Relying on only one scaling approach can create gaps in capacity planning. Proactive scaling prepares the system for scheduled events and expected load, while reactive scaling helps the platform adjust when traffic patterns differ from what was forecast. Each approach covers a different part of the workload profile, so using both together creates a more consistent operating environment.

In practice, we’ve seen that when only one approach is in place, the system can reach its limits faster than expected. For example, during one high-traffic event, additional nodes were added only after queues had already started growing. They eventually came online, but order-processing workflows had stalled by that point, and engineers had to restart the processes manually. A combination of planned capacity and responsive scaling would have reduced the impact in that situation.

Pro tips:

Use granular caching strategies

Caching works best when it supports the system’s overall design. You can’t avoid database calls entirely, but a good caching strategy cuts down how often they happen during heavy traffic. When the cache is set up carefully and placed in the right layers, it takes pressure off the database and helps keep response times steady.

Pro tips:

Implement event-driven processing for heavy workflows

During peak traffic, synchronous processes must stay lean. Checkout, cart operations, and payments shouldn’t wait for long-running workflows like fraud detection, invoice generation, or ERP synchronization.

That’s why we push heavy workflows into asynchronous queues using event-driven architecture, for example, SAP Event Mesh. An order is created and confirmed immediately, and only then does the system publish events like “order placed” or “payment confirmed” for downstream services to process.

Pro tips:

Add real-time stock alerts and backorder/bundle rules

To prevent customers from buying items that are already gone, the system needs up-to-date stock data synchronization, especially when multiple channels and high traffic are involved.

For example, an ecommerce platform can send inventory-reservation events to an external warehouse solution, so it automatically increases inventory if possible. For bundle products, availability must consider every component — if even one item runs out, the bundle should adapt or temporarily switch off before orders start failing.

Pro tips:

Set up real-time system monitoring

As platforms grow, observability has to grow with them, giving teams a real-time understanding of system behavior so that early issues are noticed and addressed long before they reach customers.

For SAP Commerce Cloud projects, Dynatrace is our go-to tool. It lets us watch technical metrics, like CPU, memory, threads, DB time, alongside business issues like failed checkouts or stuck orders. When alerts are set up right, our engineers usually fix incidents in under an hour, which our expert calls a solid, achievable target.

We also combine reactive and proactive monitoring practices to ensure comprehensive observability and fast issue discovery.

Pro tips:

Prepare rollback scenarios for critical releases

No peak event should happen with untested code being deployed without a way back. Even minor updates, such as a new discount rule or a payment change, can break order flow.

On large-scale projects, rollback plans are mandatory. This includes having a stable previous build available, database or configuration backups if needed, and tested rollback instructions. If things go wrong, the system should be back to the last stable state in minutes, not hours.

Pro tips:

System Testing Best Practices

Before peak traffic, testing should reveal how the system handles stress, including slowdowns and failures. The points below outline what teams should keep in mind to run meaningful tests and avoid common traps.

Prevent positive bias concerns

What can go wrong:
It’s easy to trust a load test that looks “too clean”: low traffic, no visible errors, flat response times, and conclude the system is ready. In reality, this often means the load was insufficient or the wrong signals were monitored. Teams might test with a few hundred virtual users and assume the same setup will handle tens of thousands, or focus only on API response time while asynchronous queues quietly build up a backlog that only shows under real peak traffic.

How to avoid it:
Design load tests to actively challenge the system, not to confirm that everything is fine. Ramp load gradually toward realistic and peak levels, and monitor more than just HTTP 2xx/response time. Include queue depth, background job latency, and processing backlogs in Kafka/RabbitMQ or similar systems. The goal is to see how the platform behaves when pressure grows over time, so “smooth” test results reflect real robustness.

Increase the load gradually

What can go wrong:
A common mistake in load testing is jumping straight to max traffic. When you do, the system usually collapses all at once, and you can’t tell which part broke first. Ramping up the load slowly reveals problems in order, so each one can be understood and fixed.

How to avoid it:
The right approach is to scale traffic in phases while monitoring how response time, error rate, queue size, CPU, DB latency, and thread usage change over time.

In SAP Commerce Cloud projects, we use Dynatrace and Cloud Portal dashboards to watch these metrics in real time. The goal is to identify the system’s saturation point — the moment performance starts degrading and record that data for scaling thresholds.

Design stateful user sessions

What can go wrong:
Stateless load tests, where virtual users send isolated GET requests, don’t simulate real ecommerce use. They don’t create sessions, fill carts, apply promotions, or trigger stock reservations, so the system appears more stable than it actually is.

How to avoid it:
Tests should simulate full user journeys with session tracking: browsing, adding items to cart, logging in, applying promo codes, checking stock, selecting delivery options, and completing payment. This produces real database writes, cache invalidations, stock changes, and integration calls. Only then you can see if session data persists, carts are retained, and inventory updates happen properly under stress.

Incorporate pauses between actions

What can go wrong:
If virtual users execute every action instantly, the test ignores how real shoppers behave — browsing, comparing, and pausing between steps. Without these natural gaps, all requests hit the system at the same moment, creating a synthetic load spike that distorts the results.

How to avoid it:
Introducing think time — small, randomized pauses between actions — makes simulations closer to real-life traffic patterns. These pauses prevent synchronized spikes and reveal timing-related issues, like session expiry, race conditions, or delayed cache updates.

Test failure scenarios

What can go wrong:
Most platforms are only tested for successful orders, but during traffic peaks, payment gateways go down, inventory runs out, and third-party services fail. So, you need to understand what will happen after.

How to avoid it:
It’s a good rule of thumb to simulate failure intentionally. That includes cutting payment gateway responses, delaying ERP or tax service integrations, forcing inventory to hit zero, or letting two users try to buy the last available unit, which is a classic inventory race condition.

During these tests, it’s crucial to verify that no duplicate orders are created, carts aren’t lost, data remains consistent, and the system recovers normally after services come back online.

Infrastructure Automation Best Practices

Preparing infrastructure for peak traffic goes far beyond adding servers or tweaking Cloud Portal settings the night before a sale. Systems need to scale themselves, verify that every service is healthy, and roll back changes if something goes wrong without waiting for an engineer to step in.

Automate infrastructure provisioning with IaC

During peak traffic, manually provisioning nodes or changing configurations in production introduces unnecessary risk. Infrastructure should be reproducible, consistent, and scalable by design. Using Infrastructure as Code (IaC) tools such as Terraform, AWS CloudFormation, or Azure Resource Manager, entire environments can be defined in code, from compute resources and databases to networking and autoscaling rules, ensuring that deployments behave predictably every time.

Integrate automated health checks into deployment pipelines

Deploying new code before a peak without automated validation exposes production to unnecessary risk. CI/CD health checks ensure a build meets stability requirements before real traffic reaches it, verifying core APIs.

Implement gradual release and environment segmentation

Ahead of major sales, teams often introduce changes, such as new product types, updated pricing rules, or campaign-specific logic. Even these pre-planned updates can introduce instability if pushed live all at once. Gradual rollout strategies, such as blue/green and canary, help validate changes safely before full exposure.

To Sum Up

Peak traffic is the moment that shows whether your platform is architected for scale or held together by quick fixes. Under real load, performance bottlenecks become obvious: slow database queries, missing indexes, overloaded services, and third-party integrations that stop responding when demand spikes.

Systems that hold up under pressure share a few traits: clean database design without heavy joins, integrations that stay responsive as load grows, consistent caching across layers, and monitoring that follows business outcomes. All the practices described in the article, when combined, keep teams focused on performance when it matters most.

If you’re preparing for a sales peak or want a reality check on how your platform behaves under load, we’re always happy to jump on a call if you want to dig into it.