If your platform is crashing during major sales events, your infrastructure is costing you more than just monthly server bills. It’s costing you revenue. It’s costing you brand reputation. And most importantly, it’s costing you customer trust. You don’t get a second chance when a user’s cart times out on Black Friday or a highly anticipated product drop.
For technical decision-makers and cloud architects, building an e-commerce platform that survives sudden, violent traffic spikes is the ultimate test of system resilience. I’ve spent years parachuting into war rooms. I’ve seen traditional monolithic architectures melt down under the sheer volume of incoming requests. I’ve watched local-storage relational databases crack under the pressure of concurrent read/write operations, cache stampedes, and distributed transaction bottlenecks. It’s never pretty, and it’s almost always preventable.
Alibaba Cloud offers a distinct, battle-tested advantage in this arena. Look, I’m not here to sell you on marketing copy. The reality is that their core infrastructure services are the exact same primitive building blocks that power the Alibaba Group’s own Singles’ Day shopping festival—an event that routinely processes hundreds of thousands of transactions per second at its peak. They built these tools internally to solve their own massive scaling nightmares before ever offering them to the public.
Having deployed these systems at scale, I can tell you a hard truth: succeeding here isn’t about throwing money at larger instance types. You can’t vertically scale your way out of bad architecture. It’s about ruthlessly decoupling your systems.
This comprehensive guide dissects how to architect, deploy, and optimize a high-traffic e-commerce infrastructure on Alibaba Cloud. These aren’t theoretical best practices. These are hard-won lessons from actual production environments where downtime is measured in thousands of dollars per minute.
Skip the learning curve. If you need to scale your infrastructure before your next major sales event, our team of certified cloud experts can design and build it for you.👉 Talk to a Cloud Strategist Today
1. The Core Architecture of a High-Traffic E-commerce Platform
In production deployments, any state held at the compute layer is a ticking time bomb. I cannot stress this enough. If you are relying on sticky sessions, local file storage, or in-memory caches that aren’t distributed, your platform will fail when the load balancer inevitably shifts traffic or a node dies.
To handle massive concurrency, an e-commerce architecture must be strictly decoupled, completely stateless at the application layer, and capable of horizontal scaling at the storage layer. We use JSON Web Tokens (JWT) for stateless authentication. We use distributed caches. The compute nodes themselves should be entirely disposable.
1.1. Architectural Layer Breakdown
When we deploy across a Multi-Zone Virtual Private Cloud (VPC) for high availability, we strictly segment the architecture into three primary domains. Blurring the lines between these domains is where most teams get into trouble.
1.1.1. Edge & Traffic Ingestion Layer
- Alibaba Cloud DCDN (Dynamic Route for CDN): This isn’t just for caching images. DCDN caches static assets at the edge, yes, but crucially, it routes dynamic API calls over Alibaba’s private backbone. This bypasses the unpredictable latency of public routing.
- Anti-DDoS Pro & WAF 3.0: Inspects ingress traffic. During flash sales, you will be targeted by bots trying to scrape inventory or launch volumetric attacks to hold your site ransom. You need Layer 7 exploit protection that updates in real-time, alongside strict rate-limiting rules.
- Application Load Balancer (ALB): Routes HTTP/HTTPS and gRPC traffic. We don’t use Classic Load Balancers (CLB) anymore. They choke on high concurrent connections and lack advanced Layer 7 routing rules.
1.1.2. Compute & Application Layer
- Container Service for Kubernetes (ACK Pro): This is where your decoupled microservices live. It’s the engine room.
- Application High Availability Service (AHAS): The ultimate safety net. It injects Sentinel rules for real-time traffic shaping and graceful degradation. If you don’t have graceful degradation, your system is brittle.
1.1.3. Data & Asynchronous Processing Layer
- PolarDB: Handles core transactional data. It leverages decoupled storage and compute, which we’ll dive into later. It is a fundamental shift from traditional database engines.
- ApsaraDB for Redis (Cluster Edition): Manages session states and flash-sale inventory. This must be a cluster. A single Redis node will become a CPU bottleneck instantly under heavy load.
- Apache RocketMQ: Asynchronously decouples order creation and payment callbacks. Without a robust message broker, your microservices are just a distributed monolith communicating over fragile HTTP calls.
1.2. Infrastructure as Code (IaC) Base: The Non-Negotiable Foundation
I have seen teams attempt to manually provision VPCs for “quick tests” that inevitably end up in production six months later. Never do this. Always provision your network backbone using Terraform. If an entire region goes down, or if a junior engineer accidentally deletes a critical routing table, your IaC state is your only recovery mechanism. Clicking around the web console is for hobbies, not production.
Here is a baseline, production-grade configuration for a multi-zone VPC. Notice we are explicitly setting up a NAT Gateway. Your Kubernetes worker nodes should never have public IPs attached directly to them. Security groups alone are not enough; physical network isolation is mandatory.
Terraform
# Provider Configuration
provider "alicloud" {
region = "ap-southeast-1"
}
# VPC Definition
# We use a /8 or /16 block to ensure we never run out of IP addresses
# when Terway CNI assigns IPs directly to thousands of pods.
resource "alicloud_vpc" "ecommerce_vpc" {
vpc_name = "prod-ecommerce-vpc"
cidr_block = "10.0.0.0/8"
}
# Multi-AZ VSwitches for Application Tier
# Spreading across 3 zones ensures we survive a single datacenter failure.
resource "alicloud_vswitch" "app_vswitches" {
count = 3
vpc_id = alicloud_vpc.ecommerce_vpc.id
cidr_block = cidrsubnet(alicloud_vpc.ecommerce_vpc.cidr_block, 8, count.index + 1)
zone_id = element(["ap-southeast-1a", "ap-southeast-1b", "ap-southeast-1c"], count.index)
vswitch_name = "prod-vswitch-app-${count.index + 1}"
}
# NAT Gateway for Outbound Internet (Crucial for private ACK nodes)
# If your pods need to pull external Docker images, hit third-party payment APIs,
# or send SMS notifications, they need this.
resource "alicloud_nat_gateway" "nat" {
vpc_id = alicloud_vpc.ecommerce_vpc.id
nat_gateway_name = "prod-nat-gateway"
payment_type = "PayAsYouGo"
vswitch_id = alicloud_vswitch.app_vswitches[0].id
}
resource "alicloud_eip_address" "nat_eip" {
bandwidth = "100" # Don't bottleneck your outbound traffic here
internet_charge_type = "PayByTraffic"
}
resource "alicloud_eip_association" "eip_assoc" {
allocation_id = alicloud_eip_address.nat_eip.id
instance_id = alicloud_nat_gateway.nat.id
}
2. Traffic Ingestion and Edge Optimization
The first line of defense is the edge. Every single request absorbed by the edge saves backend compute cycles and database IOPS. If a request hits your database that could have been cached at the edge, you are wasting money and risking stability.
2.1. DCDN vs. Standard CDN: The SPA Reality
A standard CDN is insufficient for modern Single Page Applications (SPAs). I’ve audited architectures where aggressive standard CDN caching inadvertently cached user-specific shopping carts. Imagine logging in and seeing someone else’s items. It’s a massive data leak and causes instant panic.
Alibaba Cloud DCDN distinguishes between static and dynamic content automatically. It accelerates API calls by routing them via the shortest path across Alibaba’s internal network to your ALB, effectively bypassing public internet routing latency and jitter.
Let’s look at the numbers. When you rely on the public internet, you are at the mercy of dozens of hops, peering disputes, and submarine cable congestion. By terminating the TLS handshake at the edge node rather than the origin server, you shave off hundreds of milliseconds before the request even reaches your application logic.
2.2. We Build Optimized Global Infrastructure
Entering new markets requires a lot more than just translating your frontend. If your global users are experiencing high latency when routing to your regional servers, you are actively losing conversions. Time is literally money in e-commerce. A 100ms delay can drop conversion rates by 7%. We build fully compliant, highly optimized infrastructure that navigates the complexities of cross-border latency and local routing without breaking a sweat.
👉 Schedule an Architecture Review
2.3. Load Balancing Strategy: ALB Ingress Controller
Do not manually configure Load Balancers through the web console. It creates massive configuration drift. Next week, a developer will change a routing rule manually to push a hotfix, and your IaC state will be completely out of sync.
Instead, use the ALB Ingress Controller in ACK. It maintains a single source of truth inside your Kubernetes manifests.
When you terminate TLS at the ALB, you offload the heavy cryptographic lifting from your worker nodes. Let the load balancer handle the encryption math; save your CPU cycles for business logic.
YAML
# ALB Ingress Class Configuration
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: ecommerce-ingress
annotations:
kubernetes.io/ingress.class: alb
alb.ingress.kubernetes.io/listen-ports: |
[{"HTTPS": 443}]
alb.ingress.kubernetes.io/certificate-id: "cert-123456789" # Terminate TLS at ALB
alb.ingress.kubernetes.io/healthcheck-enabled: "true"
# Ensure you configure health checks properly.
# A bad health check will take healthy pods out of rotation during a spike.
spec:
rules:
- host: api.ecommerce.com
http:
paths:
- path: /order
pathType: Prefix
backend:
service:
name: order-service
port:
number: 8080
3. Compute Layer: Elasticity via ACK (Kubernetes)
Kubernetes is complex, yes. But for this scale, it’s the only logical choice. Bare VMs are too slow to boot. Auto-scaling groups based on standard VM images can take 3 to 5 minutes to become healthy. A lightweight Go or Node.js container can boot and start serving traffic in under 3 seconds.
3.1. Provisioning the Cluster via CLI
Always use the Terway CNI over Flannel. Flannel’s overlay network introduces an unnecessary latency hop via packet encapsulation. Terway assigns native VPC IP addresses directly to the pods. This means your Application Load Balancer can route traffic directly to the pod IP, skipping the NodePort mapping entirely. This shaves off precious milliseconds and reduces CPU overhead on your nodes.
Bash
# Create ACK Pro Cluster with Terway CNI
# Notice we are explicitly using the 'Pro' profile.
# Standard clusters have smaller API server limits. Don't skimp here.
aliyun cs POST /clusters \
--header "Content-Type=application/json" \
--body '{
"name": "prod-ecommerce-ack",
"cluster_type": "ManagedKubernetes",
"profile": "Pro",
"vpcid": "vpc-12345",
"vswitch_ids": ["vsw-1a", "vsw-1b", "vsw-1c"],
"worker_instance_types": ["ecs.c8i.2xlarge"],
"num_of_nodes": 3,
"snat_entry": true,
"container_cidr": "172.16.0.0/16",
"service_cidr": "172.19.0.0/20"
}'
3.2. Pod Readiness and Liveness: The Silent Killers
I need to interject here with a critical lesson that I learned the hard way. If you do not configure your Kubernetes readiness and liveness probes correctly, your scaling strategy will actually destroy your cluster.
During a massive traffic spike, a pod might become slow to respond because it’s processing a heavy queue of requests. If your liveness probe is too aggressive (for example, expecting an HTTP 200 response in under 1 second), Kubernetes will assume the pod is dead and kill it.
Now, think about what happens next. That heavy load is distributed to the remaining pods. Those pods instantly become slow. Kubernetes kills them too. Within 60 seconds, your entire cluster commits suicide in an endless CrashLoopBackOff cycle. This is called a cascading failure.
Be conservative with liveness probes. Be aggressive with readiness probes. If a pod is overwhelmed, the readiness probe should fail, which temporarily takes it out of the ALB rotation. This stops new traffic from hitting the busy pod, allowing it to catch its breath and process its current queue before accepting more requests.
3.3. The Dual-Scaling Strategy: A Harsh Reality Check
If you rely on standard Horizontal Pod Autoscaling (HPA) during a flash sale, your system will be dead before the new nodes even finish booting.
I’ve watched clusters literally melt because engineering teams trusted reactive scaling against an instant 100x traffic multiplier. HPA looks at CPU, decides to scale, tells the deployment, which schedules pods. If there’s no node space, the Cluster Autoscaler talks to the cloud API to boot a virtual machine. That VM takes 2 minutes to boot, join the cluster, and pull the Docker image.
In e-commerce, 2 minutes of downtime during a flash sale is catastrophic. Users hit refresh, get a 502 Bad Gateway, and go to your competitor. You must pre-scale. Use CronHPA.
YAML
# CronHPA for Preemptive Scaling before a Flash Sale
apiVersion: autoscaling.alibabacloud.com/v1beta1
kind: CronHorizontalPodAutoscaler
metadata:
name: order-service-cronhpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: order-service
jobs:
- name: "scale-up-for-sale"
# Pre-warm the cluster 30 minutes before the sale hits.
# Eat the cost of the compute for 30 minutes. It's an insurance policy.
schedule: "30 23 * * *" # Trigger at 11:30 PM for a midnight sale
targetSize: 100
- name: "scale-down-post-sale"
schedule: "0 2 * * *" # Trigger at 2:00 AM once traffic subsides
targetSize: 10
4. Database & Caching: The High-Concurrency Bottleneck
This is where architectures live or die. Compute is easy to scale; state is incredibly difficult.
4.1. The Power of PolarDB (Cloud-Native Shared Storage)
Standard relational databases with binary log (binlog) replication will kill your e-commerce platform. I don’t say that lightly. During a heavy write spike, I’ve seen traditional MySQL logical replication lag hit 15 seconds.
Think about the user experience: A user places an order. The write goes to the primary database node. The user is immediately redirected to their order history page. That page reads from a read-replica node to save primary DB CPU. Because of the 15-second replication lag, the order doesn’t exist yet on the read node. The user sees an empty list, panics, hits the back button, and frantically tries to checkout again. Now you have duplicate charges, angry customers, and a customer support queue that is hundreds of tickets deep.
PolarDB’s shared storage architecture eliminates this entirely.
In PolarDB, the primary node and the read-only nodes mount the exact same underlying storage volume via a high-speed RoCE (RDMA over Converged Ethernet) network. There is no binlog to parse and replay for replication. The read node just reads the exact same data blocks the primary just wrote. Lag is measured in microseconds, not seconds.
Terraform
# Terraform: Provisioning a PolarDB MySQL 8.0 Cluster
resource "alicloud_polardb_cluster" "order_db" {
db_type = "MySQL"
db_version = "8.0"
pay_type = "PostPaid"
db_node_class = "polar.mysql.x4.large"
vswitch_id = alicloud_vswitch.app_vswitches[0].id
description = "Prod Order Database"
}
4.2. Handling Flash Sales: The Redis Lua Pattern
Never let a flash sale hit your relational database directly. Even PolarDB has limits on row-level locks. If 10,000 people try to decrement the inventory of the same promotional item concurrently, the database will spend all its CPU managing lock contention and doing zero actual work.
I once troubleshot a deployment where standard Redis GET and SET commands led to a 5% oversell rate during a major holiday sale. Why? Because between the time the application reads the inventory (GET) and updates it (DECR), another thread has already read the old value. It’s a classic race condition.
Lua scripts aren’t optional here; they are mandatory for atomic consistency. Redis evaluates the entire Lua script as a single, isolated operation. No other command can run while the script is executing.
Lua
-- Redis Lua Script for Atomic Inventory Deduction
local inventory_key = KEYS[1]
local requested_qty = tonumber(ARGV[1])
-- Read current inventory. Default to 0 if key doesn't exist.
local current_inventory = tonumber(redis.call('get', inventory_key) or "0")
if current_inventory >= requested_qty then
-- We have enough stock. Deduct it atomically.
redis.call('decrby', inventory_key, requested_qty)
return 1 -- Success: Let the checkout proceed
else
-- Not enough stock.
return 0 -- Failed: Out of stock, inform the user immediately
end
5. Asynchronous Processing with Apache RocketMQ
Clients often ask me: “Why not just use Kafka? We already know Kafka.” Kafka is phenomenal for high-throughput log aggregation and stream processing. But RocketMQ was literally built to solve this exact transactional e-commerce flow.
Here is the nightmare scenario with standard message queues: Your Order Service creates a record in the database, commits the transaction, and then tries to publish the “Order Created” message to the queue so the Payment and Shipping services can take over. But right after the DB commit, the Kubernetes pod crashes or the network blips. The message is never sent. You now have an “orphaned” order in the database that will never be fulfilled. The customer has paid, but the shipping warehouse knows nothing about it.
Alternatively, you send the message first, then try to write to the DB. The DB write fails. Now shipping is trying to send out a package for an order that doesn’t exist in your database.
RocketMQ solves this elegantly with its “Half Message” transactional protocol. It guarantees distributed transaction integrity between your relational database and the message broker.
5.1. The Decoupling Flow (The Half-Message Protocol)
This is the exact sequence you need to implement in your code:
5.1.1. Prepare
The Order Service sends a “Half Message” to RocketMQ. This message is stored by the broker but is invisible to downstream consumers.
5.1.2. Execute Local Transaction
The Order Service executes the local PolarDB transaction (inserting the order row into the core database).
5.1.3. Commit or Rollback
If the local DB commit succeeds, the Order Service sends a “Commit” signal to RocketMQ. The message becomes visible to consumers. If the local DB commit fails, the Order Service sends a “Rollback” signal. RocketMQ discards the Half Message completely.
5.1.4. The Failsafe (Callback)
What if the Order Service crashes before it can send the commit signal? RocketMQ has a built-in fallback. After a timeout, it will actively ping the Order Service and ask, “Hey, what happened to this transaction ID?” The Order Service checks the database, sees if the order exists, and replies with Commit or Rollback. Furthermore, if messages continuously fail to process downstream, RocketMQ seamlessly moves them to a Dead Letter Queue (DLQ) so your engineering team can manually inspect them without blocking the main event pipeline.
This guarantees absolute eventual consistency without requiring you to build complex saga patterns or two-phase commit coordinators from scratch.
5.2. Need help implementing this?
Refactoring a legacy monolith into a RocketMQ and PolarDB-backed microservices architecture is risky if you haven’t done it before. One misconfigured transaction protocol, a poorly sized connection pool, or a missing dead-letter queue can lead to dropped orders and database lockups. Our DevOps team specializes in migrating and optimizing high-throughput platforms with zero downtime.
👉 Explore Our Cloud Migration & DevOps Services
6. Real-World Cost Optimization
You don’t need to burn your entire cloud budget to survive peak traffic. I hate seeing companies over-provision their hardware by 500% year-round just to survive one day in November. That is lazy engineering.
Procure your baseline compute, PolarDB clusters, and ALB instances on a 1-year Subscription (Reserved Instances). You get heavy discounts for the traffic you know you’ll have every Tuesday at 3 PM.
For the flash sale spike capacity, rely heavily on Spot Instances (Preemptible Instances) in your ACK node pools. Stateless microservices handle Spot interruptions beautifully. Spot instances can be up to 90% cheaper than on-demand instances.
But you must handle the termination notices gracefully. When the cloud provider reclaims a spot instance, they give you a brief warning. Your Kubernetes setup should run a daemonset that listens to the metadata server for this notice, cordons the node, and gracefully evicts the pods before the server is killed. We typically aim for a 70/30 split between Spot and On-Demand instances for our worker pools.
Bash
# CLI command to create an auto-scaling node pool with Spot instances
# We use multiple instance types to increase the chances of fulfilling the spot request.
aliyun cs POST /clusters/<cluster_id>/nodepools \
--body '{
"nodepool_info": {"name": "spot-pool-api"},
"scaling_group": {
"instance_types": ["ecs.c7.xlarge", "ecs.c8i.xlarge", "ecs.g7.xlarge"],
"multi_az_policy": "COST_OPTIMIZED",
"spot_strategy": "SpotWithPriceLimit"
}
}'
7. When NOT to Use This Architecture
I will actively advise against this architecture under certain conditions. Cloud native isn’t a religion; it’s a toolset. Do not use this blueprint if:
- You have predictable, low-volume traffic. A well-written monolithic application running on two compute instances behind a load balancer with a standard managed database is significantly cheaper. It won’t require a dedicated DevOps team to maintain. Don’t build a Ferrari to go to the grocery store.
- Your team lacks Kubernetes expertise. The operational overhead of managing ACK, Istio/Ingress, Prometheus, and distributed tracing is non-trivial. If your team doesn’t know how to debug a CrashLoopBackOff or read an OpenTelemetry trace, stick to simpler PaaS offerings.
- Strict Multi-Cloud mandates. Leveraging PolarDB’s shared storage and RocketMQ’s transactional features creates a degree of vendor lock-in. You have to decide if peak performance outweighs platform portability. In high-stakes e-commerce, it usually does.
8. Production Best Practices & Hard Lessons Learned
8.1. Database Connection Pooling: The Immediate OOM
This is the most common self-inflicted wound I see. I’ve seen HPA scale up 500 pods in two minutes during a massive ad campaign. Each of those pods runs a backend application configured with a standard connection pool of 100 database connections. Instantly, the database is hit with 50,000 connection requests.
The database proxy runs out of memory, the database crashes, and your entire site goes down. Never let microservices connect directly to PolarDB at scale. You must use PolarProxy to multiplex connections, or implement a tool like ProxySQL. Let the proxy handle the thousands of idle frontend connections, while maintaining a strict, limited pool of backend connections to the actual database engine.
8.2. Zero-Trust Network Policies
If your API gateway is compromised via a zero-day exploit, your database shouldn’t be exposed on a flat network. This is a basic security posture that many startups ignore. Enforce strict Kubernetes NetworkPolicies. The only thing that should be able to talk to the Order Database is the Order Service.
YAML
# Kubernetes NetworkPolicy to restrict DB access
# This blocks everything except the designated app.
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-order-service-to-db
spec:
podSelector:
matchLabels:
app: polardb-proxy
policyTypes:
- Ingress
ingress:
- from:
- podSelector:
matchLabels:
app: order-service
ports:
- protocol: TCP
port: 3306
8.3. Observability is Not Optional
If you deploy this architecture without distributed tracing, you are flying blind. When a checkout takes 4 seconds instead of 400ms, you need to know exactly which microservice is dragging its feet. Is it the inventory check? The payment gateway call? The database insert?
Implement Application Real-Time Monitoring Service (ARMS) or set up OpenTelemetry with Prometheus and Jaeger. If you can’t trace a request ID from the ALB, through the pods, into RocketMQ, and down to the database, you are not ready for production. Logging is not enough. You need distributed traces.
8.4. Chaos Engineering
Do not wait for a live sale to find out if your auto-scaling works. Use ChaosBlade to intentionally terminate pods, spike CPU utilization, and drop network packets in your staging environment. If you aren’t breaking your own systems on purpose, your customers will do it for you.
9. Common Failures (And Exactly How We Fix Them)
9.1. Ignoring Cache Stampedes (The Dog-Pile Effect)
This is the silent killer of cloud databases. Imagine you have a highly popular product—say, a limited edition sneaker—and its cache TTL (Time To Live) is set to 5 minutes.
At exactly 12:05 PM, that cache key expires. At that exact second, 10,000 users are hitting refresh on the product page. Because the cache is empty, all 10,000 application threads bypass Redis and query PolarDB simultaneously for the exact same data. The database spikes to 100% CPU, stalls, and takes the site down.
The Fix: Implement “mutex locks” (using SETNX in Redis). When the cache expires, the first thread that tries to read it attempts to acquire a lock. It gets the lock, goes to the database, and rebuilds the cache. The other 9,999 threads fail to get the lock. Instead of hitting the database, they are forced to sleep for 50ms and check the cache again. One database query instead of 10,000. It’s elegant, simple, and absolutely essential. Additionally, always add a random “jitter” to your cache TTLs so thousands of keys don’t expire on the exact same millisecond.
9.2. Lack of Graceful Degradation
When systems hit 100% capacity, letting them cascade and crash is an architectural failure. You should decide what breaks, not the server.
The Fix: Implement Application High Availability Service (AHAS) using Sentinel rules. You define the thresholds. If the order queue CPU hits 90%, or if the latency of the recommendation engine exceeds 500ms, AHAS should automatically disable the non-essential features.
Hide the “Customers who bought this also bought” section. Return cached placeholder data for user reviews. Strip the page down to its bare essentials to keep the checkout API alive. Your users won’t care if the reviews take a minute to load, but they will absolutely care if their credit card is declined due to a gateway timeout. Protect the checkout flow at all costs.
10. Conclusion: Stop Guessing, Start Scaling
Running a high-traffic e-commerce infrastructure is an exercise in managed degradation. You build a system that bends without breaking. By decoupling compute from storage, leveraging asynchronous RocketMQ queues, protecting your state with Redis Lua scripts, and preemptively scaling your ACK clusters, you build a platform that actually thrives in the chaos of flash sales.
This architecture isn’t theoretical; it’s forged in the fires of massive global retail events and proven in enterprise environments every single day. You can survive the spike, but you have to architect for it today, not the week before a major marketing campaign.
But you don’t have to build it through trial and error.
Are specific components of your current infrastructure acting as bottlenecks during your traffic spikes? Is your database locking up, or are your pods failing to scale in time? Don’t let bad architecture dictate your revenue ceiling. Let’s fix it before your next peak season.
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Read more: 👉 Implementing Zero Trust Architecture on Alibaba Cloud
Read more: 👉 Building a SaaS Platform on Alibaba Cloud: Architecture & Cost Guide