Most affordable · Message queues
- 01SQS · StandardAWS$1.20 USD / M messages
- 02Cloudflare Queues · PaidCloudflare$1.20 USD / M messages
- 03SQS · FIFOAWS$1.50 USD / M messages
How to handle traffic spikes: queues, databases and hard limits
Absorb traffic bursts without dropping requests or the database
The data, today
Most affordable · Databases
- 01Supabase · ProSupabase$25.00 USD / month
The context
A traffic spike rarely breaks what you expect first. Web servers scale out with relative ease; what gives way is usually the database running out of connections, a query without an index that used to take milliseconds, a third-party API with a fixed quota, or a pod that Kubernetes kills for exceeding its memory limit. Preparing for a spike means finding that weak link before your users find it for you.
The general strategy has three parts: absorb, protect and observe. Absorb with queues that decouple incoming requests from heavy work. Protect finite resources with connection limits, retries with exponential backoff, and idempotency so a retry never duplicates an effect. And observe with SLOs that tell you when the system is degraded, not only when it is already down and support is fielding complaints.
What to decide
- 1
Which work should move to a queue?
Anything the user does not need finished in the response: emails, image processing, AI model calls, third-party integrations. The request records the intent and returns quickly; consumers process at their own pace. Track queue depth and the age of the oldest message during the spike.
- 2
How do I make retries safe?
Attach an idempotency key to every operation with side effects, such as a charge or an order, and store it with the result. If the same key arrives again, return the stored result. Retry with exponential backoff and jitter, cap the attempts and route failures to a dead-letter queue for review.
- 3
How do I keep the database from becoming the bottleneck?
Use a connection pool or a proxy such as PgBouncer, and size total connections across every application replica. Review the slowest queries under load and add the missing indexes. Move heavy reads to replicas and cache data that rarely changes.
- 4
Which Kubernetes limits should I set?
Set realistic requests based on measured usage and memory limits with headroom, because exceeding memory gets the pod killed. Configure horizontal autoscaling on metrics that lead load and confirm the cluster has room to grow. Measure how long a new pod takes to become ready for traffic.
- 5
Which SLOs should I watch during a spike?
p95 and p99 latency on critical paths, error rate and the age of queued messages. Set thresholds before the event and decide which features get switched off when they are crossed. Give the load balancer real health checks that verify dependencies, not just that the process answers.
Common mistakes
- Scaling out web servers without checking how many connections the database can take.
- Retrying charges or orders without an idempotency key to prevent duplicates.
- Running the first load test on the day of the event.
Tools and comparators
Guides to go deeper
- Distributed systems · 5 minQueues, Retries, and Idempotency: Stop Processing Messages Twice
- Distributed systems · 9 minHow to Choose a Message Queue: SQS vs Cloudflare vs QStash
- Containers & Kubernetes · 10 minKubernetes Limits in Production: A Guide with Verifiable Thresholds
- Architecture · 8 minWhat is a load balancer? What it solves and what it doesn't
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