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There is a moment that every eWallet engineering team dreads and every product team secretly anticipates. It arrives every year with the predictability of a calendar event and the destructive potential of a natural disaster. Diwali. Eid. Christmas. New Year's Eve. The moment when a year's worth of user acquisition, marketing spend, and product development is stress-tested not by a controlled load test in a staging environment but by millions of real users, real transactions, and real money — all at the same time.

The numbers are not subtle. During India's festival season, digital payment volumes spike by factors that would be extraordinary in any other industry. UPI transaction counts on Dhanteras and Diwali regularly shatter records. eWallet platforms see gift transfers, merchant payments, bill settlements, and top-up transactions converge into a single, thunderous peak that can last hours. The platforms that handle it gracefully earn trust that compounds for months. The platforms that do not handle it make headlines for entirely the wrong reasons — failed transactions, frozen accounts, error screens where payment confirmations should be.
The difference between these two outcomes is not luck. It is architecture.

Why Festival Traffic Is Different From Every Other Traffic

Before getting into solutions, it is worth being precise about what makes festival season traffic uniquely difficult for eWallet platforms — because it is not simply a matter of volume.

Regular traffic growth is gradual and distributed. Users trickle in across time zones, devices, and use cases. Even a strong Monday morning spike in transaction volume has a shape — it builds over minutes and hours, giving autoscaling systems time to respond. Festival traffic does not build. It arrives. At midnight on Diwali, the entire user base effectively decides to transact simultaneously, and the spike from baseline to peak can happen in seconds rather than minutes.

The transaction mix changes too. Festival payments skew heavily toward peer-to-peer transfers and merchant payments — the highest-value, most latency-sensitive, and most fraud-prone transaction types in any eWallet system. These are not passive reads from a product catalogue. They are writes that touch the ledger, trigger fraud checks, initiate bank integrations, and require atomic consistency. Every single one of them needs to complete correctly or roll back cleanly, under load conditions that are simultaneously the highest of the year.

Add to this the human behaviour layer: users who encounter a slow or failed transaction do not wait patiently. They retry. Often immediately, repeatedly, and with increasing frustration. Retry storms — cascading waves of duplicate requests generated by users hammering a submit button on a frozen screen — can take a system that is merely struggling and push it into complete failure. The platform that cannot handle the initial spike faces a second, larger spike of retry traffic moments later.

The Ledger Is the Bottleneck — Design It Accordingly

Every eWallet transaction ultimately touches the ledger. Money leaves one account and arrives in another, and that movement must be recorded accurately, atomically, and in a way that is consistent with every other transaction happening simultaneously. The ledger is where festival season traffic most often breaks eWallet systems, and it is where architectural investment delivers the most impact.

The naive ledger design — a single relational database table where every transaction appends a row and every balance query aggregates those rows — works acceptably at low volume and fails catastrophically at scale. The aggregation queries that produce account balances become progressively more expensive as transaction history grows, and under peak load, the combination of high write volume and expensive read queries creates contention that degrades the entire system.

The solution is a two-layer ledger architecture. The first layer is an append-only event log — an immutable record of every transaction event, implemented in a write-optimised store like Apache Kafka or a purpose-built financial event log. This layer is designed purely for high-throughput writes and never queried directly for balance information.

The second layer is a materialised balance store — a separate database that maintains current account balances, updated asynchronously from the event log. Balance queries hit this layer exclusively, and because it stores pre-computed balances rather than deriving them from transaction history, read performance is constant regardless of transaction volume.

This separation means the write path and the read path are never competing for the same database resources, which is the single most impactful architectural change available for eWallet app development that need to survive festival season.

Autoscaling Is Not Enough — Pre-Scale

Cloud autoscaling is a powerful tool, but it has a fundamental limitation for festival season traffic: it is reactive. Autoscaling triggers when load exceeds a threshold, provisions new instances, waits for them to initialise and warm up, and only then begins absorbing traffic. This process takes time — typically two to five minutes for a complete scale-out cycle — and festival season spikes do not wait.

The engineering practice that separates prepared teams from unprepared ones is pre-scaling: deliberately provisioning infrastructure above anticipated peak load before the spike arrives, based on historical data and expected growth. If last Diwali saw three times normal peak transaction volume and this year's user base is thirty percent larger, you pre-scale to handle at least five times normal volume and maintain that capacity for the duration of the festival window.