High-Concurrency Live Data Caching Engine
Millions of concurrent users requesting live cricket scores made direct, per-request calls to a paid data feed cost-prohibitive. We re-architected delivery around a Redis caching layer, cutting API cost by over 95% with no downtime at peak load.
The Challenge
During live high-traffic cricket matches, millions of concurrent users requested real-time score updates simultaneously. Querying paid third-party sports data feeds directly on every single user request was cost-prohibitive, resulting in exponential API billing spikes and severe risks of hitting provider rate limits or system downtime during peak moments.
Our Solution
We re-architected data delivery from direct third-party API querying to an intermediary in-memory caching and buffering layer, configuring an automated background polling service that queries the paid data vendor at calibrated, short intervals to capture live ball-by-ball updates without multiplying individual API calls. Fresh match data is ingested into a high-speed in-memory cache (Redis) capable of serving millions of simultaneous client reads without touching the vendor's paid endpoint, with client applications engineered to pull exclusively from this centralized cache.
Key Results
- Third-party API consumption and data licensing costs cut by over 95% on live match days
- Massive user concurrency and traffic surges handled without downtime or service degradation
- Near-real-time score updates maintained while fully shielding backend infrastructure
- No compounding vendor fees despite millions of simultaneous users
Client Overview
A live IPL cricket score-tracking platform needed to serve millions of concurrent users during peak matches without paying for a live API call on every single request.
Impact
Third-party API consumption and licensing costs dropped by over 95% on live match days, with the system handling massive concurrency and traffic surges smoothly and no compounding vendor fees.
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