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2025Distributed Systems

High-ScaleInteroperableDiscoveryEngine

ONDC / UHI — India's Digital Public Infrastructure

Distributed SystemsGolangAI OrchestrationObservability

0ms

P99 Latency

0+

Providers

0

QPS Internal

0.9%

Cache Hit Rate

Overview

IengineeredadiscoveryengineforIndia'sDigitalPublicInfrastructuretosolvethe"TailLatency"probleminherentindecentralizednetworks.

Unlike Amazon, which hits a single internal database, ONDC requires fanning out queries to 100+ external providers simultaneously. My implementation ensures the user experience never suffers from a single slow provider — using speculative Hedged Requests and Semantic Caching to stabilise P99 at 475ms while handling 160+ QPS.

Optimization Journey

FourStepsto475ms

Identifying the Bottleneck01 — BaselineClick to expand

Identifying the Bottleneck

P99 = 747ms. Provider mfine clocking 625ms was dragging the entire network's tail latency.

Semantic Caching Layer02 — AI OptimizationClick to expand

Semantic Caching Layer

99.9% cache hit rate in Redis. Deduplicated LLM calls, pushing throughput to 247 req/s.

Circuit Breaker Proof03 — ResilienceClick to expand

Circuit Breaker Proof

Simulated 30+ QPS until the breaker tripped at state=1, proving self-healing under stress.

Breaking the 500ms Wall04 — BreakthroughClick to expand

Breaking the 500ms Wall

Hedged Requests fire speculative backup calls to beat slow nodes. P99 stabilised at 475ms.

Proof of Work

PerformanceMetrics

#MetricResultDecisionΔ
01P99 Latency0msSpeculative Hedged Requests↓ 36%
02AI Inference Cost0%Semantic Redis Caching↓ 60%
03Internal Throughput0 QPSGolang Goroutines↑ 54%
04Uptime0%Heartbeats & Circuit Breaker✓

Core Stack

GolangRedisgRPCGrafanak6DockerONDC ProtocolLLM Scoring