🔥 𝗔𝗱-𝗧𝗲𝗰𝗵 𝗔𝘂𝗰𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲 — 𝗕𝘂𝗶𝗹𝘁 𝗼𝗻 𝗝𝗮𝘃𝗮 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀

Every time you open an app or a website, an invisible war happens.

Hundreds of advertisers compete — and all of it ends in under 120 milliseconds.

That’s real-time bidding (RTB).

And Java? Still one of the best tools to power it.

◾ The Challenge

◾ Handle 1M+ bid requests per second (QPS)

◾ Complete auction logic, scoring, pricing, and response

◾ Maintain latency <100ms end-to-end

◾ Avoid revenue leaks from concurrency, GC, or network lag

◾ System Blueprint

1️⃣ Ingress Layer (Bid Gateway)

Built using Netty or Vert.x — lightweight, async, blazing fast.

Accepts OpenRTB 2.x / 3.x JSON bid requests from SSPs or exchanges.

Parses + validates payloads, then publishes to internal Kafka topic (bids.incoming).

2️⃣ Enrichment & Targeting Service

Microservice in Spring Boot + Reactor or Micronaut.

Adds geo, device, user segments, campaign eligibility.

Fetches feature vectors from Redis or Aerospike in <10ms.

3️⃣ Real-Time Auction Service (The Heart)

Written in Java with low-GC tuning (ZGC / Shenandoah).

Implements second-price (Vickrey) or first-price auctions.

Filters 1000+ bids → sorts by eCPM → applies business constraints (budget, pacing, frequency caps).

Uses in-memory priority queues or skip-lists for O(log n) selections.

Publishes the winning bid to Kafka (bids.won) and returns the ad markup immediately.

4️⃣ Budget & Pacing Engine

Tracks spend per campaign / advertiser in Redis + Kafka Streams.

Prevents overspending by reserving budgets atomically.

Supports real-time pacing (budget/time) with leaky bucket logic.

5️⃣ Analytics & Feedback Loop

Impression, click, conversion events flow into Kafka → Flink for aggregation.

ML models update bid multipliers in near-real time (CTR, CVR predictions).

Model outputs stored in feature store (Feast / Redis / Cassandra).

◾ Key Tech Stack

Java 21 (virtual threads for concurrency)

Spring Boot / Micronaut / Quarkus for microservices

Kafka + Flink for streaming and feedback

Redis / Aerospike for ultra-low latency key lookups

gRPC for internal service calls

ClickHouse / Pinot for analytics

Prometheus + Grafana + OpenTelemetry for observability

◾ Performance Tricks

◾ Keep auction logic pure in-memory — no DB calls on critical path

◾ Use object pooling to avoid GC pauses

◾ Co-locate data (geo + campaign data in RAM)

◾ Batch outbound responses using Netty channels

◾ Fraud detection hooks every 10ms (bot, domain spoofing, invalid traffic)

◾ Fail open (show fallback ad) — never blank screen

💡 Takeaway:

The fastest ad auction wins the dollar.

Java still dominates here , not because it’s fancy, but because it’s predictable, tunable, and fast.