𝗟𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲 𝗡𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝗻 𝗝𝗮𝘃𝗮 — 𝘁𝗵𝗲 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗜’𝗱 𝘂𝘀𝗲 𝘁𝗼𝗱𝗮𝘆

Billions of events. Millions of users. Dozens of channels (push, email, SMS, in-app, web).

Here’s a production-ready architecture that scales without catching fire 👇

𝗪𝗵𝗮𝘁 “𝗹𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲” 𝗿𝗲𝗮𝗹𝗹𝘆 𝗺𝗲𝗮𝗻𝘀

• Fan-out to 1M–50M recipients per campaign

• <2s end-to-end latency for real-time AND guaranteed delivery for batched jobs

• Channel mix: APNS/FCM, Email (SES/SendGrid), SMS (Twilio), WebPush, in-app/WebSocket

𝗖𝗼𝗿𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (𝗝𝗮𝘃𝗮 / 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁)

1. Ingestion API (Spring Boot, gRPC/REST)

Receives events (signup, purchase, alert), validates, writes to Kafka.

1. Event Bus → Kafka

Topics per domain (user.events, order.events, alerts.*).

Partitions sized for throughput; compaction for idempotent keys.

1. Orchestrator (Java + Kafka Streams/Flink)

Enrich: user profile, preferences, quiet hours, locale

Decide channel(s) via rules/ML

Produce a Notification Job (jobId, audience, templates, ttl)

1. Fan-out Workers (Spring Boot + Reactor)

Pull from jobs.* topics

Chunk audiences (e.g., 10k/user batch)

Rate-limit per vendor + per tenant

Write to Provider Queues (Redis Streams/SQS)

1. Channel Adaptors (stateless Java services)

Push: FCM/APNS SDKs (batch send, collapse keys)

Email: SES/SendGrid with templates + DKIM/DMARC

SMS: Twilio + per-country sender pools

𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿

• Idempotency everywhere (messageId, vendor dedupeKey)

• Backpressure: control with Kafka consumer lag + token buckets per provider

• Retry strategy: exponential backoff, max attempts, DLQ (jobs.dlq.*)

• Cold start? Pre-warm connection pools to APNS/FCM, keep-alive TLS

𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗺𝗮𝘁𝗵 (𝘀𝗮𝗻𝗶𝘁𝘆 𝗰𝗵𝗲𝗰𝗸)

If a provider caps at 5k req/s and your median payload fans out 200 users per request,

you can move ~1M users/min per adaptor. Horizontal scale by shards.

𝗧𝗲𝗰𝗵 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 (𝗯𝗮𝘁𝘁𝗹𝗲-𝘁𝗲𝘀𝘁𝗲𝗱)

Spring Boot + WebFlux/Reactor (high-concurrency IO)
Temporal (optional) for complex campaign workflows

𝗣𝗶𝘁𝗳𝗮𝗹𝗹𝘀 𝘆𝗼𝘂’𝗹𝗹 𝗵𝗶𝘁 (𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗱𝗼𝗱𝗴𝗲 𝘁𝗵𝗲𝗺)

• APNS/FCM token decay → nightly cleanup job with feedback services

• Compliance (GDPR/CCPA) → data minimization + TTL on personal data

• Quiet hours / time-zone drift → schedule by user local time

𝗠𝗩𝗣 𝗿𝗼𝗹𝗹𝗼𝘂𝘁 𝗽𝗹𝗮𝗻 (𝟰 𝘀𝗽𝗿𝗶𝗻𝘁𝘀)

Email + in-app, single tenant, end-to-end instrumentation

Push (FCM/APNS) + delivery tracking + retries

If you’re planning a notification platform for millions of users, Java gives you the ergonomics of Spring and the horsepower of the JVM to keep latency low and reliability high.