𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽’𝘀 𝗝𝗮𝘃𝗮 𝘀𝘁𝗮𝗰𝗸 𝗳𝗼𝗿 𝗹𝗼𝘄-𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 (𝘄𝗵𝗮𝘁 𝗶𝘁 𝘄𝗼𝘂𝗹𝗱 𝘁𝗮𝗸𝗲)

This content originally appeared on DEV Community and was authored by Narednra Reddy Yadama

Everyone says: “WhatsApp runs on Erlang.”

True. For the core.

But let’s flip it.
If you had to build a WhatsApp-class messaging system on Java today,how would you do it?

Here’s the playbook I’d reach for

The path to sub-100ms chat

Transport & fan-out

• Netty for long-lived TCP/WebSocket/MQTT connections. Zero‐copy, epoll/kqueue.

• Protobuf frames. Tiny. Predictable.

• gRPC for service-to-service RPC with deadlines + retries.

• Aeron (optional) for ultra-low-latency one-way streams.

Broker & persistence

• Kafka for durable event pipelines (chat events, receipts, presence).

• Kafka Streams / Flink for real-time fan-out & stateful ops.

• RocksDB as local state store for fast lookups.

• Cassandra (or Scylla) for message history & wide-row access patterns.

• Redis for hot presence, routing tables, rate limits.

Gateways & services

• Micronaut / Quarkus for tiny memory footprints and fast cold starts.

• Vert.x for reactive, back-pressure aware services.

• CQRS split: write path optimized for append, read path for conversation timelines.

• Idempotency keys everywhere (message resends happen).

Delivery semantics

• At-least-once on the wire, exactly-once UX via de-dup DB keys.

• Ack/receipt model: sent → delivered → read.

• Store-and-forward for offline clients; resend on reconnect.

• Sticky routing to keep a user on the same edge node when possible.

Latency budget (single region)

• Serialization + network hop: ~2–5ms

• Gateway enqueue: ~1–3ms

• Broker to fan-out: ~5–15ms

• Fan-out to recipient gateway: ~5–15ms

• Device push/write: ~10–30ms

Target: <60–80ms P50, <150ms P95 end-to-end.

• JVM tuning (the unsexy part that wins)

• ZGC/G1 with small regions; avoid massive heaps.

• Thread pinning for IO vs compute; prefer virtual threads for RPC fan-out.

• Off-heap buffers (Netty/Aeron) + pooled allocators.

• NUMA awareness on big boxes; pin brokers separately.

• SLOs + load-shedding when queues grow (don’t let the tail kill you).

Reliability

• Quorum/RAFT for metadata (topic maps, routing).

Privacy & safety

• E2E encryption at the edge; servers carry opaque blobs + metadata envelope.

• Abuse detection runs on metadata & user-reported content only.

• Key transparency to prevent MITM on device keys.

Why this matters

Java isn’t “too slow.” With the right stack, it’s a latency weapon: mature tooling, world-class profilers, rock-solid GC, and libraries built for billions of sockets. The craft is in back-pressure, memory locality, and aggressive simplification.

Want me to drop a tiny Netty + WebSocket fan-out skeleton and a Kafka + Cassandra timeline demo? I can share a repo next.

This content originally appeared on DEV Community and was authored by Narednra Reddy Yadama