DEV Community

Narednra Reddy Yadama
Narednra Reddy Yadama

Posted on

๐—ช๐—ต๐—ฎ๐˜๐˜€๐—”๐—ฝ๐—ฝโ€™๐˜€ ๐—๐—ฎ๐˜ƒ๐—ฎ ๐˜€๐˜๐—ฎ๐—ฐ๐—ธ ๐—ณ๐—ผ๐—ฟ ๐—น๐—ผ๐˜„-๐—น๐—ฎ๐˜๐—ฒ๐—ป๐—ฐ๐˜† ๐—บ๐—ฒ๐˜€๐˜€๐—ฎ๐—ด๐—ถ๐—ป๐—ด ๐—ฎ๐˜ ๐˜€๐—ฐ๐—ฎ๐—น๐—ฒ (๐˜„๐—ต๐—ฎ๐˜ ๐—ถ๐˜ ๐˜„๐—ผ๐˜‚๐—น๐—ฑ ๐˜๐—ฎ๐—ธ๐—ฒ)

Everyone says: โ€œWhatsApp runs on Erlang.โ€

True. For the core.

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

Hereโ€™s the playbook Iโ€™d reach for ๐Ÿ‘‡

The path to sub-100ms chat

Transport & fan-out

โ€ข Netty for long-lived TCP/WebSocket/MQTT connections. Zeroโ€copy, epoll/kqueue.

โ€ข Protobuf frames. Tiny. Predictable.

โ€ข gRPC for service-to-service RPC with deadlines + retries.

โ€ข Aeron (optional) for ultra-low-latency one-way streams.

Broker & persistence

โ€ข Kafka for durable event pipelines (chat events, receipts, presence).

โ€ข Kafka Streams / Flink for real-time fan-out & stateful ops.

โ€ข RocksDB as local state store for fast lookups.

โ€ข Cassandra (or Scylla) for message history & wide-row access patterns.

โ€ข Redis for hot presence, routing tables, rate limits.

Gateways & services

โ€ข Micronaut / Quarkus for tiny memory footprints and fast cold starts.

โ€ข Vert.x for reactive, back-pressure aware services.

โ€ข CQRS split: write path optimized for append, read path for conversation timelines.

โ€ข Idempotency keys everywhere (message resends happen).

Delivery semantics

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

โ€ข Ack/receipt model: sent โ†’ delivered โ†’ read.

โ€ข Store-and-forward for offline clients; resend on reconnect.

โ€ข Sticky routing to keep a user on the same edge node when possible.

Latency budget (single region)

โ€ข Serialization + network hop: ~2โ€“5ms

โ€ข Gateway enqueue: ~1โ€“3ms

โ€ข Broker to fan-out: ~5โ€“15ms

โ€ข Fan-out to recipient gateway: ~5โ€“15ms

โ€ข Device push/write: ~10โ€“30ms

๐Ÿ‘‰ Target: <60โ€“80ms P50, <150ms P95 end-to-end.

โ€ข JVM tuning (the unsexy part that wins)

โ€ข ZGC/G1 with small regions; avoid massive heaps.

โ€ข Thread pinning for IO vs compute; prefer virtual threads for RPC fan-out.

โ€ข Off-heap buffers (Netty/Aeron) + pooled allocators.

โ€ข NUMA awareness on big boxes; pin brokers separately.

โ€ข SLOs + load-shedding when queues grow (donโ€™t let the tail kill you).

Reliability

โ€ข Quorum/RAFT for metadata (topic maps, routing).

Privacy & safety

โ€ข E2E encryption at the edge; servers carry opaque blobs + metadata envelope.

โ€ข Abuse detection runs on metadata & user-reported content only.

โ€ข Key transparency to prevent MITM on device keys.

Why this matters

Java isnโ€™t โ€œtoo slow.โ€ With the right stack, itโ€™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.

Top comments (0)