Showing Post From Distributed Systems
Wide-column databases in 2026: revisit Apache Cassandra, ScyllaDB and YugabyteDB YCQL before you design another partition key
Most teams meet a wide-column database through one table that grew too fast for everything else: an event log per user, a delivery history, a feed or a “last 90 days of activity” screen.
Read moreDistributed coordination in 2026: revisit etcd, ZooKeeper and Consul before you write another leader election
Most teams do not decide to build a distributed coordination system. It creeps in. A scheduled job runs on one VM.
Read moreDistributed tracing in 2026: revisit Jaeger, Grafana Tempo and SigNoz before you store another span
Picture a bus, train and hotel booking platform in Kochi (an invented example, not a real client). Customers book from an Android app, an iOS app and a web app.
Read moreDistributed SQL in 2026: revisit CockroachDB, YugabyteDB and TiDB before you shard or go multi-region
Picture a mutual fund and stockbroking platform in Mumbai (an invented example, not a real client). Its order book and client ledger run on PostgreSQL 16 with Patroni across three VMs in one cloud region, and a rewards service runs on MySQL 8.
Read moreDurable execution in 2026: revisit Temporal, Restate and DBOS before you hand-roll retries, sagas and cron jobs
Picture an online pharmacy and diagnostics booking company in Ahmedabad (an invented example, not a real client). An order goes through five steps: hold the medicine stock, take the UPI payment, book a courier slot, send the customer an SMS, and, if the courier cannot pick up within four hours, release the stock and refund.
Read moreRabbitMQ, NATS and Kafka queues: revisit your message queue before you add another broker
A health insurance company in Hyderabad processes claims through RabbitMQ: three nodes on 3.13 with classic mirrored queues, about 40 lakh messages a day, from claim documents to OCR, OCR results to fraud checks and approvals to payments.
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