Event Sourcing
Event sourcing persists every change as an immutable event in an append-only log rather than overwriting current state. Current state is derived by replaying events. The pattern shines for audit, debugging, and integration — every state change is reproducible — but it adds projection and consistency complexity. TantraDev uses it where audit-completeness or replay matter more than write simplicity.
Concepts that travel with this one.
Architecture rarely lives in isolation — these are the terms that come up in the same conversation.
CQRS
Command Query Responsibility Segregation (CQRS) splits the write model from the read model. Commands mutate state through a narrow validated path; queries read from projections optimised for the access pattern. The benefit is independently scalable reads and writes, plus the freedom to denormalise reads aggressively. The cost is two models and the synchronisation between them.
Apache Kafka
Kafka is a distributed, partitioned, replayable log used as the backbone for event-driven systems. Producers append to topics, consumers read at their own pace, and the log retains messages for a configured window — so any consumer that fails can replay history rather than lose it. TantraDev uses Kafka (or Kafka-compatible Redpanda) where ordering, durability, and replay all matter at once.
Idempotency
An operation is idempotent when invoking it twice with the same input produces the same effect as invoking it once. In distributed systems an idempotency key — a client-supplied unique identifier per request — lets a server safely deduplicate retries without producing two payments, two emails, or two database rows. Every TantraDev write API that crosses a network boundary takes an idempotency key.
Building a system where Event Sourcing is the load-bearing decision?
30 minutes on the phone, one page in your inbox — what to build, what to skip, what it will cost. You keep the audit even if we are not the right fit.