Home region and write forwarding

Each record has a home region that owns its writes, often for legal or consistency reasons. Other regions serve reads from local copies and forward writes to the home region asynchronously, trading a few seconds of freshness for local latency.

Where it shows up in real systems

  • In-memory cache

    The hit ratio depends on how much of the working set fits in memory, the TTL and the eviction policy. If the cache goes down or is flushed, every read falls on the database at once: plan for that stampede. Write-behind is fast but loses writes if a node dies without a replica.

    In production: Redis (Amazon ElastiCache, Azure Cache), Valkey, Memcached, Dragonfly

  • Message queue

    A queue absorbs spikes: the API answers as soon as the message is stored, and workers drain the backlog at their own pace. Standard queues deliver at least once and may reorder; FIFO queues keep order but cap throughput. Messages that fail repeatedly go to a dead-letter queue.

    In production: Amazon SQS, RabbitMQ, Azure Service Bus, Google Cloud Tasks

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