Choose how fresh each piece of shared data must be

How to do it

Strong consistency means every read sees the latest write. It costs speed and stops writes when a copy is unreachable. Eventual consistency stays fast and available, but a read can return old data for a while. Most data sits between them. A stock count at checkout needs strong reads. A shopper must see their own new review, which is read-your-writes. A product's review count may lag by minutes. Replicas follow Read from replicas only what may lag, and caches follow Decide how stale a cache may be.

Examples

TypeScript example

// Read-your-writes: the shopper who just posted reads from the primary
async function reviewsFor(productId: ProductId, viewer: Viewer) {
  const source = viewer.wroteRecently(productId) ? primary : replica;
  return source.query('SELECT * FROM reviews WHERE product_id = $1', [productId]);
}

Python example

# Read-your-writes: the shopper who just posted reads from the primary
def reviews_for(product_id: ProductId, viewer: Viewer) -> list[Review]:
    source = primary if viewer.wrote_recently(product_id) else replica
    return source.query("SELECT * FROM reviews WHERE product_id = %s", (product_id,))

How to check

A data inventory lists each shared table, cache and event stream with its consistency model and the longest delay. A test writes through each read-your-writes path and reads the value back in the same request flow.

Sources

• Microsoft: Azure Cosmos DB consistency levels

Related rules

• DAT-46 Read from replicas only what may lag in Data and databases
• SCL-35 Decide how stale a cache may be in Scaling