# database-design Published articles for database-design. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Soft deletes affect database constraints, indexes, and application queries DevFeed: [Soft deletes affect database constraints, indexes, and application queries]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-20T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [data-processing](), [Database](), [MySQL]() Tags: [data-modelling](), [database-design](), [foreign-keys](), [indexes](), [indexing](), [mysql](), [orm](), [postgresql](), [predicate](), [schema](), [schema-design](), [soft-delete]() ### AI overview The article explains how soft deletion affects database design beyond adding a deleted_at column. It discusses duplicate-key failures, foreign-key behavior, queries that may return deleted rows, and index inefficiency, noting that PostgreSQL partial indexes help while MySQL requires a workaround. ### Source excerpt A deleted_at column turns every future query into a conditional one, and the cost is not the extra predicate. Unique constraints stop working because the deleted row still occupies the key, foreign keys start pointing at rows the application considers gone, and any query written by someone who does not know about the column silently returns deleted data. Soft delete is a data lifecycle decision, and treating it as a boolean column is what makes it expensive. ## Consistency Models in Azure Cosmos DB: From Strong to Eventual DevFeed: [Consistency Models in Azure Cosmos DB: From Strong to Eventual]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [consistency](), [Databases](), [Availability](), [Latency](), [Replication](), [Azure]() Tags: [availability](), [cloud-architecture](), [consistency](), [cosmosdb](), [database-design](), [databases](), [distributed-systems](), [latency](), [linearizable](), [pacelc](), [replication](), [semantics](), [system-design]() ### AI overview This article explains how Azure Cosmos DB uses five consistency levels to expose PACELC trade-offs between consistency, availability, latency, and read freshness. It describes Strong consistency, bounded staleness, and Session consistency, including their operational trade-offs and suitable use cases. ### Source excerpt Cosmos DB exposes the PACELC trade-off as five explicit levels instead of forcing a strong-or-eventual choice. Strong gives linearizable reads at the cost of write latency and availability. Session, the practical default for user-facing apps, gives read-your-writes within a session via per-partition session tokens while staying highly available. ## Building transactional voice agents at Nector DevFeed: [Building transactional voice agents at Nector]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2026-03-07T13:39:58Z Content type: article Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [Model Context Protocol](), [Latency](), [Multi-tenancy](), [Back end](), [observability](), [Database]() Tags: [agents](), [api](), [backend](), [database-design](), [json-rpc](), [latency](), [latency-optimization](), [mcp](), [multi-tenancy](), [observability](), [pii](), [production](), [proxy](), [rest-api](), [voice]() ### AI overview An engineer describes building customer-facing transactional voice agents for ecommerce at Nector. The article covers a REST-to-JSON-RPC MCP proxy that improved observability, PII control, context reduction, latency, and correctness, plus experiments that reduced one agent's p90 latency from more than 8 seconds to 2 seconds. ### Source excerpt Building production ready voice agents, latency optimization, multi tenancy, MCP proxies - six months of building customer facing voice agents ## When Your Source of Truth is Reality DevFeed: [When Your Source of Truth is Reality]() Original publisher: [Read original article]() Author: Dan Lew Published: 2025-07-01T13:00:26Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Databases](), [data]() Tags: [data](), [database-design](), [databases](), [source-of-truth]() ### AI overview This opinion article argues that databases tracking real-world entities can never be perfectly authoritative because reality changes and records may be incomplete or inaccurate. It uses a community orchestra roster and other examples to explain the limits of the single source of truth concept. ### Source excerpt Let me tell you a story about why one should be cautious with databases that track the real world. I play trombone in a local community orchestra. For the past two years, I've been the board secretary; part of my duties is keeping a roster of who is