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Developer tools · #json
Learn how Couchbase’s Sub‑Document API lets you update tiny JSON fragments in milliseconds, why it matters for high‑write workloads, and how to verify the performance boost.
Integrate JSON Schema validation into a microservice with a stateless validator, caching, and robust observability. This architecture note covers requirements, minimal design, trust boundaries, operational checks, failure modes, and when to adjust the design.
JSONC lets you add comments to JSON configs, but you must strip them before feeding the file to a strict parser. Learn the tooling, verify the output, and weigh the trade‑offs in this practical guide.
A diagnostic guide to JSON parse failures: how to recognize common error signatures, map causes (trailing commas, encoding, truncation, type mismatches), ordered checks, fixes tied to findings, and when to escalate to schema or infrastructure investigation.
Template Haskell can auto‑generate Aeson JSON instances, cutting boilerplate and reducing errors. See a step‑by‑step example, trade‑offs, and best practices for safe, maintainable usage.
Replace nested conditionals with declarative case/in patterns when processing varied webhook payloads in Ruby 3.x.
Crystal's JSON::Serializable module generates to_json/from_json at compile time. A worked struct example, key renaming, strict parsing, and the nil/circular-reference pitfalls to avoid.
Learn how Ktor’s ContentNegotiation plugin removes manual JSON boilerplate by automatically serializing Kotlin data classes to JSON.
Replace scattered inline validation with a single declarative contract. A practical look at JSON Schema at the API boundary, with a working ajv example and the additionalProperties trade-off.
couchbase
nodejs
haskell
ruby
crystal
ktor
kotlin