Serialization 201
Short, problem-driven essays on how serialization mechanisms work. They sit between the 101 home / three lenses and later courses (301 production judgment, 401 implementers) plus suite categories + language Results.
Theory alone does not decide production choices. Use these pages to build mechanism-level models, then validate with measured libraries and—when choosing under multi-constraint production pressure—continue to advanced courses as they ship.
How to use this track
- Skim Serialization 101 (definitions and trade-off axes).
- Optionally read one lens: Historical, Data science, or Engineering.
- Work the articles below when you need how or why.
- Open Serialization categories and a language Results page for numbers on this harness.
Honesty rules (same as Serialization 101): no universal winners; implementation beats brand name; payload shape matters; compare within paradigm and language; prose numbers are illustrative—Results own suite truth.
Suggested order (MVP path)
| Step | Article | You should be able to… |
|---|---|---|
| 1 | Memory layout | Explain why a raw memory dump is not a portable format |
| 2 | Encode/decode cost | Name the real cost centers (parse, numbers, alloc, copy)—not an unqualified claim that JSON is slow |
| 3 | Self-describing vs schema | Say who carries field identity: payload or shared contract |
| 4 | Schema evolution | Plan additive change without breaking old readers/writers |
| 5 | Dynamic vs IDL binary | Choose MessagePack/CBOR-class vs Protobuf-class for a workload |
| 6 | Zero-copy | Explain what “no deserialize” means—and what it still costs |
| 7 | Compression vs format | Separate gzip-on-the-wire from format-aware density |
By module
Representation
Contracts & change
Families in practice
Systems concerns
Where to go next
- Core path: Serialization 301 — production judgment under constraints
- Implementer elective: Serialization 401 — wire + language paths + lab
- Serialization categories · Engineering · Data science · Benchmarks