Compare: session-revised-plan

Nearly every caching layer supports TTLs, so that requirement alone doesn't narrow it much. The main options:

- **Redis** – de facto default. Per-key TTLs, rich data types, pub/sub, persistence options, mature clients everywhere. Managed versions on every cloud (ElastiCache, MemoryStore, Azure Cache).
- **Memcached** – simpler, multi-threaded, pure key/value with TTLs. Slightly better raw throughput for trivial string caching, but no persistence, no replication, fewer features. Choose only if you want minimalism.
- **Valkey** – the open-source Redis fork (post-license change), now backed by the Linux Foundation and AWS/Google. API-compatible with Redis 7.2; worth preferring over Redis proper if licensing (RSALv2/SSPL) matters to you.
- **In-process** (Caffeine for JVM, `lru-cache` for Node, etc.) – if the cache doesn't need to be shared across processes/hosts, this is far simpler and faster than any network cache. All support TTL/expiry.

Default recommendation: **Valkey (or Redis) via a managed service** unless you only need a single-process cache, in which case use an in-process library.

To give a sharper answer, a few things matter more than TTL support:
1. Does the cache need to be shared across multiple app instances?
2. Roughly what scale (data size, request rate)?
3. Self-hosted or managed/cloud, and which cloud?
4. Is licensing (SSPL) a concern for you?

`options.md` covers three of the options and only two attributes:

- **memcached** – TTLs: yes; persistence: no
- **redis** – TTLs: yes; persistence: yes
- **in-process LRU** – TTLs: yes; persistence: no; single-node only

It doesn't mention Valkey, and it doesn't cover scale, licensing, or client support.

That eliminates two of the three options in `options.md`:

- **memcached** – persistence: no → out
- **in-process LRU** – persistence: no (and single-node) → out
- **redis** – persistence: yes → the only survivor in the file

So from the documented options, **Redis** is the pick. Practical notes:

- **Valkey** also qualifies (same RDB snapshot + AOF persistence as Redis; API-compatible). It's just not in `options.md`. Prefer it over Redis proper if SSPL licensing matters.
- Redis persistence has two modes: **RDB** (periodic snapshots — can lose the last few minutes on crash) and **AOF** (append-only log — near-zero loss with `appendfsync everysec`). For "must survive a restart," enable AOF, or use both.
- On a **managed service** (ElastiCache, MemoryStore, Azure Cache), persistence/backups are a config toggle and you also get replication + failover, which protects against more than just a clean restart.

One caveat: if this data *only* lives in the cache and losing it is unacceptable, that's a datastore requirement, not a cache one — you'd want a real database as the source of truth with Redis in front. If the cache can repopulate from an underlying store, plain Redis/Valkey with AOF is fine.

**Redis** (or **Valkey**, if SSPL licensing is a concern for you).

It's the only option in `options.md` that meets both requirements — TTLs and persistence — and memcached and the in-process LRU are both ruled out by the persistence requirement.

Recommended setup:
- Use a **managed service** (ElastiCache / MemoryStore / Azure Cache) so you also get replication and failover, not just restart survival.
- Enable **AOF** persistence (`appendfsync everysec`) for near-zero data loss.
- If the cached data has no other source of truth, back it with a real database — that's beyond what a cache should guarantee.

Nearly every mainstream caching layer supports TTLs, so that alone won't narrow it much. A quick decision guide:

**In-process (single app instance, simplest)**
- **Caffeine** (Java) / **`lru-cache`** (Node) / **`cachetools`** (Python) — per-entry TTL, no network hop, no ops burden. Cache is lost on restart and not shared across instances.

**Distributed (multiple instances, shared state)**
- **Redis** — per-key TTL (`EX`/`PX`, `EXPIRE`), by far the most common choice. Rich data types, pub/sub, persistence optional. Single-threaded but very fast.
- **Valkey** — drop-in Redis fork (BSD-licensed), now the community-governed path after Redis's license change. Prefer this over Redis if licensing matters to you.
- **Memcached** — per-item TTL, multithreaded, pure LRU cache with no persistence. Simpler and lighter than Redis but far fewer features.

**Managed options**
- AWS ElastiCache (Redis/Valkey/Memcached), MemoryDB (durable), GCP Memorystore, Upstash (serverless Redis, pay-per-request).

**Default recommendation:** if you need a shared cache, use **Valkey (or Redis)** — TTL support is first-class and it's the least likely to be a limiting factor later. If it's truly single-instance, an in-process library avoids running another service.

If you can share scale, language/stack, and whether the cache must be shared across instances, I can be more specific.

`options.md` lists three options, each on TTLs and persistence:

- **memcached** — TTLs: yes; persistence: no
- **redis** — TTLs: yes; persistence: yes
- **in-process LRU** — TTLs: yes; persistence: no; single-node only

All three meet your TTL requirement. The differentiators in the file are persistence (only Redis) and the fact that in-process LRU can't be shared across nodes.

That narrows it to one option from `options.md`: **Redis** is the only one listed with persistence.

- **memcached** — no persistence, eliminated
- **in-process LRU** — no persistence (and single-node), eliminated
- **redis** — TTLs + persistence ✅

So: go with **Redis**, or **Valkey** (the BSD-licensed drop-in fork) if the Redis license change matters to you. Both give you persistence via RDB snapshots and/or AOF (append-only file). For "must survive a restart," enable **AOF** (`appendonly yes`) — RDB alone can lose the window since the last snapshot.

One caveat: treat a persistent Redis as a warm cache, not a source of truth. AOF fsync is periodic by default, and a crash (vs. clean restart) can still drop the last second or so of writes. If that's unacceptable, you'd want MemoryDB (AWS) or a real database rather than a cache.