Skill: samber/golang-samber-hot (version 1.0.3)

--- name: golang-samber-hot description: "In-memory caching in Golang using samber/hot — eviction algorithms (LRU, LFU, TinyLFU, W-TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, FIFO), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Apply when using or adopting samber/hot, when the codebase imports github.com/samber/hot, or when the project repeatedly loads the same medium-to-low cardinality resources at high frequency and needs to reduce latency or backend pressure." user-invocable: true license: MIT compatibility: Designed for Claude Code or similar AI coding agents, and for projects using Golang. metadata: author: samber version: "1.0.3" openclaw: emoji: "🔥" homepage: https://github.com/samber/cc-skills-golang requires: bins: - go install: [] skill-library-version: "0.13.0" allowed-tools: Read Edit Write Glob Grep Bash(go:*) Bash(golangci-lint:*) Bash(git:*) Agent WebFetch mcp__context7__resolve-library-id mcp__context7__query-docs AskUserQuestion --- **Persona:** You are a Go engineer who treats caching as a system design decision. You choose eviction algorithms based on measured access patterns, size caches from working-set data, and always plan for expiration, loader failures, and monitoring. # Using samber/hot for In-Memory Caching in Go Generic, type-safe in-memory caching library for Go 1.22+ with 9 eviction algorithms, TTL, loader chains with singleflight deduplication, sharding, stale-while-revalidate, and Prometheus metrics. **Official Resources:** - [pkg.go.dev/github.com/samber/hot](https://pkg.go.dev/github.com/samber/hot) - [github.com/samber/hot](https://github.com/samber/hot) This skill is not exhaustive. Please refer to library documentation and code examples for more information. Context7 can help as a discoverability platform. ```bash go get -u github.com/samber/hot ``` ## Algorithm Selection Pick based on your access pattern — the wrong algorithm wastes memory or tanks hit rate. | Algorithm | Constant | Best for | Avoid when | | --- | --- | --- | --- | | **W-TinyLFU** | `hot.WTinyLFU` | General-purpose, mixed workloads (default) | You need simplicity for debugging | | **LRU** | `hot.LRU` | Recency-dominated (sessions, recent queries) | Frequency matters (scan pollution evicts hot items) | | **LFU** | `hot.LFU` | Frequency-dominated (popular products, DNS) | Access patterns shift (stale popular items never evict) | | **TinyLFU** | `hot.TinyLFU` | Read-heavy with frequency bias | Write-heavy (admission filter overhead) | | **S3FIFO** | `hot.S3FIFO` | High throughput, scan-resistant | Small caches (<1000 items) | | **ARC** | `hot.ARC` | Self-tuning, unknown patterns | Memory-constrained (2x tracking overhead) | | **TwoQueue** | `hot.TwoQueue` | Mixed with hot/cold split | Tuning complexity is unacceptable | | **SIEVE** | `hot.SIEVE` | Simple scan-resistant LRU alternative | Highly skewed access patterns | | **FIFO** | `hot.FIFO` | Simple, predictable eviction order | Hit rate matters (no frequency/recency awareness) | **Decision shortcut:** Start with `hot.WTinyLFU`. Switch only when profiling shows the miss rate is too high for your SLO. For detailed algorithm comparison, benchmarks, and a decision tree, see [Algorithm Guide](./references/algorithm-guide.md). ## Core Usage ### Basic Cache with TTL ```go import "github.com/samber/hot" cache := hot.NewHotCache[string, *User](hot.WTinyLFU, 10_000). WithTTL(5 * time.Minute). WithJanitor(). Build() defer cache.StopJanitor() cache.Set("user:123", user) cache.SetWithTTL("session:abc", session, 30*time.Minute) value, found, err := cache.Get("user:123") ``` ### Loader Pattern (Read-Through) Loaders fetch missing keys automatically with singleflight deduplication — concurrent `Get()` calls for the same missing key share one loader invocation: ```go cache := hot.NewHotCache[int, *User](hot.WTinyLFU, 10_000). WithTTL(5 * time.Minute). WithLoaders(func(ids []int) (map[int]*User, error) { return db.GetUsersByIDs(ctx, ids) // batch query }). WithJanitor(). Build() defer cache.StopJanitor() user, found, err := cache.Get(123) // triggers loader on miss ``` ## Capacity Sizing Before setting the cache capacity, estimate how many items fit in the memory budget: 1. **Estimate single-item size** — estimate size of the struct, add the size of heap-allocated fields (slices, maps, strings). Include the key size. A rough per-entry overhead of ~100 bytes covers internal bookkeeping (pointers, expiry timestamps, algorithm metadata). 2. **Ask the developer** how much memory is dedicated to this cache in production (e.g., 256 MB, 1 GB). This depends on the service's total memory and what else shares the process. 3. **Compute capacity** — `capacity = memoryBudget / estimatedItemSize`. Round down to leave headroom. ``` Example: *User struct ~500 bytes + string key ~50 bytes + overhead ~100 bytes = ~650 bytes/entry 256 MB budget → 256_000_000 / 650 ≈ 393,000 items ``` If the item size is unknown, ask the developer to measure it with a unit test that allocates N items and checks `runtime.ReadMemStats`. Guessing capacity without measuring leads to OOM or wasted memory. ## Common Mistakes 1. **Forgetting `WithJanitor()`** — without it, expired entries stay in memory until the algorithm evicts them. Always chain `.WithJanitor()` in the builder and `defer cache.StopJanitor()`. 2. **Calling `SetMissing()` without missing cache config** — panics at runtime. Enable `WithMissingCache(algorithm, capacity)` or `WithMissingSharedCache()` in the builder first. 3. **`WithoutLocking()` + `WithJanitor()`** — mutually exclusive, panics. `WithoutLocking()` is only safe for single-goroutine access without background cleanup. 4. **Oversized cache** — a cache holding everything is a map with overhead. Size to your working set (typically 10-20% of total data). Monitor hit rate to validate. 5. **Ignoring loader errors** — `Get()` returns `(zero, false, err)` on loader failure. Always check `err`, not just `found`. ## Best Practices 1. Always set TTL — unbounded caches serve stale data indefinitely because there is no signal to refresh 2. Use `WithJitter(lambda, upperBound)` to spread expirations — without jitter, items created together expire together, causing thundering herd on the loader 3. Monitor with `WithPrometheusMetrics(cacheName)` — hit rate below 80% usually means the cache is undersized or the algorithm is wrong for the workload 4. Use `WithCopyOnRead(fn)` / `WithCopyOnWrite(fn)` for mutable values — without copies, callers mutate cached objects and corrupt shared state For advanced patterns (revalidation, sharding, missing cache, monitoring setup), see [Production Patterns](./references/production-patterns.md). For the complete API surface, see [API Reference](./references/api-reference.md). If you encounter a bug or unexpected behavior in samber/hot, open an issue at <https://github.com/samber/hot/issues>. ## Cross-References - → See `samber/cc-skills-golang@golang-performance` skill for general caching strategy and when to use in-memory cache vs Redis vs CDN - → See `samber/cc-skills-golang@golang-observability` skill for Prometheus metrics integration and monitoring - → See `samber/cc-skills-golang@golang-database` skill for database query patterns that pair with cache loaders - → See `samber/cc-skills@promql-cli` skill for querying Prometheus cache metrics via CLI


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)