/golang-samber-hot

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, Codex or similar harness, and for projects using Golang.
metadata:
  author: samber
  version: "1.1.2"
  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 Bash(godig:*) Bash(gopls:*) LSP mcp__gopls__*
paths:
  - "**/*.go"

**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 — refer to library documentation and code examples for more information:

• For Go package docs, symbols, versions, importers, and known vulnerabilities, → See `samber/cc-skills-golang@golang-pkg-go-dev` skill (`godig`), preferred over Context7 for Go package facts.
• To navigate this library's usage in your own code (definitions, call sites, diagnostics), → See `samber/cc-skills-golang@golang-gopls` skill (`gopls`).
• Context7 remains a fallback for docs not indexed on pkg.go.dev.

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

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:

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(