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
• github.com/samber/hot

This skill is not exhaustive. Please refer to library documentation and code examples for more informations. Context7 can help as a discoverability platform.

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.

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(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.

For the complete API surface, see API Reference.

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