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examples

README.md

httpcache Examples

This directory contains practical examples demonstrating different ways to use httpcache.

Available Examples

1. Basic

The simplest example using in-memory caching. Great for getting started.

Features:

  • In-memory cache setup
  • Basic GET requests
  • Cache hit detection
  • ETag validation

When to use:

  • Quick prototyping
  • Testing
  • Single-instance applications
  • When persistence is not needed

2. Disk Cache

Persistent caching using filesystem storage.

Features:

  • Persistent storage
  • Survives application restarts
  • Multiple clients sharing cache
  • Cache directory management

When to use:

  • Desktop applications
  • CLI tools
  • When you need persistence
  • Single-machine deployments

3. Redis Cache

Distributed caching using Redis.

Features:

  • Distributed cache
  • Connection pooling
  • Multiple instances sharing cache
  • Production-ready setup

When to use:

  • Microservices
  • Distributed systems
  • High availability requirements
  • When you need cache sharing across instances

4. LevelDB Cache

High-performance persistent cache.

Features:

  • Fast persistent storage
  • Embedded database
  • No external dependencies
  • Compact storage

When to use:

  • High-performance requirements
  • Embedded applications
  • When disk cache is too slow
  • When Redis is overkill

5. Freecache

High-performance, zero-GC overhead caching for large-scale applications.

Features:

  • Zero GC overhead
  • Automatic LRU eviction
  • Millions of entries support
  • Built-in statistics

When to use:

  • Caching millions of responses
  • Performance-critical applications
  • When GC is a bottleneck
  • High-concurrency environments

6. Custom Backend

Learn how to create custom cache backends.

Features:

  • Statistics tracking
  • TTL-based expiration
  • Decorator pattern examples
  • Custom implementations

When to use:

  • Learning how to extend httpcache
  • Need custom functionality
  • Building specialized caching strategies
  • Adding monitoring/metrics

7. Compress Cache

Automatic compression for cached data using Gzip, Brotli, or Snappy.

Features:

  • Multiple compression algorithms (Gzip, Brotli, Snappy)
  • Algorithm-specific configuration
  • Compression statistics tracking
  • Cross-algorithm compatibility
  • Works with any cache backend

When to use:

  • Distributed cache backends (Redis, PostgreSQL, cloud storage)
  • Large response bodies
  • Bandwidth-constrained environments
  • Storage cost optimization
  • When network transfer is expensive

8. Cache Key Headers

Differentiate cache entries based on request header values.

Features:

  • Per-user caching with Authorization headers
  • Multi-language support with Accept-Language
  • Multiple header combinations
  • Header-based cache isolation

When to use:

  • Multi-tenant applications
  • User-specific API responses
  • Internationalized content
  • API versioning by header
  • Any scenario requiring cache separation by request headers

9. NATS K/V Cache

Distributed caching using NATS JetStream Key/Value store.

Features:

  • Distributed cache with NATS
  • JetStream persistence
  • Multiple instances sharing cache
  • Built-in TTL support
  • NATS clustering support

When to use:

  • Already using NATS in your infrastructure
  • Need distributed caching with messaging
  • Microservices with NATS communication
  • When you want NATS' simplicity over Redis

8. Hazelcast Cache

Distributed caching using Hazelcast in-memory data grid.

Features:

  • Distributed in-memory cache
  • Automatic data distribution across cluster
  • High availability with replication
  • Scalable architecture
  • Enterprise-grade performance

When to use:

  • Already using Hazelcast in your infrastructure
  • Need high-performance distributed caching
  • Enterprise applications requiring HA
  • When you need automatic data partitioning

11. Multi-Tier Cache

Combine multiple cache backends with automatic fallback and promotion.

Features:

  • Multi-tiered caching strategy
  • Automatic fallback from fast to slow tiers
  • Automatic promotion to faster tiers
  • Write-through to all tiers
  • CDN-like architecture

When to use:

  • Performance + Persistence requirements
  • Local + Distributed caching
  • CDN-like edge caching
  • Complex caching strategies with multiple storage levels
  • When you need both speed and resilience

12. PostgreSQL Cache

Persistent distributed caching using PostgreSQL.

Features:

  • SQL-based persistent cache
  • ACID compliance
  • Connection pool support
  • Distributed cache shared across instances
  • Works with existing PostgreSQL infrastructure

When to use:

  • Already using PostgreSQL
  • Need ACID compliance for cache
  • SQL-based systems
  • When you need persistent distributed cache

13. MongoDB Cache

Persistent distributed caching using MongoDB.

Features:

  • Document-based persistent cache
  • Automatic TTL expiration support
  • Distributed cache shared across instances
  • Context-aware operations
  • Works with existing MongoDB infrastructure

When to use:

  • Already using MongoDB
  • Need automatic cache expiration (TTL)
  • Document-based systems
  • When you need flexible schema for cache entries

14. BlobCache - Cloud Storage

Cloud-agnostic caching using blob storage (S3, GCS, Azure).

Features:

  • Multi-cloud support (AWS S3, Google Cloud Storage, Azure Blob Storage)
  • S3-compatible services (MinIO, Ceph, SeaweedFS)
  • SHA-256 key hashing for cloud storage compatibility
  • Local development with file:// and mem://
  • Context-aware operations with timeouts

When to use:

  • Cloud-native applications
  • Multi-cloud deployments
  • Serverless functions (Lambda, Cloud Functions)
  • Long-term cache storage
  • When you need vendor-independent storage

15. Security Best Practices

Secure cache implementation with encryption and key hashing.

Features:

  • SHA-256 key hashing
  • AES-256-GCM encryption
  • Multi-user scenarios
  • Compliance requirements (GDPR, HIPAA)

When to use:

  • Multi-tenant applications
  • Storing sensitive data
  • Compliance requirements
  • Shared cache backends

Running Examples

Each example has its own directory with:

  • main.go - Runnable example code
  • README.md - Detailed documentation

All examples use the main project's go.mod. To run an example from the project root:

go run ./examples/<example-name>/main.go

Or navigate to the example directory and run:

cd examples/<example-name>
go run main.go

Quick Comparison

Backend Speed Persistence Distributed Setup Complexity Best For
Memory ⚡⚡⚡ ❌ ❌ ⭐ < 100k entries
Freecache ⚡⚡⚡ ❌ ❌ ⭐ Millions of entries, zero GC
Disk ⚡ ✅ ❌ ⭐ Persistence needed
LevelDB ⚡⚡ ✅ ❌ ⭐⭐ Fast + persistent
Redis ⚡⚡ ✅* ✅ ⭐⭐⭐ Distributed systems
PostgreSQL ⚡⚡ ✅ ✅ ⭐⭐⭐ SQL infrastructure
MongoDB ⚡⚡ ✅ ✅ ⭐⭐⭐ MongoDB infrastructure, TTL
Memcache ⚡⚡ ❌ ✅ ⭐⭐⭐ Distributed, no persistence
NATS K/V ⚡⚡ ✅* ✅ ⭐⭐⭐ NATS users
Hazelcast ⚡⚡⚡ ✅* ✅ ⭐⭐⭐ Enterprise, HA
BlobCache ⚡ ✅ ✅ ⭐⭐⭐ Cloud storage, multi-cloud
MultiCache ⚡⚡⚡→⚡ ✅ ✅ ⭐⭐ Multi-tier strategies

*Redis, NATS K/V, and Hazelcast persistence depends on configuration

MultiCache: Speed varies by tier (fastest tier = fastest speed), combines benefits of all configured backends

Common Patterns

Basic Setup

transport := httpcache.NewMemoryCacheTransport()
client := transport.Client()

Custom Cache Backend

cache := customcache.New()
transport := httpcache.NewTransport(cache)
client := &http.Client{Transport: transport}

Detecting Cache Hits

resp, _ := client.Get(url)
if resp.Header.Get(httpcache.XFromCache) == "1" {
    // Response came from cache
}

Custom Underlying Transport

customTransport := &http.Transport{
    MaxIdleConns: 100,
    // ... other settings
}
transport := httpcache.NewTransport(cache)
transport.Transport = customTransport

Best Practices

  1. Choose the right backend for your use case
  2. Use connection pooling with Redis/Memcache
  3. Monitor cache hit rates to validate effectiveness
  4. Set appropriate timeouts on the HTTP client
  5. Handle errors gracefully from cache operations
  6. Consider cache size limits to prevent memory issues
  7. Use persistent cache for expensive or slow APIs

Testing Your Cache

All examples include verification that the cache is working:

// First request - cache miss
resp1, _ := client.Get(url)
fmt.Printf("From cache: %s\n", resp1.Header.Get(httpcache.XFromCache))
// Output: From cache: 

// Second request - cache hit
resp2, _ := client.Get(url)
fmt.Printf("From cache: %s\n", resp2.Header.Get(httpcache.XFromCache))
// Output: From cache: 1

Contributing

Found a useful pattern or use case? Feel free to contribute additional examples!

  1. Create a new directory under examples/
  2. Include main.go, go.mod, and README.md
  3. Make sure the example is runnable and well-documented
  4. Update this README with a link to your example

Need Help?

  • Check the main README for general information
  • See the GoDoc for API documentation