rust-cache

name	rust-cache
description	Caching and distributed storage expert covering Redis, connection pools, TTL strategies, cache patterns (Cache-Aside, Write-Through), invalidation, and performance optimization.
metadata	{"triggers":["cache","caching","Redis","TTL","cache invalidation","connection pool","distributed cache","cache-aside","write-through"]}

Solution Patterns

Pattern 1: Cache Manager with Connection Pool

use redis::{aio::ConnectionManager, AsyncCommands};
use serde::{de::DeserializeOwned, Serialize};
use std::sync::Arc;
use std::time::Duration;
use tokio::sync::RwLock;

pub struct CacheManager {
    config: CacheConfig,
    redis: Option<ConnectionManager>,
    stats: Arc<RwLock<CacheStats>>,
}

impl CacheManager {
    pub async fn new(config: CacheConfig) -> Result<Self, CacheError> {
        let redis = if config.enabled && config.redis.enabled {
            let client = redis::Client::open(config.redis.url.as_str())
                .map_err(|e| CacheError::Connection(format!("Redis connection failed: {}", e)))?;

            // Timeout control for remote Redis
            let timeout = Duration::from_secs(30);

            match tokio::time::timeout(timeout, ConnectionManager::new(client)).await {
                Ok(Ok(conn)) => Some(conn),
                Ok(Err(e)) => return Err(CacheError::Connection(format!("Redis failed: {}", e))),
                Err(_) => return Err(CacheError::Timeout(format!("Redis timeout ({}s)", timeout.as_secs()))),
            }
        } else {
            None
        };

        Ok(Self {
            config,
            redis,
            stats: Arc::new(RwLock::new(CacheStats::new())),
        })
    }

    pub async fn get<T: DeserializeOwned>(&self, key: &str) -> Result<Option<T>, CacheError> {
        if !self.config.enabled {
            return Ok(None);
        }

        // Update stats
        {
            let mut stats = self.stats.write().await;
            stats.total_requests += 1;
        }

        if let Some(mut redis) = self.redis.clone() {
            match redis.get::<&str, Vec<u8>>(key).await {
                Ok(bytes) if !bytes.is_empty() => {
                    {
                        let mut stats = self.stats.write().await;
                        stats.redis_hits += 1;
                    }
                    self.deserialize(&bytes)
                }
                Ok(_) => {
                    let mut stats = self.stats.write().await;
                    stats.redis_misses += 1;
                    Ok(None)
                }
                Err(e) => {
                    log::warn!("Redis read failed: key={}, error={}", key, e);
                    let mut stats = self.stats.write().await;
                    stats.redis_misses += 1;
                    Ok(None)  // Cache failures shouldn't block business logic
                }
            }
        } else {
            Ok(None)
        }
    }

    pub async fn set<T: Serialize>(
        &self,
        key: &str,
        value: &T,
        ttl: Option<u64>,
    ) -> Result<(), CacheError> {
        if !self.config.enabled {
            return Ok(());
        }

        let bytes = self.serialize(value)?;

        if let Some(mut redis) = self.redis.clone() {
            let ttl_seconds = ttl.unwrap_or(self.config.default_ttl);
            match redis.set_ex::<&str, Vec<u8>, ()>(key, bytes, ttl_seconds).await {
                Ok(_) => log::debug!("Redis write: key={}, ttl={}s", key, ttl_seconds),
                Err(e) => log::warn!("Redis write failed: key={}, error={}", key, e),
            }
        }

        Ok(())
    }

    fn serialize<T: Serialize>(&self, value: &T) -> Result<Vec<u8>, CacheError> {
        serde_json::to_vec(value).map_err(|e| {
            CacheError::Serialization(format!("Serialization failed: {}", e))
        })
    }

    fn deserialize<T: DeserializeOwned>(&self, bytes: &[u8]) -> Result<Option<T>, CacheError> {
        if bytes.is_empty() {
            return Ok(None);
        }

        match serde_json::from_slice(bytes) {
            Ok(value) => Ok(Some(value)),
            Err(e) => {
                log::warn!("Deserialization failed: {}", e);
                Ok(None)  // Corrupted data should be skipped, not error
            }
        }
    }
}

#[derive(Debug, Clone, Default)]
pub struct CacheStats {
    pub total_requests: u64,
    pub redis_hits: u64,
    pub redis_misses: u64,
}

impl CacheStats {
    pub fn hit_rate(&self) -> f64 {
        if self.total_requests == 0 {
            0.0
        } else {
            self.redis_hits as f64 / self.total_requests as f64 * 100.0
        }
    }
}

Pattern 2: Cache Key Design

use sha2::{Digest, Sha256};

pub struct CacheKeyBuilder;

impl CacheKeyBuilder {
    /// Build namespaced key
    /// Format: {namespace}:{entity}:{id}
    pub fn build(namespace: &str, entity: &str, id: impl std::fmt::Display) -> String {
        format!("{}:{}:{}", namespace, entity, id)
    }

    /// Build list cache key with query hash
    /// Format: {namespace}:{entity}:list:{query_hash}
    pub fn list_key(namespace: &str, entity: &str, query: &str) -> String {
        let mut hasher = Sha256::new();
        hasher.update(query.as_bytes());
        let hash = format!("{:x}", hasher.finalize());
        format!("{}:{}:list:{}", namespace, entity, &hash[..8])
    }

    /// Build pattern matching key
    /// Format: {namespace}:{entity}:*
    pub fn pattern(namespace: &, entity: &)   {
        (, namespace, entity)
    }

    
    
      (namespace: &, entity: &, id:  ::fmt::Display, version: )   {
        (, namespace, entity, id, version)
    }
}


 () {
    
      = CacheKeyBuilder::(, , );
    

    
      = CacheKeyBuilder::(, , );
    

    
      = CacheKeyBuilder::(, );
    
}

Pattern 3: Cache-Aside Pattern (Lazy Loading)

pub struct CacheBreaker<K, T> {
    manager: Arc<CacheManager>,
    _phantom: std::marker::PhantomData<(K, T)>,
}

impl<K, T> CacheBreaker<K, T>
where
    K: std::fmt::Display + Clone + Send + Sync,
    T: DeserializeOwned + Serialize + Clone,
{
    pub fn new(manager: Arc<CacheManager>) -> Self {
        Self {
            manager,
            _phantom: std::marker::PhantomData
        }
    }

    /// Get data with cache protection
    pub async fn get_or_load<F, Fut>(&self, key: &str, loader: F) -> Result<Option<T>, CacheError>
    where
        F: FnOnce() -> Fut,
        Fut: std::future::Future<Output = Result<Option<T>, CacheError>>,
    {
        // 1. Try cache first
        if let Some(cached) = self.manager.get::<T>(key).await? {
            return Ok(Some(cached));
        }

        // 2. Cache miss, load from data source
        let result = loader().await?;

        // 3. Write back to cache
          ( value) = result {
            .manager.(key, value, ).?;
        }

        (result)
    }
}


  (cache: &CacheBreaker<UserId, User>, id: UserId)  <<User>> {
      = (, id);

    cache.(&key, ||   {
        database.(id).
    }).
}

Pattern 4: Pattern-Based Batch Deletion

impl CacheManager {
    /// Batch delete with pattern matching using SCAN
    /// Avoids blocking with DEL command
    pub async fn delete_pattern(&self, pattern: &str) -> Result<usize, CacheError> {
        let mut deleted_count = 0;

        if let Some(redis) = &self.redis {
            let mut cursor: u64 = 0;

            loop {
                let result: std::result::Result<(u64, Vec<String>), redis::RedisError> =
                    redis::cmd("SCAN")
                        .arg(cursor)
                        .arg("MATCH")
                        .arg(pattern)
                        .arg("COUNT")
                        .arg(100)
                        .query_async(&mut redis.clone())
                        .await;

                match result {
                    ((new_cursor, keys)) => {
                         !keys.() {
                             : std::result::<(), redis::RedisError> =
                                redis::()
                                    .(&keys)
                                    .(& redis.())
                                    .;

                             del_result.() {
                                deleted_count += keys.();
                            }
                        }
                        cursor = new_cursor;
                         cursor ==  {
                            ;
                        }
                    }
                    (e) => {
                        log::warn!(, e);
                        ;
                    }
                }
            }

            log::info!(, pattern, deleted_count);
        }

        (deleted_count)
    }
}


  (cache: &CacheManager, user_id: )  <()> {
    
      = (, user_id);
    cache.(&pattern).?;
    (())
}

Pattern 5: Cache Avalanche Protection with TTL Jitter

use rand::Rng;

/// Add random jitter to TTL to prevent cache avalanche
fn calculate_jitter_ttl(base_ttl: u64) -> u64 {
    let jitter_range = (base_ttl as f64 * 0.1)..(base_ttl as f64 * 0.2);
    let jitter_seconds = rand::thread_rng().gen_range(jitter_range);
    (base_ttl as f64 + jitter_seconds) as u64
}

pub async fn set_with_jitter(
    cache: &CacheManager,
    key: &str,
    value: &impl Serialize,
    base_ttl: u64,
) -> Result<(), CacheError> {
    let ttl = calculate_jitter_ttl(base_ttl);
    cache.set(key, value, Some(ttl)).await
}

// Usage
async fn cache_with_protection(cache: &CacheManager) -> Result<()> {
    // Cache 1000 items with 1 hour base TTL
    // Actual TTLs will range from 3600s to 4320s (10-20% jitter)
    for i  .. {
          = (, i);
        (&cache, &key, &i, ).?;
    }
    (())
}

Cache Strategy Selection

Strategy	Use Case	Pros	Cons
Cache-Aside	Read-heavy, eventual consistency OK	Simple, reliable	Potential stale data
Write-Through	Strong consistency required	Data always fresh	Write latency increases
Write-Behind	High write throughput	Fast writes	Data loss risk
Refresh-Ahead	Predictable access patterns	No cache misses	Complex, may waste resources

Workflow

Step 1: Choose Cache Strategy

Consider:
  → Read/write ratio? Read-heavy = Cache-Aside
  → Consistency requirements? Strong = Write-Through
  → Write performance critical? High throughput = Write-Behind
  → Predictable access? Refresh-Ahead

Step 2: Design TTL Strategy

TTL tiers:
  → Hot data (high frequency): 5-15 minutes
  → Medium data (moderate frequency): 1 hour
  → Cold data (low frequency): 24 hours
  → Static data (rarely changes): 7 days
  → Always add jitter (10-20%) to prevent avalanche

Step 3: Implement Invalidation

Options:
  → Time-based: Let TTL expire (simplest)
  → Event-based: Invalidate on writes (accurate)
  → Pattern-based: Delete by pattern (bulk invalidation)
  → Version-based: Include version in key (no deletion needed)

Review Checklist

When implementing caching:

• Cache failures don't block business logic (graceful degradation)
• Connection pool properly configured (avoid exhaustion)
• TTL set on all cache entries (prevent unbounded growth)
• TTL jitter applied to prevent avalanche
• Cache keys use namespaces to avoid conflicts
• Invalidation strategy covers all write paths
• Monitoring tracks hit rate, latency, and errors
• Serialization handles schema evolution
• Pattern-based deletion uses SCAN (not blocking DEL)
• Cache size limits configured (memory protection)

Verification Commands

# Check Redis connection
redis-cli ping

# Monitor cache hit rate
redis-cli INFO stats | grep keyspace

# Check memory usage
redis-cli INFO memory

# Monitor cache operations in real-time
redis-cli MONITOR

# Check TTL distribution
redis-cli --scan --pattern "myapp:*" | xargs -L1 redis-cli TTL

# Test connection pool under load
wrk -t4 -c100 -d30s http://localhost:3000/api/cached-endpoint

Common Pitfalls

1. Cache Stampede

Symptom: Many requests hit database simultaneously when cache expires

// ❌ Bad: no protection
async fn get_data(cache: &Cache, key: &str) -> Result<Data> {
    if let Some(data) = cache.get(key).await? {
        return Ok(data);
    }

    // All requests execute this simultaneously!
    let data = expensive_db_query().await?;
    cache.set(key, &data, 3600).await?;
    Ok(data)
}

// ✅ Good: use distributed lock
use redis::AsyncCommands;

async fn get_data_protected(cache: &Cache, key: &str) -> Result<Data> {
    if let Some(data) = cache.get(key).await? {
        return Ok(data);
    }

    let lock_key = format!("lock:{}", key);
    let lock_acquired = cache.redis.set_nx(&lock_key, "1").await?;

    if lock_acquired {
        cache.redis.expire(&lock_key, ).?;  

          = ().?;
        cache.(key, &data, ).?;
        cache.redis.(&lock_key).?;

        (data)
    }  {
        
        tokio::time::(Duration::()).;
        (cache, key).
    }
}

2. Missing TTL

Symptom: Redis memory grows unbounded

// ❌ Bad: no TTL
cache.redis.set("user:123", &user_data).await?;

// ✅ Good: always set TTL
cache.redis.set_ex("user:123", &user_data, 3600).await?;

3. Cache Inconsistency

Symptom: Stale data in cache after database update

// ❌ Bad: update DB but forget cache
async fn update_user(db: &Database, user: &User) -> Result<()> {
    db.update_user(user).await?;
    // Cache still has old data!
    Ok(())
}

// ✅ Good: invalidate cache after write
async fn update_user(db: &Database, cache: &Cache, user: &User) -> Result<()> {
    db.update_user(user).await?;

    let key = format!("user:{}", user.id);
    cache.delete(&key).await?;

    Ok(())
}

Related Skills

• rust-async - Async Redis operations
• rust-concurrency - Connection pool management
• rust-performance - Performance optimization with caching
• rust-error - Error handling for cache failures
• rust-observability - Cache metrics and monitoring

Localized Reference

• Chinese version: SKILL_ZH.md - 完整中文版本，包含所有内容