71 lines
2.3 KiB
Markdown
71 lines
2.3 KiB
Markdown
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---
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title: Use Client-Side Caching for Frequently Read Data
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impact: HIGH
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impactDescription: Reduces network round-trips for repeated reads
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tags: caching, performance, client-side, tracking
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description: Use Client-Side Caching for Frequently Read Data
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alwaysApply: true
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---
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## Use Client-Side Caching for Frequently Read Data
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Use a connection with client-side caching enabled for any data that will be read frequently but written only occasionally. Client-side caching avoids contacting the server for repeated access to data that has recently been read, reducing network traffic and improving performance.
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**Correct:** Enable client-side caching with RESP3 protocol for frequently accessed data.
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**Python** (redis-py):
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```python
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import redis
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# Enable client-side caching with RESP3
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client = redis.Redis(
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host='localhost',
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port=6379,
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protocol=3, # RESP3 required for client-side caching
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cache_config=redis.CacheConfig(max_size=1000)
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)
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# Cached reads avoid server round-trips
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value = client.get("frequently:read:key")
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```
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**Java** (Jedis):
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```java
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import redis.clients.jedis.DefaultJedisClientConfig;
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import redis.clients.jedis.UnifiedJedis;
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import redis.clients.jedis.HostAndPort;
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import redis.clients.jedis.CacheConfig;
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HostAndPort endpoint = new HostAndPort("localhost", 6379);
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DefaultJedisClientConfig config = DefaultJedisClientConfig
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.builder()
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.password("secretPassword")
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.protocol(RedisProtocol.RESP3)
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.build();
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CacheConfig cacheConfig = CacheConfig.builder().maxSize(1000).build();
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UnifiedJedis client = new UnifiedJedis(endpoint, config, cacheConfig);
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```
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**When to use:**
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- Configuration data read frequently, updated rarely
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- User session data accessed on every request
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- Feature flags or settings checked repeatedly
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- Any read-heavy workload with low write frequency
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**When NOT needed:**
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- Data that changes frequently (cache invalidation overhead outweighs benefits)
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- Write-heavy workloads
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- Simple applications where network latency is not a bottleneck
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- When you need guaranteed real-time consistency
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**Trade-offs:**
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- Adds memory overhead on the client
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- Requires RESP3 protocol
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- Cache invalidation adds complexity for frequently changing data
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Reference: [Client-side caching](https://redis.io/docs/latest/develop/clients/client-side-caching/)
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