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@ -15,7 +15,9 @@ The data is sourced from Docker containers, local file systems, and remote authe
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### Problem Statement
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When you’re dealing with numerous Minecraft servers, querying real-time server data can become very expensive in terms of performance. Each query requires:
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When you’re dealing with numerous Minecraft servers, querying real-time server data can become very expensive in terms of performance.
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Each query requires:
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- Accessing container information via Docker.
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- Checking server online status via network requests.
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@ -28,7 +30,9 @@ This system mitigates those delays by introducing an efficient in-memory cache.
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## The Caching Mechanism
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The system employs a caching layer that stores various pieces of server information (MOTD, online status, etc.) in memory. This avoids repeated heavy I/O and network operations, thus drastically reducing the request time.
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The system employs a caching layer that stores various pieces of server information (MOTD, online status, etc.) in memory.
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This avoids repeated heavy I/O and network operations, thus drastically reducing the request time.
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### Key Components of the Cache
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@ -196,7 +200,9 @@ The cache holds both the value and a timestamp, allowing future improvements lik
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## Findings
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By introducing an in-memory caching system, this application was able to reduce request times from over 2 minutes to just 2 milliseconds. This system efficiently caches key server data, eliminating the need to repeatedly query external services like Docker, network services, and file systems.
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By introducing an in-memory caching system, this application was able to reduce request times from over 2 minutes to just 2 milliseconds.
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This system efficiently caches key server data, eliminating the need to repeatedly query external services like Docker, network services, and file systems.
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This approach is especially useful in applications with high I/O overhead and network latency, allowing for faster and more responsive interactions.
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