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Awesome GitHub RepositoriesMemory-Efficient Big Data Algorithms
Algorithms designed for analyzing massive datasets with minimal memory overhead, such as Bloom filters and Top-K extraction.
Distinct from Big Data Processing: Focuses on algorithmic memory efficiency for massive data rather than general big data processing frameworks.
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Awesome Memory-Efficient Big Data Algorithms GitHub Repositories
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- doocs/advanced-java
doocs/advanced-java
78,987Ver en GitHubThis project is a comprehensive Java backend engineering guide and technical reference focused on high-concurrency design, distributed systems, and microservices architecture. It provides detailed strategies for decomposing monolithic applications, managing service discovery, and implementing the architectural patterns required for scalable backend environments. The repository distinguishes itself through an extensive collection of big data algorithmic references and database scaling strategies. It covers memory-efficient techniques for analyzing massive datasets, such as Top-K element extrac
Provides a comprehensive reference for memory-efficient algorithms to solve large-scale data problems like frequency counting and rank extraction.
Javaadvanced-javadistributed-search-enginedistributed-systems
