Awesome GitHub RepositoriesDistributed Data Caching Layers

Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

q is a command-line utility for the processing, filtering, and aggregation of tabular text and database files using standard SQL syntax. It functions as a query engine that treats CSV and TSV files, as well as standard input, as relational database tables. The tool distinguishes itself by providing a persistent cache layer that stores processed tabular data in a binary format to accelerate repeated queries on large datasets. It also maps individual filenames or stream identifiers to relational table names, enabling SQL joins across disparate text files. The project covers a broad range of da

Alluxio is a virtual distributed file system and data orchestration layer that serves as a high-performance caching layer between cloud storage and compute clusters. It acts as a distributed data cache designed to accelerate data access for large-scale analytics and machine learning workloads. The system provides a unified interface that presents multiple heterogeneous storage backends as a single coherent namespace. This allows for the unification of diverse storage systems, enabling computation engines to access data from different providers without changing application code. The project c

CppGuide is a curated collection of educational resources and practical guides focused on C++ server development, Linux kernel internals, concurrent programming, network protocols, and security exploitation. It provides structured learning paths for backend developers, covering everything from interview preparation to building high-performance network servers and understanding operating system fundamentals. The guide distinguishes itself by offering in-depth, hands-on tutorials that walk through real-world implementations, including building a Redis-like server from scratch, designing custom

Concurrent-map is a lock-striped hash map and sharded concurrent cache for Go, designed as a high-performance key-value store that enables thread-safe parallel reads and writes with minimal blocking. It replaces a single global mutex with per-shard locking, using hash-based key distribution to assign entries to independent segments, allowing multiple goroutines to operate simultaneously without race conditions. The library achieves its performance through fine-grained locking and a lock-free read path, where each shard operates independently with its own lock, enabling parallel reads and writ

DashMap is a concurrent hash map for Rust, providing a thread-safe associative array designed for high-performance multi-threaded access. It serves as a concurrent data structure that allows simultaneous reads and writes without requiring a global lock. The project utilizes a sharded lock architecture to reduce thread contention, employing fine-grained locking at the shard level. It is a Serde-compatible map, implementing serialization and deserialization to convert map data to and from common formats. The library covers capabilities for concurrent data storage, shared state management, and

Cinder is a high-performance Python runtime implementation based on CPython. It is designed as an execution environment optimized for large-scale distributed systems and cloud environments. The project integrates a distributed memory cache and an asynchronous memory layer to manage data across multiple network nodes. It also provides a native C extension framework for developing high-performance compiled modules that link directly into the interpreter memory space. The system covers capabilities for asynchronous data retrieval, large-scale execution, and the integration of embedded scripting

This project is a disk-backed key-value store and persistent data structure library for Python. It provides a mechanism for persisting mappings, sets, and queues to the local filesystem to bypass memory limitations and cache expensive function results across threads and processes. The system serves as a cross-process synchronization tool, offering distributed locks, semaphores, and barriers to coordinate shared resource access. It implements advanced caching strategies such as probabilistic stampede prevention, sharded data partitioning to increase throughput, and least-recently-used eviction