5 Repos
Techniques for grouping multiple database commands into a single network request to improve throughput.
Distinct from Batch Data Operations: Distinct from general batch data operations: focuses on network-level command pipelining rather than bulk record modification.
Explore 5 awesome GitHub repositories matching data & databases · Pipeline Batching. Refine with filters or upvote what's useful.
Redisson is a Java library and Redis client that functions as a distributed Java object mapper, caching provider, and locking framework. It maps Java collections and concurrency primitives to distributed implementations backed by Redis and Valkey, providing synchronous, asynchronous, and reactive APIs for interacting with these data stores. The project distinguishes itself by providing a comprehensive suite of distributed coordination tools, including a locking framework for managing semaphores and countdown latches across multiple application nodes. It also serves as a distributed messaging
Groups multiple operations into a single request or pipeline to reduce network round-trips and increase throughput.
This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha
Groups multiple operations into a single request to reduce network round-trips and improve throughput for bulk data processing.
Vector is a high-performance observability data pipeline designed to collect, transform, and route logs, metrics, and traces across distributed infrastructure. It functions as a modular engine that decouples data ingestion from processing and transmission, utilizing a component-based architecture to connect diverse sources to multiple destinations. The project distinguishes itself through a focus on reliability and flow control. It implements backpressure-aware data movement to prevent data loss during traffic spikes and utilizes disk-backed event buffering to ensure durability during network
Groups events into batches based on size or time thresholds to optimize network throughput.
Jedis is a Java library for connecting to Redis servers to execute commands and manage key-value data structures. It serves as a Java client and connection manager that facilitates the storage and retrieval of high-performance data. The project provides a cluster client for distributing data and requests across multiple nodes to ensure scaling and high availability. It includes a dedicated pub-sub client for real-time messaging through channel subscriptions and a pipelining tool to increase throughput by sending multiple commands in a single network round-trip. The library covers a broad ran
Increases throughput by sending multiple commands in a single stream without waiting for individual responses.
phpredis is a C-based native extension that bridges PHP applications with Redis servers for high-performance data storage and retrieval. It serves as an interface for manipulating strings, hashes, lists, sets, and sorted sets while providing a direct path for executing Redis commands and server-side scripts. The extension provides comprehensive support for distributed environments and high availability. It interfaces with Redis Cluster to distribute data across multiple nodes using hash slots and manages Redis Sentinel for service discovery and automatic failover. It also enables shared state
Groups multiple requests into a single network write to significantly reduce round-trip latency.