This guide walks through creating a FastAPI application that both publishes and consumes messages via RabbitMQ, detailing the setup of a Pika client, asynchronous consumer, message handling, API routes, and deployment with uvicorn, complete with code snippets and testing instructions.
This article introduces Pika 3.5.x’s major enhancements—including a new sync mechanism, expanded Redis command support, RocksDB upgrades, BlobDB, containerization, observability tools, ACL and transaction support—while providing a step‑by‑step upgrade procedure, optional data synchronization, and two rollback strategies for safe migration.
The Pika community announced version 3.5.2, fixing long‑standing bugs and adding features such as Redis transaction support, a cache layer for hot‑cold data separation, dynamic Codis‑Proxy configuration, and performance improvements, while also outlining the planned enhancements for the upcoming 3.5.3 release.
Ximalaya’s KV storage progressed from a simple Redis master‑slave setup to client‑side sharding, then adopted Codis clustering for elastic scaling, integrated Pika’s disk‑based store with cold‑hot separation, introduced KV‑blob separation, fast‑slow command pools, second‑level expansion, ehash fields, large‑key circuit breaking, multi‑active data‑center replication, and now targets cloud‑native deployment, advanced features, and AI‑driven operations.
This article analyzes a real‑world migration from MongoDB to the open‑source Pika KV store, demonstrating how adjusting the max‑write‑buffer‑size parameter dramatically improves disk I/O characteristics and raises query‑per‑second throughput from 3 K to 40 K.
This article explains how Pika, a disk‑based Redis‑compatible KV store, overcomes Redis’s memory limits with larger capacity, faster backup, and comparable performance, and describes its deployment, management, monitoring, and migration workflows within 360’s HULK cloud platform.
Pika, a disk‑based KV database fully compatible with the Redis protocol, was created by 360 to cut storage costs, has been adopted by major Chinese internet firms, and is now an OpenAtom open‑source project aiming to become a global benchmark for cloud‑native KV databases.
This article explains Pika’s native distributed cluster architecture, detailing its deployment structure, data distribution with tables and slots, request processing flow, both non‑consistent and Raft‑based log replication, and the enhanced metadata management that enables scalable, high‑availability storage beyond single‑node Redis limitations.
The article explains the background, architecture, data distribution, processing flow, replication mechanisms, and management features of Pika's native distributed cluster, detailing how Etcd, LVS, and RocksDB are used to achieve scalable, persistent Redis-compatible storage with table isolation and flexible slot replication.
This tutorial explains RabbitMQ fundamentals, shows how to install the Pika client, provides complete producer and consumer Python examples, and covers advanced topics such as work queues, message acknowledgments, persistence, fair dispatch, exchanges, routing, and RPC usage.
The article starts with a job‑interview anecdote and then provides a comprehensive overview of NoSQL, its history, definitions, comparison with MySQL, practical scenarios, and detailed examinations of key NoSQL projects such as LevelDB, RocksDB, SSDB, and Pika, highlighting their architectures and use cases.
This article presents thirty practical recommendations for deploying, configuring, and maintaining Pika—a high‑capacity, RocksDB‑backed Redis‑compatible storage system—covering version selection, thread settings, hardware choices, key design, memory management, replication, backup, compaction, security, and monitoring to achieve reliable and high‑performance operation.
Pika 3.0, the open‑source C++ Redis‑compatible storage built on RocksDB, introduces a new Blackwidow engine, enhanced binlog, and optimized server layer, delivering higher interface performance, reduced disk usage, extended key length, full ZSET compatibility, and improved overall efficiency.
Pika 3.0, a C++ open‑source Redis‑compatible storage built on RocksDB, introduces a new blackwidow engine, an improved binlog system, and a more efficient server layer, delivering higher performance, reduced disk usage, extended key length limits, and full Redis ZSET compatibility.
Pika 3.0 introduces a new Blackwidow engine, an efficient write‑to‑file binlog, and a streamlined server layer, all designed to boost performance, reduce storage costs, and fully maintain Redis‑compatible functionality for large‑scale deployments.
The article recaps the 2017 Pika community salon in Beijing, explains Pika’s design as a high‑capacity Redis‑compatible storage, showcases real‑world deployments at 360, Huanxin and Weibo, and shares insights from four technical talks along with event highlights and resources.
This article explains Pika's master‑slave replication architecture, detailing its specialized threads, full and incremental replication workflows, key API functions, and the slaveof and trysync processes that enable persistent, large‑scale data synchronization beyond Redis limitations.
This article provides a detailed walkthrough of Pika’s write2file binlog mechanism, covering manifest and binlog file structures, record formats, core implementation classes, and step‑by‑step procedures for constructing, updating, producing, and consuming logs in a large‑capacity Redis‑compatible storage system.
This article examines 360’s explosive KV storage demand and compares three solutions—Redis, Redis Cluster, and Pika—detailing their features, performance, persistence options, eviction policies, architectures, migration tools, and ideal usage scenarios for high‑traffic, large‑scale applications.
Pika, a large‑capacity Redis‑compatible storage system developed by 360’s Web Platform DBA and infrastructure team, offers persistent disk‑based data, multi‑threaded performance, extensive Redis API compatibility, and numerous enhancements in version 2.2, making it suitable for high‑cost, massive‑data, and high‑availability scenarios.
This article examines the operational challenges of oversized Redis instances—including slow failover, prolonged slave resynchronization, network‑induced avalanches, and persistence blocking—and offers practical mitigation strategies such as key expiration, data compression, and using high‑performance alternatives like Pika.
