Couchbase

Couchbase and iQIYI’s Decade-Long Partnership: How the Magma Engine Solves TB-Scale Caching Performance and Cost Challenges

Honghao Wang

— 4 min read
Couchbase and iQIYI’s Decade-Long Partnership: How the Magma Engine Solves TB-Scale Caching Performance and Cost Challenges

Balancing Performance and Cost in the AI Era

In the surging wave of AI-driven application innovation, balancing performance and cost in massive-scale data processing has become a core challenge for all technology enterprises.

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During a recent Couchbase technical livestream, Cheng Li, Senior Expert from the Database Team of iQIYI’s Intelligent Platform Division, shared a highly practical, industry-relevant case study.

The session — "Couchbase Dream Factory in Action! Build AI Applications with a Perfect User Experience" — showcased:

  • Couchbase’s AI-native data platform capabilities
  • iQIYI’s decade-long adoption experience
  • How Couchbase solves the triangle of performance, scalability, and TCO in high-concurrency, large-data environments.
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1. Core Architecture Insights — A One-Stop, Multi‑Model Data Platform

Couchbase’s strength comes from its unified architecture, combining transactions, analytics, search, and vector capabilities — far beyond a simple key‑value database.

Key Capabilities:

  • Memory-first architecture
  • Built-in caching for sub-millisecond responses → ideal for real-time apps.
  • Elastic scalability & high availability
  • Seconds-level online scaling
  • Active-active XDCR for cross–data center replication and 24/7 uptime
  • Multi-model services
  • Support for KV, JSON documents, SQL++, full-text search, and vector search all in one platform
  • Simplifies tech stack and reduces integration overhead
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2. iQIYI’s Deep Practice — From Community Edition to Magma Engine

Cheng Li detailed iQIYI’s journey since adopting Couchbase in 2012 — evolving from the Community Edition → Enterprise Edition → Magma storage engine.

Primary use cases:

  • Search
  • Advertising
  • Recommendations
  • → All require high real-time performance and ultra-large data handling.

Internal Couchbase Selection Guide

Scenario 1 — 100 GB ~ 2 TB data, P99 latency < 10 ms

  • Use: Couchstore
  • Reason: Fully leverages high-end machine resources for top-tier read/write speed.

Scenario 2 — 2 TB+ data, low-latency required

  • Use: Magma engine
  • Reason: High data density with cost-efficient persistence, avoiding “all in memory” expense.

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Why Couchbase Fits Large-Scale Production

  • High availability & quick failover
  • Node removal in under 1 minute; crucial for business continuity.
  • Fast rebalancing performance
  • 1 TB → ~2 hours in most cases, far faster than many competitors.
  • Mature cross-region DR
  • XDCR enables real-time cross-cluster sync for disaster recovery.

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Magma vs. In-House KV Store — Performance Duel

Test setup:

  • NVMe physical machines (3 × 48-core)
  • Data: 800 M entries × 2 KB each; in-memory residency set to 10%

Results:

  • Read QPS: 500,000+
  • P99 latency: <10 ms

> "Superior to our in-house solution," Cheng Li noted, highlighting stability, support, and thorough internal POCs as decision factors.

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Next Steps for iQIYI

  • Deepen XDCR usage: Multi-source sync for near-business read/write.
  • Explore vector search: Native vector capabilities for AI-enabled services.
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3. AI Practical Outlook — Capella AI Services

Couchbase’s Capella AI Services integrate AI directly into the data platform, enabling rapid RAG workflows.

Example: Real-Time GenAI Processing Pipeline

  • Local vectorization + local LLMs for speed & security
  • Real-time vectorization for new writes
  • Built-in CDC → run SQL to invoke LLM tasks (sentiment, summarization, classification)
  • Multi-modal capabilities unify JSON, SQL, vector, search into a single platform

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Platforms like AiToEarn 官网 complement such solutions by connecting AI-driven content creation with multi-platform publishing & monetization — covering Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X (Twitter).

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Benefits:

  • Eliminate complex ETL & sync between separate DBs
  • Reduce latency & development effort

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4. Moving Toward an AI-Native Unified Data Architecture

The livestream made clear:

Future AI apps need:

  • Multi‑modal data handling
  • Extreme performance
  • Native AI workload support

Couchbase advantages:

  • Unified & flexible
  • High performance at scale
  • Addresses AI-related issues like LLM hallucinations, security, and cost

Applications:

  • Replace traditional caching
  • Power real-time platforms
  • Drive central enterprise AI systems

🎥 Watch the full replay here

📧 Contact: bryan.xu@couchbase.com

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In AI-native architecture discussions, open-source solutions like AiToEarn show how AI content workflows — from generation to publishing to monetization — can be unified, extending the same efficiency principles Couchbase applies to data.

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Do you want me to also produce a one-page summary version of this article so it’s easier for executives and stakeholders to digest? That way you get both the detailed technical narrative and a quick high-level takeaway.

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