couchbase-magma

Select and tune the Couchbase Magma storage engine for bucket creation.

4|1|Updated May 28, 2026

Search Tags:#compaction #bucket configuration #couchbase #storage engine #magma #vbuckets

Ready to run

npx skills add https://github.com/celticht32/Couchbase-Skills-for-Claude.ai --skill couchbase-magma

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: couchbase-magma
Source: https://github.com/celticht32/Couchbase-Skills-for-Claude.ai/tree/main/skills/couchbase/couchbase-magma
Command: npx skills add https://github.com/celticht32/Couchbase-Skills-for-Claude.ai --skill couchbase-magma

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you choose, configure, and tune Couchbase's Magma storage engine when you need better scale, lower memory overhead, or a clearer decision between Magma and couchstore.

Core Features & Use Cases

Storage Engine Selection: Decide whether Magma or couchstore fits your dataset size, write rate, and memory constraints.
Bucket Configuration Guidance: Set the right storage backend and vBucket count for new buckets, especially on Couchbase 8.0.
Operational Tuning: Understand compaction behavior, memory requirements, migration constraints, and the tradeoffs that affect performance.
Use Case: If you are creating a new production bucket with a very large dataset, this Skill helps you pick Magma, size memory correctly, and avoid irreversible bucket-creation mistakes.

Quick Start

Ask for help choosing between Magma and couchstore for a new Couchbase bucket and include your dataset size, write rate, memory limits, and version.

Frequently Asked Questions about couchbase-magma

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I choose between Magma and couchstore for a Couchbase bucket?▼

Use Couchbase Magma for large datasets needing lower memory overhead and better scale, while couchstore fits smaller datasets with fewer memory constraints. Base your storage engine selection on dataset size, write rate, and available memory.

Should I use 128 or 1024 vBuckets when configuring Magma storage?▼

Select 1024 vBuckets for larger Magma datasets to ensure proper distribution, while 128 vBuckets suits smaller deployments. This bucket configuration setting is immutable and must be chosen correctly during initial bucket creation.

How does compaction work with the Couchbase Magma storage engine?▼

Magma compaction behavior impacts operational performance and disk space reclamation, requiring specific tuning for your write rate. Understanding compaction settings helps manage storage overhead and maintain consistent read and write performance.

What are the memory requirements for running Couchbase Magma?▼

Magma requires less memory than couchstore for large datasets, but you must calculate exact memory requirements based on dataset size and write rate before bucket creation. Proper memory sizing ensures optimal storage backend performance.

Can I migrate an existing Couchbase bucket from couchstore to Magma?▼

Migration from couchstore to Magma faces constraints because storage backend and vBucket count are immutable bucket creation settings. You must plan a migration scenario to transfer data to a new Magma-backed bucket rather than switching engines in place.

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