A 7-dimensional trade-off analysis tool for selecting the optimal database storage engine architecture based on specific workload patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bookforge-storage-engine-selector
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bookforge-storage-engine-selector using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Storage Engine Selector?
The Storage Engine Selector is a technical framework designed to help engineers choose between log-structured merge-trees (LSM-trees), B-trees, and in-memory storage architectures. By moving beyond database brand names, this skill focuses on the underlying storage internals that determine system performance, scalability, and reliability. By utilizing Openclaw Skills, developers can perform deep-dive analysis on write amplification, compaction strategies, and latency predictability to ensure their database choice aligns with their application's physical reality.
This skill is particularly valuable when transitioning from development to production or when diagnosing performance bottlenecks like compaction stalls and write spikes. It synthesizes complex architectural concepts into a scored matrix, providing a reviewable and defensible recommendation for backend engineers and software architects.
Storage Engine Selector Use Cases
- Evaluating whether to use write-optimized storage like RocksDB or Cassandra vs read-optimized engines like PostgreSQL or InnoDB.
- Diagnosing production latency issues caused by write amplification or background compaction tasks.
- Deciding if a workload can safely move to an in-memory database like Redis or requires the durability of disk-based storage.
- Justifying architectural shifts in high-throughput systems where standard relational databases are hitting a performance ceiling.
How Storage Engine Selector Works
- Classify the workload profile by determining the write/read ratio, query patterns (point lookups vs range scans), and latency sensitivity.
- Score the three primary engine families—LSM-tree, B-tree, and In-memory—across seven technical dimensions including write throughput, space efficiency, and transactional semantics.
- Apply disqualifying filters to rule out architectures that fail critical requirements like ACID compliance or strict durability.
- Select a specific compaction strategy (Size-tiered vs Leveled) if an LSM-tree architecture is recommended to optimize background maintenance.
- Map the winning architecture to concrete database products and provide a risk assessment of the chosen engine's inherent trade-offs.
Storage Engine Selector Setup
To use this within the Openclaw Skills ecosystem, ensure you are in a project directory containing architectural artifacts.
# Navigate to your project containing docker-compose or schema files
cd path/to/project
# Invoke the selector through your AI coding agent
openclaw run storage-engine-selector
The skill will automatically scan for docker-compose.yml, schema.sql, or application configuration to identify existing data access patterns.
Storage Engine Selector Data Schema & Taxonomy
The skill generates a structured recommendation report organized as follows:
| Data Point | Purpose |
|---|---|
| Workload Classification | One-line summary of read/write intensity and query types. |
| 7-Dimension Score Matrix | Comparative scoring (1-5) for each engine family across throughput, latency, and space. |
| Disqualifier Log | Explicit reasoning for why certain engine families were eliminated from consideration. |
| Product Mapping | Identification of specific tools (e.g., RocksDB, MySQL, Redis) that fit the architecture. |
| Operational Risk Flag | Specific warnings regarding compaction lag, SSD wear, or memory limits. |
Storage Engine Selector Advanced Features
- Multi-dimensional weighted scoring that prioritizes specific needs like p99 latency or high write-throughput.
- Automated compaction strategy selection for LSM-trees based on space vs. write-amplification trade-offs.
- Codebase-aware discovery that identifies database drivers and query patterns from your source code.
- Seamless integration with other Openclaw Skills for end-to-end data architecture design, including partitioning and replication strategy selection.
SKILL.md
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