202-database-selection

Eine Entscheidungsregel für den Vergleich von drei Datenbanken, also Systemen zum Speichern und Abrufen von Daten.

In plain words
What is it for?
Zum Bewerten passender Datenbanken für neue Anwendungen anhand von Zugriffsmustern, parallelen Nutzern, Verbindungen und Anforderungen an sichere Transaktionen.
Why use it?
Sie macht die Wahl nachvollziehbarer, indem Leistung, Wachstum, Betriebsaufwand und Kosten bewertet werden. Auch gleichzeitige Lese- und Schreibzugriffe werden berücksichtigt.

Cursor rule

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/hamzaamjad/cursor-rules/202-database-selection
Clone the repo
git clone --depth 1 https://github.com/hamzaamjad/cursor-rules
Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 707 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00707
Opus 5 $0.00000 $0.00353
Sonnet 5 $0.00000 $0.00141
Haiku 4.5 $0.00000 $0.00071

Measured yesterday against content hash ca133046a8e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

202-database-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

rules/200-engineering/202-database-selection.mdc · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Database Selection Checklist

  • Purpose: To ensure appropriate database technology selection based on access patterns, concurrency requirements, and existing infrastructure. Research shows: Wrong database selection causes 60% of performance issues and 40% of scaling failures.

  • Requirements: When proposing or implementing database solutions, evaluate against these criteria:

Tree of Thoughts Database Evaluation

Generate and compare 3 database options using quantitative scoring:

Score = (Performance × 0.3) + (Scalability × 0.3) + (Operational × 0.2) + (Cost × 0.2)

Concurrency Evaluation

  • Write Concurrency: Will multiple agents/services write simultaneously?
    • Score: 0-10 based on expected concurrent writers
    • PostgreSQL: 10 (excellent), DuckDB: 1 (single-writer only)
  • Read Concurrency: Will multiple agents/services read simultaneously?
    • Score: 0-10 based on expected concurrent readers
  • Connection Limits: What are the connection pool requirements?
    • Quantify: connections_needed / max_connections
  • Transaction Isolation: What level of ACID compliance is needed?
    • Strict ACID: +3 points for PostgreSQL
    • Eventual consistency OK: +3 points for NoSQL options

Performance Requirements

  • Write Performance: Expected writes per second?
  • Read Performance: Expected queries per second?
  • Data Volume: Expected data size over 1 year?
  • Query Complexity: Simple lookups vs complex analytics?

Operational Considerations

  • Existing Infrastructure: What databases are already deployed?
  • Migration Complexity: How difficult to migrate from current state?
  • Operational Overhead: Additional services to maintain?
  • Backup/Recovery: Built-in or requires additional tooling?

Mirror-Specific Context

Current database infrastructure:

  • PostgreSQL + TimescaleDB: Primary database for time-series data (handles concurrent writes)
  • DuckDB: Analytics engine for read-only complex queries (single-writer limitation)
  • ChromaDB: Vector database for semantic memory
  • Redis: Cache and message broker

Read the full file on GitHub · 69 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 69 lines · 0 tokens per session scan A ca133046a8e2

Subscribe to this mod's changes

202-database-selection is a cursor rule published in the GitHub repository hamzaamjad/cursor-rules (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 707 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.