Requêtes PostgreSQL optimisées

VérifiéSûr

Écrire des requêtes PostgreSQL optimisées et sécurisées en utilisant des paramètres dynamiques, des jointures et des opérations vectorielles (pg-vector). Utile pour interroger efficacement la base de données du portefeuille avec une construction de requêtes flexible et une gestion d'erreurs.

Spar Skills Guide Bot
DeveloppementIntermédiaire
5002/06/2026
Claude CodeCursorWindsurf
#postgresql#pgvector#query-building#parameterized-queries#dynamic-queries

Recommandé pour

Notre avis

Créer des requêtes PostgreSQL optimisées pour la base de données du portfolio, incluant des requêtes paramétrées, des jointures et des opérations pg-vector.

Points forts

  • Requêtes paramétrées sécurisées contre les injections SQL
  • Construction dynamique de filtres et requêtes flexibles
  • Support des opérations vectorielles pour la recherche de similarité
  • Gestion d'erreurs incluse avec typage TypeScript

Limites

  • Nécessite une connaissance du schéma de base de données du projet
  • Peut devenir verbeux pour des requêtes très complexes
  • Ne couvre pas tous les cas extrêmes de construction de requêtes
Quand l'utiliser

Utilisez ce skill pour effectuer des opérations CRUD avec des filtres dynamiques ou des recherches de similarité vectorielle dans une base PostgreSQL.

Quand l'éviter

Évitez ce skill pour des requêtes statiques simples où le SQL brut est suffisant, ou lorsque l'utilisation d'un ORM serait plus maintenable.

Analyse de sécurité

Sûr
Score qualité90/100

The skill focuses on safe, parameterized SQL query patterns and does not instruct execution of destructive commands, data exfiltration, or disabling security features. It promotes best practices like avoiding string concatenation.

Aucun point d'attention détecté

Exemples

Dynamic experience query with filters
Generate a PostgreSQL query to select experiences from the database with optional filters on company name (ILIKE), position, and start year, using the project's parameterized query pattern.
Hybrid search with vector and full-text
Create a hybrid search query that combines pg-vector similarity and full-text search to find content chunks, ranking by a combined score with weights 0.3 for text rank and 0.7 for vector similarity.
Join query with skills aggregation
Write a query to fetch experiences along with their associated skills, using a LEFT JOIN and aggregating skills into a JSON array, sorted by start date descending.

Database Query

Create optimized PostgreSQL queries for the portfolio database.

Description

Write safe, optimized PostgreSQL queries using the project's database patterns. Includes parameterized queries, proper joins, and pg-vector operations.

Instructions

  1. Import query from @/lib/database
  2. Use parameterized queries with $1, $2, etc.
  3. Build dynamic queries safely
  4. Handle results with proper typing
  5. Include error handling

Parameters

  • table - Target table name
  • operation - SELECT, INSERT, UPDATE, DELETE
  • filters - Dynamic filter conditions
  • includes_vector - Whether using pg-vector operations

Basic Query Pattern

import { query } from '@/lib/database';

// Simple SELECT
const results = await query(
  'SELECT * FROM experiences WHERE id = $1',
  [experienceId]
);

// INSERT with RETURNING
const inserted = await query(
  `INSERT INTO experiences (company, position, start_date)
   VALUES ($1, $2, $3)
   RETURNING *`,
  [company, position, startDate]
);

// UPDATE
await query(
  `UPDATE experiences 
   SET company = $1, position = $2, updated_at = NOW()
   WHERE id = $3`,
  [company, position, id]
);

// DELETE
await query(
  'DELETE FROM experiences WHERE id = $1',
  [id]
);

Dynamic Query Builder

interface ExperienceFilters {
  company?: string;
  position?: string;
  startYear?: number;
  technology?: string;
  limit?: number;
}

async function getExperiences(filters: ExperienceFilters) {
  const params: any[] = [];
  let sql = 'SELECT * FROM experiences WHERE 1=1';
  
  if (filters.company) {
    sql += ` AND company ILIKE $${params.length + 1}`;
    params.push(`%${filters.company}%`);
  }
  
  if (filters.position) {
    sql += ` AND position ILIKE $${params.length + 1}`;
    params.push(`%${filters.position}%`);
  }
  
  if (filters.startYear) {
    sql += ` AND EXTRACT(YEAR FROM start_date) = $${params.length + 1}`;
    params.push(filters.startYear);
  }
  
  if (filters.technology) {
    sql += ` AND $${params.length + 1} = ANY(technologies)`;
    params.push(filters.technology);
  }
  
  sql += ' ORDER BY start_date DESC';
  
  if (filters.limit) {
    sql += ` LIMIT $${params.length + 1}`;
    params.push(filters.limit);
  }
  
  return query(sql, params);
}

Vector Search Query

import { query } from '@/lib/database';

// Find similar content using pg-vector
async function findSimilarContent(embedding: number[], limit = 5) {
  return query(
    `SELECT 
       id, 
       content, 
       metadata,
       1 - (embedding <=> $1::vector) as similarity
     FROM content_chunks
     WHERE embedding IS NOT NULL
     ORDER BY embedding <=> $1::vector
     LIMIT $2`,
    [`[${embedding.join(',')}]`, limit]
  );
}

// Hybrid search (vector + full-text)
async function hybridSearch(searchText: string, embedding: number[]) {
  return query(
    `SELECT 
       id,
       content,
       ts_rank(search_vector, plainto_tsquery($1)) as text_rank,
       1 - (embedding <=> $2::vector) as vector_similarity,
       (ts_rank(search_vector, plainto_tsquery($1)) * 0.3 + 
        (1 - (embedding <=> $2::vector)) * 0.7) as combined_score
     FROM content_chunks
     WHERE search_vector @@ plainto_tsquery($1)
        OR embedding <=> $2::vector < 0.5
     ORDER BY combined_score DESC
     LIMIT 10`,
    [searchText, `[${embedding.join(',')}]`]
  );
}

Join Queries

// Get experiences with related skills
async function getExperiencesWithSkills() {
  return query(`
    SELECT 
      e.*,
      COALESCE(
        json_agg(
          json_build_object('name', s.skill_name, 'category', s.category)
        ) FILTER (WHERE s.id IS NOT NULL),
        '[]'
      ) as skills
    FROM experiences e
    LEFT JOIN experience_skills es ON e.id = es.experience_id
    LEFT JOIN skills s ON es.skill_id = s.id
    GROUP BY e.id
    ORDER BY e.start_date DESC
  `);
}

Error Handling

async function safeQuery<T>(
  sql: string,
  params: any[] = []
): Promise<{ data: T[] | null; error: Error | null }> {
  try {
    const result = await query(sql, params);
    return { data: result as T[], error: null };
  } catch (error) {
    console.error('Database query error:', error);
    return { data: null, error: error as Error };
  }
}

Best Practices

  1. Always use parameterized queries - Never concatenate user input
  2. Use ILIKE for case-insensitive search - Better UX
  3. Limit results - Always include a LIMIT clause
  4. Index key columns - Ensure filters use indexed columns
  5. Handle NULL values - Use COALESCE when needed
  6. Type your results - Use TypeScript interfaces
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