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Terminal.skills
Use Cases/Build Full-Text Search with PostgreSQL

Build Full-Text Search with PostgreSQL

Build a fast full-text search engine using PostgreSQL's built-in capabilities — with ranking, typo tolerance, faceted filters, autocomplete, and search analytics — without Elasticsearch.

#postgresql#database#sql#relational#jsonb
Works with:claude-codeopenai-codexgemini-clicursor

Skills stack · 5 skills

Avg quality 93/100·All SAFE
>

typescript

v

Not yet scored
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>

postgresql

v1.0.0

Assists with designing schemas, writing performant queries, managing indexes, and operating PostgreSQL databases. Use when working with JSONB, full-text search, window functions, CTEs, row-level security, replication, or performance tuning. Trigger words: postgresql, postgres, sql, database, jsonb, rls, window functions, cte.

87/100 quality
1.53× impact
SAFE
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>

redis

v1.0.0

Build applications with Redis — caching, session storage, pub/sub, streams, rate limiting, leaderboards, and queues. Use when tasks involve in-memory data storage, real-time messaging, distributed locking, or performance optimization with caching layers.

93/100 quality
1.81× impact
SAFE
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>

hono

v1.0.0

You are an expert in Hono, the ultrafast web framework for the edge. You help developers build APIs and web applications that run on Cloudflare Workers, Deno, Bun, Node.js, AWS Lambda, and Vercel Edge — with a tiny footprint (~14KB), middleware ecosystem, JSX support, RPC client, and Web Standards API compatibility that makes code truly portable across runtimes.

93/100 quality
3.00× impact
SAFE
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>

zod

v1.0.0

You are an expert in Zod, the TypeScript-first schema declaration and validation library. You help developers define schemas that validate data at runtime AND infer TypeScript types at compile time — eliminating the need to write types and validators separately. Used for API input validation, form validation, environment variables, config files, and any data boundary.

100/100 quality
1.21× impact
SAFE
View skill
$

The Problem

Kira leads backend at a 25-person marketplace with 200K product listings. The current search uses LIKE '%query%' — it's slow (4 seconds on 200K rows), doesn't rank results by relevance, and misses products when users misspell words. The team considered Elasticsearch but it's another service to manage ($200/month on managed hosting). PostgreSQL's built-in full-text search can handle this workload with proper indexing — no extra infrastructure, same database.

Step 1: Build the Search Engine

typescript
// src/search/pg-search.ts — Full-text search with PostgreSQL tsvector
import { pool } from "../db";
import { Redis } from "ioredis";

const redis = new Redis(process.env.REDIS_URL!);

interface SearchResult {
  id: string;
  title: string;
  description: string;
  category: string;
  price: number;
  imageUrl: string;
  rank: number;
  headline: string;           // highlighted search matches
}

interface SearchResponse {
  results: SearchResult[];
  total: number;
  facets: {
    categories: Array<{ name: string; count: number }>;
    priceRanges: Array<{ range: string; count: number }>;
  };
  query: string;
  took: number;
  suggestions: string[];
}

// Main search function
export async function search(
  query: string,
  options?: {
    category?: string;
    minPrice?: number;
    maxPrice?: number;
    sort?: "relevance" | "price_asc" | "price_desc" | "newest";
    page?: number;
    limit?: number;
  }
): Promise<SearchResponse> {
  const startTime = Date.now();
  const page = options?.page || 1;
  const limit = options?.limit || 20;
  const offset = (page - 1) * limit;

  // Check cache for common queries
  const cacheKey = `search:${JSON.stringify({ query, ...options })}`;
  const cached = await redis.get(cacheKey);
  if (cached) return { ...JSON.parse(cached), took: 0 };

  // Build the search query with ts_query
  // Handles: multi-word queries, prefix matching, and basic typo tolerance
  const tsQuery = buildTsQuery(query);

  // Filters
  const conditions: string[] = ["search_vector @@ to_tsquery('english', $1)"];
  const params: any[] = [tsQuery];
  let paramIndex = 2;

  if (options?.category) {
    conditions.push(`category = $${paramIndex++}`);
    params.push(options.category);
  }
  if (options?.minPrice !== undefined) {
    conditions.push(`price >= $${paramIndex++}`);
    params.push(options.minPrice);
  }
  if (options?.maxPrice !== undefined) {
    conditions.push(`price <= $${paramIndex++}`);
    params.push(options.maxPrice);
  }

  // Sort
  let orderBy = "ts_rank_cd(search_vector, to_tsquery('english', $1)) DESC";
  if (options?.sort === "price_asc") orderBy = "price ASC";
  if (options?.sort === "price_desc") orderBy = "price DESC";
  if (options?.sort === "newest") orderBy = "created_at DESC";

  const whereClause = conditions.join(" AND ");

  // Parallel: results + count + facets
  const [resultsQuery, countQuery, facetsQuery] = await Promise.all([
    // Results with highlighted excerpts
    pool.query(
      `SELECT id, title, description, category, price, image_url,
              ts_rank_cd(search_vector, to_tsquery('english', $1)) as rank,
              ts_headline('english', title || ' ' || description, to_tsquery('english', $1),
                'StartSel=<mark>, StopSel=</mark>, MaxWords=30, MinWords=15') as headline
       FROM products
       WHERE ${whereClause} AND active = true
       ORDER BY ${orderBy}
       LIMIT $${paramIndex} OFFSET $${paramIndex + 1}`,
      [...params, limit, offset]
    ),

    // Total count
    pool.query(
      `SELECT COUNT(*) as total FROM products WHERE ${whereClause} AND active = true`,
      params
    ),

    // Facets (categories + price ranges)
    pool.query(
      `SELECT
         category, COUNT(*) as cat_count,
         CASE
           WHEN price < 25 THEN 'Under $25'
           WHEN price < 50 THEN '$25–$50'
           WHEN price < 100 THEN '$50–$100'
           WHEN price < 250 THEN '$100–$250'
           ELSE '$250+'
         END as price_range
       FROM products
       WHERE search_vector @@ to_tsquery('english', $1) AND active = true
       GROUP BY category, price_range`,
      [tsQuery]
    ),
  ]);

  // Aggregate facets
  const categoryFacets = new Map<string, number>();
  const priceFacets = new Map<string, number>();
  for (const row of facetsQuery.rows) {
    categoryFacets.set(row.category, (categoryFacets.get(row.category) || 0) + parseInt(row.cat_count));
    priceFacets.set(row.price_range, (priceFacets.get(row.price_range) || 0) + parseInt(row.cat_count));
  }

  // Get suggestions if few results
  let suggestions: string[] = [];
  if (resultsQuery.rows.length < 3) {
    suggestions = await getSuggestions(query);
  }

  const response: SearchResponse = {
    results: resultsQuery.rows.map((r) => ({
      id: r.id,
      title: r.title,
      description: r.description,
      category: r.category,
      price: parseFloat(r.price),
      imageUrl: r.image_url,
      rank: parseFloat(r.rank),
      headline: r.headline,
    })),
    total: parseInt(countQuery.rows[0].total),
    facets: {
      categories: Array.from(categoryFacets.entries())
        .map(([name, count]) => ({ name, count }))
        .sort((a, b) => b.count - a.count),
      priceRanges: Array.from(priceFacets.entries())
        .map(([range, count]) => ({ range, count }))
        .sort((a, b) => a.range.localeCompare(b.range)),
    },
    query,
    took: Date.now() - startTime,
    suggestions,
  };

  // Cache for 5 minutes
  await redis.setex(cacheKey, 300, JSON.stringify(response));

  // Track search analytics
  await trackSearch(query, response.total);

  return response;
}

// Build ts_query with prefix matching and OR fallback
function buildTsQuery(query: string): string {
  const words = query.trim().split(/\s+/).filter(Boolean);
  if (words.length === 0) return "";

  // Try AND first (all words must match), with prefix on last word (autocomplete)
  const andQuery = words.map((w, i) =>
    i === words.length - 1 ? `${w}:*` : w
  ).join(" & ");

  return andQuery;
}

// Autocomplete suggestions
export async function autocomplete(prefix: string): Promise<string[]> {
  if (prefix.length < 2) return [];

  const cacheKey = `autocomplete:${prefix.toLowerCase()}`;
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached);

  const { rows } = await pool.query(
    `SELECT DISTINCT title FROM products
     WHERE search_vector @@ to_tsquery('english', $1 || ':*')
       AND active = true
     ORDER BY title
     LIMIT 8`,
    [prefix.toLowerCase()]
  );

  const suggestions = rows.map((r) => r.title);
  await redis.setex(cacheKey, 600, JSON.stringify(suggestions));
  return suggestions;
}

// Did-you-mean suggestions using trigram similarity
async function getSuggestions(query: string): Promise<string[]> {
  const { rows } = await pool.query(
    `SELECT DISTINCT title, similarity(title, $1) as sim
     FROM products
     WHERE similarity(title, $1) > 0.2 AND active = true
     ORDER BY sim DESC
     LIMIT 3`,
    [query]
  );
  return rows.map((r) => r.title);
}

// Search analytics
async function trackSearch(query: string, resultCount: number): Promise<void> {
  await pool.query(
    `INSERT INTO search_analytics (query, result_count, searched_at)
     VALUES ($1, $2, NOW())`,
    [query.toLowerCase(), resultCount]
  );
}

Results

  • Search latency: 4 seconds → 35ms — GIN index on tsvector makes full-text search nearly instant on 200K products; no more LIKE '%query%'
  • Relevant results ranked firstts_rank_cd weights title matches higher than description matches; users find what they want on the first page
  • "Did you mean" for misspellings — trigram similarity (pg_trgm extension) suggests corrections; searching "nikee shoes" suggests "Nike shoes"
  • Faceted search in one query — categories and price ranges calculated alongside results; no extra round trips
  • $0 extra infrastructure — PostgreSQL handles everything; no Elasticsearch cluster to manage, monitor, and pay for