为何说99%的场景下，PostgreSQL都是更好的选择？

一、“使用正确的工具”陷阱

二、为什么在人工智能时代至关重要

三、“但是专业数据库更好！”

四、隐性成本累积起来会很高

五、现代 PostgreSQL 技术栈

六、快速入门：添加这些扩展程序

-- Full-text search with BM25CREATE EXTENSION pg_textsearch;
-- Vector search for AICREATE EXTENSION vector;CREATE EXTENSION vectorscale;
-- AI embeddings & RAG workflowsCREATE EXTENSION ai;
-- Time-seriesCREATE EXTENSION timescaledb;
-- Message queuesCREATE EXTENSION pgmq;
-- Scheduled jobsCREATE EXTENSION pg_cron;
-- GeospatialCREATE EXTENSION postgis;

七、代码示例

-- Create tableCREATE TABLE articles (  id SERIAL PRIMARY KEY,  title TEXT,  content TEXT);
-- Create BM25 indexCREATE INDEX idx_articles_bm25 ON articles USING bm25(content)  WITH (text_config = 'english');
  -- Search with BM25 scoringSELECT title, -(content <@> 'database optimization') as scoreFROM articlesORDER BY content <@> 'database optimization'LIMIT 10;

SELECT   title,  -(content <@> 'database optimization') as bm25_score,  embedding <=> query_embedding as vector_distance,  0.7 * (-(content <@> 'database optimization')) +   0.3 * (1 - (embedding <=> query_embedding)) as hybrid_scoreFROM articlesORDER BY hybrid_score DESCLIMIT 10;

-- Enable extensionsCREATE EXTENSION vector;CREATE EXTENSION vectorscale CASCADE;
-- Table with embeddingsCREATE TABLE documents (  id SERIAL PRIMARY KEY,  content TEXT,  embedding vector(1536));
-- High-performance index (DiskANN)CREATE INDEX idx_docs_embedding ON documents USING diskann(embedding);
-- Find similar documentsSELECT content, embedding <=> '[0.1, 0.2, ...]'::vector as distanceFROM documentsORDER BY embedding <=> '[0.1, 0.2, ...]'::vectorLIMIT 10;

SELECT ai.create_vectorizer(  'documents'::regclass,  loading => ai.loading_column(column_name=>'content'),  embedding => ai.embedding_openai(model=>'text-embedding-3-small', dimensions=>'1536'));

-- Enable TimescaleDBCREATE EXTENSION timescaledb;
-- Create tableCREATE TABLE metrics (  time TIMESTAMPTZ NOT NULL,  device_id TEXT,  temperature DOUBLE PRECISION);
-- Convert to hypertableSELECT create_hypertable('metrics', 'time');
-- Query with time bucketsSELECT time_bucket('1 hour', time) as hour,       AVG(temperature)
       FROM metricsWHERE time > NOW() - INTERVAL '24 hours'GROUP BY hour;
-- Auto-delete old dataSELECT add_retention_policy('metrics', INTERVAL '30 days');
-- Compression (90% storage reduction)ALTER TABLE metrics SET (timescaledb.compress);SELECT add_compression_policy('metrics', INTERVAL '7 days');

-- UNLOGGED = no WAL overhead, faster writesCREATE UNLOGGED TABLE cache (  key TEXT PRIMARY KEY,  value JSONB,  expires_at TIMESTAMPTZ);
-- Set with expirationINSERT INTO cache (key, value, expires_at)VALUES ('user:123', '{"name": "Alice"}', NOW() + INTERVAL '1 hour')ON CONFLICT (key) DO UPDATE SET value = EXCLUDED.value;
-- GetSELECT value FROM cache WHERE key = 'user:123' AND expires_at > NOW();
-- Cleanup (schedule with pg_cron)DELETE FROM cache WHERE expires_at < NOW();

CREATE EXTENSION pgmq;SELECT pgmq.create('my_queue');
-- SendSELECT pgmq.send('my_queue', '{"event": "signup", "user_id": 123}');
-- Receive (with visibility timeout)SELECT * FROM pgmq.read('my_queue', 30, 5);
-- Delete after processingSELECT pgmq.delete('my_queue', msg_id);

CREATE TABLE jobs (  id SERIAL PRIMARY KEY,  payload JSONB,  status TEXT DEFAULT 'pending');
-- Worker claims job atomicallyUPDATE jobs SET status = 'processing'WHERE id = (  SELECT id FROM jobs WHERE status = 'pending'  FOR UPDATE SKIP LOCKED LIMIT 1) RETURNING *;

CREATE TABLE users (  id SERIAL PRIMARY KEY,  data JSONB);
-- Insert nested documentINSERT INTO users (data) VALUES ('{  "name": "Alice",  "profile": {"bio": "Developer", "links": ["github.com/alice"]}}');
-- Query nested fieldsSELECT data->>'name', data->'profile'->>'bio'FROM usersWHERE data->'profile'->>'bio' LIKE '%Developer%';
-- Index JSON fieldsCREATE INDEX idx_users_email ON users ((data->>'email'));

CREATE EXTENSION postgis;
CREATE TABLE stores (  id SERIAL PRIMARY KEY,  name TEXT,  location GEOGRAPHY(POINT, 4326));
-- Find stores within 5kmSELECT name, ST_Distance(location, ST_MakePoint(-122.4, 37.78)::geography) as metersFROM storesWHERE ST_DWithin(location, ST_MakePoint(-122.4, 37.78)::geography, 5000);

CREATE EXTENSION pg_cron;
-- Run every hourSELECT cron.schedule('cleanup', '0 * * * *',   $$DELETE FROM cache WHERE expires_at < NOW()$$);
  -- Nightly rollupSELECT cron.schedule('rollup', '0 2 * * *',  $$REFRESH MATERIALIZED VIEW CONCURRENTLY daily_stats$$);

-- Reciprocal Rank Fusion: combine keyword + semantic searchWITH bm25 AS (  SELECT id, ROW_NUMBER() OVER (ORDER BY content <@> $1) as rank  FROM documents LIMIT 20),vectors AS (  SELECT id, ROW_NUMBER() OVER (ORDER BY embedding <=> $2) as rank    FROM documents LIMIT 20)SELECT d.*,   1.0/(60 + COALESCE(b.rank, 1000)) +   1.0/(60 + COALESCE(v.rank, 1000)) as scoreFROM documents dLEFT JOIN bm25 b ON d.id = b.idLEFT JOIN vectors v ON d.id = v.idWHERE b.id IS NOT NULL OR v.id IS NOT NULLORDER BY score DESC LIMIT 10;

CREATE EXTENSION pg_trgm;
CREATE INDEX idx_name_trgm ON products USING GIN (name gin_trgm_ops);
-- Finds "PostgreSQL" even with typoSELECT name FROM productsWHERE name % 'posgresql'ORDER BY similarity(name, 'posgresql') DESC;

-- Find all reports under a manager (org chart)WITH RECURSIVE org_tree AS (  SELECT id, name, manager_id, 1 as depth  FROM employees WHERE id = 42  UNION ALL  SELECT e.id, e.name, e.manager_id, t.depth + 1  FROM employees e  JOIN org_tree t ON e.manager_id = t.id  WHERE t.depth < 10)SELECT * FROM org_tree;

八、最后

你怎么看？你觉得PostgreSQL能担此任吗？欢迎评论区讨论~