database-expert ยท diff

git:20260113.90b2d6e to git:20260207.49cc324

143 added, 0 removed. Audit A to A.

---
name: database-expert
description: Advanced database design and administration for PostgreSQL, MongoDB, and Redis. Use when designing schemas, optimizing queries, managing database performance, or implementing data patterns.
---
# Database Expert
Comprehensive guide for database design, optimization, and administration.
## Database Selection
| Database | Type | Best For |
| -------------- | ---------- | ----------------------------------- |
| **PostgreSQL** | Relational | Complex queries, ACID, JSON support |
| **MongoDB** | Document | Flexible schemas, rapid iteration |
| **Redis** | Key-Value | Caching, sessions, real-time |
| **SQLite** | Embedded | Mobile, desktop, testing |
---
## PostgreSQL
### Schema Design
```sql
-- Users table with proper constraints
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
email VARCHAR(255) NOT NULL UNIQUE,
password_hash VARCHAR(255) NOT NULL,
full_name VARCHAR(100) NOT NULL,
status VARCHAR(20) NOT NULL DEFAULT 'active'
CHECK (status IN ('active', 'inactive', 'suspended')),
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
-- Index for common queries
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_status ON users(status) WHERE status = 'active';
-- Automatic updated_at trigger
CREATE OR REPLACE FUNCTION update_updated_at_column()
RETURNS TRIGGER AS $$
BEGIN
NEW.updated_at = NOW();
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
CREATE TRIGGER update_users_updated_at
BEFORE UPDATE ON users
FOR EACH ROW
EXECUTE FUNCTION update_updated_at_column();
-- Posts with full-text search
CREATE TABLE posts (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
author_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE,
title VARCHAR(255) NOT NULL,
content TEXT NOT NULL,
tags TEXT[] DEFAULT '{}',
search_vector TSVECTOR,
published_at TIMESTAMPTZ,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
-- Full-text search index
CREATE INDEX idx_posts_search ON posts USING GIN(search_vector);
```
### Query Optimization
```sql
-- EXPLAIN ANALYZE for query analysis
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT u.full_name, COUNT(p.id) as post_count
FROM users u
LEFT JOIN posts p ON p.author_id = u.id
WHERE u.status = 'active'
GROUP BY u.id
ORDER BY post_count DESC
LIMIT 10;
-- Partial index for filtered queries
CREATE INDEX idx_posts_published
ON posts(published_at)
WHERE published_at IS NOT NULL;
-- Covering index (index-only scan)
CREATE INDEX idx_posts_author_title
ON posts(author_id)
INCLUDE (title, published_at);
-- Expression index
CREATE INDEX idx_users_email_lower
ON users(LOWER(email));
```
### Advanced Features
```sql
-- Common Table Expression (CTE)
WITH active_authors AS (
SELECT DISTINCT author_id
FROM posts
WHERE published_at > NOW() - INTERVAL '30 days'
),
author_stats AS (
SELECT
u.id,
u.full_name,
COUNT(p.id) as total_posts
FROM users u
JOIN active_authors aa ON aa.author_id = u.id
LEFT JOIN posts p ON p.author_id = u.id
GROUP BY u.id
)
SELECT * FROM author_stats
ORDER BY total_posts DESC;
-- Window functions
SELECT
id,
title,
author_id,
published_at,
ROW_NUMBER() OVER (PARTITION BY author_id ORDER BY published_at DESC) as author_rank,
LAG(published_at) OVER (PARTITION BY author_id ORDER BY published_at) as prev_post_date
FROM posts
WHERE published_at IS NOT NULL;
-- JSON operations
SELECT
id,
metadata->>'source' as source,
metadata->'stats'->>'views' as views
FROM posts
WHERE metadata @> '{"featured": true}';
-- Recursive CTE (for hierarchies)
WITH RECURSIVE category_tree AS (
SELECT id, name, parent_id, 0 as depth
FROM categories
WHERE parent_id IS NULL
UNION ALL
SELECT c.id, c.name, c.parent_id, ct.depth + 1
FROM categories c
JOIN category_tree ct ON c.parent_id = ct.id
WHERE ct.depth < 10
)
SELECT * FROM category_tree ORDER BY depth;
```
---
## MongoDB
### Schema Design Patterns
```javascript
// Embedding (for 1:few relationships)
const userSchema = {
_id: ObjectId,
email: String,
profile: {
firstName: String,
lastName: String,
avatar: String,
},
addresses: [
{
type: String,
street: String,
city: String,
},
],
};
// Referencing (for 1:many relationships)
const postSchema = {
_id: ObjectId,
authorId: ObjectId,
title: String,
content: String,
tags: [String],
};
```
### Aggregation Pipeline
```javascript
// Complex aggregation
db.orders.aggregate([
{
$match: {
status: "completed",
createdAt: { $gte: ISODate("2024-01-01") },
},
},
{
$lookup: {
from: "users",
localField: "userId",
foreignField: "_id",
as: "user",
},
},
{ $unwind: "$user" },
{
$group: {
_id: { month: { $month: "$createdAt" } },
totalRevenue: { $sum: "$total" },
orderCount: { $sum: 1 },
},
},
{ $sort: { totalRevenue: -1 } },
]);
```
### Indexes
```javascript
// Compound index
db.posts.createIndex({ authorId: 1, createdAt: -1 });
// Text index
db.posts.createIndex(
{ title: "text", content: "text" },
{ weights: { title: 10, content: 1 } },
);
// Partial index
db.orders.createIndex(
{ createdAt: 1 },
{ partialFilterExpression: { status: "pending" } },
);
// TTL index (auto-expire)
db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 });
```
---
## Redis
### Data Structures
```redis
# Strings
SET user:1:name "John Doe"
GET user:1:name
SETEX session:abc123 3600 "user_data"
INCR page:home:views
# Hashes
HSET user:1 name "John" email "john@example.com"
HGETALL user:1
# Lists (queues)
LPUSH queue:jobs '{"type":"email"}'
RPOP queue:jobs
# Sets
SADD user:1:roles admin editor
SISMEMBER user:1:roles admin
# Sorted Sets (leaderboards)
ZADD leaderboard 1000 "player1" 1500 "player2"
ZREVRANGE leaderboard 0 9 WITHSCORES
# Streams (event log)
XADD events * type "user_login" user_id "123"
XREAD COUNT 10 STREAMS events 0
```
### Caching Patterns
```typescript
// Cache-aside pattern
async function getUser(userId: string): Promise<User> {
const cacheKey = `user:${userId}`;
const cached = await redis.get(cacheKey);
if (cached) {
return JSON.parse(cached);
}
const user = await db.users.findById(userId);
await redis.setex(cacheKey, 3600, JSON.stringify(user));
return user;
}
// Rate limiting
async function checkRateLimit(
userId: string,
limit: number,
window: number,
): Promise<boolean> {
const key = `ratelimit:${userId}`;
const current = await redis.incr(key);
if (current === 1) {
await redis.expire(key, window);
}
return current <= limit;
}
// Distributed lock
async function acquireLock(
resource: string,
ttl: number,
): Promise<string | null> {
const lockId = crypto.randomUUID();
const acquired = await redis.set(`lock:${resource}`, lockId, "NX", "EX", ttl);
return acquired ? lockId : null;
}
// Release lock with Lua script (atomic operation)
// Use EVALSHA with pre-loaded script for production
const releaseLockScript = `
if redis.call("get", KEYS[1]) == ARGV[1] then
return redis.call("del", KEYS[1])
else
return 0
end
`;
```
### Pub/Sub
```typescript
// Publisher
async function publishEvent(channel: string, event: object): Promise<void> {
await redis.publish(channel, JSON.stringify(event));
}
// Subscriber
const subscriber = redis.duplicate();
subscriber.subscribe("events");
subscriber.on("message", (channel, message) => {
const event = JSON.parse(message);
handleEvent(event);
});
```
---
## Query Optimization Checklist
### PostgreSQL
- [ ] Use EXPLAIN ANALYZE for slow queries
- [ ] Create indexes for WHERE, JOIN, ORDER BY columns
- [ ] Use partial indexes for filtered queries
- [ ] Use connection pooling (pgbouncer)
- [ ] Regular VACUUM and ANALYZE
### MongoDB
- [ ] Create compound indexes matching query patterns
- [ ] Use covered queries when possible
- [ ] Avoid large array fields in documents
- [ ] Monitor with explain()
### Redis
- [ ] Use appropriate data structures
- [ ] Set TTL on cache keys
- [ ] Use pipelining for bulk operations
- [ ] Monitor memory usage
+ ---
+
+ ## Vector Databases for AI/RAG Workloads
+
+ ### Overview
+
+ Vector databases store high-dimensional embeddings and enable similarity search, which is the foundation of RAG (Retrieval-Augmented Generation) and semantic search applications.
+
+ | Database | Type | Best For |
+ | --------------- | --------------------- | ---------------------------------------- |
+ | **pgvector** | PostgreSQL extension | Existing Postgres stacks, hybrid queries |
+ | **Pinecone** | Managed cloud | Production scale, serverless |
+ | **Weaviate** | Self-hosted/cloud | Multimodal, GraphQL interface |
+ | **Chroma** | Embedded/local | Prototyping, small datasets |
+ | **Qdrant** | Self-hosted/cloud | High performance, rich filtering |
+ | **Milvus** | Self-hosted/cloud | Large-scale, distributed |
+
+ ### pgvector (PostgreSQL Extension)
+
+ ```sql
+ -- Enable extension
+ CREATE EXTENSION vector;
+
+ -- Create table with vector column
+ CREATE TABLE documents (
+ id SERIAL PRIMARY KEY,
+ content TEXT NOT NULL,
+ metadata JSONB DEFAULT '{}',
+ embedding VECTOR(1536), -- OpenAI text-embedding-3-small dimension
+ created_at TIMESTAMPTZ DEFAULT NOW()
+ );
+
+ -- Create HNSW index (faster search, more memory)
+ CREATE INDEX idx_documents_embedding ON documents
+ USING hnsw (embedding vector_cosine_ops)
+ WITH (m = 16, ef_construction = 64);
+
+ -- Or IVFFlat index (less memory, requires training)
+ CREATE INDEX idx_documents_embedding_ivf ON documents
+ USING ivfflat (embedding vector_cosine_ops)
+ WITH (lists = 100);
+
+ -- Similarity search (cosine distance)
+ SELECT id, content, metadata,
+ 1 - (embedding <=> $1::vector) AS similarity
+ FROM documents
+ WHERE metadata @> '{"category": "technical"}' -- Combine with metadata filter
+ ORDER BY embedding <=> $1::vector
+ LIMIT 10;
+
+ -- Hybrid search: combine full-text + vector
+ SELECT id, content,
+ ts_rank(search_vector, plainto_tsquery($1)) AS text_score,
+ 1 - (embedding <=> $2::vector) AS vector_score
+ FROM documents
+ WHERE search_vector @@ plainto_tsquery($1)
+ ORDER BY (0.3 * ts_rank(search_vector, plainto_tsquery($1))
+ + 0.7 * (1 - (embedding <=> $2::vector))) DESC
+ LIMIT 10;
+ ```
+
+ ### Pinecone (Managed)
+
+ ```python
+ from pinecone import Pinecone, ServerlessSpec
+
+ pc = Pinecone(api_key="xxx")
+
+ # Create index
+ pc.create_index(
+ name="documents",
+ dimension=1536,
+ metric="cosine",
+ spec=ServerlessSpec(cloud="aws", region="us-east-1"),
+ )
+
+ index = pc.Index("documents")
+
+ # Upsert with metadata
+ index.upsert(vectors=[
+ {
+ "id": "doc1",
+ "values": embedding_vector,
+ "metadata": {"source": "manual", "category": "faq"},
+ },
+ ])
+
+ # Query with metadata filter
+ results = index.query(
+ vector=query_embedding,
+ top_k=5,
+ include_metadata=True,
+ filter={"category": {"$eq": "faq"}},
+ )
+ ```
+
+ ### Chroma (Local/Embedded)
+
+ ```python
+ import chromadb
+
+ client = chromadb.PersistentClient(path="./chroma_db")
+
+ collection = client.get_or_create_collection(
+ name="documents",
+ metadata={"hnsw:space": "cosine"},
+ )
+
+ # Add documents (auto-embeds with default model)
+ collection.add(
+ documents=["Document text 1", "Document text 2"],
+ metadatas=[{"source": "web"}, {"source": "pdf"}],
+ ids=["doc1", "doc2"],
+ )
+
+ # Or add pre-computed embeddings
+ collection.add(
+ embeddings=[vector1, vector2],
+ metadatas=[{"source": "web"}, {"source": "pdf"}],
+ ids=["doc1", "doc2"],
+ )
+
+ # Query
+ results = collection.query(
+ query_texts=["search query"],
+ n_results=5,
+ where={"source": "web"},
+ )
+ ```
+
+ ### Embedding Storage and Indexing Best Practices
+
+ | Consideration | Recommendation |
+ | -------------------- | ------------------------------------------------------ |
+ | **Embedding model** | text-embedding-3-small (1536d) or nomic-embed (768d) |
+ | **Index type** | HNSW for <1M vectors, IVFFlat for >1M |
+ | **Dimensionality** | Lower dims = faster search, slightly less accuracy |
+ | **Batch inserts** | Batch 100-1000 vectors per upsert call |
+ | **Metadata** | Store filterable attributes alongside vectors |
+ | **Hybrid search** | Combine keyword (BM25) + vector for best results |
+ | **Reranking** | Use cross-encoder reranker on top-k results |
+ | **Chunking** | 500-1000 tokens per chunk with 100-200 overlap |
+