wavelet

name: wavelet description: Set up and configure Wavelet projects. Use when the user wants to add events, queries, or subscriptions to a wavelet.config.ts, integrate the Wavelet SDK or React hooks, or set up MCP for agent integration.

Wavelet

Wavelet is a reactive backend that pushes pre-computed SQL query results to apps and AI agents over WebSocket.

Core Concepts

• Events -- event ingestion tables. Defined with typed columns.
• Queries -- SQL materialized views that incrementally recompute when source data changes.
• Subscriptions -- WebSocket connections that receive diffs when a query changes.
• filterBy -- JWT-based per-tenant row filtering, enforced server-side.

Config File: wavelet.config.ts

Everything is defined in a single config file. The full type signature:

import { defineConfig, sql } from '@risingwave/wavelet'

export default defineConfig({
  database: 'postgres://root@localhost:4566/dev',

  events: {
    // Event ingestion. Column types: 'string' | 'int' | 'float' | 'boolean' | 'timestamp' | 'json'
    game_events: {
      columns: {
        user_id: 'string',
        action: 'string',
        value: 'int',
        metadata: 'json',
        ts: 'timestamp',
      }
    }
  },

  // CDC from existing Postgres (optional)
  sources: {
    my_postgres: {
      type: 'postgres',
      connection: 'postgres://user:pass@host:5432/db',
      tables: ['users', 'products'],
    }
  },

  queries: {
    // Simple query -- just SQL
    totals: sql`
      SELECT user_id, SUM(value) AS total
      FROM game_events GROUP BY user_id
    `,

    // Query with per-tenant filtering
    tenant_metrics: {
      query: sql`
        SELECT tenant_id, COUNT(*) AS count
        FROM game_events GROUP BY tenant_id
      `,
      filterBy: 'tenant_id',
    },

    // Query with explicit column types (enables offline codegen without RisingWave)
    stats: {
      query: sql`SELECT ...`,
      columns: { user_id: 'string', total: 'int' },
    },
  },

  jwt: {
    secret: process.env.JWT_SECRET,
    // or: jwksUrl: 'https://.../.well-known/jwks.json'
    // optional: issuer, audience
  },

  server: {
    port: 8080,    // default
    host: '0.0.0.0',
  },
})

CLI Commands

npx wavelet init       # Create wavelet.config.ts template
npx wavelet dev        # Sync config to RisingWave + start server (auto-starts RisingWave)
npx wavelet push       # Sync config to RisingWave without starting server
npx wavelet generate   # Generate typed client at .wavelet/client.ts
npx wavelet status     # Show config summary

All commands are idempotent.

SDK Usage

After running npx wavelet generate, a typed client is available at .wavelet/client.ts.

React

import { useWavelet } from './.wavelet/client'

function Component() {
  const { data, isLoading, error } = useWavelet('query_name')
  // data is fully typed based on the query's columns
}

TypeScript Client

import { TypedWaveletClient } from './.wavelet/client'

const wavelet = new TypedWaveletClient({
  url: 'http://localhost:8080',
  token: 'jwt-token',  // or: () => getToken()
})

// Read query
const rows = await wavelet.queries.query_name.get()

// Subscribe to live updates
const unsub = wavelet.queries.query_name.subscribe({
  onData: (diff) => {
    // diff.inserted, diff.updated, diff.deleted
  },
})

// Write events
await wavelet.events.event_name.emit({ key: 'value' })
await wavelet.events.event_name.emitBatch([{ key: 'v1' }, { key: 'v2' }])

HTTP API

GET  /v1/health                    -> { status: "ok" }
GET  /v1/queries                   -> { queries: [...] }
GET  /v1/queries/{name}            -> { query: "name", rows: [...] }
GET  /v1/queries/{name}?key=value  -> filtered rows
GET  /v1/events                    -> { events: [...] }
POST /v1/events/{name}             -> { ok: true }
POST /v1/events/{name}/batch       -> { ok: true, count: N }
WS   /subscribe/{name}             -> real-time diffs

MCP Setup (for AI agents)

Add to your MCP config (Codex Desktop, Cursor, etc.):

{
  "mcpServers": {
    "wavelet": {
      "command": "npx",
      "args": ["@risingwave/wavelet-mcp"],
      "env": {
        "WAVELET_DATABASE_URL": "postgres://root@localhost:4566/dev"
      }
    }
  }
}

Available tools: list_queries, query, list_events, emit_event, emit_batch, run_sql.

SQL Patterns for Queries

Queries are standard SQL running on RisingWave. Key patterns:

-- Aggregation
SELECT user_id, SUM(amount) AS total FROM orders GROUP BY user_id

-- Windowed aggregation
SELECT user_id, COUNT(*) AS recent_orders
FROM orders WHERE created_at > NOW() - INTERVAL '1 hour'
GROUP BY user_id

-- Threshold / alert (rows appear/disappear as condition changes)
SELECT user_id, COUNT(*) AS tx_count
FROM transactions GROUP BY user_id HAVING COUNT(*) > 100

-- Join events with reference tables
SELECT o.*, u.name, p.category
FROM orders o JOIN users u ON o.user_id = u.id JOIN products p ON o.product_id = p.id

-- Window functions
SELECT symbol, price,
       AVG(price) OVER (PARTITION BY symbol ORDER BY ts ROWS 50 PRECEDING) AS ma50
FROM trades

Common Tasks

Add a new event + query

1. Add event columns to wavelet.config.ts under events
2. Add a SQL query under queries that references the event table
3. Run npx wavelet dev (or npx wavelet push if server is already running)
4. Run npx wavelet generate to update the typed client

Add per-tenant filtering

Use the object form for the query with filterBy:

queries: {
  my_query: {
    query: sql`SELECT tenant_id, ... FROM ... GROUP BY tenant_id`,
    filterBy: 'tenant_id',
  }
}

The filterBy column must appear in the SELECT. Clients with a JWT containing { tenant_id: "t1" } only receive rows where tenant_id = 't1'.

Ingest from existing Postgres

Add a CDC source:

sources: {
  prod_db: {
    type: 'postgres',
    connection: process.env.POSTGRES_URL,
    tables: ['users', 'orders'],
  }
}

Tables become available in queries as prod_db_users, prod_db_orders.