Cloudflare R2 D1

Cloudflare R2, D1 & Storage Products

Comprehensive guide for Cloudflare's edge storage products: R2 (object storage), D1 (SQLite database), and KV (key-value store).

Sources

• Cloudflare Storage Options
• D1 Limits
• R2 Workers API
• Workers Limits

When to Use What

Decision tree:

• Need SQL queries? → D1
• Storing files/blobs? → R2
• High-read, low-write config? → KV
• Real-time state coordination? → Durable Objects

D1 SQLite Database

Critical Limitations

Performance Characteristics

• Single-threaded: Each D1 database processes queries sequentially
• Throughput formula: If avg query = 1ms → ~1,000 QPS; if 100ms → 10 QPS
• Read queries: < 1ms with proper indexes
• Write queries: Several ms (must be durably persisted)

Gotchas

1. No traditional transactions

// WRONG - BEGIN TRANSACTION not supported in Workers
await db.exec('BEGIN TRANSACTION');

// CORRECT - Use batch() for atomic operations
const results = await db.batch([
  db.prepare('INSERT INTO users (name) VALUES (?)').bind('Alice'),
  db.prepare('INSERT INTO logs (action) VALUES (?)').bind('user_created'),
]);

2. Large migrations must be batched

// WRONG - Will exceed execution limits
await db.exec('DELETE FROM logs WHERE created_at < ?', oldDate);

// CORRECT - Batch in chunks
while (true) {
  const result = await db.prepare(
    'DELETE FROM logs WHERE id IN (SELECT id FROM logs WHERE created_at < ? LIMIT 1000)'
  ).bind(oldDate).run();
  if (result.changes === 0) break;
}

3. Int64 precision loss

// JavaScript numbers are 53-bit precision
// Storing 9007199254740993 may return 9007199254740992
// Use TEXT for large integers if precision matters

4. Cannot import MySQL/PostgreSQL dumps directly

• Must convert to SQLite-compatible SQL
• Cannot import raw .sqlite3 files
• Large string values (~500KB+) may fail due to SQL length limits

wrangler.toml Configuration

[[d1_databases]]
binding = "DB"
database_name = "my-database"
database_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"

# For local development (auto-creates if missing in wrangler 4.45+)
[[d1_databases]]
binding = "DB"
database_name = "my-database"

Common Patterns

Schema migrations:

// migrations/0001_initial.sql
CREATE TABLE IF NOT EXISTS users (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  email TEXT UNIQUE NOT NULL,
  created_at TEXT DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX IF NOT EXISTS idx_users_email ON users(email);

# Apply migrations
wrangler d1 migrations apply my-database

Multi-tenant pattern:

// Create per-tenant database
// D1 allows thousands of databases at no extra cost
const tenantDb = env[`DB_${tenantId}`];

R2 Object Storage

Key Features

• S3-compatible API (with some differences)
• No egress fees (major cost advantage over S3)
• Strong consistency - reads immediately see writes
• Workers integration - direct binding, no network hop

wrangler.toml Configuration

[[r2_buckets]]
binding = "BUCKET"
bucket_name = "my-bucket"

# With jurisdiction (data residency)
[[r2_buckets]]
binding = "EU_BUCKET"
bucket_name = "eu-data"
jurisdiction = "eu"

Common Operations

export default {
  async fetch(request, env) {
    const url = new URL(request.url);
    const key = url.pathname.slice(1);

    switch (request.method) {
      case 'PUT': {
        // Upload object
        await env.BUCKET.put(key, request.body, {
          httpMetadata: {
            contentType: request.headers.get('content-type'),
          },
          customMetadata: {
            uploadedBy: 'user-123',
          },
        });
        return new Response('Uploaded', { status: 201 });
      }

      case 'GET': {
        // Download object
        const object = await env.BUCKET.get(key);
        if (!object) {
          return new Response('Not Found', { status: 404 });
        }
        return new Response(object.body, {
          headers: {
            'content-type': object.httpMetadata?.contentType || 'application/octet-stream',
            'etag': object.etag,
          },
        });
      }

      case 'DELETE': {
        await env.BUCKET.delete(key);
        return new Response('Deleted', { status: 200 });
      }

      case 'HEAD': {
        const object = await env.BUCKET.head(key);
        if (!object) {
          return new Response(null, { status: 404 });
        }
        return new Response(null, {
          headers: {
            'content-length': object.size.toString(),
            'etag': object.etag,
          },
        });
      }
    }
  },
};

Gotchas

1. Memory limits when processing large files

// WRONG - Loads entire file into memory (128MB Worker limit)
const object = await env.BUCKET.get(key);
const data = await object.text();

// CORRECT - Stream for large files
const object = await env.BUCKET.get(key);
return new Response(object.body); // Stream directly

2. Request body can only be read once

// WRONG - Body already consumed
const data = await request.text();
await env.BUCKET.put(key, request.body); // Fails!

// CORRECT - Clone request first
const clone = request.clone();
const data = await request.text();
await env.BUCKET.put(key, clone.body);

3. List operations return max 1000 keys

// Paginate through all objects
let cursor;
const allKeys = [];

do {
  const listed = await env.BUCKET.list({ cursor, limit: 1000 });
  allKeys.push(...listed.objects.map(o => o.key));
  cursor = listed.truncated ? listed.cursor : null;
} while (cursor);

Presigned URLs (S3-compatible)

import { AwsClient } from 'aws4fetch';

const r2 = new AwsClient({
  accessKeyId: env.R2_ACCESS_KEY,
  secretAccessKey: env.R2_SECRET_KEY,
});

// Generate presigned upload URL
const signedUrl = await r2.sign(
  new Request(`https://${env.R2_BUCKET}.r2.cloudflarestorage.com/${key}`, {
    method: 'PUT',
  }),
  { aws: { signQuery: true } }
);

KV (Key-Value Store)

When to Use KV

• Session tokens / auth data
• Feature flags / configuration
• Cached API responses
• Data with high reads, low writes

Critical Limitation

wrangler.toml Configuration

[[kv_namespaces]]
binding = "CACHE"
id = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Common Operations

// Write (with optional TTL)
await env.CACHE.put('user:123', JSON.stringify(userData), {
  expirationTtl: 3600, // 1 hour
});

// Read
const data = await env.CACHE.get('user:123', { type: 'json' });

// Delete
await env.CACHE.delete('user:123');

// List keys with prefix
const keys = await env.CACHE.list({ prefix: 'user:' });

Automatic Resource Provisioning (2025)

As of wrangler 4.45+, resources are auto-created:

# wrangler.toml - No IDs needed for new resources
[[d1_databases]]
binding = "DB"
database_name = "my-app-db"

[[r2_buckets]]
binding = "BUCKET"
bucket_name = "my-app-files"

[[kv_namespaces]]
binding = "CACHE"

# First deploy auto-creates resources
wrangler deploy

Full-Stack Pattern: D1 + R2 + KV

export default {
  async fetch(request, env) {
    const url = new URL(request.url);

    // KV: Check cache first
    const cached = await env.CACHE.get(url.pathname);
    if (cached) return new Response(cached);

    // D1: Query database
    const { results } = await env.DB.prepare(
      'SELECT * FROM posts WHERE slug = ?'
    ).bind(url.pathname).all();

    if (!results.length) {
      return new Response('Not Found', { status: 404 });
    }

    const post = results[0];

    // R2: Get associated image
    const image = post.image_key
      ? await env.BUCKET.get(post.image_key)
      : null;

    // Cache the response
    const html = renderPost(post, image);
    await env.CACHE.put(url.pathname, html, { expirationTtl: 300 });

    return new Response(html, {
      headers: { 'content-type': 'text/html' },
    });
  },
};

Cost Optimization

Free Tier Limits

Tips

1. Use KV for caching to reduce D1 reads
2. Batch D1 writes to minimize write operations
3. Stream R2 objects instead of loading into memory
4. Set TTLs on KV to auto-expire stale data
5. Shard D1 databases per-tenant for horizontal scale

Troubleshooting

"D1_ERROR: too many SQL variables"

Split large IN clauses into batched queries.

"R2: EntityTooLarge"

Files > 5GB must use multipart upload.

"KV: Too many writes"

You're hitting 1 write/sec/key limit. Use D1 or Durable Objects.

"Worker exceeded CPU time limit"

• Add indexes to D1 queries
• Stream R2 objects instead of buffering
• Split work across multiple requests