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Cloudflare Workers KV

Status : Production Ready ✅ Last Updated : 2026-01-20 Dependencies : cloudflare-worker-base (for Worker setup) Latest Versions : wrangler@4.59.2, @cloudflare/workers-types@4.20260109.0

Recent Updates (2025) :

• August 2025 : Architecture redesign (40x performance gain, <5ms p99 latency, hybrid storage with R2)
• April 2025 : Bulk reads API (retrieve up to 100 keys in single request, counts as 1 operation)
• January 2025 : Namespace limit increased (200 → 1,000 namespaces per account for Free and Paid plans)

Quick Start (5 Minutes)

# Create namespace
npx wrangler kv namespace create MY_NAMESPACE
# Output: [[kv_namespaces]] binding = "MY_NAMESPACE" id = "<UUID>"

wrangler.jsonc:

{
  "kv_namespaces": [{
    "binding": "MY_NAMESPACE",  // Access as env.MY_NAMESPACE
    "id": "<production-uuid>",
    "preview_id": "<preview-uuid>"  // Optional: local dev
  }]
}

Basic Usage:

type Bindings = { MY_NAMESPACE: KVNamespace };

app.post('/set/:key', async (c) => {
  await c.env.MY_NAMESPACE.put(c.req.param('key'), await c.req.text());
  return c.json({ success: true });
});

app.get('/get/:key', async (c) => {
  const value = await c.env.MY_NAMESPACE.get(c.req.param('key'));
  return value ? c.json({ value }) : c.json({ error: 'Not found' }, 404);
});

KV API Reference

Read Operations

// Get single key
const value = await env.MY_KV.get('key');  // string | null
const data = await env.MY_KV.get('key', { type: 'json' });  // object | null
const buffer = await env.MY_KV.get('key', { type: 'arrayBuffer' });
const stream = await env.MY_KV.get('key', { type: 'stream' });

// Get with cache (minimum 60s)
const value = await env.MY_KV.get('key', { cacheTtl: 300 });  // 5 min edge cache

// Bulk read (counts as 1 operation)
const values = await env.MY_KV.get(['key1', 'key2']);  // Map<string, string | null>

// With metadata
const { value, metadata } = await env.MY_KV.getWithMetadata('key');
const result = await env.MY_KV.getWithMetadata(['key1', 'key2']);  // Bulk with metadata

Write Operations

// Basic write (max 1/second per key)
await env.MY_KV.put('key', 'value');
await env.MY_KV.put('user:123', JSON.stringify({ name: 'John' }));

// With expiration
await env.MY_KV.put('session', data, { expirationTtl: 3600 });  // 1 hour
await env.MY_KV.put('token', value, { expiration: Math.floor(Date.now()/1000) + 86400 });

// With metadata (max 1024 bytes)
await env.MY_KV.put('config', 'dark', {
  metadata: { updatedAt: Date.now(), version: 2 }
});

Critical Limits:

• Key: 512 bytes max
• Value: 25 MiB max
• Metadata: 1024 bytes max
• Write rate: 1/second per key (429 error if exceeded)
• Expiration: 60 seconds minimum

List Operations

// List with pagination
const result = await env.MY_KV.list({ prefix: 'user:', limit: 1000, cursor });
// result: { keys: [], list_complete: boolean, cursor?: string }

// CRITICAL: Always check list_complete, not keys.length === 0
let cursor: string | undefined;
do {
  const result = await env.MY_KV.list({ prefix: 'user:', cursor });
  processKeys(result.keys);
  cursor = result.list_complete ? undefined : result.cursor;
} while (cursor);

Delete Operations

// Delete single key
await env.MY_KV.delete('key');  // Always succeeds

// Bulk delete (CLI only, up to 10,000 keys)
// npx wrangler kv bulk delete --binding=MY_KV keys.json

Advanced Patterns

Caching Pattern with CacheTtl

async function getCachedData(kv: KVNamespace, key: string, fetchFn: () => Promise<any>, ttl = 300) {
  const cached = await kv.get(key, { type: 'json', cacheTtl: ttl });
  if (cached) return cached;

  const data = await fetchFn();
  await kv.put(key, JSON.stringify(data), { expirationTtl: ttl * 2 });
  return data;
}

Guidelines : Minimum 60s, use for read-heavy workloads (100:1 read/write ratio)

Metadata Optimization

// Store small values (<1024 bytes) in metadata to avoid separate get() calls
await env.MY_KV.put('user:123', '', {
  metadata: { status: 'active', plan: 'pro', lastSeen: Date.now() }
});

// list() returns metadata automatically (no additional get() calls)
const users = await env.MY_KV.list({ prefix: 'user:' });
users.keys.forEach(({ name, metadata }) => console.log(name, metadata.status));

Understanding Hot vs Cold Keys

KV performance varies based on key temperature:

Post-August 2025 Improvements :

• P90 for all KV Worker invocations: <12ms (was 22ms before)
• Hot reads up to 3x faster
• All operations faster by up to 20ms

Optimization : Use key coalescing to make cold keys benefit from hot key caching:

// ❌ Bad: Many cold keys (300ms each)
await kv.put('user:123:name', 'John');
await kv.put('user:123:email', 'john@example.com');
await kv.put('user:123:plan', 'pro');

// Each read of a cold key: ~100-300ms
const name = await kv.get('user:123:name');    // Cold
const email = await kv.get('user:123:email');  // Cold
const plan = await kv.get('user:123:plan');    // Cold

// ✅ Good: Single hot key (6-8ms)
await kv.put('user:123', JSON.stringify({
  name: 'John',
  email: 'john@example.com',
  plan: 'pro'
}));

// Single read, cached as hot key: ~6-8ms
const user = JSON.parse(await kv.get('user:123'));

CacheTtl helps cold keys : For infrequently-read data, cacheTtl reduces cold read latency.

Trade-off : Coalescing requires read-modify-write for updates

Pagination Helper

async function* paginateKV(kv: KVNamespace, options: { prefix?: string } = {}) {
  let cursor: string | undefined;
  do {
    const result = await kv.list({ ...options, cursor });
    yield result.keys;
    cursor = result.list_complete ? undefined : result.cursor;
  } while (cursor);
}

// Usage
for await (const keys of paginateKV(env.MY_KV, { prefix: 'user:' })) {
  processKeys(keys);
}

Rate Limit Retry with Exponential Backoff

async function putWithRetry(kv: KVNamespace, key: string, value: string, opts?: KVPutOptions) {
  let attempts = 0, delay = 1000;
  while (attempts < 5) {
    try {
      await kv.put(key, value, opts);
      return;
    } catch (error) {
      if ((error as Error).message.includes('429')) {
        attempts++;
        if (attempts >= 5) throw new Error('Max retry attempts');
        await new Promise(r => setTimeout(r, delay));
        delay *= 2;  // Exponential backoff
      } else throw error;
    }
  }
}

Understanding Eventual Consistency

KV is eventually consistent across Cloudflare's global network (Aug 2025 redesign: hybrid storage, <5ms p99 latency):

How It Works:

1. Writes immediately visible in same location (read-your-own-write consistency within same POP)
2. Other locations see update within ~60 seconds (or cacheTtl value)
3. Cached reads may return stale data during propagation

Example:

// Tokyo: Write
await env.MY_KV.put('counter', '1');
const value = await env.MY_KV.get('counter'); // "1" ✅ (same POP, RYOW)

// London (within 60s): May be stale ⚠️
const value2 = await env.MY_KV.get('counter'); // Might be old value

// After 60+ seconds: Consistent ✅

Read-Your-Own-Write (RYOW) Guarantee : Since August 2025 redesign, requests routed through the same Cloudflare point of presence see their own writes immediately. Global consistency across different POPs still takes up to 60 seconds.

Timestamp Mitigation Pattern (for critical consistency needs):

// Use timestamp in key structure to avoid consistency issues
const timestamp = Date.now();
await kv.put(`user:123:${timestamp}`, userData);

// Find latest using list with prefix
const result = await kv.list({ prefix: 'user:123:' });
const latestKey = result.keys.sort((a, b) =>
  parseInt(b.name.split(':')[2]) - parseInt(a.name.split(':')[2])
).at(0);

Use KV for : Read-heavy workloads (100:1 ratio), config, feature flags, caching, user preferences Don't use KV for : Financial transactions, strong consistency, >1/second writes per key, critical data

Need strong consistency? Use Durable Objects

Source : Redesigning Workers KV

Wrangler CLI Essentials

# Create namespace
npx wrangler kv namespace create MY_NAMESPACE [--preview]

# Manage keys (add --remote flag to access production data)
npx wrangler kv key put --binding=MY_KV "key" "value" [--ttl=3600] [--metadata='{}']
npx wrangler kv key get --binding=MY_KV "key" [--remote]
npx wrangler kv key list --binding=MY_KV [--prefix="user:"] [--remote]
npx wrangler kv key delete --binding=MY_KV "key"

# Bulk operations (up to 10,000 keys)
npx wrangler kv bulk put --binding=MY_KV data.json
npx wrangler kv bulk delete --binding=MY_KV keys.json

IMPORTANT : CLI commands default to local storage. Add --remote flag to access production/remote data.

Development vs Production

Remote Bindings for Local Development (Wrangler 4.37+)

Connect local Workers to production KV namespaces during development:

wrangler.jsonc:

{
  "kv_namespaces": [{
    "binding": "MY_KV",
    "id": "production-uuid",
    "remote": true  // Connect to live KV
  }]
}

How It Works:

• Local Worker code executes locally (fast iteration)
• KV operations route to production namespace through proxy
• No manual data seeding required

Benefits:

• Test against real production data without deploying
• Fast local code execution with production data access
• Faster feedback loop (no deploy-test cycle)

⚠️ Warning : Writes affect production data. Consider using a staging namespace with remote: true instead of production.

Version Support:

• Wrangler 4.37.0+
• @cloudflare/vite-plugin 1.13.0+
• @cloudflare/vitest-pool-workers 0.9.0+

Source : Remote bindings architecture

Limits & Quotas

Critical : 1 write/second per key (429 if exceeded), bulk operations count as 1 operation, namespace limit increased from 200 → 1,000 (Jan 2025)

Error Handling

1. Rate Limit (429 Too Many Requests)

Cause : Writing to same key >1/second Solution : Use retry with exponential backoff (see Advanced Patterns)

// ❌ Bad
await env.MY_KV.put('counter', '1');
await env.MY_KV.put('counter', '2'); // 429 error!

// ✅ Good
await putWithRetry(env.MY_KV, 'counter', '2');

2. Value Too Large

Cause : Value exceeds 25 MiB Solution : Validate size before writing

if (value.length > 25 * 1024 * 1024) throw new Error('Value exceeds 25 MiB');

3. Metadata Too Large

Cause : Metadata exceeds 1024 bytes when serialized Solution : Validate serialized size

const serialized = JSON.stringify(metadata);
if (serialized.length > 1024) throw new Error('Metadata exceeds 1024 bytes');

4. Invalid CacheTtl

Cause : cacheTtl <60 seconds Solution : Use minimum 60

// ❌ Error
await env.MY_KV.get('key', { cacheTtl: 30 });

// ✅ Correct
await env.MY_KV.get('key', { cacheTtl: 60 });

Critical Rules

Always Do ✅

1. Use bulk operations when reading multiple keys (counts as 1 operation)
2. Set cacheTtl for frequently-read, infrequently-updated data (min 60s)
3. Store small values (<1024 bytes) in metadata when using list() frequently
4. Check list_complete when paginating, not keys.length === 0
5. Use retry logic with exponential backoff for write operations
6. Validate sizes before writing (key 512B, value 25MiB, metadata 1KB)
7. Coalesce related keys for better caching performance
8. Use KV for read-heavy workloads (100:1 read/write ratio ideal)

Never Do ❌

1. Never write to same key >1/second (causes 429 rate limit errors)
2. Never assume immediate global consistency (takes ~60 seconds to propagate)
3. Never use KV for atomic operations (use Durable Objects instead)
4. Never set cacheTtl <60 seconds (will fail)
5. Never commit namespace IDs to public repos (use environment variables)
6. Never exceed 1000 operations per invocation (use bulk operations)
7. Never rely on write order (eventual consistency = no guarantees)
8. Never forget to handle null values (get() returns null if key doesn't exist)

Troubleshooting

Issue 1: "429 Too Many Requests" on writes

Cause : Writing to same key >1/second Solution : Consolidate writes or use retry with exponential backoff

// ❌ Bad: Rate limit
for (let i = 0; i < 10; i++) await kv.put('counter', String(i));

// ✅ Good: Single write
await kv.put('counter', '9');

// ✅ Good: Retry with backoff
await putWithRetry(kv, 'counter', String(i));

Issue 2: Stale reads after write

Cause : Eventual consistency (~60 seconds propagation) Solution : Accept stale reads, use Durable Objects for strong consistency, or implement app-level cache invalidation

Issue 3: "Operations limit exceeded"

Cause : >1000 KV operations in single Worker invocation Solution : Use bulk operations

// ❌ Bad: 5000 operations
for (const key of 5000keys) await kv.get(key);

// ✅ Good: 1 operation
const values = await kv.get(keys);  // Bulk read

Issue 4: List returns empty but cursor exists

Cause : Deleted/expired keys create "tombstones" that must be iterated through Solution : Always check list_complete, not keys.length

// ✅ Correct pagination
let cursor: string | undefined;
do {
  const result = await kv.list({ cursor });
  processKeys(result.keys);  // Even if empty
  cursor = result.list_complete ? undefined : result.cursor;
} while (cursor);

CRITICAL : When using prefix, you must include it in all paginated calls :

// ❌ WRONG - Loses prefix on subsequent pages
let result = await kv.list({ prefix: 'user:' });
result = await kv.list({ cursor: result.cursor });  // Missing prefix!

// ✅ CORRECT - Include prefix on every call
let cursor: string | undefined;
do {
  const result = await kv.list({ prefix: 'user:', cursor });
  processKeys(result.keys);
  cursor = result.list_complete ? undefined : result.cursor;
} while (cursor);

Source : List keys documentation

Issue 5: wrangler types Does Not Generate Types for Environment-Nested KV Bindings

Cause : KV namespaces defined within environment configurations (e.g., [env.feature.kv_namespaces]) are not included in generated TypeScript types Impact : Loss of TypeScript autocomplete and type checking for KV bindings Source : GitHub Issue #9709

Example Configuration:

# wrangler.toml
[env.feature]
name = "my-worker-feature"
[[env.feature.kv_namespaces]]
binding = "MY_STORAGE_FEATURE"
id = "xxxxxxxxxxxx"

Running npx wrangler types creates type definitions for environment variables but not for the KV namespace bindings.

Workaround:

# Generate types for specific environment
npx wrangler types -e feature

Or define KV namespaces at top level instead of nested in environments:

# Top-level (types generated correctly)
[[kv_namespaces]]
binding = "MY_STORAGE"
id = "xxxxxxxxxxxx"

Note : Runtime bindings still work correctly; this only affects type generation.

Issue 6: wrangler kv key list Returns Empty Array for Remote Data

Cause : CLI commands default to local storage , not remote/production KV Impact : Users expect to see production data but get empty array from local storage Source : GitHub Issue #10395

Solution : Use --remote flag to access production/remote data

# ❌ Shows local storage (likely empty)
npx wrangler kv key list --binding=MY_KV

# ✅ Shows remote/production data
npx wrangler kv key list --binding=MY_KV --remote

Why This Happens : By design, wrangler dev uses local KV storage to avoid interfering with production data. CLI commands follow the same default for consistency.

Applies to : All wrangler kv key commands (get, list, delete, put)

Production Checklist

• Environment-specific namespaces configured (id vs preview_id)
• Namespace IDs stored in environment variables (not hardcoded)
• Rate limit retry logic implemented for writes
• Appropriate cacheTtl values set for reads (min 60s)
• Sizes validated (key 512B, value 25MiB, metadata 1KB)
• Bulk operations used where possible
• Pagination with list_complete check (not keys.length)
• Error handling for null values
• Monitoring/alerting for rate limits

Related Documentation

• Cloudflare KV Docs
• KV API Reference
• KV Limits
• How KV Works
• Wrangler KV Commands

Last Updated : 2026-01-20 Package Versions : wrangler@4.59.2, @cloudflare/workers-types@4.20260109.0 Changes : Added 6 research findings - hot/cold key performance patterns, remote bindings (Wrangler 4.37+), wrangler types environment issue, CLI --remote flag requirement, RYOW consistency details, prefix persistence in pagination

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