This content originally appeared on DEV Community and was authored by Uma Victor
You know that moment when customers start hammering your "Check Payment Status" button during a flash sale? They're anxious about their $500 purchase, so they are refreshing every few seconds. Each click triggers a database query. What started as 1,000 requests per minute becomes 50,000. Your API response time crawls from 50ms to 800ms, leading customers to abandon their carts.
If you're building payment systems that need to handle serious traffic, you've probably hit this wall too. The solution isn't throwing more database servers at the problem; it's implementing smart caching strategies that keep your most frequent queries lightning-fast.
This guide walks you through a complete implementation of distributed caching for payment lookups. Using Redis and real-world examples with Flutterwave integration, you'll gain the knowledge to handle peak transaction loads without breaking a sweat.
What Is Distributed Caching?
A distributed data cache works by spreading your data across multiple servers, or multiple nodes, instead of being constrained by the memory limits of a single machine. This in-memory storage approach is like having checkout lanes at every entrance of a store, helping to reduce network traffic and latency by serving frequently accessed data from a location closer to the user.
In a payment system, this means your transaction data, user session data, and API responses are stored across several cache servers. When one server goes down or gets overloaded, the others keep serving requests. More importantly, you can scale your cache capacity by adding more servers.
While traditional single-server caching can be scaled vertically (adding more RAM/CPU to the existing server), it has capacity limits that distributed caching overcomes.
Here is an example of a single server cache (all data on one Redis instance):
const cache = redis.createClient({ host: 'cache-server-1' });
// Distributed cache - data spread across multiple servers
const distributedCache = new Redis.Cluster([
{ host: 'cache-1', port: 6379 },
{ host: 'cache-2', port: 6379 },
{ host: 'cache-3', port: 6379 }
]);
Distributed vs. Single-Server Caching: Why It Matters for Payments
Regular caching works fine for small applications, but payment systems have different requirements:
Normal Caching Limitations
- Single point of failure: If your cache server crashes, all cached data disappears.
- Memory limits: You're stuck with whatever RAM one server has.
- Geographic latency: Users far from your cache server get slower responses.
- Load concentration: All cache requests hit one server.
Distributed Caching Benefits
- High availability: With data distributed across multiple servers, the system remains operational and accessible even if one or more servers experience issues.
- Horizontal scaling: Add more servers when you need more capacity.
- Geographic distribution: Cache servers are closer to users.
- Load distribution: Requests are spread across multiple servers.
These differences are critical for payment systems processing thousands of transactions per minute. A single cache server might handle 10,000 requests per second, but a distributed cache can handle 100,000+ by spreading the load.
For example, all data is directed to a single cache in a standard caching setup. In contrast, with distributed caching, the data is automatically spread across multiple nodes by the caching system (like Redis Cluster). This distribution also means that if one cache server in the cluster (e.g., cache-server-2) becomes unavailable, the data remains accessible from the other active servers (e.g., cache-server-1 and cache-server-3).
// Normal caching: One server handling everything
const singleCache = redis.createClient();
await singleCache.set('tx:12345', transactionData); // All data goes here
// Distributed caching: Data automatically distributed
const cluster = new Redis.Cluster(nodes);
await cluster.set('tx:12345', transactionData); // Redis decides which server stores this
// If cache-server-2 goes down, your data is still available on cache-server-1 and cache-server-3
Architecting Your Redis Cache for Payment Lookups
Your distributed Redis cache sits as an application-level layer between your business logic and your source database. This distributed architecture is designed to intercept data requests before they hit your primary data stores.
Where Does the Cache Fit?
Your distributed cache sits as an application-level layer between your business logic and your data sources. In payment systems, you typically have one of two scenarios.
Scenario 1. Caching Database Queries: When you store payment data locally (for example, when you save data from the payment gateway into your own database for record-keeping or analysis). The cache intercepts requests before they hit Flutterwave's API, reducing external calls and improving response times. This is important for your Flutterwave integration because you're working within our rate limits and you want to minimize network dependencies.
Scenario 2. Caching External API Responses: When your payment service is your primary source of truth:
Here's how this looks in a typical payment verification flow for Scenario 2:
// Your payment verification endpoint
app.get('/api/payments/:id/status', async (req, res) => {
const paymentId = req.params.id;
// Cache sits here - between your API and Flutterwave
const status = await paymentCache.getTransactionStatus(paymentId);
res.json({ status, paymentId });
});
Common Caching Patterns and Advanced Data Structures in Redis
Different caching patterns work better for different types of payment data. Let's look at the patterns that matter most for payment integration.
Cache-Aside (Lazy Loading) — Best for payment API Calls
The Cache-Aside or Lazy Loading pattern is a common caching strategy where the application code manages the cache. When data is requested, the application first checks if it's available in the cache. If it's a "cache hit," it's returned directly. If it's a "cache miss," the application retrieves the data from the primary data source (e.g., a database or an external API like Flutterwave), stores a copy in the cache for future requests, and then returns it to the requester.
This is your go-to pattern for caching your payment service responses. Your application controls the cache completely.
Here's how to implement the three-step cache-aside flow:
async function getTransactionDetails(transactionId: string) {
const cacheKey = `flw:tx:${transactionId}`;
// 1. Check cache first
let details = await redis.get(cacheKey);
if (details) {
return JSON.parse(details); // Cache hit
}
// 2. Cache miss - call Flutterwave
const response = await flutterwave.Transaction.verify({ id: transactionId });
if (response.status === 'success') {
// 3. Store in cache for next time
const ttl = response.data.status === 'successful' ? 3600 : 300; // Terminal vs pending
await redis.set(cacheKey, ttl, JSON.stringify(response.data));
return response.data;
}
throw new Error('Transaction verification failed');
}
This implementation is ideal for Flutterwave API responses, transaction lookups, customer data, and any other external API call.
Read-Through Pattern — For Local Database Cache
In the Read-Through caching pattern, the cache fetches data from the underlying data source when a cache miss occurs. The application interacts with the cache as if it were the main data source. If the requested data is in the cache, it's returned. If not, the cache provider or a library abstracting it retrieves the data from the database, stores it in the cache, and then returns it to the application.
The read-through pattern is useful when you have local payment data. This pattern encapsulates the cache logic within your data access layer. Below is an example:
class DatabaseCacheLayer {
async getCustomerPaymentHistory(customerId: string) {
const cacheKey = `customer:${customerId}:payments`;
// Cache library handles the "read-through" logic
return await this.cache.getOrSet(cacheKey, async () => {
// This only runs on cache miss
return await this.db.query(
'SELECT * FROM payments WHERE customer_id = ? ORDER BY created_at DESC',
[customerId]
);
}, { ttl: 900 }); // 15 minutes
}
}
This implementation is ideal when you have frequently accessed local payment data that's expensive to query.
Write-Through Pattern — For Critical Payment State
The Write-Through caching pattern ensures data consistency by writing data to both the cache and the underlying data source simultaneously (or in very close succession as part of a single logical operation). When the application writes data, the operation is considered complete only after the data has been successfully written to both the cache and the database.
Use this when you need absolute consistency between the cache and the database. Both operations happen synchronously to prevent data inconsistency:
async function updatePaymentStatus(paymentId: string, status: string) {
// Write to both database and cache simultaneously
await Promise.all([
db.query('UPDATE payments SET status = ? WHERE id = ?', [status, paymentId]),
redis.hSet(`payment:${paymentId}`, 'status', status)
]);
// Both succeed or both fail - no inconsistency
}
This is mostly used for internal payment state, which must stay consistent, like order fulfillment status.
How Do You Identify What Payment Data To Cache?
Not all data is worth caching. An effective caching solution prioritizes frequently accessed data that is expensive to fetch from its source and is acceptable to be slightly stale. These are often your most frequently accessed data sets. Let’s look at some places you should add a cache during payment integrations.
1. External API Transaction Verification Responses
Transaction verification is likely your most expensive operation, as it involves communicating with the payment gateway API, and customers frequently check these transactions.
Below is an example code that caches the essential data from the Flutterwave verify response:
async function cacheTransactionVerification(transactionId: string) {
const response = await flutterwave.Transaction.verify({ id: transactionId });
const cacheData = {
id: response.data.id,
status: response.status, // 'successful', 'failed', 'pending'
amount: response.data.amount,
currency: response.data.currency,
customer: response.data.customer,
payment_type: response.data.payment_type,
tx_ref: response.data.tx_ref,
created_at: response.data.created_at,
// Don't cache sensitive data like full card details
card_info: {
first_6digits: response.data.card?.first_6digits,
last_4digits: response.data.card?.last_4digits,
type: response.data.card?.type
}
};
const cacheKey = `flw:v1:tx:${transactionId}:verification`;
const ttl = getVerificationTTL(cacheData.status);
await redis.set(cacheKey, ttl, JSON.stringify(cacheData));
return cacheData;
}
Caching the transaction verification responses helps because customers refresh payment pages constantly. A single transaction might be verified 50+ times. Caching saves those API calls and improves response time from 200ms to 5ms.
2. Terminal Payment Statuses (Successful/Failed Transactions)
Once a payment is complete, its status rarely changes. These are perfect for long-term caching.
Here, for example:
function getVerificationTTL(status: string): number {
switch (status) {
case 'successful':
case 'failed':
// Terminal states - cache for 24 hours since they rarely change
return 86400; // 24 hours
case 'pending':
// Pending transactions change frequently - short cache
return 300; // 5 minutes
case 'processing':
// Very volatile - cache briefly to reduce API spam
return 60; // 1 minute
default:
return 120; // 2 minutes for unknown states
}
}
// Usage example
const ttl = status === 'successful' || status === 'failed' ? 86400 : 300; // 24h vs 5min
await redis.set(`flw:tx:${txId}:status`, ttl, status);
80% of transactions end up in terminal states. By caching these long-term, you eliminate recurring API calls for completed payments.
3. Customer Payment Profiles
Information such as customer profiles from Flutterwave is often used during checkout for personalization, but the data itself doesn't change significantly. Here is an example code:
async function cacheCustomerProfile(customerId: string, flutterwaveCustomer: any) {
// Structure the cache data for efficient access
const customerCache = {
id: customerId,
email: flutterwaveCustomer.email,
name: flutterwaveCustomer.name,
phone: flutterwaveCustomer.phone_number,
// Cache payment methods for faster checkout
payment_methods: flutterwaveCustomer.payment_methods?.map(method => ({
type: method.type,
last_4: method.last_4,
brand: method.brand,
expiry: method.expiry_month + '/' + method.expiry_year
})) || [],
verification_status: flutterwaveCustomer.verification_status,
created_at: flutterwaveCustomer.created_at,
cached_at: new Date().toISOString()
};
const cacheKey = `flw:v1:customer:${customerId}:profile`;
// Customer data changes rarely - cache for 6 hours
await redis.set(cacheKey, 21600, JSON.stringify(customerCache));
return customerCache;
}
Customer checkout flows access this data multiple times. Caching eliminates redundant API calls during the payment process.
Implementation Deep Dive
Let's get into the practical details of implementing your cache layer. This includes designing cache keys, handling serialization, and setting the right TTL values for different types of payment data.
Designing Effective Cache Keys
Cache keys are like file paths; they need to be unique, organized, and meaningful. Poor key design can lead to collisions, debugging nightmares, and inefficient cache usage.
Here are some cache key design principles to follow for good cache usage:
- Use a hierarchical structure with prefixes.
- Include all necessary identifiers.
- Keep keys readable and debuggable.
- Handle multi-tenancy properly.
Here is an example of how to do this when you use Flutterwave as your payment provider. The cache key should follow the pattern of {service}:{version}:{entity}:{id}:{attribute}:
// Pattern: {service}:{version}:{entity}:{id}:{attribute}
const cacheKeys = {
// Transaction data
transactionDetails: (txId: string) => `flw:v1:tx:${txId}:details`,
transactionStatus: (txId: string) => `flw:v1:tx:${txId}:status`,
// Customer data
customerProfile: (customerId: string) => `flw:v1:customer:${customerId}:profile`,
customerTransactions: (customerId: string, page: number) =>
`flw:v1:customer:${customerId}:transactions:page:${page}`,
// Rate limiting
apiRateLimit: (merchantId: string, hour: string) =>
`flw:v1:rate:${merchantId}:${hour}`,
// Multi-tenant support
merchantTransaction: (merchantId: string, txId: string) =>
`flw:v1:merchant:${merchantId}:tx:${txId}:details`
};
Data Serialization and Storage
Redis stores everything as strings, so you need to serialize your JavaScript objects. The choice of serialization format affects performance, storage size, and compatibility.
Here is an example using JSON:
class FlutterwaveCache {
async storeTransactionDetails(txId: string, details: any): Promise<void> {
const key = `flw:v1:tx:${txId}:details`;
// Serialize to JSON string
const serialized = JSON.stringify({
id: details.id,
status: details.status,
amount: details.amount,
currency: details.currency,
customer: details.customer,
cached_at: new Date().toISOString()
});
await this.redis.set(key, this.getTTL(details.status), serialized);
}
async getTransactionDetails(txId: string): Promise<any | null> {
const key = `flw:v1:tx:${txId}:details`;
const serialized = await this.redis.get(key);
if (!serialized) return null;
try {
return JSON.parse(serialized);
} catch (error) {
console.error('Failed to deserialize transaction details:', error);
// Clean up corrupted cache entry
await this.redis.del(key);
return null;
}
}
}
Here is an example using Redis Hash for complex Objects:
// Store transaction as Redis hash for granular access
async storeTransactionAsHash(txId: string, details: any): Promise<void> {
const key = `flw:v1:tx:${txId}:hash`;
await this.redis.hSet(key, {
id: details.id,
status: details.status,
amount: details.amount.toString(),
currency: details.currency,
customer_email: details.customer.email,
customer_name: details.customer.name,
cached_at: Date.now().toString()
});
await this.redis.expire(key, this.getTTL(details.status));
}
// Get just the status without deserializing the entire object
async getTransactionStatus(txId: string): Promise<string | null> {
const key = `flw:v1:tx:${txId}:hash`;
return await this.redis.hGet(key, 'status');
}
Smart TTL Strategies
TTL (Time To Live) determines how long data stays in cache. For payment data, TTL should reflect how likely the data is to change.
Here, for example:
class TTLStrategy {
getTTLForTransaction(status: string): number {
switch (status) {
case 'successful':
case 'failed':
// Terminal states - cache for 24 hours
return 86400;
case 'pending':
// Might change soon - cache for 5 minutes
return 300;
case 'processing':
// Very volatile - cache for 1 minute
return 60;
default:
// Unknown status - short cache
return 120;
}
}
getTTLForCustomerData(lastActivity: Date): number {
const hoursSinceActivity = (Date.now() - lastActivity.getTime()) / (1000 * 60 * 60);
if (hoursSinceActivity < 1) {
return 1800; // 30 minutes for active customers
} else if (hoursSinceActivity < 24) {
return 7200; // 2 hours for recent customers
} else {
return 21600; // 6 hours for inactive customers
}
}
}
Using a smart TTL strategy ensures that volatile data is refreshed quickly, while stable data remains in the cache for longer, thereby optimizing performance and data consistency.
Wrapping Up
Distributed caching transforms payment systems from database-bound bottlenecks into lightning-fast APIs that can handle serious traffic. Start by caching your most frequently accessed lookups (such as transaction status and customer validation), measuring the impact, and then expanding to other operations. Your database will thank you, your customers will notice faster responses, and you'll sleep better during traffic spikes.
Want to dive deeper into payment system optimization? Check out Flutterwave's documentation for more integration patterns and best practices.
This content originally appeared on DEV Community and was authored by Uma Victor
Uma Victor | Sciencx (2025-06-30T08:54:36+00:00) How To Integrate A Distributed Cache For Payment Lookups. Retrieved from https://www.scien.cx/2025/06/30/how-to-integrate-a-distributed-cache-for-payment-lookups/
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