This week’s issue brings you:

• Top 5 Database Caching Strategies You Should Know

• 6 Software Architecture Patterns You Should Know (Recap)

• Stateful vs Stateless Design — What’s the Difference? (Recap)

READ TIME: 5 MINUTES

Top 5 Database Caching Strategies You Should Know

Caching is a core performance optimization technique. Done right, it reduces latency, lowers database load, and improves throughput, without sacrificing availability.

But caching isn’t one-size-fits-all.

The right strategy depends on your system’s access patterns, consistency requirements, and tolerance for stale data.

Let’s dive into five of the most widely used caching strategies.

1) Cache-Aside

In a cache-aside strategy, the application explicitly manages reads and writes to the cache.

It queries the cache first, and on a cache miss, it fetches data from the database, returns it, and updates the cache.

The cache only stores data after it’s been read by the app.

Pros:

• Total control—great for irregular access patterns and dynamic data.

• Only caches what’s actually requested.

Cons:

• You’re on the hook for managing cache invalidation and consistency.

• Adds extra logic to application code.

Cache-aside shines in systems where usage patterns are unpredictable or data freshness varies. However, beware that sloppy cache management can lead to stale reads or wasted memory.

2) Write-Through

In a write-through strategy, every write updates both the cache and the database at the same time. This ensures the cache always stays in sync with the database.

Pros:

• Data is always consistent between cache and database.

• Simplifies reads—no risk of stale cache data.

Cons:

• Higher write latency – Every write incurs the cost of writing to two systems (cache and database).

• Cache can be flooded with infrequently read data.

Best used when data integrity and consistency are critical—think e-commerce transactions or financial apps where stale reads aren’t an option.

3) Write-Behind (Write-Back)

Write-behind (or write-back) caching updates the cache first and the database later in the background. “Dirty” data is flushed based on a schedule or specific triggers like time or batch size.

Pros:

• Supercharges write performance—perfect for high-write workloads.

• Relieves the database from immediate pressure.

Cons:

• Risk of data loss if the cache node crashes before the database syncs.

• Can complicate troubleshooting if writes lag too long.

Write-behind is ideal for apps where slight delays in database updates are tolerable—like activity feeds, logs, or ephemeral data.

4) Read-Through

A read-through cache acts as a middle layer. The application queries the cache directly, and on a miss, the cache fetches the data from the database, stores it in the cache, and returns it to the application.

Pros:

• Application code is simplified—no need for explicit cache logic on reads.

• Reduces database load for repeat queries.

Cons:

• Initial cache misses still hit the database, causing a slight delay.

• Risk of stale data if underlying data changes frequently and no expiration policy is in place.

This is a solid choice for read-heavy applications with stable data, like content libraries or product catalogues.

5) Write-Around

Write-around caching skips the cache during writes, sending data straight to the database.

Data enters the cache only when it’s read later. This means new or updated data isn’t cached until requested.

Pros:

• Keeps the cache lean by avoiding writes that may never be read.

• Prevents unnecessary churn for write-heavy scenarios.

Cons:

• First reads after a write always miss the cache, causing read latency.

• Inconsistent performance if reads for new data spike unexpectedly.

Best for apps with frequent writes and infrequent reads, like analytics logs or activity events.

Choosing the Right Strategy

Each caching strategy comes with its own trade-offs. To decide which one fits your system, consider:

• Read vs write ratio: Is your workload read-heavy, write-heavy, or balanced?

• Consistency requirements: Can your system tolerate stale data, or is strict consistency a must?

• Operational complexity: How much logic are you willing to manage in the application vs the cache layer?

Understanding these trade-offs is key to making caching an enabler, not a liability.

Final Thoughts

Caching isn’t just a performance boost, it’s a critical part of system design. The right strategy reduces load, improves user experience, and scales with your needs.

Whether you're designing for high throughput, low latency, or data integrity, matching your caching strategy to your access patterns will help your application perform predictably under pressure.

6 Software Architecture Patterns You Should Know (Recap)

Choosing the right architecture isn’t about following trends. It’s about aligning with your application’s needs, your team’s expertise, and long-term scalability and maintainability. And it's one of the biggest decisions you'll make for your application.

Monolithic: A single-unit application, simple to develop and deploy but harder to scale and maintain over time. Some teams use a modular monolith for better maintainability.

Serverless: Developers focus on code while cloud providers manage infrastructure and scaling. It reduces operational overhead.

Event-Driven (EDA): Asynchronous communication through events enables real-time, scalable applications, ideal for microservices but can be complex to debug.

Microservices: Independently deployable services offer flexibility and scalability but introduce challenges in communication, data consistency, and orchestration.

Domain-Driven Design (DDD): Not an architecture itself but a design approach that structures systems based on business domains.

Layered (N-Tier): Divides applications into logical layers (e.g., presentation, business logic, and data).

See the full post here for an expanded explanation of each.

Stateful vs Stateless Design — What’s the Difference? (Recap)

“State” refers to stored information that systems use to process requests.

Stateful applications store data like user IDs, session information, configurations, and preferences to help process requests for a given user.

As applications grew in complexity and received increasing amounts of traffic, the limitations of stateful design became apparent. The rapid need for scalability and efficiency drove the popularity of stateless design.

With stateless design requests contain all the information needed to process it.

Stateless design has been pivotal in several areas including Microservices and Serverless Computing.

It does have it’s challenges though including larger requests sizes, and transmission inefficiencies.

Most applications pick a hybrid approach between stateful and stateless design depending on the needs and constraints of each component of the system.

Check out the full post here for an extended breakdown stateless and stateful design.

That wraps up this week’s issue of Level Up Coding’s newsletter!

Join us again next week where we’ll explore and visually distill more important engineering concepts.