What is Pogocache software caching and what is it used for?

If you've ever wondered why certain applications respond at top speed, the answer is usually the same: a good caching strategy. And now, with the emergence of modern solutions like Pogocache, that speedup is even more noticeable. Combining classic caching concepts with new efficient implementations marks the difference between a normal app and one that flies.

Beyond the typical storage temporary, the cache is a whole layer of architecture that affects browsers, proxies, APIs, databases and to the edge of the network. Understand each level, its headers and usage patterns It allows you to reduce server load, reduce latency, and save costs without sacrificing reliable data.

What is Pogocache and why is it causing a stir?

Pogocache is a caching software built from the ground up with one clear goal: to minimize latency and CPU usage. Its proposal is to be faster than popular solutions such as Memcached, Valkey, Redis, Garnet or Dragonfly, and do so with a modern and light architecture.

One of its strengths is its protocol compatibility: it understands the wire protocols of Memcache, Redis, HTTP, and PostgreSQL, which opens the door to easy integration with existing stacks. That means it can act as a replacement in scenarios where you already talk to Redis or Memcache., or be consulted from clients that communicate via HTTP or with the Postgres protocol.

It's flexible to deploy: it can be run as a managed service, installed locally, or integrated as an embedded library into your application. This versatility makes it easy to use as a shared cache, process cache, or even in hybrid topologies. where edge and backend coexist. It's also free software licensed under the AGPL.

In practice, Pogocache is a good fit for cases such as caching API responses, frequently used query results, temporary sessions, or data structures that are hot-queried. Being CPU and latency efficient, it shines especially in read-heavy workloads. and high-rotation keys.

The idea of ​​​​cache: a simple metaphor that explains it all

Imagine you're shopping online and you're going to pick up your order. If each product has to be picked up in the warehouse, it takes time; if the order is already in a bag with your name on it, you'll be out in seconds. That's exactly what the cache does: it prepares responses in advance to deliver them without waiting..

Global-scale services, like search engines, take advantage of this logic: many of the answers you see have already been calculated and saved in a pre-prepared state. When you make the request, you only have to return the stored result., do not repeat the calculation every time.

This idea also applies to everyday applications. Apps Messaging tools save downloaded files to avoid repeating transfers; creative tools keep working copies to make everything smoother. However, when the cache grows too large or becomes corrupted, it must be purged to recover performance..

The cache is not eternal: it has a configured expiration date. Define lifetimes, invalidations and regenerations It is key to balancing freshness and speed, avoiding serving expired data when it is not needed.

Cache layers and locations in a modern system

The cache does not live in a single place. It is distributed in layers that complement each other., from the user's device to the database or network edge.

• Client (browser or app): Saves static resources such as images, CSS, or JS to speed up subsequent visits.
• DNS: Resolvers store the conversion of domain names to IP addresses for faster response times.
• Web and CDN (edge): Replicas close to users reduce latency and offload the origin.
• Applications: Cache API responses, views, and intensive calculations.
• Databases: Inner and outer layers minimize repetitive reads and storage latency.

At the edge, current CDNs add strategies such as tiered caching, with intermediate layers between the point closest to the user and the origin. This reduces the number of trips to the server and improves availability even if the source fails..

HTTP caching: the foundation of web performance

The HTTP protocol integrates caching mechanisms to reuse previous responses when it is safe to do so. The typical process is to first look in cache, serve if there is a hit, and if not, go to the source and store the copy. for next time.

There are several HTTP caching locations: browser (private), intermediate shared proxy, and gateway or reverse proxy in front of the application. Each one obeys directives that are sent through headers, crucial for controlling expiration, validation and sharing.

The most common directives in the 'Cache-Control' header include:

• public / private: Indicates whether the response can be saved in shared caches or only on the client.
• Max-age: seconds of freshness allowed in any cache; s-maxage does the same only for shared caches.
• no-cache: forces you to revalidate before using the copy; no store prevents the response from being stored.
• must-revalidate / proxy-revalidate: requires respecting the expiration date and revalidating upon expiration.
• max-stale: Accepts expired content up to a limit, useful in case of failures or intermittence.
• min-fresh: asks for content that stays fresh for at least some time.
• only-if-cached: The client only wants an already cached copy; if there isn't one, 504.
• no-transform: Prohibits the cache from modifying the body (e.g., recompressing).

Other related headers are equally important. 'Expires' sets an absolute expiration date'ETag' provides a unique tag per resource version for conditional validation. 'Last-Modified' specifies the last modification date. 'Vary' instructs the cache to maintain variants based on headers such as 'Accept-Encoding' or 'User-Agent'. Combined, they allow precision: serving quickly without losing consistency.

Caching APIs: When it's worth it and how to do it right

Many modern applications are built around HTTP APIs. Not all requests require calculating business logic or accessing the database every time.; if the nature of the data allows it, it is preferable to return a cached copy.

The key is to align the TTLs with the rate of data change. If the category list is updated once a day, caching that response for 24 hours is reasonable. This way you reduce the pressure on application servers and databases, you improve latency and cut costs.

In managed environments, there are services that facilitate this pattern. API gateway platforms allow you to publish, monitor, secure, and cache endpoints at any scale. Enabling caching at the platform entrance offloads the backend and simplifies the operation..

For highly dynamic endpoints, revalidation techniques (ETag/If-None-Match, Last-Modified/If-Modified-Since) help avoid recalculating responses when nothing has changed. 304 is returned and the client reuses its copy, saving transfer and time.

Database caching: local, external, and cloud

Caching frequently accessed query results and data is one of the most effective ways to improve performance. It consists of keeping in high-speed memory what is frequently requested., reducing access to main storage.

Many databases incorporate internal caches (for example, disk page or result caches), and it is also common to use specialized external solutions such as Redis or Memcached. These outer layers serve key-values ​​in memory with micro/millisecond latencies, ideal for hot readings.

Cloud providers typically offer managed engines with caching and fine-tuning options, from relational to NoSQL. This makes it easy to deploy caching strategies without setting up the entire infrastructure yourself., maintaining metrics and automatic scaling.

The operating pattern is simple: if a query is successful, it is returned immediately; if it is unsuccessful, the database is queried and the response is stored with its TTL. It relies on temporal locality: what was recently requested is likely to be requested again..

Typical cases include product sheets, lists with common filters, or results from popular e-commerce searches. Especially when the data changes little between queries, the latency improvement is noticeable..

Where to implement the cache in your architecture

There are three very practical places to start if you build web services.

Navigator

Control how the client caches using 'Cache-Control' (e.g., 'max-age' to define lifetime). Conditional validation with 'ETag' and 'Last-Modified' prevents downloading what hasn't changedProperly configured, the front end becomes much more agile between visits.

Reverse proxy or gateway

Placing an intermediate layer in front of the backend (reverse proxy) allows you to cache public responses and reduce the number of requests reaching your application. You can combine it with CDNs and tiered strategies to gain global scale with minimal latencies.

Application

Integrating caching into your service layers (controller, use cases, repositories) gives you fine-grained control over what to cache and when to invalidate. In-memory solutions like Redis or Pogocache itself fit perfectly here., with dynamic TTLs and key tagging.

Clear advantages… and limits that are worth knowing

The reasons for caching are compelling: faster response times, less load on the database, and a better user experience. In addition, network travel, bandwidth and infrastructure bills are reduced. in high traffic scenarios.

But not everything is worth it: if you need data strictly in real time or there is a high sensitivity to freshness, there is a risk of serving obsolete copies. It also increases operational complexity: overrides, consistency, and security. you have to think them through carefully.

Another point is debugging: with caches in between, reproducing errors can be more difficult. And be careful with sensitive data: depending on where and how you cache, you must comply with policies and encrypt to prevent unintentional leaks.

HTTP headers in detail: how to talk to caches

Designing headers well makes all layers behave as you expect. A static resource can be 'public, max-age=31536000, immutable' to extend its lifespan on clients and proxies. A private response containing user data should be marked 'private, no-store'.

For content that changes often but not on every request, combine moderated 'max-age' with validation ('ETag' or 'Last-Modified'). Thus, while it is fresh it is served from cache and, upon expiration, it is revalidated very quickly with 304 if there are no changes.

When there are variations by language, compression, or device, use 'Vary' appropriately. You avoid serving the wrong version by keeping one copy per relevant header combination.. Properly implemented, it maintains an effective cache without mixing responses.

Edge caching: bringing speed closer to the user

Edge caching stores responses on globally distributed nodes to minimize distance to users. Reduce latencies, accelerate deliveries and absorb peaks without stressing your origin, both for static files and for streaming live or on demand.

With a reverse proxy architecture and tiered caching, content will travel to the origin less often. Even if the original server goes down temporarily, many requests will continue to be served. from the intermediate or edge layers.

Best practices when using Pogocache alongside the rest of the ecosystem

Plan TTLs based on the pattern of changes in your data, not on intuition. Use keys with consistent names and, where possible, selective invalidation (by tags or prefixes) to avoid emptying the entire cache with specific changes.

Take advantage of protocol support: If your application already supports Redis or Memcache, consider using this plugin as a drop-in replacement; if you prefer HTTP, expose cacheable endpoints with proper headers; if low-level integration is more convenient for you, use the embedded option. This flexibility allows you to test without redoing your entire architecture..

Measures cache hits and misses, key collisions, and CPU and memory usage. Observability is essential for adjusting TTL, sizes, and eviction policies. (LRU, LFU, FIFO, etc.) to your actual traffic pattern.

Keep in mind the AGPL license: it's free software, but with specific obligations if you distribute or provide a service. Consult your legal team to confirm how this affects your deployment model. in SaaS or redistributed products.

For highly dynamic data, combine short caching with revalidation and prewarming of hot keys. Warm-up reduces known latency spikes after mass deployments or expirations, and prevents avalanches at the source.

Real performance comes from a combination of: an effective application cache (e.g., with Pogocache), well-thought-out HTTP headers, a CDN/edge that absorbs the bulk of the traffic, and, ultimately, a data caching policy that takes pressure off your storage. With those pieces aligned you will gain speed, resilience and cost per request. without sacrificing freshness when it matters.