Improve Speed & Scalability with Smart Caching Strategies

In this blog post, we’ll explore:

• What caching is and why it’s important
• Internal vs external cache: key differences
• Examples of each (in-memory, distributed, database-based caching)
• Pros and cons of each type
• When to use internal vs external caching
• Real-world use cases and best practices

What Is Caching?

Caching is the process of storing frequently accessed data in a temporary storage layer so it can be retrieved faster the next time it’s needed. The goal of caching is to minimize the time and resources required to access that data from its original source, such as a database or remote API. However, not all caches are created equal—in particular, the distinction between internal (built-in) and external (standalone or distributed) caches shapes the performance, consistency, complexity, and security profile of a system.

For example, fetching user profile data from a database might take 150ms, but fetching it from a cache might take just 5ms. Multiply that over millions of users and you get significant performance improvements.

Internal vs External Cache: The Core Distinction

Internal Caching (In-Process)

Internal caching stores data directly within the memory of the application process (RAM). It’s typically implemented using local data structures such as hash maps or through in-language libraries that provide automatic caching behavior.

Common Internal Caching Tools

• Java: ConcurrentHashMap, Caffeine, Ehcache (when embedded)
• Python: functools.lru_cache, Flask-Caching (in-process mode)
• Node.js: node-cache, simple JS object storage
• .NET: MemoryCache

For example, imagine a function that fetches weather data from an API. You could use a dictionary to cache results:

cache = {}

def get_weather(city):
    if city in cache:
        return cache[city]
    data = call_weather_api(city)
    cache[city] = data
    return data

Benefits of Internal Caching

• Speed: Accessing RAM is lightning-fast.
• Simplicity: Easy to implement, no additional infrastructure.
• Cost-effective: No external system or network latency.

Drawbacks

• Not shared across instances: If your app is running on multiple servers or containers, each has its own separate cache.
• Memory limits: Tied to the application’s memory.
• Volatility: Cache is lost if the app restarts or crashes.
• No centralized eviction policy.

External Caching (Out-of-Process)

External caches are standalone services or systems that the application communicates with over a network. They live outside the application’s memory and are often optimized for high-performance read/write operations.

Types of External Caches

1. Distributed In-Memory Caches

• Redis
• Memcached

1. Persistent or Database-backed Caches

• PostgreSQL materialized views
• MySQL query cache (deprecated in newer versions)
• Hibernate 2nd-level cache with database storage

1. Edge/Proxy Caches

• CDN caches like Cloudflare, Akamai
• Varnish HTTP accelerator

1. Object/Blob Storage Caching

• AWS S3 + CloudFront
• Google Cloud CDN with GCS backend

For example, using Redis with Python:

import redis

r = redis.StrictRedis(host='localhost', port=6379, db=0)

def get_user(user_id):
    key = f"user:{user_id}"
    cached = r.get(key)
    if cached:
        return deserialize(cached)
    data = get_user_from_db(user_id)
    r.set(key, serialize(data), ex=3600)  # 1 hour TTL
    return data

Benefits

• Shared across services: Ideal for microservices and horizontally scaled systems.
• Large capacity: Can store more data than in-process memory.
• Durability (optional): Persistent options like Redis RDB/AOF.
• Advanced features: Eviction policies, TTLs, clustering, etc.

Drawbacks

• Network latency: Slower than in-memory access.
• Complexity: Requires separate setup, monitoring, and failover strategies.
• Cost: Hosting external caches incurs cost—especially for distributed systems.

Types of Caches

Modern systems employ a range of cache types, from process-local in-memory maps to external, distributed data grids. Here’s how key categories break down.

In-Memory Cache

• What: Stores data in local RAM, offering the fastest possible access due to absence of network/disk latency.
• Examples: In-process application cache, single-node Redis/Memcached.
• Common Use: Frequently accessed data, session state, computed results.
• Characteristics:
• Ultra-fast access.
• Capacity limited by available RAM.
• Prone to data loss on process/server restarts.
• Usually internal, but can also be set up as external.

Distributed Cache

• What: Spans multiple nodes and often multiple servers, forming a cluster.
• Examples: Redis Cluster, Memcached Cluster, Hazelcast, Apache Ignite, In-memory Data Grids.
• Common Use: Scalable caching for web applications, microservices, real-time analytics.
• Characteristics:
• Provides high availability and horizontal scalability.
• Shared data, consistent hashing or partitioning.
• Can be used as external cache (shared between applications/services).

Database (Internal) Cache

• What: Caching embedded within a database engine, storing hot data (and sometimes indexes) in RAM or dedicated storage.
• Examples: PostgreSQL buffer pool, MySQL InnoDB cache, MongoDB WiredTiger cache.
• Common Use: Speeding up repetitive queries, reducing disk I/O.
• Characteristics:
• Internal to the database process; transparent to applications.
• Directly benefits database performance.
• Can be rendered less effective if an external cache “shields” most reads from reaching the database, causing its internal cache to stay cold.

Near Cache

• What: A local cache placed “near” the application, often acting as a front-end to a remote or distributed cache.
• Examples: JVM-local cache with Hazelcast/Redis as main cache.
• Use Case: Reducing latency and network hops even when using distributed solutions.

Persistent/Databased Cache

• What: Caches with persistent backing, often using a fast database (e.g., RocksDB-backed cache in Facebook’s TAO, or Redis with persistence enabled).
• Use Case: Scenarios where losing cached data is unacceptable.
• Characteristics: Often blurs the line between cache and data store.

In-Depth Analysis

How Internal Caches Work

Internal caches operate inside the boundaries of a system component. For databases, this means a segment of RAM reserved for frequently accessed tables, indexes, or computation results. For CPUs, the L1/L2/L3 caches are hardware-resident buffers that sit between processor and main memory.

• Transparency & Efficiency: Applications (or queries) are not aware of the cache; the database automatically loads hot data into cache and evicts cold data using policies like LRU (least recently used).
• Simplicity: Developers do not have to manage data placement or eviction.
• Performance: No serialization/network bottleneck—the cache is as fast as the server’s RAM.

Limitations of Internal Caches

• Scope: Only useful to the process/component containing the cache. Cannot share between nodes or scale beyond single server RAM.
• Data Loss: Cache content is lost if the process restarts.
• Ineffective with External Caching: If an external cache intercepts the majority of reads, the database cache never gets populated (“cold”), decreasing internal efficiency.
• Lack of Cross-Service Sharing: Each service/node has its own cache, leading to duplicated data and wasted memory.

How External Caches Work

External caches are typically deployed as independent services—either on the same server, across multiple nodes, or as managed cloud solutions. Examples include Redis, Memcached, or more advanced data grids like Hazelcast.

• Network Scope: Applications, services, or even microservices can consult a single cache cluster, offloading repetitive backend requests.
• Distributed Nature: Data partitions and replication schemes allow scaling to very large datasets, high throughput, and high availability.
• Controlled Eviction & Policies: TTLs, explicit invalidation, and fine-grained access controls.
• Optional Durability: Some external caches support persistent storage (e.g., Redis with AOF or RDB snapshots), making cache “warm” after process/node restarts.

Limitations of External Caches

• Network Overhead: Even if running on the same machine, there is TCP/IP or IPC overhead compared to internal RAM pointers.
• Data Consistency: Explicit invalidation and synchronization are necessary (e.g., database writes must evict/invalidate cache entries); this adds complexity and may cause stale data if not handled properly.
• Single Point of Failure: Centralized caches, unless clustered and replicated, can become a bottleneck or risk to reliability.
• Security Risks: More attack surface—requires careful configuration of access control, encryption, and isolation.
• Cost & Operations: Running an external cache is an operational burden—deployment, scaling, monitoring, and failover need attention.

Real-World Use Cases and Cache Strategy

When to Use Internal Cache

• Component Isolation: Component-level optimization is vital (e.g., high write/read ratio in a transactional DB).
• No Network Overhead: Ultra-low latency, such as CPU L1/L2 cache, or local app-level cache for session state.
• Simple Architectures: Smaller teams or less complex products can keep operations simple.

When to Use External Cache

• Microservices & Distributed Systems: When multiple components or services need access to the same data.
• Large-Scale Systems: To scale beyond the RAM of a single machine, enable cache sharing, and support high availability.
• Reducing Backend Load: Offload frequent requests to a cache, reducing load and scaling needs for slow or expensive-to-query backends.
• Session Sharing: Web applications need user session state accessible behind multiple load-balanced frontends.

Combining Both: Hybrid Approaches

• Multi-Level Caching: A near-cache (local, internal) sits in front of a distributed external cache, which itself sits in front of the database. This stacks fast access (from app loc mem), scalable sharing (from external cache), and authoritative storage (from DB).
• Cache-aside Pattern: The app attempts to fetch from the cache, loading from backend and updating external cache on miss.
• Write-Through/Write-Behind: Patterns to synchronize cache and persistence for consistency.

Challenges and Caveats

• Cache Invalidation: “There are only two hard things in Computer Science: cache invalidation and naming things.” Keeping caches consistent with source-of-truth data is tricky, especially as the number of layers grows.
• Stale Data Risk: External caches must have explicit policies for time-to-live, eviction, and consistency enforcement.
• Operational Complexity: External caches come with extra deployment, scaling, and maintenance overhead.
• Security: Any additional networked service introduces attack risks; care must be taken with authentication, encryption, and access controls.

Conclusion

Internal and external caches are both vital but serve best in specific scenarios. Internal caches shine when tightly bound to component logic and lifecycle, offering unmatched speed and simplicity. External caches provide scalable sharing and fault tolerance for distributed, high-availability architectures but come with added complexity and operational needs. Most large-scale systems deploy a combination of both, carefully tuned for their access patterns and trade-offs.

Successful cache strategy depends on deeply understanding workload patterns (read/write mix, data freshness requirements, scale), failure modes, maintainability, and security. The right cache, configured with the proper policies and combined with a solid invalidation and synchronization approach, can boost system performance and reliability considerably.