Implementing custom Cache bins for specialised needs

Drupal’s default cache bins-such as cache. render and cache. Entities are designed for generalised use cases.

However, complex applications often require specialised caching strategies that transcend these built-in systems.

Imagine a scientific research platform that processes large datasets for computational models.

Default caching might reduce database queries, but it fails to address the computational overhead of recalculating results. In such cases, custom cache bins become essential to balance performance with precision.

Limitations of default Cache bins

Default cache bins are optimised for simplicity, but they struggle with niche requirements:

Shared storage: All cached data shares the same backend, risking interference (e.g., a heavy computation overwriting frequently accessed data).
One-size-fits-all expiration: Default bins use universal rules for cache invalidation, which may not align with domain-specific needs (e.g., invalidating a weather API cache every hour vs. a financial report cache every day).
Backend constraints: Default bins often rely on the database (cache.backend.database), which may not suit high-throughput scenarios (e.g., real-time analytics requiring Redis).

When custom Cache bins add value

Custom bins shine in scenarios where default bins fall short:

High-volume, low-change data: Scientific computations or machine learning predictions that take seconds to generate but change infrequently.
Specialised backends: Applications requiring Redis for speed or file-based storage for large binary objects.
Granular invalidation: Systems needing precise cache clearing (e.g., refreshing only weather forecasts for a specific region).

Tip: Avoid custom bins for rapidly changing data (like live chat messages) or sensitive information (such as user-specific financial records), where default mechanisms or no caching may be safer.

Understanding Cache bins

What are Cache bins?

Think of cache bins as labelled storage boxes in a warehouse. Each box holds a different type of item, and you can choose where to store each box for maximum efficiency. In Drupal, cache bins:

Separate Cached content: Prevent interference between unrelated data (e.g., page content vs. API responses).
Allow custom backends: Use different storage systems (e.g., Redis for fast access, database for persistence).
Support unique policies: Define rules like expiration times, invalidation strategies, and access controls.

Default Drupal Cache bins

Drupal ships with predefined bins for common use cases:

cache.default: Generic storage for arbitrary data.
cache.render: Stores rendered HTML fragments.
cache.entity: Caches raw entity data.
cache.bootstrap: Critical data needed during early bootstrapping.

While these bins handle most scenarios, they lack flexibility for specialised needs.

Why create custom Cache bins?

Advantages of specialised storage

Isolation: Prevent cache interference between unrelated systems (e.g., a weather API cache won’t evict a machine learning model cache).
Tailored backends: Use Redis for low-latency access or file storage for large binary objects.
Fine-grained control: Define custom expiration rules (e.g., “cache weather data for 15 minutes”) and invalidation logic.

Real-world scenarios

Scientific computation results: A research platform caches complex simulations for 24 hours, invalidating them only when input parameters change.
Machine learning predictions: Store model outputs in a Redis-backed bin to reduce redundant calculations.
Geospatial data: Cache map tiles in a file-based bin to offload database pressure.

Cache bin architecture

Core components

A custom cache bin relies on four foundational elements:

Cache backend: Defines where data is stored (e.g., database, Redis, file system).
Cache tags: Determine what triggers cache invalidation (e.g., scientific_computation, ml_model:42).
Metadata management: Includes rules like max-age and dependencies (tags, contexts).
Isolation mechanism: Enforced through unique service definitions and backend configurations.

Dependency injection: why it matters

Drupal’s service container allows cache logic to be decoupled from business logic. By injecting dependencies like CacheBackendInterface, services become:

Testable: Easily mocked in unit tests.
Flexible: Replace backends (e.g., switching from database to Redis) without rewriting logic.

Example:
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use Drupal\Core\Cache\CacheBackendInterface;

class SpecializedCacheBin implements CacheBackendInterface {
  protected $cacheBin;

  public function __construct(CacheBackendInterface $cache_bin) {
    $this->cacheBin = $cache_bin;
  }

  public function get($cid, $allow_invalid = FALSE) {
    return $this->cacheBin->get($cid, $allow_invalid);
  }

  // Implement all other required CacheBackendInterface methods...
}

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Official Documentation: Service Container

Step-by-step: creating a custom Cache bin

1. Register the service

Define the bin in your module’s mymodule.services.yml:
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services:
  cache.specialized_bin:
    class: Drupal\mymodule\Cache\SpecializedCacheBin
    arguments: ['@cache_factory']
    tags:
      - { name: cache.bin }

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Note: The cache backend (e.g., Redis, database) is configured in settings.php, not in the service definition.

2. Configure the backend in settings.php
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$settings['cache']['bins']['specialized_bin'] = 'cache.backend.redis'; // or 'cache.backend.database'

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3. Implement the Cache bin class

Here’s an example for a scientific computation cache:
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use Drupal\Core\Cache\CacheBackendInterface;
use Drupal\Core\Cache\CacheFactoryInterface;

class ScientificComputationCache implements CacheBackendInterface {
  protected $cacheBin;

  public function __construct(CacheFactoryInterface $cache_factory) {
    $this->cacheBin = $cache_factory->get('scientific_computation');
  }

  public function get($cid, $allow_invalid = FALSE) {
    $cached = $this->cacheBin->get($cid, $allow_invalid);
    if ($cached && $this->validateScientificData($cached->data)) {
      return $cached;
    }
    return FALSE;
  }

  public function set($cid, $data, $expire = CacheBackendInterface::CACHE_PERMANENT, array $tags = []) {
    $processed_data = $this->preprocessData($data);
    $this->cacheBin->set(
      $cid,
      $processed_data,
      $expire,
      array_merge($tags, ['scientific_computation'])
    );
  }

  protected function validateScientificData($data) {
    return is_array($data) && isset($data['result']) && isset($data['timestamp']);
  }

  protected function preprocessData($data) {
    return [
      'result' => $data,
      'timestamp' => time(),
      'source_hash' => md5(serialize($data)),
    ];
  }

  // Implement all other required CacheBackendInterface methods...
}

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Practical implementation examples

Machine learning model Cache

Scenario: A recommendation engine generates predictions that take 5 seconds to compute.

Solution: Cache predictions with Redis for fast retrieval.
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use Drupal\Core\Cache\CacheBackendInterface;

class MachineLearningModelCache {
  protected $cache;

  public function __construct(CacheBackendInterface $cache) {
    $this->cache = $cache;
  }

  public function cacheModelPrediction($model_id, $input, $prediction) {
    $cache_id = 'ml_prediction:' . md5($model_id . serialize($input));
    $this->cache->set(
      $cache_id,
      [
        'model' => $model_id,
        'input' => $input,
        'prediction' => $prediction,
        'timestamp' => time(),
      ],
      CacheBackendInterface::CACHE_PERMANENT,
      ['machine_learning_predictions']
    );
  }
}

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Impact:

Latency Reduction: Cuts prediction time from 5s to milliseconds.
Scalability: Redis handles concurrent requests without database bottlenecks.

Geospatial data Caching

Scenario: A mapping application serves pre-rendered tiles that take 2 seconds to generate.

Solution: Use a file-based bin to offload the database.
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use Drupal\Core\Cache\CacheBackendInterface;
use Drupal\Core\File\FileSystemInterface;

class MapTileCache implements CacheBackendInterface {
  protected $cacheBin;
  protected $fileSystem;

  public function __construct(CacheBackendInterface $cache_bin, FileSystemInterface $file_system) {
    $this->cacheBin = $cache_bin;
    $this->fileSystem = $file_system;
  }

  public function set($cid, $data, $expire, array $tags) {
    $uri = "public://cache/tiles/$cid.png";
    $this->fileSystem->saveData($data, $uri, FileSystemInterface::EXISTS_REPLACE);
    $this->cacheBin->set($cid, ['uri' => $uri], $expire, $tags);
  }

  public function get($cid, $allow_invalid = FALSE) {
    $cache = $this->cacheBin->get($cid, $allow_invalid);
    if ($cache && isset($cache->data['uri']) && file_exists($cache->data['uri'])) {
      return file_get_contents($cache->data['uri']);
    }
    return FALSE;
  }

  // Implement all other required CacheBackendInterface methods...
}

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Impact:

Resource Efficiency: Reduces database load by storing large binary objects on disk.
Resilience: Falls back to metadata for invalidation during file system failures.

Performance optimisation techniques

Best practices

Backend selection: Use Redis for low-latency access, file storage for large binaries.
Granular tags: Use specific tags like ml_model:42 instead of broad tags.
Dynamic expiration: Set max-age based on data volatility (e.g., weather data every 15 minutes).
Monitor: Track hit rates and generation times for proactive tuning.

Common pitfalls

Overly Broad Tags: Using ml_model:all clears unrelated predictions.
Inappropriate Backends: Storing large files in the database instead of the filesystem.

Debugging and monitoring

Logging Cache operations

Track cache activity for auditing and debugging:
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class CacheBinDebugger {
  public static function logCacheOperation($bin, $operation, $cid) {
    \Drupal::logger('cache_debug')->info(
      'Cache Operation: @operation in @bin for @cid',
      [
        '@operation' => $operation,
        '@bin' => $bin,
        '@cid' => $cid,
      ]
    );
  }
}

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Why it works:

Operational Visibility: Identifies slow or frequently invalidated entries.
Troubleshooting: Diagnoses missing dependencies during invalidation.

Performance monitoring

Use backend-specific tools to measure effectiveness:
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function mymodule_monitor_cache_bin_performance($bin_name) {
  $cache = \Drupal::cache($bin_name);

  // Example: Redis-specific stats (if using Redis)
  if (method_exists($cache, 'getConnection')) {
    $stats = $cache->getConnection()->info();
    \Drupal::logger('cache_performance')->info(
      'Redis Stats for @bin: @stats',
      [
        '@bin' => $bin_name,
        '@stats' => print_r($stats, TRUE),
      ]
    );
  }
}

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Metrics to monitor:

Hit rate: High hit rates indicate effective caching.
Generation time: Identify slow-to-generate content for optimisation.

Series navigation

This article is part of a comprehensive 10-part series on Drupal caching:

Introduction to Drupal Caching
Understanding Drupal’s Cache API
Choosing the Right Cache Backend for Your Drupal Site
Mastering Page and Block Caching in Drupal
Optimising Drupal Views and Forms with Caching
Entity and Render Caching for Drupal Performance
Building Custom Caching Services in Drupal
Implementing Custom Cache Bins for Specialised Needs (You are here)
Advanced Drupal Cache Techniques (Coming soon)‍
Drupal Caching Best Practices and Performance Monitoring (Coming soon)

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Wrapping up and what’s ahead?

As we wrap up, remember that implementing custom cache bins in Drupal is surprisingly straightforward once you understand the core concepts. Define your service in your module's services.yml file, create your cache bin class extending the appropriate backend, and register everything properly. These three steps will get you most of the way there. The real power comes when you fine tune your bin's configuration to match your specific caching needs and invalidation patterns. Don't hesitate to experiment with different backends and expiration strategies. Sometimes the best implementation reveals itself through trial and optimization. Your site's performance metrics will tell the story of your success.

Next, we’ll look at how advanced caching techniques build on this foundation. We’ll walk through cache hierarchies (memory → Redis → database), event-driven invalidation, and integrating with CDNs or edge networks to extend performance beyond the application layer.

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