Cache Framework

Flow offers a caching framework to cache data. The system offers a wide variety of options and storage solutions for different caching needs. Each cache can be configured individually and can implement its own specific storage strategy.

If configured correctly the caching framework can help to speed up installations, especially in heavy load scenarios. This can be done by moving all caches to a dedicated cache server with specialized cache systems like the Redis key-value store (a.k.a. NoSQL database), or shrinking the needed storage space by enabling compression of data.


The caching framework can handle multiple caches with different configurations. A single cache consists of any number of cache entries. A single cache entry is defined by these parts:

A string as unique identifier within this cache. Used to store and retrieve entries.
The data to be cached.
A lifetime in seconds of this cache entry. The entry can not be retrieved from cache if lifetime expired.
Additional tags (an array of strings) assigned to the entry. Used to remove specific cache entries.

The difference between identifier and tags is hard to understand at first glance, it is illustrated with an example.

About the Identifier

The identifier used to store (“set”) and retrieve (“get”) entries from the cache holds all information to differentiate entries from each other. For performance reasons, it should be quick to calculate. Suppose there is an resource-intensive extension added as a plugin on two different pages. The calculated content depends on the page on which it is inserted and if a user is logged in or not. So, the plugin creates at maximum four different content outputs, which can be cached in four different cache entries:

  • page 1, no user logged in
  • page 1, a user is logged in
  • page 2, no user logged in
  • page 2, a user is logged in

To differentiate all entries from each other, the identifier is build from the page id where the plugin is located, combined with the information whether a user is logged in. These are concatenated and hashed (with sha1(), for example). In PHP this could look like this:

$identifier = sha1((string)$this->getName() . (string)$this->isUserLoggedIn());

When the plugin is accessed, the identifier is calculated early in the program flow. Next, the plugin looks up for a cache entry with this identifier. If there is such an entry, the plugin can return the cached content, else it calculates the content and stores a new cache entry with this identifier. In general the identifier is constructed from all dependencies which specify an unique set of data. The identifier should be based on information which already exist in the system at the point of its calculation. In the above scenario the page id and whether or not a user is logged in are already determined during the frontend bootstrap and can be retrieved from the system quickly.

About Tags

Tags are used to drop specific cache entries if the information an entry is constructed from changes. Suppose the above plugin displays content based on different news entries. If one news entry is changed in the backend, all cache entries which are compiled from this news row must be dropped to ensure that the frontend renders the plugin content again and does not deliver old content on the next frontend call. If for example the plugin uses news number one and two on one page, and news one on another page, the according cache entries should be tagged with these tags:

  • page 1, tags news_1, news_2
  • page 2, tag news_1

If entry two is changed, a simple backend logic could be created, which drops all cache entries tagged with “news_2”, in this case the first entry would be invalidated while the second entry still exists in the cache after the operation. While there is always exactly one identifier for each cache entry, an arbitrary number of tags can be assigned to an entry and one specific tag can be assigned to mulitple cache entries. All tags a cache entry has are given to the cache when the entry is stored (set).

System Architecture

The caching framework architecture is based on these classes:

Factory class to instantiate caches.
Returns the cache frontend of a specific cache. Implements methods to handle cache instances.
Interface to handle cache entries of a specific cache. Different frontends exist to handle different data types.
Interface for different storage strategies. A set of implementations exist with different characteristics.

In your code you usually rely on dependency injection to have your caches injected. Thus you deal mainly with the API defined in the FrontendInterface.


The cache framework is configured in the usual Flow way through YAML files. The most important is Caches.yaml, although you may of course use Objects.yaml to further configure the way your caches are used. Caches are given a (unique) name and have three keys in their configuration:

The frontend to use for the cache.
The backend to use for the cache.
The backend options to use.
If the cache should stay persistent.

As an example for such a configuration take a look at the default that is inherited for any cache unless overridden:

Example: Default cache settings

# Default cache configuration
# If no frontend, backend or options are specified for a cache, these values
# will be taken to create the cache.
  frontend: TYPO3\Flow\Cache\Frontend\VariableFrontend
  backend: TYPO3\Flow\Cache\Backend\FileBackend
    defaultLifetime: 0

Some backends have mandatory as well as optional parameters (which are documented below). If not all mandatory options are defined, the backend will throw an exception on the first access. To override options for a cache, simply set them in Caches.yaml in your global or package Configuration directory.

Example: Configuration to use RedisBackend for FooCache

  backend: TYPO3\Flow\Cache\Backend\RedisBackend
    database: 3

Persistent Cache

Caches can be marked as being “persistent” which lets the Cache Manager skip the cache while flushing all other caches or flushing caches by tag. Persistent caches make for a versatile and easy to use low-level key-value-store. Simple data like tokens, preferences or the like which usually would be stored in the file system, can be stored in such a cache. Flow uses a persistent cache for storing an encryption key for the Hash Service. The configuration for this cache looks like this:

Example: Persistent cache settings

# Cache configuration for the HashService
# If no frontend, backend or options are specified for a cache, these values
# will be taken to create the cache.
  backend: TYPO3\Flow\Cache\Backend\SimpleFileBackend
  persistent: true

Note that, because the cache has been configured as “persistent”, the SimpleFileBackend will store its data in Data/Persistent/Cache/Flow_Security_Cryptography_HashService/ instead of using the temporary directory Data/Temporary/Production/Cache/Flow_Security_Cryptography_HashService/. You can override the cache directory by specifying it in the cache’s backend options.

Cache Frontends

Frontend API

All frontends must implement the API defined in the interface TYPO3\Flow\Cache\Frontend\FrontendInterface. All cache operations must be done with these methods.

Returns the cache identifier.
Returns the backend instance of this cache. It is seldom needed in usual code.
Sets/overwrites an entry in the cache.
Return the cache entry for the given identifier.
Finds and returns all cache entries which are tagged by the specified tag.
Check for existence of a cache entry.
Remove the entry for the given identifier from the cache.
Removes all cache entries of this cache.
Flush all cache entries which are tagged with the given tag.
Call the garbage collection method of the backend. This is important for backends which are unable to do this internally.
Checks if a given identifier is valid.
Checks if a given tag is valid.

Check the API documentation for details on these methods.

Available Frontends

Currently three different frontends are implemented, the main difference is the data types which can be stored using a specific frontend.

The string frontend accepts strings as data to be cached.
Strings, arrays and objects are accepted by this frontend. Data is serialized before it is given to the backend. The igbinary serializer is used transparently (if available in the system) which speeds up the serialization and unserialization and reduces data size. The variable frontend is the most frequently used frontend and handles the widest range of data types. While it can also handle string data, the string frontend should be used in this case to avoid the additional serialization done by the variable frontend.

This is a special frontend to cache PHP files. It extends the string frontend with the method requireOnce() and allows PHP files to be require()‘d if a cache entry exists.

This can be used to cache and speed up loading of calculated PHP code and becomes handy if a lot of reflection and dynamic PHP class construction is done. A backend to be used with the PHP frontend must implement the

Currently the file backend is the only backend which fulfills this requirement.


The PHP frontend can only be used to cache PHP files, it does not work with strings, arrays or objects.

Cache Backends

Currently already a number of different storage backends exists. They have different characteristics and can be used for different caching needs. The best backend depends on given server setup and hardware, as well as cache type and usage. A backend should be chosen wisely, a wrong decision could slow down an installation in the end.

Common Options

Common cache backend options

Options Description Mandatory Type Default
defaultLifeTime Default lifetime in seconds of a cache entry if it is not specified for a specific entry on set() No integer 3600


The file backend stores every cache entry as a single file to the file system. The lifetime and tags are added after the data part in the same file.

By default, cache entries will be stored in a directory below Data/Temporary/{context}/Cache/. For caches which are marked as persistent, the default directory is Data/Persistent/Cache/. You may override each of the defaults by specifying the cacheDirectory backend option (see below).

As main advantage the file backend is the only backend which implements the PhpCapableInterface and can be used in combination with the PhpFrontend. The backend was specifically adapted to these needs and has low overhead for get and set operations, it scales very well with the number of entries for those operations. This mostly depends on the file lookup performance of the underlying file system in large directories, and most modern file systems use B-trees which can easily handle millions of files without much performance impact.

A disadvantage is that the performance of flushByTag() is bad and scales just O(n). This basically means that with twice the number of entries the file backend needs double time to flush entries which are tagged with a given tag. This practically renders the file backend unusable for content caches. The reason for this design decision in Flow is that the file backend is mainly used as AOP cache, where flushByTag() is only used if a PHP file changes. This happens very seldom on production systems, so get and set performance is much more important in this scenario.


Under heavy load the maximum set() performance depends on the maximum write and seek performance of the hard disk. If for example the server system shows lots of I/O wait in top, the file backend has reached this bound. A different storage strategy like RAM disks, battery backed up RAID systems or SSD hard disks might help then.


File cache backend options

Option Description Mandatory Type Default

Full path leading to a custom cache directory.


  • /tmp/my-cache-directory/
No string  


The PDO backend can be used as a native PDO interface to databases which are connected to PHP via PDO. The garbage collection is implemented for this backend and should be called to clean up hard disk space or memory.


There is currently very little production experience with this backend, especially not with a capable database like Oracle. We appreciate any feedback for real life use cases of this cache.


When not using SQLite, you have to create the needed caching tables manually. The table definition (as used automatically for SQLite) can be found in the file TYPO3.Flow/Resources/Private/Cache/SQL/DDL.sql. It works unchanged for MySQL, for other RDBMS you might need to adjust the DDL manually.


When not using SQLite the maximum length of each cache entry is restricted. The default in TYPO3.Flow/Resources/Private/Cache/SQL/DDL.sql is MEDIUMTEXT (16mb on MySQL), which should be sufficient in most cases.


Pdo cache backend options

Option Description Mandatory Type Default

Data source name for connecting to the database.


  • mysql:host=localhost;dbname=test
  • sqlite:/path/to/sqlite.db
  • sqlite::memory:
Yes string  
username Username to use for the database connection No    
password Password to use for the database connection No    


Redis is a key-value storage/database. In contrast to memcached, it allows structured values.Data is stored in RAM but it allows persistence to disk and doesn’t suffer from the design problems which exist with the memcached backend implementation. The redis backend can be used as an alternative of the database backend for big cache tables and helps to reduce load on database servers this way. The implementation can handle millions of cache entries each with hundreds of tags if the underlying server has enough memory.

Redis is known to be extremely fast but very memory hungry. The implementation is an option for big caches with lots of data because most important operations perform O(1) in proportion to the number of keys. This basically means that the access to an entry in a cache with a million entries is not slower than to a cache with only 10 entries, at least if there is enough memory available to hold the complete set in memory. At the moment only one redis server can be used at a time per cache, but one redis instance can handle multiple caches without performance loss when flushing a single cache.

The garbage collection task should be run once in a while to find and delete old tags.

The implementation is based on the phpredis module, which must be available on the system. It is recommended to build this from the git repository. Currently redis version 2.2 is recommended.


It is important to monitor the redis server and tune its settings to the specific caching needs and hardware capabilities. There are several articles on the net and the redis configuration file contains some important hints on how to speed up the system if it reaches bounds. A full documentation of available options is far beyond this documentation.


The redis implementation is pretty young and should be considered as experimental. The redis project itself has a very high development speed and it might happen that the Flow implementation changes to adapt to new versions.


Redis cache backend options

Option Description Mandatory Type Default
hostname IP address or name of redis server to connect to No string
port Port of the Redis server. Yes integer 6379
database Number of the database to store entries. Each cache should use its own database, otherwise all caches sharing a database are flushed if the flush operation is issued to one of them. Database numbers 0 and 1 are used and flushed by the core unit tests and should not be used if possible. No integer 0
password Password used to connect to the redis instance if the redis server needs authentication. Warning: The password is sent to the redis server in plain text. No string  
compressionLevel Set gzip compression level to a specific value. No integer (0 to 9) 0


Memcached is a simple key/value RAM database which scales across multiple servers. To use this backend, at least one memcache daemon must be reachable, and the PHP module memcache must be loaded. There are two PHP memcache implementations: memcache and memcached, only memcache is currently supported by this backend.

Warning and Design Constraints

Memcached is by design a simple key-value store. Values must be strings and there is no relation between keys. Since the caching framework needs to put some structure in it to store the identifier-data-tags relations, it stores, for each cache entry, an identifier-to-data, an identifier-to-tags and a tag-to-identifiers entry.

This leads to structural problems:

  • If memcache runs out of memory but must store new entries, it will toss some other
    entry out of the cache (this is called an eviction in memcached speak).
  • If data is shared over multiple memcache servers and some server fails, key/value pairs
    on this system will just vanish from cache.

Both cases lead to corrupted caches: If, for example, a tags-to-identifier entry is lost, dropByTag() will not be able to find the corresponding identifier-to-data entries which should be removed and they will not be deleted. This results in old data delivered by the cache. Additionally, there is currently no implementation of the garbage collection which can rebuild cache integrity. It is thus important to monitor a memcached system for evictions and server outages and to clear clear caches if that happens.

Furthermore memcache has no sort of namespacing. To distinguish entries of multiple caches from each other, every entry is prefixed with the cache name. This can lead to very long runtimes if a big cache needs to be flushed, because every entry has to be handled separately and it is not possible to just truncate the whole cache with one call as this would clear the whole memcached data which might even hold non Flow related entries.

Because of the mentioned drawbacks, the memcached backend should be used with care or in situations where cache integrity is not important or if a cache has no need to use tags at all.


The current native debian squeeze package (probably other distributions are affected, too) suffers from PHP memcache bug 16927.


Since memcached has no sort of namespacing and access control, this backend should not be used if other third party systems do have access to the same memcached daemon for security reasons. This is a typical problem in cloud deployments where access to memcache is cheap (but could be read by third parties) and access to databases is expensive.


Memcached cache backend options

Option Description Mandatory Type Default

Array of used memcached servers, at

least one server must be defined. Each server definition is a string, allowed syntaxes:

  • host
    TCP connect to host on memcached default port (usually 11211, defined by PHP ini variable memcache.default_port
  • host:port
    TCP connect to host on port
  • tcp://hostname:port
    Same as above
  • unix:///path/to/memcached.sock
    Connect to memcached server using unix sockets
Yes array  
compression Enable memcached internal data compression. Can be used to reduce memcached memory consumption but adds additional compression / decompression CPU overhead on the according memcached servers. No boolean FALSE


APC is mostly known as an opcode cache for PHP source files but can be used to store user data as well. As main advantage the data can be shared between different PHP processes and requests. All calls are direct memory calls. This makes this backend lightning fast for get() and set() operations. It can be an option for relatively small caches (few dozens of megabytes) which are read and written very often and becomes handy if APC is used as opcode cache anyway.

The implementation is very similar to the memcached backend implementation and suffers from the same problems if APC runs out of memory.

The garbage collection is currently not implemented. In its latest version, APC will fail to store data with a PHP warning if it runs out of memory. This may change in the future. Even without using the cache backend, it is advisable to increase the memory cache size of APC to at least 64MB when working with Flow, simply due to the large number of PHP files to be cached. A minimum of 128MB is recommended when using the additional content cache. Cache TTL for file and user data should be set to zero (disabled) to avoid heavy memory fragmentation.


It is not advisable to use the APC backend in shared hosting environments for security reasons: The user cache in APC is not aware of different virtual hosts. Basically every PHP script which is executed on the system can read and write any data to this shared cache, given data is not encapsulated or namespaced in any way. Only use the APC backend in environments which are completely under your control and where no third party can read or tamper your data.


The APC backend has no options.


The transient memory backend stores data in a local array. It is only valid for one request. This becomes handy if code logic needs to do expensive calculations or must look up identical information from a database over and over again during its execution. In this case it is useful to store the data in an array once and just lookup the entry from the cache for consecutive calls to get rid of the otherwise additional overhead. Since caches are available system wide and shared between core and extensions they can profit from each other if they need the same information.

Since the data is stored directly in memory, this backend is the quickest backend available. The stored data adds to the memory consumed by the PHP process and can hit the memory_limit PHP setting.


The transient memory backend has no options.


The null backend is a dummy backend which doesn’t store any data and always returns FALSE on get().


The null backend has no options.

How to Use the Caching Framework

This section is targeted at developers who want to use caches for arbitrary needs. It is only about proper initialization, not a discussion about identifier, tagging and lifetime decisions that must be taken during development.

Register a Cache

To register a cache it must be configured in Caches.yaml of a package:

  frontend: TYPO3\Flow\Cache\Frontend\StringFrontend

In this case \TYPO3\Flow\Cache\Frontend\StringFrontend was chosen, but that depends on individual needs. This setting is usually not changed by users. Any option not given is inherited from the configuration of the “Default” cache. The name (MyPackage_FooCache in this case) can be chosen freely, but keep possible name clashes in mind and adopt a meaningful schema.

Retrieve and Use a Cache

Using dependency injection

A cache is usually retrieved through dependency injection, either constructor or setter injection. Which is chosen depends on when you need the cache to be available. Keep in mind that even if you seem to need a cache in the constructor, you could always make use of initializeObject(). Here is an example for setter injection matching the configuration given above. First you need to configure the injection in Objects.yaml:

        factoryObjectName: TYPO3\Flow\Cache\CacheManager
        factoryMethodName: getCache
            value: MyPackage_FooCache

This configures what will be injected into the following setter:

 * Sets the foo cache
 * @param \TYPO3\Flow\Cache\Frontend\StringFrontend $cache Cache for foo data
 * @return void
public function setFooCache(\TYPO3\Flow\Cache\Frontend\StringFrontend $cache) {
        $this->fooCache = $cache;

To make it even simpler you could omit the setter method and annotate the member with the Inject annotations. The injected cache is fully initialized, all available frontend operations like get(), set() and flushByTag() can be executed on $this->fooCache.

Using the CacheFactory

Of course you can also manually ask the CacheManager (have it injected for your convenience) for a cache:

$this->fooCache = $this->cacheManager->getCache('MyPackage_FooCache');