A disk cache object is a key-value store that saves the values as files in a directory on disk. Objects can be stored and retrieved using the get() and set() methods. Objects are automatically pruned from the cache according to the parameters max_size, max_age, max_n, and evict.

cache_disk(
dir = NULL,
max_size = 1024 * 1024^2,
max_age = Inf,
max_n = Inf,
evict = c("lru", "fifo"),
destroy_on_finalize = FALSE,
missing = key_missing(),
prune_rate = 20,
warn_ref_objects = FALSE,
logfile = NULL
)

## Arguments

dir Directory to store files for the cache. If NULL (the default) it will create and use a temporary directory. Maximum size of the cache, in bytes. If the cache exceeds this size, cached objects will be removed according to the value of the evict. Use Inf for no size limit. The default is 1 gigabyte. Maximum age of files in cache before they are evicted, in seconds. Use Inf for no age limit. Maximum number of objects in the cache. If the number of objects exceeds this value, then cached objects will be removed according to the value of evict. Use Inf for no limit of number of items. The eviction policy to use to decide which objects are removed when a cache pruning occurs. Currently, "lru" and "fifo" are supported. If TRUE, then when the cache_disk object is garbage collected, the cache directory and all objects inside of it will be deleted from disk. If FALSE (the default), it will do nothing when finalized. A value to return when get(key) is called but the key is not present in the cache. The default is a key_missing() object. It is actually an expression that is evaluated each time there is a cache miss. See section Missing keys for more information. How often to prune the cache. See section Cache Pruning for more information. Should a warning be emitted when a reference is stored in the cache? This can be useful because serializing and deserializing a reference object (such as environments and external pointers) can lead to unexpected behavior. An optional filename or connection object to where logging information will be written. To log to the console, use stderr() or stdout().

## Value

A disk caching object, with class cache_disk.

## Missing keys

The missing parameter controls what happens when get() is called with a key that is not in the cache (a cache miss). The default behavior is to return a key_missing() object. This is a sentinel value that indicates that the key was not present in the cache. You can test if the returned value represents a missing key by using the is.key_missing() function. You can also have get() return a different sentinel value, like NULL. If you want to throw an error on a cache miss, you can do so by providing an expression for missing, as in missing = stop("Missing key").

When the cache is created, you can supply a value for missing, which sets the default value to be returned for missing values. It can also be overridden when get() is called, by supplying a missing argument. For example, if you use cache\$get("mykey", missing = NULL), it will return NULL if the key is not in the cache.

The missing parameter is actually an expression which is evaluated each time there is a cache miss. A quosure (from the rlang package) can be used.

If you use this, the code that calls get() should be wrapped with tryCatch() to gracefully handle missing keys.

## Cache pruning

Cache pruning occurs when set() is called, or it can be invoked manually by calling prune().

The disk cache will throttle the pruning so that it does not happen on every call to set(), because the filesystem operations for checking the status of files can be slow. Instead, it will prune once in every prune_rate calls to set(), or if at least 5 seconds have elapsed since the last prune occurred, whichever is first.

When a pruning occurs, if there are any objects that are older than max_age, they will be removed.

The max_size and max_n parameters are applied to the cache as a whole, in contrast to max_age, which is applied to each object individually.

If the number of objects in the cache exceeds max_n, then objects will be removed from the cache according to the eviction policy, which is set with the evict parameter. Objects will be removed so that the number of items is max_n.

If the size of the objects in the cache exceeds max_size, then objects will be removed from the cache. Objects will be removed from the cache so that the total size remains under max_size. Note that the size is calculated using the size of the files, not the size of disk space used by the files --- these two values can differ because of files are stored in blocks on disk. For example, if the block size is 4096 bytes, then a file that is one byte in size will take 4096 bytes on disk.

Another time that objects can be removed from the cache is when get() is called. If the target object is older than max_age, it will be removed and the cache will report it as a missing value.

## Eviction policies

If max_n or max_size are used, then objects will be removed from the cache according to an eviction policy. The available eviction policies are:

"lru"

Least Recently Used. The least recently used objects will be removed. This uses the filesystem's mtime property. When "lru" is used, each get() is called, it will update the file's mtime using Sys.setFileTime(). Note that on some platforms, the resolution of Sys.setFileTime() may be low, one or two seconds.

"fifo"

First-in-first-out. The oldest objects will be removed.

Both of these policies use files' mtime. Note that some filesystems (notably FAT) have poor mtime resolution. (atime is not used because support for atime is worse than mtime.)

## Sharing among multiple processes

The directory for a cache_disk can be shared among multiple R processes. To do this, each R process should have a cache_disk object that uses the same directory. Each cache_disk will do pruning independently of the others, so if they have different pruning parameters, then one cache_disk may remove cached objects before another cache_disk would do so.

Even though it is possible for multiple processes to share a cache_disk directory, this should not be done on networked file systems, because of slow performance of networked file systems can cause problems. If you need a high-performance shared cache, you can use one built on a database like Redis, SQLite, mySQL, or similar.

When multiple processes share a cache directory, there are some potential race conditions. For example, if your code calls exists(key) to check if an object is in the cache, and then call get(key), the object may be removed from the cache in between those two calls, and get(key) will throw an error. Instead of calling the two functions, it is better to simply call get(key), and check that the returned object is not a key_missing() object, using is.key_missing(). This effectively tests for existence and gets the object in one operation.

It is also possible for one processes to prune objects at the same time that another processes is trying to prune objects. If this happens, you may see a warning from file.remove() failing to remove a file that has already been deleted.

## Methods

A disk cache object has the following methods:

get(key, missing)

Returns the value associated with key. If the key is not in the cache, then it evaluates the expression specified by missing and returns the value. If missing is specified here, then it will override the default that was set when the cache_mem object was created. See section Missing Keys for more information.

set(key, value)

Stores the key-value pair in the cache.

exists(key)

Returns TRUE if the cache contains the key, otherwise FALSE.

remove(key)

Removes key from the cache, if it exists in the cache. If the key is not in the cache, this does nothing.

size()

Returns the number of items currently in the cache.

keys()

Returns a character vector of all keys currently in the cache.

reset()

Clears all objects from the cache.

destroy()

Clears all objects in the cache, and removes the cache directory from disk.

prune()

Prunes the cache, using the parameters specified by max_size, max_age, max_n, and evict.