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NAME
persistent_term - Persistent terms.
DESCRIPTION
This module is similar to ets in that it provides a storage for Erlang terms that can be
accessed in constant time, but with the difference that persistent_term has been highly
optimized for reading terms at the expense of writing and updating terms. When a
persistent term is updated or deleted, a global garbage collection pass is run to scan all
processes for the deleted term, and to copy it into each process that still uses it.
Therefore, persistent_term is suitable for storing Erlang terms that are frequently
accessed but never or infrequently updated.
Warning:
Persistent terms is an advanced feature and is not a general replacement for ETS tables.
Before using persistent terms, make sure to fully understand the consequence to system
performance when updating or deleting persistent terms.
Term lookup (using get/1), is done in constant time and without taking any locks, and the
term is not copied to the heap (as is the case with terms stored in ETS tables).
Storing or updating a term (using put/2) is proportional to the number of already created
persistent terms because the hash table holding the keys will be copied. In addition, the
term itself will be copied.
When a (complex) term is deleted (using erase/1) or replaced by another (using put/2), a
global garbage collection is initiated. It works like this:
* All processes in the system will be scheduled to run a scan of their heaps for the
term that has been deleted. While such scan is relatively light-weight, if there are
many processes, the system can become less responsive until all process have scanned
their heaps.
* If the deleted term (or any part of it) is still used by a process, that process will
do a major (fullsweep) garbage collection and copy the term into the process. However,
at most two processes at a time will be scheduled to do that kind of garbage
collection.
Deletion of atoms and other terms that fit in one machine word is specially optimized to
avoid doing a global GC. It is still not recommended to update persistent terms with such
values too frequently because the hash table holding the keys is copied every time a
persistent term is updated.
Some examples are suitable uses for persistent terms are:
* Storing of configuration data that must be easily accessible by all processes.
* Storing of references for NIF resources.
* Storing of references for efficient counters.
* Storing an atom to indicate a logging level or whether debugging is turned on.
STORING HUGE PERSISTENT TERMS
The current implementation of persistent terms uses the literal allocator also used for
literals (constant terms) in BEAM code. By default, 1 GB of virtual address space is
reserved for literals in BEAM code and persistent terms. The amount of virtual address
space reserved for literals can be changed by using the +MIscs option when starting the
emulator.
Here is an example how the reserved virtual address space for literals can be raised to 2
GB (2048 MB):
erl +MIscs 2048
BEST PRACTICES FOR USING PERSISTENT TERMS
It is recommended to use keys like ?MODULE or {?MODULE,SubKey} to avoid name collisions.
Prefer creating a few large persistent terms to creating many small persistent terms. The
execution time for storing a persistent term is proportional to the number of already
existing terms.
Updating a persistent term with the same value as it already has is specially optimized to
do nothing quickly; thus, there is no need compare the old and new values and avoid
calling put/2 if the values are equal.
When atoms or other terms that fit in one machine word are deleted, no global GC is
needed. Therefore, persistent terms that have atoms as their values can be updated more
frequently, but note that updating such persistent terms is still much more expensive than
reading them.
Updating or deleting a persistent term will trigger a global GC if the term does not fit
in one machine word. Processes will be scheduled as usual, but all processes will be made
runnable at once, which will make the system less responsive until all process have run
and scanned their heaps for the deleted terms. One way to minimize the effects on
responsiveness could be to minimize the number of processes on the node before updating or
deleting a persistent term. It would also be wise to avoid updating terms when the system
is at peak load.
Avoid storing a retrieved persistent term in a process if that persistent term could be
deleted or updated in the future. If a process holds a reference to a persistent term when
the term is deleted, the process will be garbage collected and the term copied to process.
Avoid updating or deleting more than one persistent term at a time. Each deleted term will
trigger its own global GC. That means that deleting N terms will make the system less
responsive N times longer than deleting a single persistent term. Therefore, terms that
are to be updated at the same time should be collected into a larger term, for example, a
map or a tuple.
EXAMPLE
The following example shows how lock contention for ETS tables can be minimized by having
one ETS table for each scheduler. The table identifiers for the ETS tables are stored as a
single persistent term:
%% There is one ETS table for each scheduler.
Sid = erlang:system_info(scheduler_id),
Tid = element(Sid, persistent_term:get(?MODULE)),
ets:update_counter(Tid, Key, 1).
DATA TYPES
key() = term()
Any Erlang term.
value() = term()
Any Erlang term.
EXPORTS
erase(Key) -> Result
Types:
Key = key()
Result = boolean()
Erase the name for the persistent term with key Key. The return value will be true
if there was a persistent term with the key Key, and false if there was no
persistent term associated with the key.
If there existed a previous persistent term associated with key Key, a global GC
has been initiated when erase/1 returns. See Description.
get() -> List
Types:
List = [{key(), value()}]
Retrieve the keys and values for all persistent terms. The keys will be copied to
the heap for the process calling get/0, but the values will not.
get(Key) -> Value
Types:
Key = key()
Value = value()
Retrieve the value for the persistent term associated with the key Key. The lookup
will be made in constant time and the value will not be copied to the heap of the
calling process.
This function fails with a badarg exception if no term has been stored with the key
Key.
If the calling process holds on to the value of the persistent term and the
persistent term is deleted in the future, the term will be copied to the process.
get(Key, Default) -> Value
Types:
Key = key()
Default = Value = value()
Retrieve the value for the persistent term associated with the key Key. The lookup
will be made in constant time and the value will not be copied to the heap of the
calling process.
This function returns Default if no term has been stored with the key Key.
If the calling process holds on to the value of the persistent term and the
persistent term is deleted in the future, the term will be copied to the process.
info() -> Info
Types:
Info = #{count := Count, memory := Memory}
Count = Memory = integer() >= 0
Return information about persistent terms in a map. The map has the following keys:
count:
The number of persistent terms.
memory:
The total amount of memory (measured in bytes) used by all persistent terms.
put(Key, Value) -> ok
Types:
Key = key()
Value = value()
Store the value Value as a persistent term and associate it with the key Key.
If the value Value is equal to the value previously stored for the key, put/2 will
do nothing and return quickly.
If there existed a previous persistent term associated with key Key, a global GC
has been initiated when put/2 returns. See Description.