# counters

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Module

counters

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Module Summary

Counter Functions

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Since

Module counters was introduced in OTP 21.2.

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Description

This module provides a set of functions to do operations towards shared mutable counter variables. The implementation does not utilize any software level locking, which makes it very efficient for concurrent access. The counters are organized into arrays with the following semantics:

- Counters are 64 bit signed integers.

- Counters wrap around at overflow and underflow operations.

- Counters are initialized to zero.

- Write operations guarantee atomicity. No intermediate results can be seen from a single write operation.

- Two types of counter arrays can be created with options atomics or write_concurrency. The atomics counters have good allround performance with nice consistent semantics while write_concurrency counters offers even better concurrent write performance at the expense of some potential read inconsistencies. See new/2.

- Indexes into counter arrays are one-based. A counter array of size N contains N counters with index from 1 to N.

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Data Types

####
counters_ref()

Identifies a counter array returned from new/2.

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Exports

####
new(Size, Opts) -> counters_ref()
OTP 21.2

### Types

Size = integer() >= 1
Opts = [Opt]
Opt = atomics | write_concurrency

Create a new counter array of Size counters. All counters in the array are initially set to zero.

Argument Opts is a list of the following possible options:

atomics (Default)

  Counters will be sequentially consistent. If write operation A is done sequentially before write operation B, then a concurrent reader may see the result of none of them, only A, or both A and B. It cannot see the result of only B.

write_concurrency

  This is an optimization to achieve very efficient concurrent add and sub operations at the expense of potential read inconsistency and memory consumption per counter.

  Read operations may see sequentially inconsistent results with regard to concurrent write operations. Even if write operation A is done sequentially before write operation B, a concurrent reader may see any combination of A and B, including only B. A read operation is only guaranteed to see all writes done sequentially before the read. No writes are ever lost, but will eventually all be seen.

  The typical use case for write_concurrency is when concurrent calls to add and sub toward the same counters are very frequent, while calls to get and put are much less frequent. The lack of absolute read consistency must also be acceptable.

Counters are not tied to the current process and are automatically garbage collected when they are no longer referenced.

####
get(Ref, Ix) -> integer()
OTP 21.2

### Types

Ref = counters_ref()
Ix = integer()

Read counter value.

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add(Ref, Ix, Incr) -> ok
OTP 21.2

### Types

Ref = counters_ref()
Ix = Incr = integer()

Add Incr to counter at index Ix.

####
sub(Ref, Ix, Decr) -> ok
OTP 21.2

### Types

Ref = counters_ref()
Ix = Decr = integer()

Subtract Decr from counter at index Ix.

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put(Ref, Ix, Value) -> ok
OTP 21.2

### Types

Ref = counters_ref()
Ix = Value = integer()

Write Value to counter at index Ix.

Note

Despite its name, the write_concurrency optimization does not improve put. A call to put is a relatively heavy operation compared to the very lightweight and scalable add and sub. The cost for a put with write_concurrency is like a get plus a put without write_concurrency.

####
info(Ref) -> Info
OTP 21.2

### Types

Ref = counters_ref()
Info = #{size := Size, memory := Memory}
Size = Memory = integer() >= 0

Return information about a counter array in a map. The map has the following keys (at least):

size

  The number of counters in the array.

memory

  Approximate memory consumption for the array in bytes.