#synchronization #spsc #multithreading #non-blocking #wait-free


An implementation of triple buffering, useful for sharing frequently updated data between threads

18 releases (6 stable)

3.0.1 Aug 27, 2018
2.0.0 Feb 11, 2018
1.1.1 Nov 19, 2017
0.3.4 Jun 25, 2017
0.1.1 Mar 10, 2017

#79 in Algorithms

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MPL-2.0 license

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Triple buffering in Rust

On crates.io On docs.rs Build status

What is this?

This is an implementation of triple buffering written in Rust. You may find it useful for the following class of thread synchronization problems:

  • There is one producer thread and one consumer thread
  • The producer wants to update a shared memory value periodically
  • The consumer wants to access the latest update from the producer at any time

The simplest way to use it is as follows:

// Create a triple buffer:
let buf = TripleBuffer::new(0);

// Split it into an input and output interface, to be respectively sent to
// the producer thread and the consumer thread:
let (mut buf_input, mut buf_output) = buf.split();

// The producer can move a value into the buffer at any time

// The consumer can access the latest value from the producer at any time
let latest_value_ref = buf_output.read();
assert_eq!(*latest_value_ref, 42);

In situations where moving the original value away and being unable to modify it after the fact is too costly, such as if creating a new value involves dynamic memory allocation, you can opt into the lower-level "raw" interface, which allows you to access the buffer's data in place and precisely control when updates are propagated.

This data access method is more error-prone and comes at a small performance cost, which is why you will need to enable it explicitly using the "raw" cargo feature.

// Create and split a triple buffer
use triple_buffer::TripleBuffer;
let buf = TripleBuffer::new(String::with_capacity(42));
let (mut buf_input, mut buf_output) = buf.split();

// Mutate the input buffer in place
    // Acquire a reference to the input buffer
    let raw_input = buf_input.raw_input_buffer();

    // In general, you don't know what's inside of the buffer, so you should
    // always reset the value before use (this is a type-specific process).

    // Perform an in-place update
    raw_input.push_str("Hello, ");

// Publish the input buffer update

// Manually fetch the buffer update from the consumer interface

// Acquire a mutable reference to the output buffer
let raw_output = buf_output.raw_output_buffer();

// Post-process the output value before use

Give me details! How does it compare to alternatives?

Compared to a mutex:

  • Only works in single-producer, single-consumer scenarios
  • Is nonblocking, and more precisely bounded wait-free. Concurrent accesses will be slowed down by cache contention, but no deadlock, livelock, or thread scheduling induced slowdown is possible.
  • Allows the producer and consumer to work simultaneously
  • Uses a lot more memory (3x payload + 4 integers vs 1x payload + 1 bool)
  • Does not allow in-place updates, as the producer and consumer do not access the same memory location
  • Should be slower if updates are rare and in-place updates are much more efficient than moves, faster otherwise.

Compared to the read-copy-update (RCU) primitive from the Linux kernel:

  • Only works in single-producer, single-consumer scenarios
  • Has higher dirty read overhead on relaxed-memory architectures (ARM, POWER...)
  • Does not require accounting for reader "grace periods": once the reader has gotten access to the latest value, the synchronization transaction is over
  • Does not use the inefficient compare-and-swap hardware primitive on update
  • Does not suffer from the ABA problem, allowing much simpler code
  • Allocates memory on initialization only, rather than on every update
  • May use more memory (3x payload + 4 integers vs 1x pointer + amount of payloads and refcounts that depends on the readout and update pattern)
  • Should be slower if updates are rare, faster if updates are frequent

Compared to sending the updates on a message queue:

  • Only works in single-producer, single-consumer scenarios (queues can work in other scenarios, although the implementations are much less efficient)
  • Consumer only has access to the latest state, not the previous ones
  • Consumer does not need to get through every previous state
  • Is nonblocking AND uses bounded amounts of memory (with queues, it's a choice)
  • Can transmit information in a single move, rather than two
  • Should be faster for any compatible use case

In short, triple buffering is what you're after in scenarios where a shared memory location is updated frequently by a single writer, read by a single reader who only wants the latest version, and you can spare some RAM.

  • If you need multiple producers, look somewhere else
  • If you need multiple consumers, you may be interested in my related "SPMC buffer" work, which basically extends triple buffering to multiple consumers
  • If you can't tolerate the RAM overhead or want to update the data in place, try a Mutex instead (or possibly an RWLock)
  • If the shared value is updated very rarely (e.g. every second), try an RCU
  • If the consumer must get every update, try a message queue

How do I know your unsafe lock-free code is working?

By running the tests, of course! Which is unfortunately currently harder than I'd like it to be.

First of all, we have sequential tests, which are very thorough but obviously do not check the lock-free/synchronization part. You run them as follows:

$ cargo test --tests && cargo test --features raw

Then we have a concurrent test where a reader thread continuously observes the values from a rate-limited writer thread, and makes sure that he can see every single update without any incorrect value slipping in the middle.

This test is more important, but it is also harder to run because one must first check some assumptions:

  • The testing host must have at least 2 physical CPU cores to test all possible race conditions
  • No other code should be eating CPU in the background. Including other tests.
  • As the proper writing rate is system-dependent, what is configured in this test may not be appropriate for your machine.

Taking this and the relatively long run time (~10-20 s) into account, this test is ignored by default.

Finally, we have benchmarks, which allow you to test how well the code is performing on your machine. Because cargo bench has not yet landed in Stable Rust, these benchmarks masquerade as tests, which make them a bit unpleasant to run. I apologize for the inconvenience.

To run the concurrent test and the benchmarks, make sure no one is eating CPU in the background and do:

$ cargo test --release -- --ignored --nocapture --test-threads=1

(As before, you can also test with --features raw)

Here is a guide to interpreting the benchmark results:

  • clean_read measures the triple buffer readout time when the data has not changed. It should be extremely fast (a couple of CPU clock cycles).
  • write measures the amount of time it takes to write data in the triple buffer when no one is reading.
  • write_and_dirty_read performs a write as before, immediately followed by a sequential read. To get the dirty read performance, substract the write time from that result. Writes and dirty read should take comparable time.
  • concurrent_write measures the write performance when a reader is continuously reading. Expect significantly worse performance: lock-free techniques can help against contention, but are not a panacea.
  • concurrent_read measures the read performance when a writer is continuously writing. Again, a significant hit is to be expected.

On my laptop's CPU (Intel Core i7-4720HQ), typical results are as follows:

  • Write: 7.4 ns
  • Clean read: 0.75 ns
  • Dirty read: 9.4 ns
  • Concurrent write: 41 ns
  • Concurrent read: 98 ns


This crate is distributed under the terms of the MPLv2 license. See the LICENSE file for details.

More relaxed licensing (Apache, MIT, BSD...) may also be negociated, in exchange of a financial contribution. Contact me for details at knights_of_ni AT gmx DOTCOM.