# ConcurrentCollections [](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/) ConcurrentCollections.jl provides the following concurrent collections for Julia ≥ 1.7. Most of their operations are (almost) lock-free whenever appropriate. * [`DualLinkedConcurrentRingQueue`](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/#ConcurrentCollections.DualLinkedConcurrentRingQueue) * [`DualLinkedQueue`](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/#ConcurrentCollections.DualLinkedQueue) * [`LinkedConcurrentRingQueue`](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/#ConcurrentCollections.LinkedConcurrentRingQueue) * [`ConcurrentQueue`](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/#ConcurrentCollections.ConcurrentQueue) * [`ConcurrentStack`](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/#ConcurrentCollections.ConcurrentStack) * [`WorkStealingDeque`](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/#ConcurrentCollections.WorkStealingDeque) * [`ConcurrentDict`](https://juliaconcurrent.github.io/ConcurrentCollections.jl/dev/#ConcurrentCollections.ConcurrentDict) **NOTE**: If you are trying to find a way to improve performance (especially the *throughput*) of your program, it is highly recommended to look for ways to **avoid** using concurrent collections first. In particular, consider applying the [data-parallel](https://juliafolds.github.io/data-parallelism/) pattern to dodge the difficulties in concurrent programming. For example, it is often a better idea to use task-local **non**-thread-safe `Dict`s instead of a `ConcurrentDict` shared across tasks. One of the most important techniques in data-parallel programming is how to merge such task-local states. For more information, see, e.g., [Efficient and safe approaches to mutation in data parallelism](https://juliafolds.github.io/data-parallelism/tutorials/mutations/).