Earlier quoted context omitted.
This part... > for the high frame-rate, heavy animations this puts a lot of pressure on CPU and memory ...does seem to suggest that the "garbage multiplier" effect of immutability is an ill fit for applications that also create a lot of garbage naturally. Note that this is about as close to an apples-to-apples comparison as we're likely to get - the same application implemented two different ways - so it's not the ap…
Clojure developers are not unaware of the tradeoffs between immutable and mutable data structures. You'll see them use mutable data structures, particularly in tight loops inside functions that take immutable inputs, mutate them, and produce immutable outputs (thereby preserving the promise of immutability, while leveraging the performance of mutability internally). You'll rarely see apps designed like this up-front…
The Erlang runtime exposes complex mutable resources like ETS tables through opaque handles, where the handle can be freely shared, but the resource backing the handle can never actually be touched by "clients." Instead, the resource backing the handle lives in its own heap, which is owned by a manager object; and accesses to the resources in that heap are done by handing the manager references to data that it then copies into the heap; or querying it by handing it a reference to a key, whereupon the manager will copy data back out and return it.
It's not really the same abstraction as e.g. a Concurrent container-class in the Java stdlib, as it's not implemented through the client ever acquiring (the moral equivalent of) a mutex and then touching the data itself; nor does it involve the client adding object references to an atomic queue, where some async process then weaves those references into the backing object. Neither the client's execution-thread, nor its passed-in data, ever touches the handle's backing data.
Instead, ETS and the like have guarantees almost as if the mutable resources they hold were living in a separate OS process (similar to e.g. data in a nearby Redis instance), where you need to "view" and "update" the resources living in that separate process through commands issued to that server, over a socket, using a wire protocol; where that serialization over the socket guarantees that the data reaching the other end is a copy of your client-owned data, rather than a shared-memory representation of it. The same semantics, but without the actual overhead of serialization or kernel context-switching, because the "other end" is still inside the managed runtime of your OS process.
And, to be clear, Erlang ETS accesses aren't linearized by a "message inbox" queue sitting in-between, the way that regular Erlang inter-actor message-passing is. The ETS table manager can handle multiple concurrent requests to the same table, from different processes, simultaneously, without locking the whole table—just like an RDBMS would. (Instead, it uses row-batch locks for writes, like RDBMSes do.) The concurrency strategy is a concern internal to each particular black-box-with-a-handle, rather than something general to the abstraction. The only thing guaranteed by the black-box-with-a-handle abstraction, is that nobody can mutate the data "inside" the black box without its manager's knowledge, because nobody ever holds a live reference to the data "inside" the black box.