Except in those languages it can be just as brutally painful to allocate. Start modifying strings in the render loop on Android and see how quickly you get destroyed by constant GC pauses.
The only way to address it is with extensive pooling and heuristics.
Or you can just mutate.
Really it's no wonder that the web is so slow if the common conception is that allocating is no big deal. If you really want to do performance right allocations should be at the top of your list right next to how cache friendly your data structures are.
Because in reality it is no big deal. Modern GCs are incredibly efficient.
When a GC allocates memory all it does is check if there is enough memory in the "young generation" if yes it will increment a pointer and then it will just return the last position in the young generation. If there is not enough memory in the young generation the GC will start traversing the GC root nodes on the stack and only the "live" memory has to be traversed and copied over to the old generation. In other words in the majority of cases allocation memory costs almost as much as mutating memory.
There is a huge difference between “algorithmically efficient” (aka big o) and “real life efficient” (aka actual cycle counts). In real life, constant factors are a huge deal. Real developers don’t just work with CS, they work with the actual underlying hardware. Big O has no concept of cache, no SMP, no hypertreading, no pipeline flushes, no branch prediction, or anything else that actually matters to creating performance libraries and applications in real life.
The only way to address it is with extensive pooling and heuristics.
Or you can just mutate.
Really it's no wonder that the web is so slow if the common conception is that allocating is no big deal. If you really want to do performance right allocations should be at the top of your list right next to how cache friendly your data structures are.