Acknowledged

ByteArray addAll() not Memory Efficient

When using the addAll() method of Lang.ByteArray, peak memory usage implies that the ConnectIQ framework is doubling the size of the array being appended to instead of extending it by the size of the appended array.

For example, let's say we create a 1K byteArray called myArray and run the following code starting with an empty byte array, longByteArray:

while(longByteArray.size() < 32768) {
    longByteArray.addAll(myArray);
}

An efficient implementation would grow peak memory usage by ~1K more than longByteArray.size(). Instead, we see peak memory usage growing with longByteArray.size() * 2 + 1024.

It may also be worth noting that the Monkey C extension for Visual Studio code will time out as the array grows if a developer sets a breakpoint on the addAll function above while the watch window is open to longByteArray.

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  • , I'm sure the collective intelligence in this forum can come up with creative workarounds, but I'm hoping an improvement could be made in native CIQ code since anything we'd do in the VM would be less efficient. A class or a loop like the one below "works" but isn't as scalable or as time efficient. The VS Code extension issue inspecting large arrays is also a problem regardless.

    I'm trying to keep this thread closer to theory than specific application, but to answer your question, in the case where I discovered it, I was working with static data. Back then, I'd tried plenty of tricks to coax addAll() to act how I wanted, like pre-allocating the array as  had suggested more recently, but native functions like addAll() don't seem to compare to the loop below in terms of peak memory efficiency. It may go without saying that I ended up not using addAll(), but the API might be stronger if it were improved.

Comment

  • , I'm sure the collective intelligence in this forum can come up with creative workarounds, but I'm hoping an improvement could be made in native CIQ code since anything we'd do in the VM would be less efficient. A class or a loop like the one below "works" but isn't as scalable or as time efficient. The VS Code extension issue inspecting large arrays is also a problem regardless.

    I'm trying to keep this thread closer to theory than specific application, but to answer your question, in the case where I discovered it, I was working with static data. Back then, I'd tried plenty of tricks to coax addAll() to act how I wanted, like pre-allocating the array as  had suggested more recently, but native functions like addAll() don't seem to compare to the loop below in terms of peak memory efficiency. It may go without saying that I ended up not using addAll(), but the API might be stronger if it were improved.

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