QAbstractItemModel::beginResetModel与endResetModel是否存在性能隐患？及dataChanged与该方法的优劣对比

Let’s start by recapping your setup to set the context: you’re working with Qt 5.15.0 on embedded Linux, storing view data in a QList<MyStruct> member m_lstData. Your initial update logic was:

void MyModelClass::SomeInsertMethod(const QList<MyStruct>& aNewData) {
    beginResetModel();
    m_lstData = aNewData;
    endResetModel();
}

Then you added a smart optimization to skip resets when data hasn’t changed:

void MyModelClass::SomeInsertMethod(const QList<MyStruct>& aNewData) {
    if (m_lstData != aNewData) {
        beginResetModel();
        m_lstData = aNewData;
        endResetModel();
    }
}

Great question—let’s break down the remaining performance concerns and the tradeoffs between beginResetModel and dataChanged.

Potential Performance Issues with beginResetModel/endResetModel #

Even with your data check, calling beginResetModel() and endResetModel() can still cause noticeable latency on embedded systems, and here’s why:

• When you trigger a model reset, the view completely tears down and rebuilds its entire visual representation. That means it re-queries every item’s data (for all roles), redoes layout calculations, and redraws every element from scratch. On resource-constrained embedded hardware (limited CPU/GPU, lower memory), this full rebuild can lead to the slight delay you’re seeing.
• The Qt docs don’t explicitly call out this performance hit because it’s highly context-dependent—what’s trivial on a desktop might be expensive on embedded. Resetting is designed to be a safe catch-all for large data changes, not the most efficient option.

Your current check prevents unnecessary resets when data is identical, which is a fantastic first step, but when data does change, the full reset still carries that heavy overhead.

Why dataChanged Is Better (When Done Right) #

Your concern about extra loops and comparisons is valid, but in most embedded scenarios, the tradeoff favors dataChanged over reset. Here are its key advantages:

• Partial, targeted updates: Instead of rebuilding the entire view, dataChanged tells the view exactly which indices and roles have changed. The view only redraws those specific items, skipping all unchanged elements. This drastically reduces CPU/GPU usage—critical on embedded systems where every cycle counts.
• Preserves view state: A model reset wipes out the view’s current state: scroll position, selected items, expanded nodes (if using a tree view). dataChanged keeps all that intact, which is a huge win for user experience.
• Lower overhead for incremental changes: If only a small subset of your data changes, the loop to find those changes is negligible compared to the cost of a full view rebuild. Even for larger datasets, optimizing your comparison logic (e.g., first check list length, then compare elements only if lengths match) can keep the comparison cost low.

Addressing Your Comparison Concern #

You’re right that looping through elements adds some work, but let’s put this in perspective:

• For a list of 1000 items, comparing each element might take a few microseconds. A full view reset, on the other hand, could take tens or hundreds of milliseconds (depending on how complex your delegate/view is). The math clearly favors dataChanged here.
• Make sure your MyStruct has an efficient operator== (and by extension, operator!=). If your struct contains complex data, optimize the comparison to check the most likely changing fields first, or use a hash of the struct to compare quickly.

Additional Optimization Tips #

• Combine granular signals where possible: If you’re adding/removing rows instead of replacing the entire list, use beginInsertRows()/endInsertRows() or beginRemoveRows()/endRemoveRows() instead of dataChanged—these are even more efficient for structural changes.
• Use view caching: Enable caching on your view (e.g., QListView::setCacheMode(QListView::CacheAllRows)). This stores rendered items in memory, reducing redraw time for unchanged elements.
• Batch data changes: If you have multiple small updates, collect the changed indices first, then emit a single dataChanged signal instead of multiple ones. Minimizing signal emissions reduces overhead.

内容的提问来源于stack exchange，提问作者BikashRDas