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Every time a new patient walks in, the system must instantly integrate them into the queue while maintaining the invariant that the most critical patient is always accessible for immediate treatment. The challenge isn't just *recording* the new patient—it's *positioning* them correctly among potentially thousands of others, and doing so in a fraction of a second. This is the insert operation in a heap: the ability to add a new element while preserving the heap's structural and ordering properties. What makes heaps remarkable is that this seemingly complex reorganization happens in **O(log n)** time—regardless of heap size. A heap with 10 elements and a heap with 10 million elements both require the same bounded number of comparisons relative to their height. In this page, we'll dissect the insertion algorithm completely: the intuition behind it, the precise mechanics, the proof of correctness, the complexity analysis, and the subtle implementation details that distinguish robust code from buggy code.73:T557,Before we examine the heap's insertion strategy, let's understand why insertion is non-trivial by considering what *doesn't* work. **Attempted Strategy 1: Insert at Root** Suppose we tried inserting a new element at the root (index 0). This would immediately violate the complete binary tree property. The root already has children, and we'd need to "push" the entire tree down, displacing every element. This is an O(n) operation—completely unacceptable for a data structure designed for efficiency. **Attempted Strategy 2: Insert at Random Position** What if we found a random empty spot? But heaps don't *have* random empty spots. The array is densely packed because heaps are complete binary trees—every index from 0 to n-1 is occupied. We can't just place an element anywhere. **Attempted Strategy 3: Find Correct Position First** We might try scanning the heap to find where the new element "should" go based on its value. But this would require O(n) comparisons in the worst case, and after insertion, we'd still need to shift elements. This is no better than a sorted array. The heap's solution is elegant: **insert at the end, then fix upward**. By adding the element at the first available position (maintaining the complete tree structure) and then "bubbling" it up to its correct location, we achieve O(log n) insertion with minimal disruption.74:T594,**Maintenance (each iteration preserves the invariant):** Suppose the invariant holds at the start of an iteration. Let `i = index` be the current position of the element being bubbled, and `p = parent(i)`. **Case 1:** `heap[i] ≤ heap[p]` (no violation) The heap property is satisfied at (p, i). Combined with the invariant (all other pairs are valid), the entire heap is valid. The loop terminates. ✓ **Case 2:** `heap[i] > heap[p]` (violation exists) We swap `heap[i]` and `heap[p]`. After the swap: 1. **The pair (p, i) is now valid** — The larger value is at p (the parent), and the smaller value is at i (the child). 2. **The pair (grandparent, p) might be invalid** — The value now at p came from i. If this value is larger than the grandparent, we have a new violation. 3. **The pair (p, sibling of i) is valid** — The value at p increased (we swapped in a larger value). Since `new_heap[p] ≥ old_heap[p] ≥ heap[sibling(i)]` (by the old heap property), the sibling relationship holds. 4. **The subtree rooted at i is valid** — The value now at i was previously at p. This value was larger than all descendants before the swap. The children of i (which were children of i before, not p) still satisfy: they were less than old_heap[i] ≤ old_heap[p] = new_heap[i]. ✓ We update `index = p`. The only possible violation is now between the new position and *its* parent. ✓ Invariant maintained.75:T426,The time complexity of heap insertion is **O(log n)** in the worst case. Let's analyze this thoroughly. **Step 1: Adding to the End — O(1)** Appending an element to the end of a dynamic array is amortized O(1). If the array needs resizing, the amortized cost is still O(1) due to the geometric growth strategy (typically doubling capacity when full). **Step 2: Bubble-Up — O(log n)** The key question: how many times can the while loop execute? Each iteration moves the element from one level of the tree to its parent level. The maximum number of levels in a complete binary tree with n elements is ⌊log₂(n)⌋ + 1, which means the maximum number of ancestor nodes (and thus potential swaps) from any leaf is ⌊log₂(n)⌋. **Formal Height Analysis:** For a complete binary tree with n nodes: - The height h = ⌊log₂(n)⌋ - The number of nodes at full height h: 2^h - 1 + k, where k is the number of nodes in the last level - Thus n satisfies: 2^h ≤ n < 2^(h+1) Taking logarithms: h ≤ log₂(n) < h + 1, confirming h = ⌊log₂(n)⌋.76:T6d4,Let's examine production-quality implementations of heap insertion in multiple languages, highlighting important details and common pitfalls. **Python Implementation:** ```python class MaxHeap: def __init__(self): self.heap = [] def insert(self, value): """Insert a value into the max-heap in O(log n) time.""" # Step 1: Add to end (O(1) amortized) self.heap.append(value) # Step 2: Bubble up to restore heap property self._bubble_up(len(self.heap) - 1) def _bubble_up(self, index): """Restore heap property by bubbling element up.""" while index > 0: parent_index = (index - 1) // 2 if self.heap[index] > self.heap[parent_index]: # Swap child and parent self.heap[index], self.heap[parent_index] = ( self.heap[parent_index], self.heap[index] ) index = parent_index else: break # Heap property satisfied ``` **JavaScript Implementation:** ```javascript class MaxHeap { constructor() { this.heap = []; } insert(value) { this.heap.push(value); // Add to end this._bubbleUp(this.heap.length - 1); } _bubbleUp(index) { while (index > 0) { const parentIndex = Math.floor((index - 1) / 2); if (this.heap[index] > this.heap[parentIndex]) { // Swap [this.heap[index], this.heap[parentIndex]] = [this.heap[parentIndex], this.heap[index]]; index = parentIndex; } else { break; } } } } ```77:T708,**Java Implementation (with generics):** ```java import java.util.ArrayList; import java.util.List; public class MaxHeap> { private List heap; public MaxHeap() { this.heap = new ArrayList<>(); } public void insert(T value) { heap.add(value); // Add to end bubbleUp(heap.size() - 1); } private void bubbleUp(int index) { while (index > 0) { int parentIndex = (index - 1) / 2; if (heap.get(index).compareTo(heap.get(parentIndex)) > 0) { // Swap T temp = heap.get(index); heap.set(index, heap.get(parentIndex)); heap.set(parentIndex, temp); index = parentIndex; } else { break; } } } } ``` **TypeScript Implementation (with full type safety):** ```typescript class MaxHeap { private heap: T[] = []; private compare: (a: T, b: T) => number; constructor(compareFn?: (a: T, b: T) => number) { // Default comparison for numbers/strings this.compare = compareFn ?? ((a, b) => (a > b ? 1 : a < b ? -1 : 0)); } insert(value: T): void { this.heap.push(value); this.bubbleUp(this.heap.length - 1); } private bubbleUp(index: number): void { while (index > 0) { const parentIndex = Math.floor((index - 1) / 2); if (this.compare(this.heap[index], this.heap[parentIndex]) > 0) { [this.heap[index], this.heap[parentIndex]] = [this.heap[parentIndex], this.heap[index]]; index = parentIndex; } else { break; } } } } ```78:T4ff,The only difference between max-heap and min-heap insertion is the comparison direction. Everything else—the structure, the algorithm, the complexity—remains identical. **Max-Heap Comparison:** ```python if self.heap[index] > self.heap[parent_index]: # Child larger → swap swap() ``` **Min-Heap Comparison:** ```python if self.heap[index] < self.heap[parent_index]: # Child smaller → swap swap() ``` **Generic Approach Using Comparators:** The cleanest implementation uses a comparator function, allowing a single codebase for both heap types: ```python class Heap: def __init__(self, comparator=lambda a, b: a > b): # Default: max-heap self.heap = [] self.compare = comparator # Returns True if a should be higher than b def _should_swap(self, child_idx, parent_idx): return self.compare(self.heap[child_idx], self.heap[parent_idx]) # ... rest of implementation uses self._should_swap() ... # Usage: max_heap = Heap() # Default max-heap min_heap = Heap(comparator=lambda a, b: a < b) # Min-heap ``` This pattern is how Python's `heapq` module works (though it only provides min-heap; for max-heap, you negate values) and how Java's `PriorityQueue` uses `Comparator` objects.79:T481,While the basic bubble-up algorithm is efficient, real-world implementations often incorporate additional optimizations and considerations. **Optimization 1: Shift Instead of Swap** Instead of swapping at each level, we can "bubble" the value by shifting parents down and only placing the new value once: ```python def _bubble_up_optimized(self, index): value = self.heap[index] # Save the new value while index > 0: parent_index = (index - 1) // 2 if value > self.heap[parent_index]: # max-heap self.heap[index] = self.heap[parent_index] # Shift parent down index = parent_index else: break self.heap[index] = value # Place value in final position ``` This reduces memory writes from 3 per swap to 1 per level, plus 1 final write. 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This separation is what makes heaps efficient—shape maintenance is O(1), and order restoration is O(log n).","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":1},{"id":"bubble-up-algorithm","title":"The Bubble-Up Algorithm (Heapify-Up)","content":[{"data":{"content":"The bubble-up algorithm (also called heapify-up, sift-up, up-heap, or percolate-up) is the core mechanism for restoring the heap property after insertion. Let's define it precisely.\n\n**Algorithm: Bubble-Up for Max-Heap**\n\n```\nBUBBLE-UP(heap, index):\n while index > 0:\n parent_index = (index - 1) // 2\n if heap[index] > heap[parent_index]:\n swap heap[index] and heap[parent_index]\n index = parent_index\n else:\n break // Heap property satisfied\n```\n\n**Step-by-Step Mechanics:**\n\n1. **Start at the newly inserted element** (at the last index of the array)\n2. **Compare with parent**: Calculate the parent's index using `(index - 1) // 2`\n3. **If violating heap property**: For a max-heap, if the child is greater than the parent, swap them\n4. **Move up**: Update the index to the parent's index and repeat\n5. **Terminate**: When the element is at the root (index 0) or no longer violates the heap property with its parent"},"type":"text"},{"data":{"rows":[["Parent of index i","(i - 1) // 2","(5 - 1) // 2 = 2"],["Left child of index i","2 * i + 1","2 * 2 + 1 = 5"],["Right child of index i","2 * i + 2","2 * 2 + 2 = 6"]],"caption":"Parent-Child Index Relationships in Array-Based Heap","headers":["Relationship","Formula","Example (index = 5)"]},"type":"table"},{"data":{"content":"**Why These Formulas Work:**\n\nThe formulas derive from the level-order indexing of a complete binary tree:\n\n- Level 0: 1 node (index 0—the root)\n- Level 1: 2 nodes (indices 1, 2)\n- Level 2: 4 nodes (indices 3, 4, 5, 6)\n- Level k: 2^k nodes (indices 2^k - 1 to 2^(k+1) - 2)\n\nThe parent formula `(i - 1) // 2` works because within each level, nodes are paired with their parent from the previous level. The integer division naturally handles both left and right children: left child `2i + 1` and right child `2i + 2` both map back to parent `i` when the formula is applied."},"type":"text"}],"titleType":"number","titleNumber":2},{"id":"visualizing-bubble-up","title":"Visualizing Bubble-Up: A Complete Example","content":[{"data":{"content":"Let's trace through a complete insertion example with a max-heap. We'll start with a valid max-heap and insert a new element that needs to bubble all the way to the root.\n\n**Initial Max-Heap (array representation):**\n```\nIndex: 0 1 2 3 4 5 6\nValue: [90, 70, 80, 30, 40, 50, 60]\n```\n\n**Tree visualization:**\n```\n 90\n / \n 70 80\n / \\ / \n 30 40 50 60\n```\n\n**Operation: Insert value 95**\n\nSince 95 is larger than the current root (90), it should become the new root. Let's watch the algorithm discover this."},"type":"text"},{"data":{"type":"ordered","items":["**Step 1: Insert at end** — Add 95 at index 7. Array: `[90, 70, 80, 30, 40, 50, 60, 95]`\nTree: 95 is now the left child of node 30 (at index 3)","**Step 2: Compare with parent** — 95 at index 7, parent at index (7-1)//2 = 3, value 30. Since 95 > 30, swap.\nArray: `[90, 70, 80, 95, 40, 50, 60, 30]`\nNow 95 is at index 3.","**Step 3: Compare with new parent** — 95 at index 3, parent at index (3-1)//2 = 1, value 70. Since 95 > 70, swap.\nArray: `[90, 95, 80, 70, 40, 50, 60, 30]`\nNow 95 is at index 1.","**Step 4: Compare with new parent** — 95 at index 1, parent at index (1-1)//2 = 0, value 90. Since 95 > 90, swap.\nArray: `[95, 90, 80, 70, 40, 50, 60, 30]`\nNow 95 is at index 0 (the root).","**Step 5: Terminate** — Index is 0, no more parents. Bubble-up complete."],"title":{"icon":"ArrowUp","text":"Step-by-Step Bubble-Up Trace","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**Final Max-Heap (array representation):**\n```\nIndex: 0 1 2 3 4 5 6 7\nValue: [95, 90, 80, 70, 40, 50, 60, 30]\n```\n\n**Final tree visualization:**\n```\n 95\n / \n 90 80\n / \\ / \n 70 40 50 60\n /\n 30\n```\n\n**Observations:**\n\n- The new element (95) traveled from the bottom (index 7) to the top (index 0)\n- Each step moved it one level up in the tree\n- The tree height was 4 levels (log₂(8) = 3, so height = 3, requiring at most 3 swaps)\n- The complete binary tree structure was preserved throughout\n- At every intermediate state, the heap property was maintained for all nodes *except* possibly the node being bubbled"},"type":"text"}],"titleType":"number","titleNumber":3},{"id":"loop-invariant","title":"Correctness Proof: The Loop Invariant","content":[{"data":{"content":"To rigorously prove that bubble-up correctly restores the heap property, we use loop invariant analysis. This is the gold standard for algorithm correctness proofs.\n\n**Definition: Max-Heap Property**\n\nA max-heap satisfies: for every node i (except the root), `heap[parent(i)] ≥ heap[i]`.\n\n**Loop Invariant for Bubble-Up:**\n\n> At the start of each iteration of the while loop, the array `heap[0..n-1]` satisfies the max-heap property everywhere EXCEPT possibly between node `index` and its parent `parent(index)`. All other parent-child relationships satisfy the heap property.\n\n**Initialization (before first iteration):**\n\nAfter inserting the new element at position n-1 (the end), the only possible violation is between this new node and its parent. All other relationships were valid before insertion and remain valid because we only added a leaf—no existing parent-child relationships were affected.\n\n✓ Invariant holds initially."},"type":"text"},{"data":{"content":"$74"},"type":"text"},{"data":{"content":"**Termination:**\n\nThe loop terminates when either:\n1. `index = 0` (we've reached the root, no parent to violate)\n2. `heap[index] ≤ heap[parent(index)]` (no violation with parent)\n\nIn both cases, since the invariant held and there's no remaining violation, the entire heap satisfies the max-heap property.\n\n✓ Algorithm is correct."},"type":"text"},{"data":{"icon":"Lightbulb","title":"Why Invariant Proofs Matter","content":"Loop invariant proofs aren't just academic exercises. They force you to understand *exactly* what the algorithm maintains at each step. When you debug heap code that isn't working, the invariant tells you what to check: is there a unique violation point? Are all other relationships intact? This structured thinking prevents hours of confused debugging.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"time-complexity","title":"Time Complexity Analysis","content":[{"data":{"content":"$75"},"type":"text"},{"data":{"rows":[["7","2","2","3"],["15","3","3","4"],["1,023","9","9","10"],["1,048,575 (~1M)","19","19","20"],["1,073,741,823 (~1B)","29","29","30"]],"caption":"Worst-Case Bubble-Up Operations by Heap Size","headers":["Heap Size (n)","Height (⌊log₂n⌋)","Max Swaps","Max Comparisons"]},"type":"table"},{"data":{"content":"**Work Per Iteration:**\n\nEach iteration of bubble-up performs:\n- 1 parent index calculation (arithmetic, O(1))\n- 1 comparison between child and parent (O(1))\n- At most 1 swap (3 assignments, O(1))\n- 1 index update (O(1))\n\nTotal work per iteration: O(1)\n\n**Combined Complexity:**\n\nTotal time = O(1) for adding + O(log n) × O(1) for bubble-up = **O(log n)**\n\n**Worst-Case vs. Average-Case:**\n\nThe worst case occurs when the inserted element must bubble all the way to the root (e.g., inserting the new maximum in a max-heap). However, the average case can be significantly better.\n\nIf elements are inserted in random order, the expected number of swaps is approximately O(1) to O(log log n), because a random element is more likely to stop early in the bubble-up process. However, we typically state the worst-case O(log n) because adversarial inputs can always trigger it."},"type":"text"},{"data":{"icon":"Info","title":"Space Complexity","content":"Heap insertion is an **in-place** operation. Beyond the O(n) space for the heap array itself, insertion requires only O(1) auxiliary space: a few index variables and a temporary for swapping. No additional data structures are needed.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"implementation-details","title":"Implementation in Code","content":[{"data":{"content":"$76"},"type":"text"},{"data":{"content":"$77"},"type":"text"}],"titleType":"number","titleNumber":6},{"id":"common-pitfalls","title":"Common Implementation Pitfalls","content":[{"data":{"content":"Heap insertion is conceptually simple, but implementation bugs are common. Let's examine the most frequent mistakes and how to avoid them."},"type":"text"},{"data":{"items":["**Off-by-One in Parent Calculation** — Using `index / 2` instead of `(index - 1) / 2`. This places the parent incorrectly for all non-root indices. Remember: with 0-based indexing, the formula is `(i - 1) // 2`.","**Wrong Comparison Direction** — For a max-heap: `child > parent` means swap. For a min-heap: `child < parent` means swap. Mixing these up creates an invalid heap that passes basic tests but fails edge cases.","**Forgetting the Break Condition** — The loop must exit when no swap is needed. Without `break` (or while condition update), the loop becomes infinite when the element reaches its correct position mid-tree.","**Integer Division Errors (JavaScript)** — JavaScript's `/` operator returns floats. Use `Math.floor((index - 1) / 2)` or bitwise `(index - 1) >> 1` for correct integer division. Failing to do so causes index errors.","**Not Updating Index After Swap** — The line `index = parent_index` is essential. Without it, the algorithm checks the same position repeatedly instead of progressing up the tree.","**Modifying During Iteration (in some languages)** — In languages where array modification can invalidate indices, ensure your implementation handles this correctly. For heaps, in-place swapping is safe."],"title":{"icon":"XCircle","text":"Common Bugs and Their Fixes","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"glass"},"type":"list"},{"data":{"icon":"AlertCircle","title":"Testing Heap Implementations","content":"Basic tests like 'insert one element' or 'insert in sorted order' often pass even with buggy code. Robust testing requires: (1) Insert elements that must bubble to root; (2) Insert elements that stay at leaf; (3) Insert elements that stop mid-tree; (4) Insert sequences that create all tree shapes (left-heavy, right-heavy, balanced); (5) Verify heap property after every operation.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"min-heap-variation","title":"Adapting for Min-Heap","content":[{"data":{"content":"$78"},"type":"text"},{"data":{"left":[{"data":{"items":["Largest element at root","Large values bubble up","Small values stay low","Good for: largest K elements"],"title":{"icon":"ArrowUp","text":"Max-Heap Insert Behavior","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["Smallest element at root","Small values bubble up","Large values stay low","Good for: smallest K elements"],"title":{"icon":"ArrowDown","text":"Min-Heap Insert Behavior","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"}],"titleType":"number","titleNumber":8},{"id":"practical-considerations","title":"Practical Considerations and Optimizations","content":[{"data":{"content":"$79"},"type":"text"},{"data":{"content":"**Consideration 3: Memory Allocation Strategy**\n\nFor high-frequency insertions, pre-allocating array capacity avoids repeated resizing:\n\n```python\nclass HeapWithCapacity:\n def __init__(self, initial_capacity=16):\n self.heap = [None] * initial_capacity\n self.size = 0\n \n def insert(self, value):\n if self.size >= len(self.heap):\n self._resize(len(self.heap) * 2) # Double capacity\n self.heap[self.size] = value\n self.size += 1\n self._bubble_up(self.size - 1)\n```\n\n**Consideration 4: Thread Safety**\n\nFor concurrent priority queues, insertion must be atomic or properly synchronized:\n- Lock the heap during insert\n- Or use lock-free algorithms (significantly more complex)\n- Java's `PriorityBlockingQueue` handles this internally\n\n**Consideration 5: Duplicate Handling**\n\nHeaps naturally support duplicates—the comparison determines relative position, and equal elements can exist anywhere relative to each other. No special handling needed."},"type":"text"},{"data":{"icon":"Lightbulb","title":"When to Use Library Implementations","content":"In production code, prefer battle-tested library implementations: Python's `heapq`, Java's `PriorityQueue`, C++'s `std::priority_queue`. Write custom heaps only when: (1) You need operations not provided (like decreaseKey); (2) You're learning; (3) You have specific performance requirements the library doesn't meet.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":9},{"id":"summary","title":"Summary: Heap Insertion Mastered","content":[{"data":{"content":"We've thoroughly examined heap insertion—from intuition through implementation to optimization. Let's consolidate the key takeaways:"},"type":"text"},{"data":{"type":"ordered","items":["**Insert at End, Then Fix** — The separation of concerns (shape maintenance vs. order maintenance) is the key to heap efficiency.","**Bubble-Up Restores Order** — By comparing with parent and swapping if necessary, the new element finds its correct position.","**Loop Invariant Ensures Correctness** — At each step, only one potential violation exists, and the algorithm eliminates it or moves it up.","**O(log n) Worst Case** — The element can bubble through at most log₂(n) levels, with O(1) work per level.","**Parent Index Formula: (i - 1) // 2** — This formula is fundamental to array-based heap navigation.","**Min-Heap vs. Max-Heap** — Only the comparison direction differs; all other mechanics are identical.","**Standard Libraries Exist** — Use them in production; implement yourself to learn."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**What's Next:**\n\nNow that we understand how to add elements to a heap, we need to understand how to remove them. The next page covers the extract operation—removing the root element (the maximum in a max-heap or minimum in a min-heap) and restoring the heap property using the bubble-down (heapify-down) algorithm. This is the complementary operation that makes heaps useful as priority queues."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now have a complete understanding of heap insertion: the algorithm, its correctness, its complexity, and its implementation. You can implement insert for both max-heaps and min-heaps, trace through examples, and avoid common pitfalls. Next, we'll tackle the equally important extract operation.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":10}],"isLocked":false},{"id":"seed_3efcee9725446bfaab38136a4a1275cd","title":"ExtractMax/ExtractMin — Remove Root, Bubble Down (Heapify Down)","order":1,"content":null,"isLocked":true},{"id":"seed_b7d21ce8c74498568ef7bcadef55742d","title":"Time Complexity — O(log n) for Both Insert and Extract","order":2,"content":null,"isLocked":true},{"id":"seed_3062645c98ea12323bd446003d78b9bb","title":"Maintaining the Heap Property","order":3,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792304196885,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"heap-operations-insert-and-extract"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"heap-operations-insert-and-extract\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304196922,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792304196886,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_96837da7396946fa01ea3704b2e58af4"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_96837da7396946fa01ea3704b2e58af4\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304196922,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792304196886,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["feedback","get"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_3efcee9725446bfaab38136a4a1275cd"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_3efcee9725446bfaab38136a4a1275cd\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304196922,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792304196886,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["feedback","getUserLike"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_3efcee9725446bfaab38136a4a1275cd"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_3efcee9725446bfaab38136a4a1275cd\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304196922,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792304196886,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["highlights","getHighlights"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_3efcee9725446bfaab38136a4a1275cd"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_3efcee9725446bfaab38136a4a1275cd\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792304196922,"state":{"data":"$undefined","dataUpdateCount":0,"dataUpdatedAt":0,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"pending","fetchStatus":"fetching"},"queryKey":[["highlights","getPopularHighlights"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_3efcee9725446bfaab38136a4a1275cd","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_3efcee9725446bfaab38136a4a1275cd\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@7a"}]},"children":"$L7b"}]