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Args: arr: The array to sort Returns: A new sorted array """ # Base case: arrays of length 0 or 1 are already sorted if len(arr) <= 1: return arr # Divide: find the midpoint and split the array mid = len(arr) // 2 left_half = arr[:mid] right_half = arr[mid:] # Conquer: recursively sort each half sorted_left = merge_sort(left_half) sorted_right = merge_sort(right_half) # Combine: merge the sorted halves return merge(sorted_left, sorted_right) def merge(left, right): """ Merges two sorted arrays into a single sorted array. Args: left: First sorted array right: Second sorted array Returns: A new sorted array containing all elements from both inputs """ result = [] i = j = 0 # Compare elements from both arrays and add the smaller one while i < len(left) and j < len(right): if left[i] <= right[j]: result.append(left[i]) i += 1 else: result.append(right[j]) j += 1 # Add any remaining elements from either array while i < len(left): result.append(left[i]) i += 1 while j < len(right): result.append(right[j]) j += 1 return result73:T4f4,Initial call: merge_sort([5, 2, 8, 1, 9, 4]) DIVIDE PHASE (Recursive Calls Going Down) ========================================= Call 1: merge_sort([5, 2, 8, 1, 9, 4]) → mid = 3 → Splitting into [5, 2, 8] and [1, 9, 4] Call 2: merge_sort([5, 2, 8]) → mid = 1 → Splitting into [5] and [2, 8] Call 3: merge_sort([5]) → Base case: return [5] Call 4: merge_sort([2, 8]) → mid = 1 → Splitting into [2] and [8] Call 5: merge_sort([2]) → Base case: return [2] Call 6: merge_sort([8]) → Base case: return [8] → merge([2], [8]) = [2, 8] → merge([5], [2, 8]) = [2, 5, 8] Call 7: merge_sort([1, 9, 4]) → mid = 1 → Splitting into [1] and [9, 4] Call 8: merge_sort([1]) → Base case: return [1] Call 9: merge_sort([9, 4]) → mid = 1 → Splitting into [9] and [4] Call 10: merge_sort([9]) → Base case: return [9] Call 11: merge_sort([4]) → Base case: return [4] → merge([9], [4]) = [4, 9] → merge([1], [4, 9]) = [1, 4, 9] → merge([2, 5, 8], [1, 4, 9]) = [1, 2, 4, 5, 8, 9] FINAL RESULT: [1, 2, 4, 5, 8, 9]46:["$","$L71",null,{"owner":"anonymous","state":{"mutations":[],"queries":[{"dehydratedAt":1792306404979,"state":{"data":{"json":{"archivedAt":null,"id":"seed_a45eefd7ace053183db2aca078e8dfbc","slug":"data-structures-algorithms","title":"Data Structures & Algorithms","description":"Build strong algorithmic foundations, trace algorithms visually, solve progressive DSA problems, and apply invariants in real repository and code-review 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Structures & Algorithms","topic":"Sorting Algorithms","title":"Merge Sort — Divide & Conquer Sorting","description":"Master Merge Sort, the first efficient O(n log n) sorting algorithm, which revolutionized computer science. Learn its divide-and-conquer structure, understand the merge operation, analyze its guaranteed time complexity, and explore its space requirements.","difficulty":"INTERMEDIATE","estimatedMinutes":90,"pages":[{"id":"seed_f39afc1d9dc9584792e2749e8087922d","title":"The Merge Sort Algorithm","order":0,"content":[{"id":"opening-hook","title":"The First Efficient Sorting Algorithm","content":[{"data":{"content":"In 1945, mathematician **John von Neumann** invented Merge Sort while working on the EDVAC computer—one of the earliest electronic general-purpose computers. This algorithm marked a profound shift in computer science: it was the first practically efficient sorting algorithm, demonstrating that **O(n log n)** sorting was achievable and opening the door to the field of algorithm design as we know it today.\n\nMerge Sort isn't just historically significant—it remains one of the most important algorithms in computer science. Its elegance lies in a simple yet powerful idea: **if you can efficiently merge two sorted lists into one sorted list, then you can sort anything by repeatedly dividing and merging**. This insight exemplifies the divide-and-conquer paradigm that underpins countless other algorithms."},"type":"text"},{"data":{"icon":"Target","title":"What You Will Learn","content":"By the end of this page, you will understand Merge Sort's fundamental mechanism, appreciate why it was revolutionary, and grasp the intuition behind its guaranteed O(n log n) performance. You'll see how this algorithm transforms the sorting problem from a seemingly intractable n² puzzle into a structured, logarithmic-depth solution.","variant":"info"},"type":"callout"}]},{"id":"quadratic-barrier","title":"The Quadratic Barrier and How Merge Sort Breaks It","content":[{"data":{"content":"Before Merge Sort, the known sorting algorithms—Bubble Sort, Selection Sort, and Insertion Sort—all shared a common limitation: **they required O(n²) time in the worst case**. For small datasets, this was acceptable. But as computing evolved and data grew, the quadratic time complexity became a severe bottleneck.\n\nTo understand why O(n²) is problematic, consider sorting a modest dataset of 1 million elements:"},"type":"text"},{"data":{"rows":[["Quadratic (O(n²))","O(n²)","10¹² operations","~16.7 minutes"],["Linearithmic (O(n log n))","O(n log n)","~20 × 10⁶ operations","~0.02 seconds"],["Speedup Factor","-","-","~50,000× faster"]],"caption":"Comparison of Time Complexities for 1 Million Elements","headers":["Algorithm Class","Time Complexity","Operations Required","Time at 10⁹ ops/sec"]},"type":"table"},{"data":{"content":"**The difference is staggering.** A quadratic algorithm that takes 17 minutes can be reduced to 20 milliseconds with an O(n log n) approach. This isn't a minor optimization—it's the difference between software that works and software that fails.\n\n**Von Neumann's insight** was that sorting could be accomplished in O(n log n) time by exploiting a key observation: **merging two already-sorted sequences is a linear-time operation**. If we can recursively reduce unsorted data to sorted subsequences and then merge them, we achieve dramatically better performance."},"type":"text"},{"data":{"icon":"BookOpen","title":"The Optimal Lower Bound","content":"It has been mathematically proven that any comparison-based sorting algorithm must perform at least Ω(n log n) comparisons in the worst case. This means Merge Sort is asymptotically optimal—you cannot do fundamentally better with comparison-based sorting. This theoretical result makes Merge Sort's guaranteed O(n log n) performance especially significant.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":1},{"id":"core-intuition","title":"The Core Intuition: Why Merging Is Powerful","content":[{"data":{"content":"The brilliance of Merge Sort stems from a simple observation about sorted sequences. Consider two sorted lists:\n\n**List A:** [1, 4, 7, 9]\n**List B:** [2, 3, 5, 8]\n\nTo combine these into a single sorted list, you don't need to compare every element with every other element (which would be quadratic). Instead, you can use a linear-time process:\n\n1. Compare the **first elements** of both lists (1 vs 2)\n2. The smaller one (1) goes into the result\n3. Advance the pointer in that list\n4. Repeat until both lists are exhausted\n\nThis process requires only **n comparisons** to merge two sorted lists of total size n, regardless of how the elements are distributed."},"type":"text"},{"data":{"code":"List A: [1, 4, 7, 9] List B: [2, 3, 5, 8] Result: []\n ^ ^\n\nStep 1: Compare 1 vs 2 → Take 1\nList A: [1, 4, 7, 9] List B: [2, 3, 5, 8] Result: [1]\n ^ ^\n\nStep 2: Compare 4 vs 2 → Take 2\nList A: [1, 4, 7, 9] List B: [2, 3, 5, 8] Result: [1, 2]\n ^ ^\n\nStep 3: Compare 4 vs 3 → Take 3\nList A: [1, 4, 7, 9] List B: [2, 3, 5, 8] Result: [1, 2, 3]\n ^ ^\n\nStep 4: Compare 4 vs 5 → Take 4\nList A: [1, 4, 7, 9] List B: [2, 3, 5, 8] Result: [1, 2, 3, 4]\n ^ ^\n\n... continues until Result: [1, 2, 3, 4, 5, 7, 8, 9]","title":"Visualizing the Merge Process","language":"text"},"type":"code"},{"data":{"content":"**Why is this powerful?** Because the merge operation is **linear** in the total size of the input. No matter how the elements are arranged within the sorted sublists, we only traverse each element once.\n\nThe key insight is that **sorted data carries information**. When we know a list is sorted, we know that once we've used an element, everything remaining in that list is larger. This constraint eliminates the need for exhaustive comparisons.\n\n**The recursive leap:** If merging two sorted lists is linear-time, then we can sort any data by:\n1. Dividing it into two halves\n2. Recursively sorting each half (producing two sorted lists)\n3. Merging the sorted halves"},"type":"text"},{"data":{"icon":"Lightbulb","title":"The Two-Pointer Technique","content":"The merge operation uses what's called the 'two-pointer technique'—maintaining a pointer into each sorted list and advancing the appropriate one at each step. This technique appears throughout computer science: in merging sorted arrays, finding pairs with specific sums, detecting cycles in linked lists, and many other applications. Mastering it here will serve you well in countless other problems.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":2},{"id":"algorithm-structure","title":"The Algorithm Structure: Recursive Decomposition","content":[{"data":{"content":"Merge Sort follows the classic **divide-and-conquer** paradigm with three distinct phases:\n\n**1. Divide:** Split the input array into two halves\n**2. Conquer:** Recursively sort each half\n**3. Combine:** Merge the two sorted halves into a single sorted result\n\nThe beauty of this structure is its simplicity. The 'hard work' of sorting is distributed across many simple merge operations, and the recursive decomposition naturally organizes this work in a logarithmic depth tree."},"type":"text"},{"data":{"code":" [38, 27, 43, 3, 9, 82, 10]\n SPLIT\n / \\\n [38, 27, 43, 3] [9, 82, 10]\n SPLIT SPLIT\n / \\ / \\\n [38, 27] [43, 3] [9, 82] [10]\n SPLIT SPLIT SPLIT (BASE)\n / \\ / \\ / \\\n [38] [27] [43] [3] [9] [82]\n (BASE) (BASE) (BASE) (BASE) (BASE) (BASE)\n\n MERGE (going back up)\n\n [38] [27] [43] [3] [9] [82] [10]\n \\ / \\ / \\ / |\n [27, 38] [3, 43] [9, 82] [10]\n \\ / \\ /\n [3, 27, 38, 43] [9, 10, 82]\n \\ /\n [3, 9, 10, 27, 38, 43, 82]","title":"Merge Sort Decomposition Tree","language":"text"},"type":"code"},{"data":{"content":"**Observing the tree structure:**\n\n1. **Depth:** The tree has approximately log₂(n) levels. Each level halves the problem size until we reach single-element arrays (the base case).\n\n2. **Work per level:** At each level, we perform O(n) total work. Even though work is distributed across multiple merge operations, each element is merged exactly once per level.\n\n3. **Total work:** With log₂(n) levels and O(n) work per level, the total work is **O(n log n)**.\n\nThis analysis reveals why Merge Sort is fundamentally different from quadratic algorithms. Where Bubble Sort performs n passes of n comparisons each (n × n = n²), Merge Sort performs log(n) levels of n work each (n × log n)."},"type":"text"},{"data":{"icon":"Info","title":"The Base Case","content":"A single-element array is inherently sorted—there's nothing to arrange when there's only one element. This trivial observation provides the recursion's base case. Arrays of size 0 or 1 are returned unchanged, and the merging process builds sorted arrays from these atomic components.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"algorithm-pseudocode","title":"Algorithm Pseudocode and Implementation","content":[{"data":{"content":"Let's formalize the Merge Sort algorithm with clear pseudocode. The algorithm consists of two main functions: the recursive **mergeSort** function that handles the divide-and-conquer structure, and the **merge** function that combines sorted subsequences."},"type":"text"},{"data":{"code":"$72","title":"Merge Sort Implementation","language":"python"},"type":"code"},{"data":{"content":"**Understanding the code structure:**\n\n**The merge_sort function:**\n- **Lines 8-9:** The base case handles trivially sorted arrays\n- **Lines 12-14:** The divide phase splits the array at the midpoint\n- **Lines 17-18:** The conquer phase recursively sorts each half\n- **Line 21:** The combine phase merges the sorted results\n\n**The merge function:**\n- **Lines 32-33:** Two pointers track positions in each input array\n- **Lines 36-42:** The main loop compares elements and adds the smaller one\n- **Lines 45-51:** After one array is exhausted, copy remaining elements\n\n**Critical detail:** Note the use of `<=` in line 38 rather than `<`. This ensures **stability**—when elements are equal, we take from the left array first, preserving the original relative order of equal elements."},"type":"text"},{"data":{"icon":"AlertCircle","title":"Creating New Arrays vs. In-Place","content":"This implementation creates new arrays at each step, which is clear and correct but uses O(n) auxiliary space. We'll explore the space implications in detail later in this module. For now, understand that this version prioritizes clarity over space optimization.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"trace-example","title":"Detailed Trace Through an Example","content":[{"data":{"content":"Let's trace through Merge Sort step by step on a concrete example. We'll sort the array **[5, 2, 8, 1, 9, 4]** and observe every recursive call and merge operation."},"type":"text"},{"data":{"code":"$73","title":"Complete Execution Trace","language":"text"},"type":"code"},{"data":{"content":"**Observations from the trace:**\n\n1. **Call structure:** We made 11 total calls to merge_sort. For an array of size n, the recursive structure creates approximately 2n - 1 total nodes in the call tree.\n\n2. **Base cases dominate:** 6 of the 11 calls hit the base case immediately. This is typical—about half the calls are on single-element arrays.\n\n3. **Merge operations:** We performed 5 merge operations, combining increasingly large sorted sequences:\n - [2] + [8] → [2, 8]\n - [5] + [2, 8] → [2, 5, 8]\n - [9] + [4] → [4, 9]\n - [1] + [4, 9] → [1, 4, 9]\n - [2, 5, 8] + [1, 4, 9] → [1, 2, 4, 5, 8, 9]\n\n4. **Level-by-level work:** Each element participates in log₂(6) ≈ 3 merge operations, matching our O(n log n) analysis."},"type":"text"},{"data":{"icon":"Lightbulb","title":"Visualizing the Call Stack","content":"When tracing recursive algorithms, imagine a stack of function calls. Each recursive call pushes a frame onto the stack, and returning pops it off. The maximum stack depth for Merge Sort is O(log n), which is important for understanding both space usage and the risk of stack overflow on extremely large inputs.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"why-special","title":"What Makes Merge Sort Special","content":[{"data":{"content":"Merge Sort occupies a unique position among sorting algorithms due to several distinctive properties. Understanding these helps you recognize when Merge Sort is the right choice—and when another algorithm might be preferable."},"type":"text"},{"data":{"items":["**Guaranteed O(n log n):** Unlike Quick Sort (O(n²) worst case) or Heap Sort (same complexity but worse cache behavior), Merge Sort is always O(n log n). The input arrangement cannot cause degradation.","**Stability:** Equal elements maintain their original relative order. This matters when sorting objects by multiple criteria—sort by secondary key first, then by primary key, and the secondary order is preserved within primary groups.","**Parallelization:** The recursive structure naturally parallelizes. Each half can be sorted on a different processor core, and modern parallel merge algorithms can even parallelize the merge step.","**External Sorting:** Merge Sort is the foundation for sorting data larger than memory. Data is divided into memory-sized chunks, each sorted independently, then merged using a k-way merge that requires minimal memory.","**Linked List Efficiency:** Unlike Quick Sort (which needs random access), Merge Sort works excellently on linked lists because it only requires sequential access and pointer manipulation."],"title":{"icon":"Star","text":"Distinctive Properties of Merge Sort","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"left":[{"data":{"items":["Stable sort is required","Worst-case guarantee matters","Sorting linked lists","External sorting (disk-based)","Parallel sorting needed","Counting inversions in data"],"title":{"icon":"CheckCircle","text":"When to Choose Merge Sort","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["Memory is severely constrained","Cache efficiency is critical","Data is nearly sorted (use Insertion Sort)","In-place sorting is mandatory","Average case performance preferred (Quick Sort)"],"title":{"icon":"XCircle","text":"When to Consider Alternatives","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"},{"data":{"icon":"Briefcase","title":"Real-World Usage","content":"Python's built-in sort (Timsort) uses a hybrid approach that incorporates Merge Sort for its stability and worst-case guarantees, combined with Insertion Sort for small runs. Java's Arrays.sort() for objects uses Timsort as well (since Java 7). The choice of Merge Sort in these implementations reflects its practical value in production systems.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"comparison","title":"Comparison with Quadratic Sorting Algorithms","content":[{"data":{"content":"To fully appreciate Merge Sort's significance, let's compare it directly with the quadratic sorting algorithms we've already studied. This comparison highlights the paradigm shift that divide-and-conquer brings to the sorting problem."},"type":"text"},{"data":{"rows":[["Best Case","O(n)","O(n²)","O(n)","O(n log n)"],["Average Case","O(n²)","O(n²)","O(n²)","O(n log n)"],["Worst Case","O(n²)","O(n²)","O(n²)","O(n log n)"],["Space","O(1)","O(1)","O(1)","O(n)"],["Stable","Yes","No","Yes","Yes"],["Adaptive","Yes","No","Yes","No"],["Method","Exchange","Selection","Insertion","Merge"]],"caption":"Merge Sort vs. Quadratic Algorithms","headers":["Property","Bubble Sort","Selection Sort","Insertion Sort","Merge Sort"]},"type":"table"},{"data":{"content":"**Key insights from the comparison:**\n\n1. **Consistent performance:** Merge Sort's O(n log n) in all cases provides predictability. Production systems benefit from knowing an operation won't suddenly become slow due to pathological input.\n\n2. **The space trade-off:** The O(n) auxiliary space is the price of guaranteed performance. This is a fundamental trade-off in algorithm design—Merge Sort trades memory for time consistency.\n\n3. **Not adaptive:** Unlike Insertion Sort, Merge Sort doesn't benefit from partially sorted input. It always performs the same number of operations. This is the flip side of its consistency.\n\n4. **Different paradigms:** The quadratic algorithms work by incrementally building a sorted portion. Merge Sort works by decomposing and recombining. This paradigm difference is what enables the complexity breakthrough."},"type":"text"},{"data":{"icon":"Lightbulb","title":"The Right Tool for the Job","content":"Despite Merge Sort's superior asymptotic performance, Insertion Sort can be faster for small arrays (say, n < 20) due to lower constant factors and better cache behavior. This is why efficient implementations often switch to Insertion Sort when recursing to small subarrays—a technique called 'cutoff' or 'hybrid sorting'.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"historical-context","title":"Historical Context and Impact","content":[{"data":{"content":"Understanding Merge Sort's historical context helps appreciate its significance in the development of computer science as a discipline.\n\n**The birth of computational thinking:**\n\nWhen von Neumann developed Merge Sort in 1945, computer science as an academic field didn't exist. Computers were room-sized machines used for ballistics calculations and code-breaking. The idea of systematically analyzing algorithms—studying their efficiency, proving their correctness, understanding their fundamental limits—was nascent.\n\nMerge Sort was among the first algorithms to be formally analyzed in terms of operation counts. Von Neumann's analysis showing O(n log n) performance established a methodology that would become central to computer science: **abstract analysis of computational cost**."},"type":"text"},{"data":{"content":"**Impact on the field:**\n\n1. **Established divide-and-conquer:** Merge Sort demonstrated the power of breaking problems into smaller subproblems. This paradigm now underpins algorithms from binary search to FFT to the closest pair problem.\n\n2. **Inspired complexity theory:** The analysis of Merge Sort helped establish the foundation for algorithm analysis. Questions like 'What is the fundamental lower bound for sorting?' arose from considering whether Merge Sort could be improved.\n\n3. **Proved optimality is achievable:** By matching the Ω(n log n) lower bound for comparison-based sorting, Merge Sort showed that theoretical limits could be achieved in practice.\n\n4. **Enabled external sorting:** Before solid-state storage, sorting data larger than RAM was crucial. Merge Sort's ability to work with sequential access made it the foundation of data processing for decades."},"type":"text"},{"data":{"icon":"BookOpen","title":"Standing on Giants' Shoulders","content":"Every time you use a computer to search, sort, or organize data, you benefit from ideas pioneered by von Neumann and his contemporaries. Merge Sort isn't just an algorithm—it's a piece of intellectual heritage that shaped how we think about computation itself.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":8},{"id":"summary","title":"Summary and What's Next","content":[{"data":{"content":"We've established the conceptual foundation of Merge Sort—one of the most important algorithms in computer science. Let's consolidate the key insights:"},"type":"text"},{"data":{"type":"ordered","items":["**Merge Sort breaks the quadratic barrier** by exploiting the efficiency of merging sorted sequences, achieving guaranteed O(n log n) performance.","**The core insight is that merging sorted lists is linear-time.** If you can merge efficiently, you can sort by dividing and merging recursively.","**The algorithm has three phases:** divide (split at midpoint), conquer (recursively sort halves), and combine (merge sorted results).","**The recursion tree has log₂(n) levels** with O(n) work at each level, giving O(n log n) total work.","**Merge Sort is stable and has guaranteed worst-case performance,** making it ideal for applications requiring predictability.","**The trade-off is O(n) auxiliary space,** used to store merged results during the combine phase."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**What's next:**\n\nIn the following pages, we'll dive deeper into Merge Sort's mechanics. We'll examine the three-phase structure in detail, dissect the merge operation with precision, formally analyze the time complexity, and understand the space requirements. By the end of this module, you'll have complete mastery of this foundational algorithm."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now understand why Merge Sort was revolutionary and how its fundamental mechanism achieves O(n log n) sorting. In the next page, we'll explore the divide-sort-merge structure in greater depth, examining each phase with precision and understanding how they work together to produce sorted output.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":9}],"isLocked":false},{"id":"seed_2e06110649931e52a760d2363d91ecd1","title":"Divide, Sort Halves, Merge","order":1,"content":null,"isLocked":true},{"id":"seed_4bd5b29f0143b6f7f177dec5dd5b11d7","title":"The Merge Operation in Detail","order":2,"content":null,"isLocked":true},{"id":"seed_d27e981cb7a6279c2cb4c00b94063b3c","title":"Time Complexity — O(n log n) Guaranteed","order":3,"content":null,"isLocked":true},{"id":"seed_f8e9c22f7a41afdb89e9ce90210e8389","title":"Space Complexity — O(n)","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792306404873,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"merge-sort-divide-conquer"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"merge-sort-divide-conquer\"},\"type\":\"query\"}]"},{"dehydratedAt":1792306405013,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792306404873,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_d56678b517a5ebb801218183bb06e372"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_d56678b517a5ebb801218183bb06e372\"},\"type\":\"query\"}]"},{"dehydratedAt":1792306405013,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792306404873,"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_d27e981cb7a6279c2cb4c00b94063b3c"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_d27e981cb7a6279c2cb4c00b94063b3c\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792306405013,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792306404873,"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_d27e981cb7a6279c2cb4c00b94063b3c"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_d27e981cb7a6279c2cb4c00b94063b3c\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792306405013,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792306404873,"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_d27e981cb7a6279c2cb4c00b94063b3c"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_d27e981cb7a6279c2cb4c00b94063b3c\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792306405013,"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_d27e981cb7a6279c2cb4c00b94063b3c","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_d27e981cb7a6279c2cb4c00b94063b3c\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@74"}]},"children":"$L75"}]