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# If we go left-to-right: dp[w - weight] might already be updated # for the current item, essentially allowing multiple copies of the item! # Right-to-left ensures we use values from the "previous row" conceptually.73:T5c9,// 2D Solution — O(n × W) space function knapsack2D(weights, values, W) { const n = weights.length; // dp[i][w] = max value using first i items with capacity w const dp = Array.from({ length: n + 1 }, () => Array(W + 1).fill(0) ); for (let i = 1; i <= n; i++) { for (let w = 0; w <= W; w++) { // Don't take item i dp[i][w] = dp[i-1][w]; // Take item i (if possible) if (w >= weights[i-1]) { dp[i][w] = Math.max( dp[i][w], dp[i-1][w - weights[i-1]] + values[i-1] ); } } } return dp[n][W]; // Space: O(n × W) } // 1D Solution — O(W) space function knapsack1D(weights, values, W) { const n = weights.length; // dp[w] = max value with capacity w (using items processed so far) const dp = Array(W + 1).fill(0); for (let i = 0; i < n; i++) { // CRITICAL: Iterate RIGHT to LEFT! // This ensures we use dp[w - weights[i]] from PREVIOUS iteration for (let w = W; w >= weights[i]; w--) { dp[w] = Math.max(dp[w], dp[w - weights[i]] + values[i]); } } return dp[W]; // Space: O(W) } // Why right-to-left? // If we go left-to-right: dp[w - weight] might already be updated // for the current item, essentially allowing multiple copies! // Right-to-left ensures we use values from the "previous row" conceptually.74:T679,public class Knapsack { // 2D Solution — O(n × W) space public static int knapsack2D(int[] weights, int[] values, int W) { int n = weights.length; // dp[i][w] = max value using first i items with capacity w int[][] dp = new int[n + 1][W + 1]; for (int i = 1; i <= n; i++) { for (int w = 0; w <= W; w++) { // Don't take item i dp[i][w] = dp[i-1][w]; // Take item i (if possible) if (w >= weights[i-1]) { dp[i][w] = Math.max( dp[i][w], dp[i-1][w - weights[i-1]] + values[i-1] ); } } } return dp[n][W]; // Space: O(n × W) } // 1D Solution — O(W) space public static int knapsack1D(int[] weights, int[] values, int W) { int n = weights.length; // dp[w] = max value with capacity w (using items processed so far) int[] dp = new int[W + 1]; for (int i = 0; i < n; i++) { // CRITICAL: Iterate RIGHT to LEFT! // Ensures we use dp[w - weights[i]] from PREVIOUS iteration for (int w = W; w >= weights[i]; w--) { dp[w] = Math.max(dp[w], dp[w - weights[i]] + values[i]); } } return dp[W]; // Space: O(W) } // Why right-to-left? // If we go left-to-right: dp[w - weight] might already be updated // for the current item, essentially allowing multiple copies! // Right-to-left ensures we use values from the "previous row". }75:T569,# Space-optimized Edit Distance — O(min(m, n)) space def edit_distance_optimized(s1: str, s2: str) -> int: m, n = len(s1), len(s2) # Ensure s2 is the shorter string (we use O(len(s2)) space) if m < n: s1, s2, m, n = s2, s1, n, m # Current row of the DP table dp = list(range(n + 1)) # Base case: dp[0..n] = 0..n for i in range(1, m + 1): # 'diagonal' holds dp[i-1][j-1] as we iterate diagonal = dp[0] dp[0] = i # Base case: editing first i chars of s1 to empty string for j in range(1, n + 1): # Save the current dp[j] before overwriting (it becomes next diagonal) temp = dp[j] if s1[i-1] == s2[j-1]: dp[j] = diagonal # No operation needed else: dp[j] = 1 + min( dp[j], # dp[i-1][j] — delete (above, not yet updated) dp[j-1], # dp[i][j-1] — insert (left, already updated) diagonal # dp[i-1][j-1] — replace ) diagonal = temp # Move diagonal for next iteration return dp[n] # Key insight: We iterate LEFT-TO-RIGHT because we need dp[j-1] from # the current row. But we also need dp[i-1][j-1] (diagonal) from the # previous row. We save it in a variable before overwriting!76:T5ca,// Space-optimized Edit Distance — O(min(m, n)) space function editDistanceOptimized(s1, s2) { let m = s1.length, n = s2.length; // Ensure s2 is the shorter string (we use O(len(s2)) space) if (m < n) { [s1, s2] = [s2, s1]; [m, n] = [n, m]; } // Current row of the DP table const dp = Array.from({ length: n + 1 }, (_, i) => i); // Base case for (let i = 1; i <= m; i++) { // 'diagonal' holds dp[i-1][j-1] as we iterate let diagonal = dp[0]; dp[0] = i; // Base case: editing first i chars of s1 to empty string for (let j = 1; j <= n; j++) { // Save the current dp[j] before overwriting const temp = dp[j]; if (s1[i-1] === s2[j-1]) { dp[j] = diagonal; // No operation needed } else { dp[j] = 1 + Math.min( dp[j], // dp[i-1][j] — delete (above, not yet updated) dp[j-1], // dp[i][j-1] — insert (left, already updated) diagonal // dp[i-1][j-1] — replace ); } diagonal = temp; // Move diagonal for next iteration } } return dp[n]; } // Key insight: We iterate LEFT-TO-RIGHT because we need dp[j-1] from // the current row. 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You'll learn to analyze dependency patterns that enable this optimization, implement the transformation systematically, and recognize the dramatic space savings this technique enables—often reducing O(n²) or O(n × m) space to O(n) or O(m).","variant":"info"},"type":"callout"}]},{"id":"foundation-dependencies","title":"The Foundation — Understanding DP Table Dependencies","content":[{"data":{"content":"Before we can optimize space, we must deeply understand **how cells in a DP table depend on each other**. This dependency structure is the key that unlocks space optimization.\n\nIn bottom-up DP, we fill a table cell by cell. Each cell `dp[i][j]` depends on previously computed cells. The pattern of these dependencies determines whether we can reduce dimensions.\n\n**The Critical Insight:** If every cell in row `i` depends **only** on cells in row `i-1` (the immediately previous row), then we don't need to store all rows—we only need the current row and the previous row. And with careful implementation, we can often reduce this further to a single row!"},"type":"text"},{"data":{"items":["**Single-row dependency:** `dp[i][j]` depends only on cells in row `i-1`. Examples: Knapsack, Coin Change, Edit Distance.","**Same-row + previous-row:** `dp[i][j]` depends on `dp[i-1][...]` and `dp[i][j-1]` (cells in the same row to the left). Examples: Unique Paths, minimum path sum.","**Multi-row dependency:** `dp[i][j]` depends on rows `i-1`, `i-2`, or earlier. These typically cannot be reduced to 1D directly.","**Diagonal dependency:** `dp[i][j]` depends on `dp[i-1][j-1]` (previous row, previous column). Requires careful handling."],"title":{"icon":"Target","text":"Common Dependency Patterns in 2D DP","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"Lightbulb","title":"The Golden Rule of Space Optimization","content":"**If each row only depends on the immediately previous row, the 2D table can be reduced to two 1D arrays (current and previous). If we're even more careful with our iteration order, we can often reduce it to a single 1D array.**","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":1},{"id":"visualizing-transformation","title":"Visualizing the Transformation","content":[{"data":{"content":"Let's visualize what happens when we reduce a 2D DP table to 1D. Consider the 0/1 Knapsack problem with the following recurrence:\n\n```\ndp[i][w] = max(dp[i-1][w], dp[i-1][w - weight[i]] + value[i])\n```\n\nNotice that `dp[i][w]` depends only on values from row `i-1`. The current row's values are computed entirely from the previous row."},"type":"text"},{"data":{"rows":[["Item 0 (base)","0","0","0","0","0","0"],["Item 1 (w=2,v=3)","0","0","3←","3","3","3"],["Item 2 (w=3,v=4)","0","0","3","4←","4","7←"],["Item 3 (w=4,v=5)","0","0","3","4","5←","7"]],"caption":"2D Knapsack Table — Row Dependencies","headers":["Capacity →","0","1","2","3","4","5"]},"type":"table"},{"data":{"content":"**The arrows (←) indicate dependencies from the previous row.** Cell `dp[2][5] = 7` comes from `max(dp[1][5]=3, dp[1][2]+4=7)`.\n\nSince row `i` only needs row `i-1`, after computing row `i`, row `i-2` becomes useless. We're wasting memory storing all those old rows!\n\n**The insight:** Instead of storing all `n` rows, we could:\n1. Use two arrays: `prev[]` and `curr[]`, alternating between them\n2. Or better: use a single array and update it in-place (with the right iteration order)"},"type":"text"},{"data":{"icon":"Info","title":"Why Iteration Order Matters","content":"When reducing to a single 1D array, we must process elements in an order that doesn't overwrite values we still need. For Knapsack, this means iterating **from right to left** (decreasing capacity) so we don't accidentally use 'new' values when we need 'old' values.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":2},{"id":"technique-step-by-step","title":"The Technique — Step by Step","content":[{"data":{"content":"Let's formalize the process of reducing a 2D DP solution to 1D. This is a systematic technique you can apply to many problems."},"type":"text"},{"data":{"type":"ordered","items":["**Analyze dependencies:** For each cell `dp[i][j]`, identify exactly which cells it depends on. List them: `dp[i-1][j]`? `dp[i-1][j-1]`? `dp[i][j-1]`?","**Verify single-row dependency:** Confirm that all dependencies come from the immediately previous row (possibly plus same-row cells to the left).","**Identify the dimension to eliminate:** The 'outer' dimension (typically the item/character index) can be eliminated if dependencies follow the pattern.","**Determine iteration order:** This is critical! If we need `dp[i-1][j]` (same column, previous row), we must not overwrite `dp[j]` before using it. Often this means reverse iteration.","**Transform the code:** Replace `dp[i][j]` with `dp[j]`. Remove the outer array dimension. Adjust initialization.","**Handle special cases:** Base cases, boundary conditions, and diagonal dependencies may need special attention."],"title":{"icon":"Zap","text":"The 2D → 1D Reduction Process","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**Example: 0/1 Knapsack Transformation**\n\nLet's see this in action with the 0/1 Knapsack problem."},"type":"text"},{"data":{"fileName":"knapsack_2d_vs_1d.py","languages":[{"code":"$72","label":"Python","value":"python"},{"code":"$73","label":"JavaScript","value":"javascript"},{"code":"$74","label":"Java","value":"java"}],"showLineNumbers":true},"type":"code"}],"titleType":"number","titleNumber":3},{"id":"iteration-order-critical","title":"Why Iteration Order is Critical","content":[{"data":{"content":"The choice of iteration order in 1D DP is not arbitrary—it's the **linchpin** of correctness. Let's understand this deeply.\n\nConsider what happens in Knapsack when we iterate **left-to-right** (the wrong way):"},"type":"text"},{"data":{"rows":[["Before","Item: weight=2, value=3","0","—"],["w=2","dp[2] = max(0, dp[0]+3) = 3","0","dp[2] now = 3"],["w=4","dp[4] = max(0, dp[2]+3) = 6","0","Uses NEW dp[2]=3!"],["w=5","dp[5] = max(0, dp[3]+3) = 3","3","Incorrect!"]],"caption":"Left-to-Right Iteration (WRONG for 0/1 Knapsack)","headers":["Step","Processing","dp[5]","Problem"]},"type":"table"},{"data":{"content":"**The problem:** When we computed `dp[4]`, we used `dp[2]` which had already been updated for the current item. We're essentially counting the same item twice—taking it at capacity 2 and again at capacity 4. This converts 0/1 Knapsack into Unbounded Knapsack!"},"type":"text"},{"data":{"icon":"AlertCircle","title":"The Iteration Order Rules","content":"**For 0/1 Knapsack (each item used at most once):** Iterate RIGHT-TO-LEFT. This ensures that when we access `dp[w - weight]`, it still contains the value from before processing the current item.\n\n**For Unbounded Knapsack (unlimited copies):** Iterate LEFT-TO-RIGHT. We *want* to reuse updated values, allowing multiple copies of items.","variant":"warning"},"type":"callout"},{"data":{"rows":[["Before","Item: weight=2, value=3","dp = [0,0,0,0,0,0]","—"],["w=5","dp[5] = max(0, dp[3]+3) = 3","dp[3]=0 (old)","✓ Correct"],["w=4","dp[4] = max(0, dp[2]+3) = 3","dp[2]=0 (old)","✓ Correct"],["w=3","dp[3] = max(0, dp[1]+3) = 3","dp[1]=0 (old)","✓ Correct"],["w=2","dp[2] = max(0, dp[0]+3) = 3","dp[0]=0 (old)","✓ Correct"]],"caption":"Right-to-Left Iteration (CORRECT for 0/1 Knapsack)","headers":["Step","Processing","dp values used","Result"]},"type":"table"},{"data":{"content":"By iterating right-to-left, we always access `dp[w - weight]` before we've overwritten it with the current item's computation. This preserves the semantic of the 2D recurrence where row `i` depends on row `i-1`."},"type":"text"}],"titleType":"number","titleNumber":4},{"id":"general-framework","title":"A General Framework for Iteration Order","content":[{"data":{"content":"Let's establish a general framework for determining the correct iteration order when reducing 2D DP to 1D."},"type":"text"},{"data":{"items":["**If `dp[i][j]` depends on `dp[i-1][j']` where `j' < j`:** We need values from smaller indices in the previous row. Iterate **RIGHT-TO-LEFT** (decreasing j) to preserve these values.","**If `dp[i][j]` depends on `dp[i-1][j']` where `j' > j`:** We need values from larger indices in the previous row. Iterate **LEFT-TO-RIGHT** (increasing j) to preserve these values.","**If `dp[i][j]` depends on `dp[i][j']` where `j' < j` (same row, left):** We need values already updated in the current row. Iterate **LEFT-TO-RIGHT**.","**If `dp[i][j]` depends on `dp[i-1][j]` (same column, previous row):** The value at index `j` from the previous iteration. This is automatically the current `dp[j]` before update, so either direction works for this dependency alone."],"title":{"icon":"Zap","text":"Iteration Order Decision Framework","type":"icon","color":"primary","variant":"outline-circle"},"variant":"glass"},"type":"list"},{"data":{"content":"**Combining Dependencies:**\n\nReal problems often have multiple dependencies. Analyze each one:"},"type":"text"},{"data":{"rows":[["`dp[i][w] = max(dp[i-1][w], dp[i-1][w-wt]+val)`","prev[w], prev[w-wt] (smaller)","Right-to-left"],["`dp[i][w] = dp[i-1][w] + dp[i][w-1]`","prev[w], curr[w-1]","Left-to-right"],["`dp[i][j] = dp[i-1][j] + dp[i-1][j-1]`","prev[j], prev[j-1]","Right-to-left"]],"headers":["Recurrence","Dependencies","Iteration Order"]},"type":"table"},{"data":{"content":"When dependencies conflict (some want left-to-right, others right-to-left), you typically need **two arrays** (current and previous) rather than a single array."},"type":"text"},{"data":{"icon":"Lightbulb","title":"The Mental Model","content":"Think of the 1D array as a 'sliding window' over the 2D table. Before each iteration of the outer loop, the array contains values from the previous row. After the inner loop completes, it contains values from the current row. The iteration order determines which 'version' of each cell you see during computation.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"practical-example-edit-distance","title":"Practical Example — Edit Distance","content":[{"data":{"content":"Edit Distance (Levenshtein Distance) is a classic 2D DP problem that demonstrates a more nuanced space optimization. The recurrence is:\n\n```\ndp[i][j] = dp[i-1][j-1] if s1[i-1] == s2[j-1]\ndp[i][j] = 1 + min(dp[i-1][j], // delete from s1\n dp[i][j-1], // insert into s1\n dp[i-1][j-1]) // replace in s1\n```\n\n**Dependency analysis:**\n- `dp[i-1][j-1]` — diagonal (previous row, previous column)\n- `dp[i-1][j]` — directly above (previous row, same column)\n- `dp[i][j-1]` — directly left (same row, previous column)\n\nThis is tricky! We depend on both the previous row AND the current row (for `dp[i][j-1]`)."},"type":"text"},{"data":{"fileName":"edit_distance_optimization.py","languages":[{"code":"$75","label":"Python","value":"python"},{"code":"$76","label":"JavaScript","value":"javascript"}],"showLineNumbers":true},"type":"code"},{"data":{"content":"**Notice the diagonal trick:** We save `dp[j]` in a temporary variable before updating it. This preserved value serves as `dp[i-1][j-1]` for the next column's computation. This elegant technique handles the diagonal dependency without needing a full second array."},"type":"text"},{"data":{"icon":"Info","title":"Additional Optimization","content":"We also swap strings to ensure we use the shorter string's length for the DP array. This gives O(min(m, n)) space instead of O(n). For strings of length 1000 and 10, we use 11 cells instead of 1001!","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"example-lcs","title":"Another Example — Longest Common Subsequence","content":[{"data":{"content":"The Longest Common Subsequence (LCS) problem has a similar dependency structure to Edit Distance:\n\n```\ndp[i][j] = dp[i-1][j-1] + 1 if s1[i-1] == s2[j-1]\ndp[i][j] = max(dp[i-1][j], dp[i][j-1]) otherwise\n```\n\nDependencies: diagonal `dp[i-1][j-1]`, above `dp[i-1][j]`, left `dp[i][j-1]`.\n\nWe apply the same diagonal-saving technique:"},"type":"text"},{"data":{"fileName":"lcs_space_optimized.py","languages":[{"code":"def lcs_optimized(s1: str, s2: str) -> int:\n \"\"\"\n Space-optimized LCS: O(min(m, n)) space\n \"\"\"\n m, n = len(s1), len(s2)\n \n # Use shorter string for the 1D array\n if m < n:\n s1, s2, m, n = s2, s1, n, m\n \n dp = [0] * (n + 1)\n \n for i in range(1, m + 1):\n diagonal = 0 # dp[i-1][0] = 0 always\n \n for j in range(1, n + 1):\n temp = dp[j] # Save dp[i-1][j] before overwriting\n \n if s1[i-1] == s2[j-1]:\n dp[j] = diagonal + 1 # Character matches\n else:\n dp[j] = max(dp[j], dp[j-1]) # Max of above and left\n \n diagonal = temp # dp[i-1][j] becomes dp[i-1][j-1] for next column\n \n return dp[n]\n\n# Comparison:\n# 2D version: O(m × n) space\n# 1D version: O(min(m, n)) space\n# \n# For LCS of two 10,000-character strings:\n# 2D: 100,000,000 cells × 4 bytes = 400 MB\n# 1D: 10,000 cells × 4 bytes = 40 KB (10,000x reduction!)","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"CheckCircle","title":"The Pattern Emerges","content":"Many 2D DP problems with dependencies on (i-1, j-1), (i-1, j), and (i, j-1) can be optimized using the same technique: iterate left-to-right, save the diagonal in a variable, and swap strings to minimize array size.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"summary-takeaways","title":"Summary: Reducing 2D DP to 1D","content":[{"data":{"content":"Let's consolidate the key insights from this page:"},"type":"text"},{"data":{"type":"ordered","items":["**Space optimization is often necessary** — 2D DP tables can consume prohibitive memory for large inputs.","**Analyze dependencies first** — The key to reduction is identifying that each row depends only on the previous row.","**Iteration order determines correctness** — Right-to-left for 0/1-type problems (using previous values), left-to-right when using current-row values.","**The diagonal trick** — When depending on (i-1, j-1), save the value in a variable before overwriting.","**Swap to minimize** — Use the smaller dimension for the 1D array to achieve O(min(m, n)) space.","**Correctness over optimization** — Always verify your optimized solution against example cases. It's easy to get iteration order wrong!"],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"rows":[["0/1 Knapsack","O(n × W)","O(W)","n-fold"],["Coin Change","O(n × amount)","O(amount)","n-fold"],["Edit Distance","O(m × n)","O(min(m, n))","max(m,n)-fold"],["LCS","O(m × n)","O(min(m, n))","max(m,n)-fold"],["Unique Paths","O(m × n)","O(min(m, n))","max(m,n)-fold"]],"caption":"Space Savings Summary","headers":["Problem","2D Space","1D Space","Reduction"]},"type":"table"},{"data":{"content":"**What's next:**\n\nIn the next page, we'll explore the **Rolling Array Technique** — a generalization of this approach that handles cases where you need more than just the previous row, and provides a cleaner mental model for multi-row dependencies."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now understand the fundamental technique of reducing 2D DP tables to 1D arrays. This skill will dramatically reduce memory usage in your DP solutions and is essential for handling large-scale optimization problems.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":8}],"isLocked":false},{"id":"seed_5e21e38b985875889c5685f074a20ccb","title":"Rolling Array Technique","order":1,"content":null,"isLocked":true},{"id":"seed_c8f33402cca427839da6b2190911ffd7","title":"When Space Optimization Applies","order":2,"content":null,"isLocked":true},{"id":"seed_74ca86f360b0d4dad9656c5e5547b1da","title":"Trade-offs with Complexity","order":3,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792285743096,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"space-optimization-techniques"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"space-optimization-techniques\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285743139,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792285743097,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_ebf2694091ee1533e3c3d25a0351fdc3"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_ebf2694091ee1533e3c3d25a0351fdc3\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285743139,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792285743097,"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_74ca86f360b0d4dad9656c5e5547b1da"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_74ca86f360b0d4dad9656c5e5547b1da\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285743139,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792285743097,"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_74ca86f360b0d4dad9656c5e5547b1da"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_74ca86f360b0d4dad9656c5e5547b1da\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285743139,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792285743097,"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_74ca86f360b0d4dad9656c5e5547b1da"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_74ca86f360b0d4dad9656c5e5547b1da\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285743139,"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_74ca86f360b0d4dad9656c5e5547b1da","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_74ca86f360b0d4dad9656c5e5547b1da\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@77"}]},"children":"$L78"}]