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Without any index or table of contents, what would you do? You'd start at the beginning, check if the first few characters match your word, and if not, move forward slightly and try again. This intuitive process—checking every possible position systematically—is exactly what the **brute force pattern matching algorithm** does. The brute force approach (also called the **naive algorithm**) is the simplest conceivable solution to the pattern matching problem. It requires no preprocessing, no auxiliary data structures, and no mathematical cleverness. It simply answers the question: "Does the pattern occur here? No? Then what about here? And here?" Yet this simplicity is precisely what makes it so important. The brute force algorithm serves as the **conceptual baseline** against which all optimized pattern matching algorithms are measured. You cannot truly appreciate why KMP achieves linear time or why Rabin-Karp uses hashing without first understanding what naive matching does—and what it does *wrong*.73:T653,/** * Naive (Brute Force) Pattern Matching Algorithm * * Searches for all occurrences of pattern P in text T. * Returns an array of starting indices where P is found. * * Time Complexity: O(n * m) worst case, O(n) best case * Space Complexity: O(1) auxiliary (excluding output storage) * * @param text - The text string to search within * @param pattern - The pattern string to search for * @returns Array of starting positions (0-indexed) where pattern occurs */ function naivePatternMatch(text: string, pattern: string): number[] { const n = text.length; const m = pattern.length; const occurrences: number[] = []; // Edge cases if (m === 0) { // Empty pattern matches at every position (including after last char) return Array.from({ length: n + 1 }, (_, i) => i); } if (m > n) { // Pattern longer than text: no possible match return occurrences; } // Try each valid starting position for (let i = 0; i <= n - m; i++) { let j = 0; // Compare pattern with text starting at position i while (j < m && text[i + j] === pattern[j]) { j++; } // If we matched all m characters, record this occurrence if (j === m) { occurrences.push(i); } } return occurrences; } // Example usage const text = "ABABDABACDABABCABAB"; const pattern = "ABABCABAB"; console.log(`Text: "${text}"`); console.log(`Pattern: "${pattern}"`); console.log(`Occurrences at: ${naivePatternMatch(text, pattern)}`); // Output: Occurrences at: [10]74:T5dc,def naive_pattern_match(text: str, pattern: str) -> list[int]: """ Naive (Brute Force) Pattern Matching Algorithm Searches for all occurrences of pattern P in text T. Returns a list of starting indices where P is found. Time Complexity: O(n * m) worst case, O(n) best case Space Complexity: O(1) auxiliary (excluding output storage) Args: text: The text string to search within pattern: The pattern string to search for Returns: List of starting positions (0-indexed) where pattern occurs """ n = len(text) m = len(pattern) occurrences = [] # Edge cases if m == 0: # Empty pattern matches at every position return list(range(n + 1)) if m > n: # Pattern longer than text: no possible match return occurrences # Try each valid starting position for i in range(n - m + 1): j = 0 # Compare pattern with text starting at position i while j < m and text[i + j] == pattern[j]: j += 1 # If we matched all m characters, record this occurrence if j == m: occurrences.append(i) return occurrences # Example usage if __name__ == "__main__": text = "ABABDABACDABABCABAB" pattern = "ABABCABAB" print(f'Text: "{text}"') print(f'Pattern: "{pattern}"') print(f"Occurrences at: {naive_pattern_match(text, pattern)}") # Output: Occurrences at: [10]75:T862,import java.util.ArrayList; import java.util.List; /** * Naive (Brute Force) Pattern Matching Algorithm Implementation */ public class NaivePatternMatching { /** * Naive (Brute Force) Pattern Matching Algorithm * * Searches for all occurrences of pattern P in text T. * Returns a list of starting indices where P is found. * * Time Complexity: O(n * m) worst case, O(n) best case * Space Complexity: O(1) auxiliary (excluding output storage) * * @param text The text string to search within * @param pattern The pattern string to search for * @return List of starting positions (0-indexed) where pattern occurs */ public static List naivePatternMatch(String text, String pattern) { int n = text.length(); int m = pattern.length(); List occurrences = new ArrayList<>(); // Edge cases if (m == 0) { // Empty pattern matches at every position for (int i = 0; i <= n; i++) { occurrences.add(i); } return occurrences; } if (m > n) { // Pattern longer than text: no possible match return occurrences; } // Try each valid starting position for (int i = 0; i <= n - m; i++) { int j = 0; // Compare pattern with text starting at position i while (j < m && text.charAt(i + j) == pattern.charAt(j)) { j++; } // If we matched all m characters, record this occurrence if (j == m) { occurrences.add(i); } } return occurrences; } public static void main(String[] args) { String text = "ABABDABACDABABCABAB"; String pattern = "ABABCABAB"; System.out.println("Text: \"" + text + "\""); System.out.println("Pattern: \"" + pattern + "\""); System.out.println("Occurrences at: " + naivePatternMatch(text, pattern)); // Output: Occurrences at: [10] } }76:T4b5,Why is the naive algorithm correct? This might seem obvious, but stating it precisely builds intuition for more complex algorithms. **Theorem:** The naive pattern matching algorithm finds all and only the positions where pattern `P` occurs in text `T`. **Proof (informal but rigorous):** **Completeness (finds all occurrences):** - An occurrence of `P` at position `i` means `T[i+j] = P[j]` for all `j ∈ [0, m-1]` - The outer loop considers every position `i` from 0 to `n-m` - At the correct position `i`, the inner loop will match all `m` characters - Thus, `j` will reach `m`, and we record position `i` - No occurrence is missed because we check every valid position **Soundness (only finds actual occurrences):** - We record position `i` only when `j = m` after the inner loop - `j = m` means the inner loop executed `m` iterations without breaking - Each iteration verified `T[i+j] = P[j]` for one value of `j` - Therefore, `T[i..i+m-1] = P[0..m-1]` (definition of occurrence) - No false positive is possible **Edge cases:** - If 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Structures & Algorithms","topic":"Naive Pattern Matching","title":"Naive Pattern Matching — The Fundamental Algorithm","description":"Master the brute force approach to string pattern matching. Understand exactly how the naive algorithm works, analyze its quadratic time complexity, and discover why this baseline makes optimized algorithms like KMP and Rabin-Karp so powerful.","difficulty":"BEGINNER","estimatedMinutes":60,"pages":[{"id":"seed_b6c99ce1b7c9b8c2f8382df77fb3d87d","title":"Brute Force Approach — The Foundation of Pattern Matching","order":0,"content":[{"id":"opening-hook","title":"The Most Natural Algorithm","content":[{"data":{"content":"$72"},"type":"text"},{"data":{"icon":"Target","title":"What You Will Learn","content":"By the end of this page, you will deeply understand the brute force pattern matching algorithm—its mechanics, its intuition, its implementation, and most importantly, the fundamental inefficiency that motivates every advanced string algorithm you'll encounter.","variant":"info"},"type":"callout"}]},{"id":"problem-restated","title":"The Pattern Matching Problem Restated","content":[{"data":{"content":"Before diving into the algorithm, let's crystallize the problem we're solving:\n\n**Given:**\n- A **text** string `T` of length `n`\n- A **pattern** string `P` of length `m`\n- Where typically `m ≤ n` (the pattern is no longer than the text)\n\n**Find:**\n- All positions `i` in `T` where `P` occurs as a substring\n- An occurrence at position `i` means `T[i..i+m-1] = P[0..m-1]`\n\n**Example:**\n- Text: `\"ABABDABACDABABCABAB\"`\n- Pattern: `\"ABABCABAB\"`\n- Answer: Pattern occurs at position 10 (0-indexed)\n\nThe brute force algorithm attacks this problem in the most direct way possible: **try every starting position** in the text, and for each, check if the pattern matches character by character."},"type":"text"},{"data":{"icon":"Info","title":"Indexing Convention","content":"Throughout this module, we use 0-indexed strings. Position 0 is the first character. When we say the pattern occurs at position `i`, we mean `T[i]` is the first character of the occurrence, and `T[i+m-1]` is the last.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":1},{"id":"core-idea","title":"The Algorithm's Core Idea","content":[{"data":{"content":"The brute force algorithm embodies a simple principle: **exhaustive verification at every valid position**.\n\n**Valid positions** are those where the pattern could potentially fit within the text. If the text has length `n` and the pattern has length `m`, then:\n- The pattern can start at position 0, 1, 2, ..., up to position `n - m`\n- There are exactly `n - m + 1` valid starting positions\n- At position `n - m`, the pattern extends from `T[n-m]` to `T[n-1]`, using exactly the last `m` characters\n\n**The algorithm in plain English:**\n\n1. Start at text position 0\n2. Compare `P[0]` with `T[0]`, then `P[1]` with `T[1]`, and so on\n3. If all `m` characters match, record this as an occurrence\n4. If any character mismatches, stop comparing at this position\n5. Move to the next text position and repeat\n6. Continue until you've tried all `n - m + 1` positions"},"type":"text"},{"data":{"icon":"Lightbulb","title":"The \"Brute Force\" Philosophy","content":"The term 'brute force' doesn't mean 'stupid'—it means 'complete and direct.' The algorithm makes no assumptions, uses no shortcuts, and guarantees correctness through exhaustive checking. This is often the right first approach: get something correct, then optimize.","variant":"tip"},"type":"callout"},{"data":{"content":"**Why \"at every valid position\"?**\n\nConsider searching for pattern `\"ABC\"` in text `\"AABCD\"` (n=5, m=3):"},"type":"text"},{"data":{"rows":[["0","`AAB`","No"],["1","`ABC`","Yes ✓"],["2","`BCD`","No"]],"headers":["Position","Substring Checked","Match?"]},"type":"table"},{"data":{"content":"We can't start at position 3 or beyond because `T[3..5]` wouldn't have enough characters to form a length-3 pattern. Hence, we check positions 0 through `5 - 3 = 2` (inclusive).\n\nThis exhaustive checking is the algorithm's strength (it never misses an occurrence) and weakness (it never skips an unnecessary check)."},"type":"text"}],"titleType":"number","titleNumber":2},{"id":"formal-algorithm","title":"Formal Algorithm Description","content":[{"data":{"content":"Let's formalize the brute force algorithm with precise pseudocode and then implement it in code:\n\n**Pseudocode:**\n\n```\nNAIVE-PATTERN-MATCH(T, P)\n n ← length(T)\n m ← length(P)\n occurrences ← empty list\n \n for i ← 0 to n - m do\n j ← 0\n while j < m and T[i + j] = P[j] do\n j ← j + 1\n end while\n \n if j = m then\n append i to occurrences\n end if\n end for\n \n return occurrences\n```\n\n**Explanation of the pseudocode:**\n\n- **Outer loop (`for i`)**: Iterates through all valid starting positions in the text (0 to n-m inclusive)\n- **Inner loop (`while j`)**: Compares pattern characters with text characters starting at position `i`\n- **Match condition (`j = m`)**: If we successfully matched all `m` characters, `j` will equal `m`\n- **Mismatch**: The inner while loop exits early when `T[i+j] ≠ P[j]`, meaning a mismatch occurred at pattern position `j`"},"type":"text"},{"data":{"fileName":"naive_pattern_matching","languages":[{"code":"$73","label":"TypeScript","value":"typescript"},{"code":"$74","label":"Python","value":"python"},{"code":"$75","label":"Java","value":"java"}],"showLineNumbers":true},"type":"code"}],"titleType":"number","titleNumber":3},{"id":"tracing-example","title":"Tracing Through an Example","content":[{"data":{"content":"Understanding the algorithm deeply requires tracing through an example step by step. Let's work through a complete example that reveals the algorithm's behavior:\n\n**Text:** `T = \"AABCAAB\"` \n**Pattern:** `P = \"AAB\"`\n\nHere, `n = 7` and `m = 3`, so we'll check positions 0 through 4 (inclusive)."},"type":"text"},{"data":{"rows":[["0","AAB","A=A ✓, A=A ✓, B=B ✓ → j reaches 3","MATCH at position 0"],["1","ABC","A=A ✓, B≠A ✗ → mismatch at j=1","No match"],["2","BCA","B≠A ✗ → mismatch at j=0","No match"],["3","CAA","C≠A ✗ → mismatch at j=0","No match"],["4","AAB","A=A ✓, A=A ✓, B=B ✓ → j reaches 3","MATCH at position 4"]],"caption":"Complete Trace of Naive Pattern Matching","headers":["Position i","Substring T[i..i+2]","Comparison Steps","Result"]},"type":"table"},{"data":{"content":"**Result:** Pattern `\"AAB\"` occurs at positions **0** and **4**.\n\n**Observations from this trace:**\n\n1. **Total comparisons performed:** 3 + 2 + 1 + 1 + 3 = **10 comparisons**\n\n2. **Matches found:** 2 occurrences where all 3 characters matched\n\n3. **Early termination:** Positions 2 and 3 terminated after just 1 comparison (first character mismatch)\n\n4. **No position skipped:** Even though we found a match at position 0, we still checked position 1, 2, 3, and 4 independently\n\nThis last observation is crucial. The naive algorithm treats every position independently. It doesn't \"remember\" anything from previous comparisons. We'll see in later pages how this lack of memory creates the opportunity for optimization."},"type":"text"},{"data":{"icon":"AlertCircle","title":"Wasted Work Preview","content":"At position 0, we verified that T[0..2] = \"AAB\". This tells us T[1..2] = \"AB\". When we move to position 1, we then compare T[1] with P[0] = 'A'—but we already know T[1] = 'A' from the previous match! The naive algorithm discards this information. Smarter algorithms exploit it.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"inner-loop","title":"The Inner Loop Mechanics","content":[{"data":{"content":"The inner loop of the naive algorithm deserves careful examination because its behavior determines both correctness and efficiency.\n\n**The inner loop:**\n```\nwhile j < m and T[i + j] = P[j] do\n j ← j + 1\nend while\n```\n\n**Two-condition exit:**\n\nThe loop can exit for exactly two reasons:\n\n1. **`j = m` (complete match):** We've compared all `m` characters of the pattern, and every single one matched. This means `P` occurs at position `i` in `T`.\n\n2. **`T[i + j] ≠ P[j]` (mismatch):** We found a character that doesn't match. No point continuing—this position cannot be a match.\n\n**Why this two-condition design is elegant:**\n\n- It handles both success and failure in a single construct\n- The `j < m` check comes first, preventing out-of-bounds access\n- After the loop, `j` tells us exactly how many characters matched (useful for debugging and optimization)"},"type":"text"},{"data":{"content":"**Visualizing match depth:**\n\nLet's denote the **match depth** at position `i` as the value of `j` when the inner loop exits. This tells us how far into the pattern we successfully matched.\n\n**Example:** Text = `\"ABAAC\"`, Pattern = `\"AAC\"`"},"type":"text"},{"data":{"rows":[["0","A=A","1","A≠A (T[1]='B')"],["1","...","0","B≠A (first char)"],["2","A=A, A=A, C=C","3","j=m (full match!)"]],"headers":["Position i","Characters compared","Match depth j","Why loop stopped"]},"type":"table"},{"data":{"content":"At position 0, we matched 1 character before failing. At position 1, we matched 0 characters. At position 2, we matched all 3 characters—a complete match.\n\n**Key insight:** The match depth varies dramatically across positions. When many characters match before a failure, we've done significant work that is then \"thrown away\" when we move to the next position. This wasted work is the fundamental inefficiency we'll address in later algorithms."},"type":"text"},{"data":{"icon":"Lightbulb","title":"Debugging Tip","content":"When implementing pattern matching, tracking the match depth at each position is invaluable for debugging. If you're getting wrong answers, print the match depth at each position—it immediately reveals where comparisons are going wrong.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"correctness","title":"Correctness Argument","content":[{"data":{"content":"$76"},"type":"text"},{"data":{"icon":"Info","title":"Why Completeness Matters","content":"When we later study optimized algorithms like KMP, we'll need to prove they're complete—that they don't accidentally skip valid occurrences while avoiding redundant work. The naive algorithm's completeness is trivial because it checks everything. For KMP, proving completeness requires careful reasoning about the failure function.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"alternative-formulations","title":"Alternative Formulations","content":[{"data":{"content":"The naive algorithm can be implemented in several equivalent ways. Understanding these alternatives deepens comprehension and prepares you for variations in interview problems.\n\n**Formulation 1: Using Substring Comparison**\n\nInstead of comparing character by character, compare entire substrings:"},"type":"text"},{"data":{"fileName":"naive_substring","languages":[{"code":"/**\n * Naive pattern matching using substring comparison.\n * Conceptually cleaner but typically slower due to substring creation.\n */\nfunction naiveWithSubstring(text: string, pattern: string): number[] {\n const n = text.length;\n const m = pattern.length;\n const occurrences: number[] = [];\n \n for (let i = 0; i <= n - m; i++) {\n // Extract substring and compare\n if (text.substring(i, i + m) === pattern) {\n occurrences.push(i);\n }\n }\n \n return occurrences;\n}\n\n// Note: substring() creates a new string each iteration,\n// which adds O(m) time and space per position.\n// Total: O(n * m) time, O(m) extra space per iteration.","label":"TypeScript","value":"typescript"},{"code":"def naive_with_substring(text: str, pattern: str) -> list[int]:\n \"\"\"\n Naive pattern matching using string slicing.\n Conceptually cleaner but may be slower due to slice creation.\n \"\"\"\n n = len(text)\n m = len(pattern)\n occurrences = []\n \n for i in range(n - m + 1):\n # Extract slice and compare\n if text[i:i+m] == pattern:\n occurrences.append(i)\n \n return occurrences\n\n# Note: In Python, string slicing creates a new string object.\n# The == comparison then compares character by character.\n# Total complexity is still O(n * m) in the worst case.","label":"Python","value":"python"}]},"type":"code"},{"data":{"content":"**Formulation 2: Right-to-Left Comparison**\n\nCompare characters from the end of the pattern instead of the beginning:"},"type":"text"},{"data":{"fileName":"naive_right_to_left","languages":[{"code":"/**\n * Naive pattern matching comparing right-to-left.\n * Same complexity, but different behavior on mismatches.\n * This direction choice inspires the Boyer-Moore algorithm.\n */\nfunction naiveRightToLeft(text: string, pattern: string): number[] {\n const n = text.length;\n const m = pattern.length;\n const occurrences: number[] = [];\n \n for (let i = 0; i <= n - m; i++) {\n let j = m - 1; // Start from the last character\n \n // Compare from right to left\n while (j >= 0 && text[i + j] === pattern[j]) {\n j--;\n }\n \n // If j went below 0, all characters matched\n if (j < 0) {\n occurrences.push(i);\n }\n }\n \n return occurrences;\n}\n\n// This direction doesn't change worst-case complexity,\n// but it's the foundation for the Boyer-Moore algorithm,\n// which uses the mismatch position to make smarter skips.","label":"TypeScript","value":"typescript"}]},"type":"code"},{"data":{"icon":"Lightbulb","title":"Direction Matters for Optimization","content":"Right-to-left comparison is the foundation of the Boyer-Moore algorithm. When we find a mismatch at position j, Boyer-Moore uses information about which character caused the mismatch to skip multiple positions at once. This is harder to exploit with left-to-right comparison.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"summary","title":"Summary and Key Takeaways","content":[{"data":{"content":"We've now established a complete understanding of the brute force pattern matching algorithm. Let's consolidate the key points:"},"type":"text"},{"data":{"type":"ordered","items":["**The naive algorithm is the natural baseline** — It does exactly what intuition suggests: check every position systematically.","**Outer loop scans positions, inner loop verifies matches** — The two-loop structure cleanly separates \"where to check\" from \"how to check.\"","**Complete correctness is guaranteed** — We find all occurrences and only actual occurrences because we check everything.","**No preprocessing required** — We can start matching immediately without building any auxiliary structures.","**Each position is treated independently** — The algorithm doesn't use information from previous comparisons, which is both its simplicity and its weakness.","**Match depth varies by position** — Some positions fail immediately (first character mismatch), others fail late (near-matches), and some succeed completely."],"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 next page, we'll visualize how the pattern \"slides\" over the text during the algorithm's execution. This sliding window perspective is crucial for understanding both the algorithm's behavior and the optimization opportunities that exist."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now deeply understand the mechanics of brute force pattern matching. You can implement it, trace through examples, and explain why it's correct. Next, we'll visualize the sliding window behavior that characterizes this algorithm.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":8}],"isLocked":false},{"id":"seed_819471e63de679a4bc5881da3f1d03ad","title":"Sliding the Pattern Over the Text — Visualization and Mechanics","order":1,"content":null,"isLocked":true},{"id":"seed_22ca79e7e297ef6d5444c4194140f147","title":"Time Complexity O(n × m) — A Rigorous Analysis","order":2,"content":null,"isLocked":true},{"id":"seed_d887dd630b98f883518416da9126c28e","title":"Why Improvement Is Needed — The Case for Smarter Algorithms","order":3,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792285762516,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"naive-pattern-matching"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"naive-pattern-matching\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285762624,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792285762517,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_ee877940220f8f08cd3d65116fe7fbbe"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_ee877940220f8f08cd3d65116fe7fbbe\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285762624,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792285762517,"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_d887dd630b98f883518416da9126c28e"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_d887dd630b98f883518416da9126c28e\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285762624,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792285762517,"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_d887dd630b98f883518416da9126c28e"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_d887dd630b98f883518416da9126c28e\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285762624,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792285762517,"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_d887dd630b98f883518416da9126c28e"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_d887dd630b98f883518416da9126c28e\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792285762624,"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_d887dd630b98f883518416da9126c28e","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_d887dd630b98f883518416da9126c28e\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@77"}]},"children":"$L78"}]