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There's an elderly person who needs a 15-minute financial consultation, a business owner requiring 30 minutes for a loan application review, and a student who simply needs to deposit a check—a task taking just 2 minutes. If customers are served in arrival order (FCFS), and the elderly person arrived first, the student waits 15 minutes for a 2-minute task. **Shortest Job First (SJF)** captures an intuitive optimization: **serve the shortest task first**. This minimizes the cumulative waiting time across all customers. The student gets served immediately, finishes in 2 minutes, and the total system wait time decreases dramatically. In operating systems, this concept translates directly to CPU scheduling. SJF selects the process with the **smallest CPU burst**—the process that needs the least CPU time—and schedules it next. This seemingly simple idea has profound implications for system performance and represents one of the most theoretically significant scheduling algorithms ever designed.72:T12cc,#include #include #include #define MAX_PROCESSES 100 typedef struct { int pid; // Process ID int arrival_time; // Time process enters ready queue int burst_time; // Total CPU burst time needed int remaining_time; // For tracking (equals burst_time in non-preemptive) int start_time; // When process first gets CPU int completion_time; // When process finishes int waiting_time; // Time spent in ready queue int turnaround_time; // Total time from arrival to completion int started; // Flag: has this process started? } Process; /** * Finds the process with shortest burst time among arrived processes * Returns -1 if no process is available */ int find_shortest_job(Process procs[], int n, int current_time, int completed[]) { int shortest_idx = -1; int min_burst = INT_MAX; for (int i = 0; i < n; i++) { // Process must have arrived and not be completed if (procs[i].arrival_time <= current_time && !completed[i]) { // Select if shorter, or if equal length, prefer earlier arrival if (procs[i].burst_time < min_burst || (procs[i].burst_time == min_burst && (shortest_idx == -1 || procs[i].arrival_time < procs[shortest_idx].arrival_time))) { min_burst = procs[i].burst_time; shortest_idx = i; } } } return shortest_idx; } /** * Non-preemptive SJF Scheduler * Simulates SJF scheduling and computes all metrics */ void sjf_schedule(Process procs[], int n) { int completed[MAX_PROCESSES] = {0}; // Track completed processes int completed_count = 0; int current_time = 0; printf(" SJF Scheduling Simulation: "); printf("═══════════════════════════════════════════════════════════ "); while (completed_count < n) { // Find the shortest job among arrived, uncompleted processes int idx = find_shortest_job(procs, n, current_time, completed); if (idx == -1) { // No process available - fast forward to next arrival int next_arrival = INT_MAX; for (int i = 0; i < n; i++) { if (!completed[i] && procs[i].arrival_time < next_arrival) { next_arrival = procs[i].arrival_time; } } printf("Time %3d: CPU idle (waiting for process arrival) ", current_time); current_time = next_arrival; continue; } // Schedule the selected process Process *p = &procs[idx]; p->start_time = current_time; p->completion_time = current_time + p->burst_time; p->turnaround_time = p->completion_time - p->arrival_time; p->waiting_time = p->turnaround_time - p->burst_time; printf("Time %3d: P%d starts (burst=%d, arrival=%d) ", current_time, p->pid, p->burst_time, p->arrival_time); current_time = p->completion_time; completed[idx] = 1; completed_count++; printf("Time %3d: P%d completes (wait=%d, turnaround=%d) ", current_time, p->pid, p->waiting_time, p->turnaround_time); } printf("═══════════════════════════════════════════════════════════ "); } /** * Calculate and display average metrics */ void print_metrics(Process procs[], int n) { float total_wait = 0, total_turnaround = 0; printf(" %-5s %-10s %-10s %-10s %-10s %-12s ", "PID", "Arrival", "Burst", "Wait", "Turnaround", "Completion"); printf("───────────────────────────────────────────────────────── "); for (int i = 0; i < n; i++) { printf("P%-4d %-10d %-10d %-10d %-10d %-12d ", procs[i].pid, procs[i].arrival_time, procs[i].burst_time, procs[i].waiting_time, procs[i].turnaround_time, procs[i].completion_time); total_wait += procs[i].waiting_time; total_turnaround += procs[i].turnaround_time; } printf("───────────────────────────────────────────────────────── "); printf("Average Waiting Time: %.2f ", total_wait / n); printf("Average Turnaround Time: %.2f ", total_turnaround / n); }73:Ta7d,/** * Min-Heap based SJF Ready Queue * Optimized for efficient minimum extraction */ #define HEAP_CAPACITY 1024 typedef struct { int pid; int burst_time; int arrival_time; } HeapProcess; typedef struct { HeapProcess data[HEAP_CAPACITY]; int size; } MinHeap; /** * Comparison: returns true if a should come before b * Primary: shorter burst. Tie-breaker: earlier arrival. */ static inline int compare(HeapProcess *a, HeapProcess *b) { if (a->burst_time != b->burst_time) return a->burst_time < b->burst_time; return a->arrival_time < b->arrival_time; } /** * Bubble up: restore heap property after insertion */ void heapify_up(MinHeap *heap, int index) { while (index > 0) { int parent = (index - 1) / 2; if (compare(&heap->data[index], &heap->data[parent])) { // Swap with parent HeapProcess temp = heap->data[index]; heap->data[index] = heap->data[parent]; heap->data[parent] = temp; index = parent; } else { break; } } } /** * Bubble down: restore heap property after extraction */ void heapify_down(MinHeap *heap, int index) { while (1) { int smallest = index; int left = 2 * index + 1; int right = 2 * index + 2; if (left < heap->size && compare(&heap->data[left], &heap->data[smallest])) { smallest = left; } if (right < heap->size && compare(&heap->data[right], &heap->data[smallest])) { smallest = right; } if (smallest != index) { HeapProcess temp = heap->data[index]; heap->data[index] = heap->data[smallest]; heap->data[smallest] = temp; index = smallest; } else { break; } } } /** * Insert process into ready queue - O(log n) */ void heap_insert(MinHeap *heap, int pid, int burst, int arrival) { if (heap->size >= HEAP_CAPACITY) { fprintf(stderr, "Ready queue overflow "); return; } int index = heap->size++; heap->data[index] = (HeapProcess){pid, burst, arrival}; heapify_up(heap, index); } /** * Get process with shortest burst - O(1) */ HeapProcess* heap_peek(MinHeap *heap) { if (heap->size == 0) return NULL; return &heap->data[0]; } /** * Remove and return shortest burst process - O(log n) */ HeapProcess heap_extract_min(MinHeap *heap) { HeapProcess min = heap->data[0]; heap->data[0] = heap->data[--heap->size]; heapify_down(heap, 0); return min; } int heap_empty(MinHeap *heap) { return heap->size == 0; }74:T453,SJF's performance depends heavily on workload characteristics. Understanding how it behaves under different scenarios is essential for knowing when to apply it. ### Uniform Burst Times When all processes have identical burst times, SJF degenerates to FCFS: - All processes are equally "short" - Tie-breaking (typically FCFS) determines order - No advantage over simpler algorithms **Implication:** SJF's benefits require variance in process lengths. ### High Variance Workloads With significant variation in burst times (e.g., short interactive tasks mixed with long batch jobs), SJF shines: - Short processes complete quickly, reducing total wait time - Average waiting time can be dramatically lower than FCFS - System appears more responsive to interactive users **Implication:** SJF excels in mixed workload environments. ### Continuous Arrival of Short Jobs If short jobs continuously arrive while a long job waits: - The long job is perpetually deferred - This is the **starvation problem**—covered in depth in a later page **Implication:** SJF requires starvation mitigation in production.45:["$","$L70",null,{"owner":"anonymous","state":{"mutations":[],"queries":[{"dehydratedAt":1792373963766,"state":{"data":{"json":{"archivedAt":null,"id":"seed_37d2f8441071f52e9abfc18043cd1c3b","slug":"operating-systems","title":"Operating Systems","description":"Master Operating Systems from fundamentals to advanced concepts. 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Systems","topic":"CPU Scheduling","title":"Shortest Job First (SJF) & SRTF","description":"Master the Shortest Job First and Shortest Remaining Time First scheduling algorithms. Explore their optimality properties, prediction challenges, implementation complexities, and the critical problem of starvation that affects priority-based schedulers.","difficulty":"INTERMEDIATE","estimatedMinutes":75,"pages":[{"id":"seed_9c1d79c23a916288346090ded0ac25e6","title":"SJF Algorithm","order":0,"content":[{"id":"opening-context","title":"The Intuition Behind Shortest Job First","content":[{"data":{"content":"$71"},"type":"text"},{"data":{"icon":"Target","title":"What You Will Master","content":"By completing this page, you will understand the formal definition of SJF scheduling, its decision criteria at each scheduling point, implementation data structures, the algorithm's behavior under various workloads, and its fundamental tradeoffs. You'll be equipped to analyze, trace, and compare SJF against other scheduling algorithms.","variant":"info"},"type":"callout"}]},{"id":"formal-definition","title":"Formal Definition and Core Principles","content":[{"data":{"content":"**Shortest Job First (SJF)**, also known as **Shortest Process Next (SPN)** or **Shortest Job Next (SJN)**, is a scheduling algorithm that prioritizes processes based on their expected CPU burst length. At each scheduling decision point, SJF selects the process with the shortest predicted next CPU burst.\n\n### Formal Definition\n\nLet P = {p₁, p₂, ..., pₙ} be the set of processes in the ready queue at time t. For each process pᵢ, let τᵢ represent its predicted next CPU burst length. SJF selects:\n\n**p* = argmin{τᵢ | pᵢ ∈ P}**\n\nThe process p* with the minimum predicted burst length is scheduled next.\n\n### Key Characteristics"},"type":"text"},{"data":{"items":["**Selection Criterion**: The process with the smallest next CPU burst is selected from the ready queue","**Non-Preemptive (Basic Form)**: Once a process begins execution, it runs to completion of its current CPU burst without interruption","**Priority Basis**: CPU burst length serves as a dynamic priority—shorter bursts mean higher priority","**Optimal for Average Wait Time**: Provably minimizes average waiting time among all non-preemptive algorithms","**Information Requirement**: Requires knowledge (or prediction) of future CPU burst lengths—information not directly available at runtime"],"title":{"icon":"Target","text":"SJF Defining Properties","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"Info","title":"The Prediction Problem","content":"The core challenge with SJF is that the operating system cannot know the future—it cannot know exactly how long a CPU burst will last before the process actually runs. This makes SJF an \"oracle\" algorithm in its pure form: theoretically optimal but impractical to implement exactly. Real implementations use predictions based on historical behavior, which we'll explore in a dedicated page.","variant":"note"},"type":"callout"},{"data":{"content":"### Comparison with FCFS\n\nUnderstanding SJF requires contrasting it with First-Come, First-Served (FCFS):"},"type":"text"},{"data":{"rows":[["Selection Criterion","Arrival time","CPU burst length"],["Queue Structure","Simple FIFO","Priority queue (by burst length)"],["Fairness","Fair by arrival order","Unfair to long processes"],["Average Wait Time","Can be high (convoy effect)","Provably minimal"],["Implementation","Trivial","Requires burst prediction"],["Starvation","Impossible","Possible for long processes"]],"headers":["Aspect","FCFS","SJF"]},"type":"table"},{"data":{"content":"SJF trades the simplicity and fairness of FCFS for optimal average waiting time—a tradeoff that profoundly shapes its applicability."},"type":"text"}],"titleType":"number","titleNumber":1},{"id":"algorithm-mechanics","title":"Algorithm Mechanics: Step-by-Step Execution","content":[{"data":{"content":"The non-preemptive SJF algorithm executes through a well-defined sequence of steps at each scheduling decision point.\n\n### Scheduling Decision Points\n\nIn non-preemptive SJF, scheduling decisions occur only when:\n\n1. **The running process terminates** — It has completed its current CPU burst and either exits or blocks for I/O\n2. **The running process voluntarily yields** — It requests I/O or waits for a resource\n3. **No process is currently running** — The CPU is idle and processes are waiting\n\nNotably, scheduling decisions do **not** occur when new processes arrive. Once a process starts running, it continues regardless of whether shorter processes subsequently become ready. This is the fundamental characteristic of non-preemptive scheduling."},"type":"text"},{"data":{"code":"$72","fileName":"sjf_scheduler.c","languages":[{"label":"C","value":"c"}]},"type":"code"},{"data":{"icon":"Lightbulb","title":"Tie-Breaking Strategy","content":"When multiple processes have identical burst times, a tie-breaking rule is needed. Common strategies include: (1) FCFS among ties—earlier arrival wins, (2) Process ID—lower PID wins, or (3) Random selection. The implementation above uses FCFS tie-breaking, which is most common in practice and ensures deterministic behavior.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":2},{"id":"worked-example","title":"Worked Example: Tracing SJF Execution","content":[{"data":{"content":"Let's trace through a complete SJF scheduling example to solidify understanding. Consider the following process set:"},"type":"text"},{"data":{"rows":[["P1","0","8"],["P2","1","4"],["P3","2","9"],["P4","3","5"]],"headers":["Process","Arrival Time","CPU Burst"]},"type":"table"},{"data":{"content":"### Step-by-Step Execution\n\n**Time 0:**\n- Ready Queue: {P1}\n- Only P1 has arrived; P1 starts execution (burst = 8)\n- Decision: Schedule P1\n\n**Time 0-8:**\n- P1 runs to completion (non-preemptive)\n- During this time, P2 (at t=1), P3 (at t=2), and P4 (at t=3) all arrive\n- They cannot preempt P1—they wait in the ready queue\n\n**Time 8:**\n- P1 completes\n- Ready Queue: {P2, P3, P4}\n- Burst times: P2=4, P3=9, P4=5\n- Shortest burst: P2 (4)\n- Decision: Schedule P2\n\n**Time 8-12:**\n- P2 runs to completion\n\n**Time 12:**\n- Ready Queue: {P3, P4}\n- Burst times: P3=9, P4=5\n- Shortest burst: P4 (5)\n- Decision: Schedule P4\n\n**Time 12-17:**\n- P4 runs to completion\n\n**Time 17:**\n- Ready Queue: {P3}\n- Only P3 remains\n- Decision: Schedule P3\n\n**Time 17-26:**\n- P3 runs to completion\n- Scheduling complete"},"type":"text"},{"data":{"height":"200px","mermaidSyntax":"gantt\n title SJF Scheduling Gantt Chart\n dateFormat X\n axisFormat %s\n \n section CPU Timeline\n P1 (burst=8) :active, 0, 8\n P2 (burst=4) :active, 8, 12\n P4 (burst=5) :active, 12, 17\n P3 (burst=9) :active, 17, 26"},"type":"mermaid-excalidraw"},{"data":{"content":"### Metrics Calculation\n\n**Completion Times:**\n- P1: 8\n- P2: 12\n- P3: 26\n- P4: 17\n\n**Turnaround Time** = Completion Time - Arrival Time\n- P1: 8 - 0 = 8\n- P2: 12 - 1 = 11\n- P3: 26 - 2 = 24\n- P4: 17 - 3 = 14\n\n**Waiting Time** = Turnaround Time - Burst Time\n- P1: 8 - 8 = 0\n- P2: 11 - 4 = 7\n- P3: 24 - 9 = 15\n- P4: 14 - 5 = 9\n\n**Average Waiting Time** = (0 + 7 + 15 + 9) / 4 = **7.75**\n\n**Average Turnaround Time** = (8 + 11 + 24 + 14) / 4 = **14.25**"},"type":"text"},{"data":{"icon":"CheckCircle","title":"Comparison with FCFS","content":"If we had used FCFS on this same process set, the execution order would be P1→P2→P3→P4, with average waiting time of (0 + 7 + 19 + 18) / 4 = 11.0. SJF achieves 7.75—a 29% improvement. This improvement becomes more dramatic with greater variance in burst times.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"data-structures","title":"Data Structure Considerations","content":[{"data":{"content":"Efficient SJF implementation requires careful data structure selection. The core operation is repeatedly finding the minimum element (shortest burst) from a dynamic set—a classic use case for priority queues.\n\n### Naive Approach: Unsorted List\n\nThe simplest implementation uses an unsorted list:\n- **Insert (process arrival)**: O(1)—append to end\n- **Find Minimum (scheduling decision)**: O(n)—scan entire list\n- **Delete (after selection)**: O(1)—mark as complete\n\nFor n processes, this gives O(n²) total scheduling overhead. Acceptable for small n, but problematic at scale.\n\n### Optimized Approach: Min-Heap (Priority Queue)\n\nA min-heap provides logarithmic operations:\n- **Insert**: O(log n)\n- **Find Minimum**: O(1)\n- **Delete Minimum**: O(log n)\n\nFor n processes, total scheduling overhead is O(n log n)—dramatically better for systems with many processes."},"type":"text"},{"data":{"rows":[["Unsorted Array","O(1)","O(n)","O(n)","O(n²)"],["Sorted Array","O(n)","O(1)","O(1)","O(n²)"],["Binary Heap","O(log n)","O(1)","O(log n)","O(n log n)"],["Fibonacci Heap","O(1)*","O(1)","O(log n)*","O(n log n)*"],["Self-Balancing BST","O(log n)","O(log n)","O(log n)","O(n log n)"]],"caption":"Data Structure Comparison for SJF","headers":["Data Structure","Insert","Find Min","Delete Min","Total (n ops)"]},"type":"table"},{"data":{"code":"$73","fileName":"sjf_priority_queue.c","languages":[{"label":"C","value":"c"}]},"type":"code"},{"data":{"icon":"AlertCircle","title":"Real-World Complexity","content":"Production schedulers face additional complexity: processes may have their predicted burst times updated, requiring decrease-key operations. Priority inversions and aging mechanisms add further overhead. The theoretical O(n log n) can be significantly impacted by these practical considerations.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"behavioral-analysis","title":"Behavioral Analysis Under Different Workloads","content":[{"data":{"content":"$74"},"type":"text"},{"data":{"left":[{"data":{"items":["Burst times have high variance","Many short interactive processes exist","Average response time is the primary metric","Burst prediction is reasonably accurate","Long processes can tolerate delays"],"title":{"icon":"CheckCircle","text":"SJF Excels When","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["All bursts are similar length","Burst prediction is unreliable","Long jobs have deadlines","Fairness is a hard requirement","Short jobs arrive continuously"],"title":{"icon":"XCircle","text":"SJF Struggles When","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"},{"data":{"icon":"Info","title":"Batch vs Interactive Systems","content":"SJF was originally designed for batch processing systems where job lengths were known (specified by users submitting punch cards). Modern interactive systems make burst prediction far more challenging, but the core principle—favor short tasks—remains influential in schedulers like the Linux CFS, which approximates similar behavior through virtual runtime tracking.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"advantages-limitations","title":"Advantages and Limitations","content":[{"data":{"content":"A comprehensive assessment of SJF requires balancing its theoretical optimality against its practical constraints.\n\n### Fundamental Advantages"},"type":"text"},{"data":{"items":["**Provably Optimal Average Waiting Time** — Among all non-preemptive algorithms, SJF minimizes average waiting time. This is a mathematical guarantee, not merely an observation.","**Minimizes Convoy Effect** — Unlike FCFS, short processes don't wait behind long ones. System throughput for interactive work improves.","**Reduced Average Turnaround Time** — By prioritizing quick completions, SJF maximizes the number of processes finishing early.","**Theoretical Foundation** — SJF provides the benchmark against which other algorithms are compared. Understanding it illuminates scheduler design principles.","**Applicable Beyond OS Scheduling** — The SJF principle applies to queuing systems, task scheduling in databases, network packet prioritization, and more."],"title":"Why SJF Matters","variant":"glass"},"type":"list"},{"data":{"content":"### Critical Limitations"},"type":"text"},{"data":{"items":["**Requires Future Knowledge** — True SJF needs exact CPU burst lengths, which are unknowable until execution completes. Every implementation is an approximation.","**Starvation of Long Processes** — Long processes may wait indefinitely if short processes keep arriving. This violates fairness guarantees.","**Prediction Overhead** — Maintaining burst predictions adds computational and storage overhead. Inaccurate predictions degrade performance below FCFS.","**Not Real-Time Compatible** — SJF provides no deadline guarantees. Real-time systems require different approaches.","**Non-Preemptive Delays** — A long process that starts cannot be interrupted, potentially blocking higher-priority arrivals."],"title":"Why Pure SJF Isn't Practical","variant":"glass"},"type":"list"},{"data":{"icon":"Lightbulb","title":"The Optimality Paradox","content":"SJF is theoretically optimal yet practically impossible to implement exactly. This paradox drives the field of CPU scheduling research: how do we approximate SJF's optimality while accommodating real-world constraints? SRTF, aging mechanisms, and multi-level feedback queues all represent answers to this question.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"summary","title":"Summary: The SJF Foundation","content":[{"data":{"content":"We have established a comprehensive understanding of the Shortest Job First scheduling algorithm. Let's consolidate the essential knowledge:"},"type":"text"},{"data":{"type":"ordered","items":["**SJF selects the process with the smallest predicted CPU burst** — It's a priority-based algorithm where shorter burst = higher priority","**Non-preemptive SJF runs selected processes to completion** — No interruption once a process starts, regardless of new arrivals","**SJF is provably optimal for average waiting time** — Among non-preemptive algorithms, no approach achieves lower average wait","**The algorithm requires knowledge of future burst lengths** — This fundamental limitation makes pure SJF an \"oracle\" algorithm","**Efficient implementation uses min-heap priority queues** — O(log n) operations versus O(n²) for naive list-based approaches","**SJF excels with high-variance workloads** — Benefits diminish when burst times are uniform"],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**Looking Ahead:**\n\nThe next page provides a rigorous mathematical proof of SJF's optimality. Understanding *why* SJF minimizes average waiting time—not just *that* it does—deepens comprehension and builds the analytical foundation for evaluating scheduling algorithms. We'll see that the optimality proof is elegant and illuminates the fundamental principles of algorithmic scheduling theory."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now understand the mechanics, implementation, and behavioral characteristics of Shortest Job First scheduling. The algorithm's elegant simplicity belies its profound implications—and its limitations drive the evolution of more sophisticated scheduling approaches.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":7}],"isLocked":false},{"id":"seed_8af84d46462e3575484dd39d8b353fcd","title":"Optimal for Average Wait Time","order":1,"content":null,"isLocked":true},{"id":"seed_d2d005dbbe497ebdcee442a4e9100ea8","title":"Prediction Challenges","order":2,"content":null,"isLocked":true},{"id":"seed_88f6d6501f920c73a5200eaf32c11b37","title":"SRTF (Preemptive SJF)","order":3,"content":null,"isLocked":true},{"id":"seed_85982fd92e5a87d8d0ca75995a58f12c","title":"Starvation Problem","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792373963729,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"sjf-and-srtf"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"sjf-and-srtf\"},\"type\":\"query\"}]"},{"dehydratedAt":1792373963780,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792373963753,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_a50d3097a9683b75505f14d8d122f4f6"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_a50d3097a9683b75505f14d8d122f4f6\"},\"type\":\"query\"}]"},{"dehydratedAt":1792373963780,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792373963753,"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_88f6d6501f920c73a5200eaf32c11b37"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_88f6d6501f920c73a5200eaf32c11b37\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792373963780,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792373963753,"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_88f6d6501f920c73a5200eaf32c11b37"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_88f6d6501f920c73a5200eaf32c11b37\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792373963780,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792373963753,"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_88f6d6501f920c73a5200eaf32c11b37"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_88f6d6501f920c73a5200eaf32c11b37\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792373963780,"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_88f6d6501f920c73a5200eaf32c11b37","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_88f6d6501f920c73a5200eaf32c11b37\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@75"}]},"children":"$L76"}]