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During a CPU burst, the process is in the **running state**, consuming CPU cycles to perform computations. **The mechanics of a CPU burst:** When a process receives the CPU, it begins executing its instruction stream. Each instruction—whether it's an arithmetic operation, a memory access, a conditional branch, or a function call—consumes one or more CPU cycles. The process continues executing until one of several events occurs: 1. **I/O Request**: The process needs data from a file, network, or device 2. **Synchronization Wait**: The process must wait for a lock, semaphore, or condition variable 3. **Voluntary Yield**: The process explicitly relinquishes the CPU 4. **Preemption**: The operating system forcibly removes the process (in preemptive systems) 5. **Termination**: The process completes its execution The period from when the process starts executing until any of these events is the CPU burst.73:T4a1,// Example: A sequence showing alternating CPU and I/O bursts void process_log_file(const char *filename) { // ====== CPU BURST 1 ====== // Preparing the file open request int fd = open(filename, O_RDONLY); // ====== I/O BURST 1 ====== // Process BLOCKS here waiting for: // - File system to locate the inode // - Disk to read directory entries // - Permission checks to complete // When open() returns, I/O burst ends char buffer[4096]; ssize_t bytes_read; while (1) { // ====== I/O BURST (repeated) ====== bytes_read = read(fd, buffer, sizeof(buffer)); // Process is BLOCKED during disk read // CPU is free to run other processes if (bytes_read <= 0) break; // ====== CPU BURST (repeated) ====== // Parse and process the data // This is pure computation - no waiting for (int i = 0; i < bytes_read; i++) { process_character(buffer[i]); } // CPU burst ends when loop restarts and read() is called } // ====== I/O BURST (final) ====== close(fd); // May block to flush buffers to disk }74:T725,Process execution follows a predictable structural pattern: an alternating sequence of CPU and I/O bursts that continues until the process terminates. This pattern, known as the **CPU-I/O burst cycle**, is fundamental to how operating systems manage processor resources. **The canonical execution model:** ``` Process Start │ ▼ ┌─────────────┐ ┌───────────┐ ┌─────────────┐ │ CPU Burst │ ──▶ │ I/O Burst │ ──▶ │ CPU Burst │ ──▶ ... │ (Running) │ │ (Waiting) │ │ (Running) │ └─────────────┘ └───────────┘ └─────────────┘ │ ▼ (Eventually) ┌──────────────┐ │ Termination │ │ (CPU Burst) │ └──────────────┘ ``` **Key observations about the cycle:** 1. **Every process begins with a CPU burst**: Even if the first action is an I/O request, the code to make that request must execute first. 2. **Every process ends with a CPU burst**: The final CPU burst executes the termination code (exit system call). 3. **The pattern is strictly alternating**: A CPU burst is always followed by either an I/O burst or termination; an I/O burst is always followed by a CPU burst. 4. **No overlap within a single process**: A process cannot be in CPU burst and I/O burst simultaneously (though multiple processes can be).75:T49d,// Exponential Averaging for CPU Burst Prediction // τ(n+1) = α × t(n) + (1 - α) × τ(n) // // Where: // τ(n+1) = predicted length of next burst // t(n) = actual length of the most recent burst // τ(n) = previous prediction // α = smoothing factor (0 < α < 1) typedef struct { double predicted_burst; // τ: current prediction double alpha; // α: smoothing factor (typically 0.5) } BurstPredictor; void initialize_predictor(BurstPredictor *p, double initial_estimate, double alpha) { p->predicted_burst = initial_estimate; // τ(0): initial guess p->alpha = alpha; } double predict_next_burst(BurstPredictor *p) { return p->predicted_burst; } void update_with_actual(BurstPredictor *p, double actual_burst) { // τ(n+1) = α × t(n) + (1 - α) × τ(n) p->predicted_burst = p->alpha * actual_burst + (1 - p->alpha) * p->predicted_burst; } // Example usage in scheduler: // // Process P arrives, we predict its burst: // estimated_time = predict_next_burst(&P->predictor); // // After P completes its CPU burst: // update_with_actual(&P->predictor, measured_burst_duration);76:T571,#!/bin/bash # Conceptual script to analyze CPU burst patterns using perf # Record scheduler events for a running process # This captures context switches, wakeups, and CPU migrations perf sched record -p -- sleep 10 # Generate a summary of scheduling latency and runtime perf sched latency # Example output interpretation: # ------------------------------------------------- # Task | Runtime ms | Switches | # ------------------------------------------------- # firefox | 342.52 | 1247 | # mysqld | 156.23 | 4521 | # ffmpeg | 2341.67 | 89 | # ------------------------------------------------- # # Analysis: # - firefox: 342.52ms / 1247 switches = 0.27ms average CPU burst (I/O bound) # - mysqld: 156.23ms / 4521 switches = 0.03ms average CPU burst (very I/O bound) # - ffmpeg: 2341.67ms / 89 switches = 26.3ms average CPU burst (CPU bound) # For detailed per-burst timing: perf sched timehist # Sample output showing individual CPU bursts: # time cpu task name wait time sch delay run time # [s] [msec] [msec] [msec] # 2.019329 [0001] firefox 0.000 0.012 0.342 # 2.019671 [0001] Xorg 0.129 0.003 0.089 # 2.019854 [0001] firefox 0.089 0.008 0.18346:["$","$L71",null,{"owner":"anonymous","state":{"mutations":[],"queries":[{"dehydratedAt":1792287858859,"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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This pattern is so universal, so deeply embedded in the nature of computing, that understanding it unlocks your comprehension of why CPU scheduling exists at all.\n\nWhen you open a text editor and type a document, the process handling your keystrokes isn't continuously using the CPU. It spends most of its time *waiting*—waiting for you to press the next key, waiting for the disk to load a file, waiting for the operating system to acknowledge a screen update. 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A scheduling algorithm optimized for microsecond bursts might perform poorly with second-scale bursts, and vice versa."},"type":"text"}],"titleType":"number","titleNumber":1},{"id":"defining-io-bursts","title":"Defining I/O Bursts","content":[{"data":{"content":"An **I/O burst** is the period during which a process waits for an input/output operation to complete. During an I/O burst, the process is in the **waiting (blocked) state** and cannot use the CPU, regardless of whether the CPU is available.\n\n**The anatomy of an I/O burst:**\n\nI/O bursts begin when a process makes a request that cannot be satisfied immediately and must wait for external events:\n\n1. **Disk I/O**: Reading from or writing to files, databases, or virtual memory\n2. **Network I/O**: Sending or receiving data over network connections\n3. **User Input**: Waiting for keyboard, mouse, or touch events\n4. **Device Communication**: Interacting with peripherals (printers, sensors, GPUs)\n5. **Inter-process Communication**: Waiting for messages from other processes"},"type":"text"},{"data":{"code":"$73","fileName":"io_burst_example.c","showLineNumbers":true},"type":"code"},{"data":{"icon":"Zap","title":"I/O Latency Orders of Magnitude","content":"Understanding I/O latency helps appreciate why I/O bursts dominate many workloads:\n\n• **L1 Cache**: ~1 nanosecond\n• **L3 Cache**: ~10 nanoseconds\n• **RAM**: ~100 nanoseconds\n• **SSD**: ~100 microseconds (100,000 ns)\n• **HDD**: ~10 milliseconds (10,000,000 ns)\n• **Network (LAN)**: ~1-10 milliseconds\n• **Network (Internet)**: ~10-200 milliseconds\n\nDuring a 10ms disk access, a 3 GHz CPU could have executed **30 million instructions**. This is why I/O waits are so expensive and why the OS schedules other processes during I/O bursts.","variant":"tip"},"type":"callout"},{"data":{"content":"**I/O burst characteristics:**\n\nUnlike CPU bursts, which are under the control of the application's logic, I/O burst durations depend on:\n\n- **Device speed**: SSDs vs. HDDs vs. network vs. tape\n- **Device contention**: How many other processes are competing for the same device\n- **Queue depth**: How many pending requests are ahead in the device queue\n- **Data location**: Sequential vs. random access patterns\n- **Network conditions**: Latency, bandwidth, congestion\n\nThis unpredictability makes I/O burst durations much harder to predict than CPU bursts, which has significant implications for scheduling algorithms that attempt to estimate burst times."},"type":"text"}],"titleType":"number","titleNumber":2},{"id":"cpu-io-burst-cycle","title":"The CPU-I/O Burst Cycle","content":[{"data":{"content":"$74"},"type":"text"},{"data":{"height":"350px","mermaidSyntax":"stateDiagram-v2\n [*] --> CPUBurst1: Process Created\n CPUBurst1 --> IOBurst1: I/O Request\n IOBurst1 --> CPUBurst2: I/O Complete\n CPUBurst2 --> IOBurst2: I/O Request\n IOBurst2 --> CPUBurst3: I/O Complete\n CPUBurst3 --> IOBurstN: ...\n IOBurstN --> CPUBurstN: I/O Complete\n CPUBurstN --> [*]: Process Terminates\n \n note right of CPUBurst1: Process actively\nusing CPU\n note right of IOBurst1: Process waiting\nCPU available for others"},"type":"mermaid-excalidraw"},{"data":{"content":"**Why this model matters for scheduling:**\n\nThe CPU-I/O burst cycle has profound implications:\n\n1. **Multiprogramming opportunity**: During I/O bursts, the CPU is idle *for that process*. Other processes can use it.\n\n2. **Scheduling decision points**: Every I/O burst is a scheduling opportunity—the OS must decide which ready process runs next.\n\n3. **Efficiency metric**: The fraction of time spent in CPU bursts vs. I/O bursts determines how efficiently a workload uses the processor.\n\n4. **Process classification**: Processes can be characterized by their burst patterns (CPU-bound vs. I/O-bound), informing scheduling decisions."},"type":"text"},{"data":{"icon":"AlertCircle","title":"The Multiprogramming Imperative","content":"If we ran processes one at a time to completion, the CPU would be idle during all I/O bursts. For I/O-bound workloads, this could mean **90%+ CPU idle time**. Multiprogramming—keeping multiple processes ready to run—ensures that when one process waits for I/O, another can use the CPU. This is the fundamental motivation for sophisticated CPU scheduling.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"burst-duration-distribution","title":"CPU Burst Duration Distribution","content":[{"data":{"content":"One of the most important empirical observations in operating systems research is the **distribution of CPU burst durations**. Understanding this distribution is crucial because many scheduling algorithms make decisions based on predicted burst lengths.\n\n**The empirical finding:**\n\nDecades of measurements across diverse workloads have consistently shown that CPU burst durations follow a characteristic distribution:\n\n- **Many short bursts**: The majority of CPU bursts are very short (microseconds to a few milliseconds)\n- **Few long bursts**: A small fraction of bursts are much longer (seconds or more)\n- **Exponential-like decay**: The frequency of bursts decreases roughly exponentially as duration increases\n\nThis distribution is often modeled as an **exponential distribution** or **hyperexponential distribution**, though real workloads show variations."},"type":"text"},{"data":{"rows":[["< 1 ms","~40%","40%"],["1-10 ms","~30%","70%"],["10-100 ms","~20%","90%"],["100 ms - 1 s","~8%","98%"],["> 1 second","~2%","100%"]],"caption":"Typical CPU Burst Duration Distribution (Interactive Workload)","headers":["Burst Duration","Approximate Frequency","Cumulative Percentage"]},"type":"table"},{"data":{"content":"**Why this distribution exists:**\n\nThe prevalence of short bursts isn't random—it reflects fundamental patterns in software:\n\n1. **Event-driven programming**: Interactive applications respond to events (keystrokes, clicks, messages) with brief processing bursts followed by waits for the next event.\n\n2. **Layered I/O**: Even \"compute-intensive\" programs frequently access data, hitting disk or network for each data item.\n\n3. **Operating system design**: Frequent interrupts (timer, I/O completion) fragment what might otherwise be longer bursts.\n\n4. **Memory hierarchy**: Cache misses and page faults convert long computations into series of short bursts separated by waits.\n\n**Implications for scheduling algorithm design:**"},"type":"text"},{"data":{"items":["**Favor short jobs (SJF)**: Since most bursts are short, algorithms that run short bursts first can significantly reduce average waiting time.","**Time slicing works well**: Short time quantum values (e.g., 10-100 ms) will complete most bursts without preemption, minimizing context switch overhead.","**Prediction is feasible**: The regularity of burst patterns makes exponential averaging and other prediction techniques reasonably effective.","**Long bursts are outliers**: The rare long bursts need special handling to prevent starvation while not degrading overall system performance."],"title":{"icon":"Target","text":"How Burst Distribution Shapes Scheduling","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"Lightbulb","title":"The 10ms Rule of Thumb","content":"A common heuristic: most interactive processes have CPU bursts under 10 milliseconds. This insight drove the design of time-sharing systems with quantum values around 10-100ms—long enough to complete typical bursts without preemption, short enough to maintain responsive feel.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"measuring-predicting-bursts","title":"Measuring and Predicting Bursts","content":[{"data":{"content":"Several scheduling algorithms (notably Shortest Job First and its variants) require knowledge of future CPU burst durations. Since we cannot know the future, we must **predict** burst lengths based on past behavior. This prediction challenge is central to scheduling algorithm design.\n\n**The prediction problem:**\n\nAt the moment a process becomes ready to run, the scheduler must decide:\n- How long will this process's next CPU burst be?\n- Should we run it now, or is there a shorter job waiting?\n\nWe cannot query the process—it doesn't know either. We must infer from history.\n\n**Exponential averaging (Exponential Moving Average):**\n\nThe most widely used prediction technique is exponential averaging, which weights recent history more heavily than distant past:"},"type":"text"},{"data":{"code":"$75","fileName":"burst_prediction.c","showLineNumbers":true},"type":"code"},{"data":{"content":"**Understanding the α parameter:**\n\nThe smoothing factor α controls how quickly the prediction responds to changes:"},"type":"text"},{"data":{"rows":[["α = 0.0","Prediction never changes (uses only initial estimate)","N/A (degenerate)"],["α = 0.1","Very slow adaptation, heavily weights history","Stable workloads"],["α = 0.5","Balanced weighting of recent vs. historical","General purpose"],["α = 0.9","Rapid adaptation, recent bursts dominate","Volatile workloads"],["α = 1.0","Prediction equals last burst exactly","Highly variable processes"]],"headers":["α Value","Behavior","Best For"]},"type":"table"},{"data":{"content":"**Example calculation:**\n\nConsider a process with burst history: 10ms, 8ms, 12ms, 5ms (most recent)\n\nWith α = 0.5 and initial estimate τ₀ = 10:\n- After 10ms burst: τ₁ = 0.5×10 + 0.5×10 = 10.0\n- After 8ms burst: τ₂ = 0.5×8 + 0.5×10 = 9.0\n- After 12ms burst: τ₃ = 0.5×12 + 0.5×9 = 10.5\n- After 5ms burst: τ₄ = 0.5×5 + 0.5×10.5 = 7.75\n\nNext burst prediction: **7.75ms**"},"type":"text"},{"data":{"icon":"Info","title":"Expanding the Exponential Average Formula","content":"The formula τ(n+1) = α×t(n) + (1-α)×τ(n) can be expanded to show that each historical burst contributes with exponentially decreasing weight:\n\nτ(n+1) = α×t(n) + α(1-α)×t(n-1) + α(1-α)²×t(n-2) + ... + (1-α)ⁿ×τ(0)\n\nWith α = 0.5: weights are 0.5, 0.25, 0.125, 0.0625... — recent history dominates but old behavior still influences predictions.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"burst-patterns-real-systems","title":"Burst Patterns in Real Systems","content":[{"data":{"content":"Different types of applications exhibit characteristic burst patterns. Understanding these patterns helps in workload characterization, capacity planning, and scheduler tuning.\n\n**Typical burst patterns by application type:**"},"type":"text"},{"data":{"rows":[["Text Editor","Very short (< 1ms), sporadic","Long waits for input","1:1000+ I/O dominated"],["Web Browser","Short bursts, event-driven","Network waits, render bursts","1:10 to 1:1 mixed"],["Compiler","Medium bursts, phases","File reads between phases","10:1 CPU dominated"],["Database Server","Short query processing","Disk I/O for data","1:5 I/O dominated"],["Video Encoder","Long, steady bursts","Minimal I/O (buffered)","100:1 CPU dominated"],["Scientific Simulation","Very long bursts","Checkpoint I/O only","1000:1 CPU dominated"]],"caption":"Burst Characteristics by Application Type","headers":["Application Type","CPU Burst Pattern","I/O Burst Pattern","Ratio"]},"type":"table"},{"data":{"content":"**Observing bursts with system tools:**\n\nModern operating systems provide tools to observe burst behavior. On Linux, tools like `perf`, `ftrace`, and `eBPF` can capture scheduling events. Here's a conceptual analysis approach:"},"type":"text"},{"data":{"code":"$76","fileName":"analyze_bursts.sh","showLineNumbers":true},"type":"code"},{"data":{"icon":"Lightbulb","title":"Practical Burst Analysis","content":"When optimizing real systems, measuring actual burst distributions is more valuable than theoretical models. Use tools like `perf sched`, `trace-cmd`, or Application Performance Monitoring (APM) tools to understand your workload's actual burst characteristics before tuning scheduler parameters.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"implications-multitasking","title":"Implications for Multitasking","content":[{"data":{"content":"The CPU-I/O burst model has profound implications for how operating systems achieve efficient multitasking. Understanding these implications connects the theoretical burst model to practical system design.\n\n**Why multiprogramming works:**\n\nConsider a simplified scenario with two processes:\n\n- **Process A**: 40% CPU burst, 60% I/O burst (I/O bound)\n- **Process B**: 80% CPU burst, 20% I/O burst (CPU bound)\n\nIf we run A alone:\n- CPU utilization = 40%\n- A completes some work, waits, completes more...\n\nIf we run B alone:\n- CPU utilization = 80%\n- B completes more work before waiting\n\nIf we run A and B together (multiprogramming):\n- When A waits for I/O, B uses the CPU\n- When B waits for I/O, A uses the CPU\n- CPU utilization approaches 100% (minus overhead)\n\nThis is the fundamental insight that drives all modern operating system design."},"type":"text"},{"data":{"items":["**Degree of multiprogramming**: More processes generally improves CPU utilization—there's always a process ready when others are waiting.","**Job mix matters**: Mixing I/O-bound and CPU-bound processes yields better utilization than homogeneous workloads.","**Context switch overhead is tolerable**: The cost of switching (microseconds) is tiny compared to typical I/O waits (milliseconds).","**Interactive responsiveness**: Short CPU bursts followed by I/O waits naturally create opportunities for other processes, including user interaction handlers.","**Batch throughput**: Long CPU bursts should complete without unnecessary preemption to minimize overhead."],"title":{"icon":"Target","text":"Key Implications of the Burst Model","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**The multiprogramming formula:**\n\nA classic approximation for CPU utilization with n processes, each with I/O wait fraction p:\n\n```\nCPU Utilization = 1 - pⁿ\n```\n\nFor example, if each process waits for I/O 80% of the time (p = 0.8):\n- 1 process: 1 - 0.8¹ = 20% CPU utilization\n- 2 processes: 1 - 0.8² = 36% CPU utilization\n- 5 processes: 1 - 0.8⁵ = 67% CPU utilization\n- 10 processes: 1 - 0.8¹⁰ = 89% CPU utilization\n- 20 processes: 1 - 0.8²⁰ = 99% CPU utilization\n\nThis formula, while simplified (assumes independent I/O), illustrates why systems maintain many active processes.\n\n**The scheduler's challenge:**\n\nGiven this model, the scheduler must answer: *When multiple processes are ready (not in I/O burst), which one should run?*\n\nThis is the central question of CPU scheduling, and different answers lead to different scheduling algorithms with different tradeoffs—which we'll explore throughout this module."},"type":"text"},{"data":{"icon":"AlertCircle","title":"The Overhead Reality","content":"While more processes improve utilization in theory, each adds overhead: memory for process structures, context switch costs, and cache pollution. There's a practical limit beyond which adding processes degrades rather than improves performance. Finding this balance is a key system design challenge.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"summary-looking-ahead","title":"Summary: CPU and I/O Bursts","content":[{"data":{"content":"We've established the foundational model that underlies all CPU scheduling concepts. Let's consolidate the key insights:"},"type":"text"},{"data":{"type":"ordered","items":["**CPU burst** = contiguous period of CPU execution without I/O; **I/O burst** = period waiting for I/O completion.","**Processes alternate** between CPU and I/O bursts in a strict cycle, starting and ending with CPU bursts.","**Burst distribution** is skewed: many short bursts, few long bursts—following approximately exponential distribution.","**Burst prediction** using exponential averaging enables intelligent scheduling decisions based on historical behavior.","**Multiprogramming exploits** I/O waits by running other processes, dramatically improving CPU utilization.","**Different applications** have characteristic burst patterns: I/O-bound (many short CPU bursts) vs. CPU-bound (few long CPU bursts)."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**What's next:**\n\nNow that we understand burst patterns, the next page explores the critical distinction between **CPU-bound and I/O-bound processes**. This classification, built directly on burst analysis, fundamentally shapes how schedulers prioritize different workloads and balance competing goals like throughput and responsiveness."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now understand the CPU-I/O burst model—the fundamental rhythm of process execution that all scheduling algorithms must accommodate. This model explains why scheduling exists (to utilize CPU during I/O waits), why prediction matters (to make intelligent choices), and why multiprogramming works (mathematical inevitability with independent processes).","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":8}],"isLocked":false},{"id":"seed_072d0ce3da6ae15e164b5526620d305e","title":"CPU-bound vs I/O-bound","order":1,"content":null,"isLocked":true},{"id":"seed_f687887fc0c51b3b15feabe57ef9d8ae","title":"Preemptive vs Non-preemptive","order":2,"content":null,"isLocked":true},{"id":"seed_9ef6f93eb0623d8b358a890405be5fd3","title":"Scheduling Criteria","order":3,"content":null,"isLocked":true},{"id":"seed_f4a46c572912bd0e38dbf1aecb53ca5a","title":"Dispatcher","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792287858836,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"scheduling-concepts"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"scheduling-concepts\"},\"type\":\"query\"}]"},{"dehydratedAt":1792287858874,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792287858836,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_482c67b5c885c815164497eb2a7d510c"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_482c67b5c885c815164497eb2a7d510c\"},\"type\":\"query\"}]"},{"dehydratedAt":1792287858874,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792287858836,"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_9ef6f93eb0623d8b358a890405be5fd3"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_9ef6f93eb0623d8b358a890405be5fd3\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792287858874,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792287858836,"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_9ef6f93eb0623d8b358a890405be5fd3"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_9ef6f93eb0623d8b358a890405be5fd3\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792287858874,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792287858836,"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_9ef6f93eb0623d8b358a890405be5fd3"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_9ef6f93eb0623d8b358a890405be5fd3\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792287858874,"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_9ef6f93eb0623d8b358a890405be5fd3","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_9ef6f93eb0623d8b358a890405be5fd3\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@77"}]},"children":"$L78"}]