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BufferedReader( new InputStreamReader(socket.getInputStream()) ); PrintWriter out = new PrintWriter( socket.getOutputStream(), true ) ) { String request = in.readLine(); // Simulate some processing work (10ms) Thread.sleep(10); out.println("HTTP/1.1 200 OK\r \r Hello World"); } catch (Exception e) { e.printStackTrace(); } } }72:T5f3,# ❌ ANTI-PATTERN: Thread-per-request import socket import threading class NaiveServer: def __init__(self, host='localhost', port=8080): self.host = host self.port = port self.server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) def start(self): self.server_socket.bind((self.host, self.port)) self.server_socket.listen(100) print(f"Server listening on {self.host}:{self.port}") while True: # Accept incoming connection client_socket, address = self.server_socket.accept() # ⚠️ PROBLEM: Creates a new thread for EVERY request handler = threading.Thread( target=self.handle_request, args=(client_socket,) ) handler.start() # Thread is created, runs, and then garbage collected # This happens thousands of times per second under load def handle_request(self, client_socket): try: request = client_socket.recv(1024).decode() # Simulate some processing work (10ms) import time time.sleep(0.01) response = "HTTP/1.1 200 OK\r \r Hello World" client_socket.send(response.encode()) finally: client_socket.close() # Note: Python's GIL means threads don't provide true parallelism # for CPU-bound work, but the creation overhead still applies73:T706,// ❌ ANTI-PATTERN: Thread-per-request #include #include #include #include #include #include class NaiveServer { public: void start(int port) { int serverSocket = socket(AF_INET, SOCK_STREAM, 0); sockaddr_in serverAddr{}; serverAddr.sin_family = AF_INET; serverAddr.sin_port = htons(port); serverAddr.sin_addr.s_addr = INADDR_ANY; bind(serverSocket, (sockaddr*)&serverAddr, sizeof(serverAddr)); listen(serverSocket, 100); std::cout << "Server listening on port " << port << std::endl; while (true) { // Accept incoming connection sockaddr_in clientAddr{}; socklen_t clientLen = sizeof(clientAddr); int clientSocket = accept( serverSocket, (sockaddr*)&clientAddr, &clientLen ); // ⚠️ PROBLEM: Creates a new thread for EVERY request std::thread handler([this, clientSocket]() { handleRequest(clientSocket); }); handler.detach(); // Thread runs independently // Thread is created, runs, and then destroyed // Heavy OS overhead for each request } } private: void handleRequest(int clientSocket) { char buffer[1024] = {0}; read(clientSocket, buffer, 1024); // Simulate some processing work (10ms) std::this_thread::sleep_for(std::chrono::milliseconds(10)); const char* response = "HTTP/1.1 200 OK\r \r Hello World"; write(clientSocket, response, strlen(response)); close(clientSocket); } };74:Td1c,import java.util.concurrent.CountDownLatch; /** * Benchmark to measure actual thread creation overhead * in your specific JVM and environment. */ public class ThreadCreationBenchmark { public static void main(String[] args) throws Exception { int[] threadCounts = {100, 1000, 5000, 10000}; // Warmup System.out.println("Warming up..."); benchmarkThreadCreation(1000); System.out.println(" Thread Creation Benchmark Results:"); System.out.println("=================================="); for (int count : threadCounts) { double avgMicros = benchmarkThreadCreation(count); double throughput = 1_000_000.0 / avgMicros; System.out.printf( "Threads: %5d | Avg Creation: %7.2f μs | Max Throughput: %,.0f threads/sec%n", count, avgMicros, throughput ); } System.out.println(" Memory Overhead Analysis:"); System.out.println("========================="); analyzeMemoryOverhead(); } static double benchmarkThreadCreation(int threadCount) throws Exception { Thread[] threads = new Thread[threadCount]; CountDownLatch startLatch = new CountDownLatch(1); CountDownLatch doneLatch = new CountDownLatch(threadCount); long startTime = System.nanoTime(); for (int i = 0; i < threadCount; i++) { threads[i] = new Thread(() -> { try { startLatch.await(); // Wait for signal doneLatch.countDown(); } catch (InterruptedException e) { Thread.currentThread().interrupt(); } }); threads[i].start(); } long creationTime = System.nanoTime() - startTime; // Let threads run and complete startLatch.countDown(); doneLatch.await(); // Wait for all threads to fully terminate for (Thread t : threads) { t.join(); } return (creationTime / 1000.0) / threadCount; // Avg in microseconds } static void analyzeMemoryOverhead() { Runtime runtime = Runtime.getRuntime(); System.gc(); long baseMemory = runtime.totalMemory() - runtime.freeMemory(); Thread[] threads = new Thread[1000]; CountDownLatch latch = new CountDownLatch(1); for (int i = 0; i < 1000; i++) { final int idx = i; threads[i] = new Thread(() -> { try { latch.await(); } catch (InterruptedException e) {} }); threads[i].start(); } System.gc(); long withThreads = runtime.totalMemory() - runtime.freeMemory(); long perThread = (withThreads - baseMemory) / 1000; System.out.printf("Approximate memory per thread: %,d bytes%n", perThread); System.out.printf("Estimated memory for 10,000 threads: %,d MB%n", perThread * 10000 / (1024 * 1024)); latch.countDown(); for (Thread t : threads) { try { t.join(); } catch (InterruptedException e) {} } } }75:T902,import threading import time import tracemalloc def benchmark_thread_creation(thread_count: int) -> float: """Measure average thread creation time in microseconds.""" barrier = threading.Barrier(thread_count + 1) threads = [] def worker(): barrier.wait() # Wait for all threads to be created start_time = time.perf_counter_ns() for _ in range(thread_count): t = threading.Thread(target=worker) t.start() threads.append(t) creation_time = time.perf_counter_ns() - start_time # Release all threads barrier.wait() # Wait for completion for t in threads: t.join() return (creation_time / 1000) / thread_count # Average in microseconds def analyze_memory_overhead(): """Measure memory consumption per thread.""" tracemalloc.start() base_snapshot = tracemalloc.take_snapshot() barrier = threading.Barrier(1001) threads = [] for _ in range(1000): t = threading.Thread(target=lambda: barrier.wait()) t.start() threads.append(t) with_threads_snapshot = tracemalloc.take_snapshot() # Calculate difference stats = with_threads_snapshot.compare_to(base_snapshot, 'lineno') total_diff = sum(stat.size_diff for stat in stats) per_thread = total_diff // 1000 print(f"Approximate memory per thread (Python objects): {per_thread:,} bytes") print(f"Note: OS-level stack memory is not included in this measurement") barrier.wait() for t in threads: t.join() tracemalloc.stop() def main(): thread_counts = [100, 500, 1000, 2000] # Warmup print("Warming up...") benchmark_thread_creation(100) print(" Thread Creation Benchmark Results:") print("=" * 60) for count in thread_counts: avg_micros = benchmark_thread_creation(count) throughput = 1_000_000 / avg_micros if avg_micros > 0 else 0 print(f"Threads: {count:5d} | " f"Avg 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The naive approach—spawn a new thread for each request—seems logical. After all, threads are the mechanism for concurrent execution, right? But this seemingly reasonable strategy will bring even powerful servers to their knees."},"type":"text"},{"data":{"icon":"Target","title":"What You Will Learn","content":"By the end of this page, you will deeply understand why thread creation is expensive, quantify the costs involved, and recognize the symptoms of thread creation overhead in production systems. This understanding is essential before we explore the Thread Pool solution.","variant":"info"},"type":"callout"}]},{"id":"anatomy-of-thread-creation","title":"The Anatomy of Thread Creation","content":[{"data":{"content":"To understand why thread creation is expensive, we need to examine what actually happens when you create a thread. This isn't just a simple increment of a counter—it's a complex orchestration involving the operating system kernel, memory management, and CPU scheduling.\n\n**When you create a thread, the following sequence occurs:**"},"type":"text"},{"data":{"type":"ordered","items":["**System Call Overhead** — The thread creation request transitions from user space to kernel space. This context switch alone costs hundreds to thousands of CPU cycles as the CPU saves user-space registers, switches privilege levels, and loads kernel context.","**Kernel Object Allocation** — The kernel allocates a thread control block (TCB) or similar structure to track the thread's state, including registers, priority, scheduling information, signal handlers, and thread-local storage pointers.","**Stack Memory Allocation** — Each thread requires its own stack. On Linux, the default stack size is typically 8MB (though most is only virtually mapped). On Windows, the default is 1MB. This memory must be allocated and mapped in the process's virtual address space.","**Guard Pages Setup** — The OS typically places guard pages at stack boundaries to detect stack overflows. These require additional page table entries and memory mapping operations.","**Thread-Local Storage (TLS) Initialization** — Any thread-local variables must be initialized. Modern languages and runtimes often have significant TLS requirements.","**Scheduler Integration** — The kernel must add the new thread to its scheduling data structures, potentially rebalancing priority queues or updating hierarchical scheduler state.","**Signal Handling Setup** — The thread inherits or establishes signal masks and handlers, requiring additional kernel bookkeeping.","**Return to User Space** — Another context switch to return execution to the newly created thread, loading its fresh context."],"title":{"icon":"Zap","text":"Thread Creation Steps (What the OS Does)","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"AlertCircle","title":"This Happens Every Single Time","content":"Every single thread creation performs this entire dance. There's no shortcut, no caching, no amortization. If you create 10,000 threads to handle 10,000 tasks, you pay this cost 10,000 times—and then pay similar costs to destroy each thread when done.","variant":"warning"},"type":"callout"},{"data":{"content":"**The stack allocation deserves special attention:**\n\nEach thread needs its own private stack for function calls, local variables, and return addresses. While modern operating systems use virtual memory to avoid allocating all 8MB immediately (using a technique called *demand paging*), there are still significant costs:\n\n- **Virtual address space consumption**: Even if physical memory isn't allocated, the virtual address space is consumed. On 32-bit systems (now rare), this limits you to a few hundred threads. Even on 64-bit systems, extreme thread counts can fragment the address space.\n\n- **Page table overhead**: Each mapped region requires entries in the process's page tables. With thousands of threads, page table memory consumption becomes significant.\n\n- **Memory commits on access**: As the stack grows, pages fault in and consume physical memory. Under load, this creates a cascade of page faults."},"type":"text"}],"titleType":"number","titleNumber":1},{"id":"quantifying-the-cost","title":"Quantifying the Cost","content":[{"data":{"content":"Abstract explanations only go so far. Let's look at concrete measurements to understand the real-world impact of thread creation overhead.\n\n**Typical thread creation times across platforms:**"},"type":"text"},{"data":{"rows":[["Linux (pthread_create)","10-30 μs","~30,000-100,000","Depends on kernel version, glibc"],["Windows (CreateThread)","20-50 μs","~20,000-50,000","Varies with security features"],["macOS (pthread_create)","15-40 μs","~25,000-65,000","Similar to BSD threading"],["JVM (new Thread())","50-200 μs","~5,000-20,000","Includes JVM bookkeeping, GC pressure"],[".NET (new Thread())","40-150 μs","~7,000-25,000","CLR overhead adds latency"],["Go (goroutine)","0.3-1 μs","~1,000,000+","Not OS threads—user-space green threads"]],"caption":"Thread Creation Latency by Platform","headers":["Platform","Thread Creation Time","Threads/Second (Max)","Notes"]},"type":"table"},{"data":{"content":"**What these numbers mean in practice:**\n\nLet's consider a web server receiving 10,000 requests per second (a modest load for modern systems). If we spawn a thread per request:"},"type":"text"},{"data":{"fileName":"cost-analysis.md","languages":[{"code":"# Thread Creation Cost Analysis\n\n## Scenario: 10,000 requests/second\n\n### Time Spent Just Creating Threads:\n- Linux: 10,000 × 20μs = 200ms per second (20% of CPU capacity)\n- JVM: 10,000 × 100μs = 1000ms per second (100%—IMPOSSIBLE)\n\n### Memory Overhead (Assuming 8MB stack reservation):\n- 10,000 threads × 8MB = 80GB virtual address space\n- Even with demand paging, each thread commits ~64KB minimum\n- 10,000 × 64KB = 640MB committed memory JUST for stacks\n\n### Context Switch Overhead:\n- Creating thread: ~2 context switches (user→kernel→user)\n- If each thread runs briefly before blocking: +2 more switches\n- 10,000 × 4 switches × ~5μs = 200ms additional overhead\n\n## Total Overhead: 400ms+ per second just for thread management","label":"Analysis","value":"markdown"}],"showLineNumbers":false},"type":"code"},{"data":{"icon":"AlertCircle","title":"The JVM Example Is Not Hypothetical","content":"Early Java web servers (pre-NIO, pre-thread pools) actually had this problem. They would create a new thread for each incoming connection, and under high load, they would spend more time creating and destroying threads than doing actual work. This was one of the primary motivations for servlet containers introducing thread pools.","variant":"error"},"type":"callout"},{"data":{"content":"**Memory consumption at scale:**\n\nThread memory overhead becomes even more stark when we consider real-world scenarios:"},"type":"text"},{"data":{"rows":[["100","800 MB","Easily manageable"],["1,000","8 GB","Starts to strain systems"],["10,000","80 GB","Requires 64-bit, large memory"],["100,000","800 GB","Exceeds most systems"],["1,000,000","8 TB","Physically impossible"]],"headers":["Concurrent Threads","Virtual Memory (8MB stacks)","Practical Physical Limit"]},"type":"table"},{"data":{"content":"Even with reduced stack sizes (say, 256KB), hitting the C10K (10,000 concurrent connections) problem requires careful engineering—which led directly to the development of thread pools and event-driven architectures."},"type":"text"}],"titleType":"number","titleNumber":2},{"id":"thread-per-request-antipattern","title":"The Thread-per-Request Anti-Pattern","content":[{"data":{"content":"The most common manifestation of expensive thread creation occurs in server applications that spawn a new thread for each incoming request. This pattern is intuitive but deeply flawed.\n\n**The naive implementation:**"},"type":"text"},{"data":{"fileName":"thread-per-request.java","languages":[{"code":"$71","label":"Java","value":"java"},{"code":"$72","label":"Python","value":"python"},{"code":"$73","label":"C++","value":"cpp"}],"showLineNumbers":true},"type":"code"},{"data":{"content":"**Why this pattern seems appealing but fails:**"},"type":"text"},{"data":{"left":[{"data":{"items":["Simple mental model: one request, one thread","Each request handler is isolated","No shared state between handlers (seemingly)","Easy to understand stack traces","No explicit resource management","Works fine during development with low traffic"],"title":{"icon":"CheckCircle","text":"Why Developers Choose It","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["Thread creation latency adds to every request","Memory consumption grows unbounded with load","No cap on thread count → system instability","Context switching overhead crushes CPU","Thread destruction latency on completion","GC pressure from short-lived thread objects"],"title":{"icon":"XCircle","text":"Why It Fails at Scale","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"}],"titleType":"number","titleNumber":3},{"id":"cascading-failures","title":"Cascading Failures Under Load","content":[{"data":{"content":"The thread-per-request anti-pattern doesn't fail gracefully—it cascades. Understanding this failure mode is crucial for appreciating why thread pools are essential, not optional.\n\n**The cascade sequence:**"},"type":"text"},{"data":{"type":"ordered","items":["**Normal Load**: System handles 100 requests/second. 100 concurrent threads at any moment. Everything works fine.","**Traffic Spike**: Load increases to 1,000 requests/second. Now 1,000+ concurrent threads. Thread creation time starts to become noticeable.","**Slowdown Begins**: Each request takes slightly longer due to context switching. This means threads live longer, causing thread count to increase further.","**Memory Pressure**: With 5,000+ threads, memory consumption triggers GC. GC pauses slow down all threads, extending their lifetime even more.","**Positive Feedback Loop**: More threads → more memory → more GC → slower requests → even more threads accumulating.","**System Saturation**: Thread count hits OS limits (ulimit), or the system runs out of virtual address space, or memory.","**Total Failure**: New requests fail entirely. Existing requests time out. Stack overflows may occur. System becomes unresponsive."],"title":"How Thread Explosion Crashes Systems","variant":"glass"},"type":"list"},{"data":{"icon":"AlertCircle","title":"The Tragedy: This Happens When You Need Reliability Most","content":"This cascade typically occurs during traffic spikes—product launches, viral moments, flash sales—exactly when your service MUST perform. The thread-per-request model fails precisely when success is most important.","variant":"error"},"type":"callout"},{"data":{"content":"**Real-world symptoms of thread creation overhead:**\n\nEngineers often misdiagnose thread creation problems because the symptoms appear elsewhere. Here are the telltale signs:"},"type":"text"},{"data":{"rows":[["Latency spikes","P99 latency shoots up unpredictably","Thread creation adds variable delay"],["CPU at 100% but low throughput","Server maxed but handling few requests","CPU busy context switching, not working"],["Memory climbing despite no leaks","RAM usage grows with traffic","Each thread consumes stack memory"],["`OutOfMemoryError: unable to create native thread`","JVM crash under load","OS thread limit reached"],["Slow GC, frequent Full GC","GC metrics degrade under load","Thread objects create allocation pressure"],["Slow startup of request handling","Time-to-first-byte increases","Thread creation precedes any work"]],"headers":["Symptom","What It Looks Like","Why It Happens"]},"type":"table"}],"titleType":"number","titleNumber":4},{"id":"destruction-cost","title":"The Destruction Cost (Often Forgotten)","content":[{"data":{"content":"While thread *creation* gets the most attention, thread *destruction* is equally expensive—and often overlooked. Every thread that is born must eventually die, and that death has its own costs.\n\n**What happens when a thread terminates:**"},"type":"text"},{"data":{"type":"ordered","items":["**Final Context Switch** — The thread makes a system call to exit, transitioning to kernel mode one last time.","**Thread-Local Storage Cleanup** — Any TLS variables with destructors must be invoked. In C++, this includes thread_local objects with non-trivial destructors.","**Signal Handling Cleanup** — The kernel removes the thread from signal routing structures.","**Scheduler Removal** — The kernel removes the thread from scheduling queues and updates priority structures.","**Stack Deallocation** — The stack memory must be unmapped from virtual address space. Page table entries are removed.","**TCB Deallocation** — The thread control block is freed, but may require synchronization with other kernel subsystems first.","**Join Synchronization** — If another thread is waiting to join, it must be notified and potentially unblocked.","**Object Finalization (Managed Languages)** — In Java/C#, the Thread object becomes garbage and eventually triggers GC work."],"title":{"icon":"Zap","text":"Thread Termination Sequence","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**The hidden multiplier:**\n\nFor a thread-per-request design handling N requests per second:\n\n- Create N threads: N × (creation time)\n- Destroy N threads: N × (destruction time)\n- **Total overhead: N × (creation + destruction time)**\n\nIf creation takes 50μs and destruction takes 30μs, that's 80μs of pure overhead per request—before any actual work begins. At 10,000 requests/second, that's **800ms of pure overhead every second** just for thread lifecycle management."},"type":"text"},{"data":{"icon":"Lightbulb","title":"The Simple Insight That Leads to Thread Pools","content":"If we could reuse threads instead of creating and destroying them, we would pay the creation cost once (at startup) and the destruction cost once (at shutdown). For a server handling millions of requests, this is a massive optimization—paying lifecycle costs once instead of millions of times.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"measuring-thread-creation","title":"Measuring Thread Creation in Your Environment","content":[{"data":{"content":"Before optimizing, you should measure. Here's how to quantify thread creation overhead in your specific environment:\n\n**Benchmarking thread creation:**"},"type":"text"},{"data":{"fileName":"thread-benchmark.java","languages":[{"code":"$74","label":"Java","value":"java"},{"code":"$75","label":"Python","value":"python"}],"showLineNumbers":true},"type":"code"},{"data":{"icon":"Info","title":"Your Results Will Vary","content":"Thread creation overhead depends heavily on your OS, kernel version, CPU, memory speed, and language runtime. Run these benchmarks on your production-like hardware to get accurate numbers for capacity planning.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"path-forward","title":"The Path Forward: Previewing the Solution","content":[{"data":{"content":"We've established that thread creation is expensive—prohibitively so for high-throughput systems. The core insight that leads to a solution is simple:\n\n**The disparity between thread lifecycle and task lifecycle.**\n\n- A typical HTTP request takes 10-100ms\n- Thread creation takes 20-200μs\n- That's a 100-1000x overhead ratio per task\n\nBut we're paying this overhead for every task. What if instead:\n\n- We created threads once at startup\n- We reused them for millions of tasks\n- We destroyed them once at shutdown\n\nThis is the **Thread Pool Pattern**—and it's exactly what we'll explore in the next page."},"type":"text"},{"data":{"icon":"DollarSign","title":"The Economic Framing","content":"Think of threads as expensive equipment. You wouldn't buy a new truck for every delivery and scrap it afterward—you'd buy a fleet of trucks and reuse them. Thread pools apply this same economic reasoning to concurrency: amortize the fixed costs over many uses.","variant":"tip"},"type":"callout"},{"data":{"items":["**Solution: Pool of Reusable Threads** — How thread pools work internally, with work queues and worker threads","**Pool Sizing Considerations** — How to determine optimal pool sizes for different workloads","**Thread Pool in Practice** — Real implementations in Java, Python, C++, and production patterns"],"title":{"icon":"Target","text":"What's Coming Next","type":"icon","color":"primary","variant":"outline-circle"},"variant":"glass"},"type":"list"}],"titleType":"number","titleNumber":7},{"id":"summary","title":"Summary","content":[{"data":{"content":"This page has established the critical foundation for understanding thread pools: the significant overhead of thread creation and destruction.\n\n**Key takeaways:**"},"type":"text"},{"data":{"type":"ordered","items":["**Thread creation is a complex operation** involving kernel calls, memory allocation, scheduler integration, and multiple context switches—each taking 10-200+ microseconds.","**Memory overhead is substantial**: each thread consumes significant stack space (typically 256KB-8MB) plus kernel bookkeeping structures.","**The thread-per-request pattern fails at scale** because thread lifecycle costs dominate actual work, and memory consumption grows unbounded.","**Failure cascades are non-linear**: systems under load experience positive feedback loops where slowdown causes more threads, which causes more slowdown.","**Thread destruction is equally costly** as creation—doubling the lifecycle overhead per task.","**The solution is reuse**: by amortizing thread lifecycle costs across many tasks, thread pools eliminate the overhead for individual operations."],"title":{"icon":"CheckCircle","text":"What You've Learned","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"CheckCircle","title":"Ready for the Solution","content":"You now deeply understand WHY thread pools exist—not as an optimization, but as a fundamental requirement for scalable concurrent systems. In the next page, we'll explore HOW thread pools work: their architecture, components, and the elegance of the worker thread model.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":8}],"isLocked":false},{"id":"seed_c0e4d5d900b352df7cf41201e4afd80e","title":"Solution: Pool of Reusable Threads","order":1,"content":null,"isLocked":true},{"id":"seed_b47bf69841dcbb4fd2b09a3bcdca8f08","title":"Pool Sizing Considerations","order":2,"content":null,"isLocked":true},{"id":"seed_45998715e3451e6ff398f0f0b82190a1","title":"Thread Pool in Practice","order":3,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792196503466,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"thread-pool-pattern"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"thread-pool-pattern\"},\"type\":\"query\"}]"},{"dehydratedAt":1792196503480,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792196503467,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_b4c1e7580bba0451d5ecf4a7b9358883"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_b4c1e7580bba0451d5ecf4a7b9358883\"},\"type\":\"query\"}]"},{"dehydratedAt":1792196503480,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792196503467,"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_c0e4d5d900b352df7cf41201e4afd80e"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_c0e4d5d900b352df7cf41201e4afd80e\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792196503480,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792196503467,"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_c0e4d5d900b352df7cf41201e4afd80e"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_c0e4d5d900b352df7cf41201e4afd80e\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792196503480,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792196503467,"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_c0e4d5d900b352df7cf41201e4afd80e"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_c0e4d5d900b352df7cf41201e4afd80e\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792196503480,"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_c0e4d5d900b352df7cf41201e4afd80e","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_c0e4d5d900b352df7cf41201e4afd80e\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@76"}]},"children":"$L77"}]