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It begins as electrical signals or photons, traverses cables, routers, switches, and sometimes satellites. Each component in this chain can fail: - **Network Interface Cards (NICs)** can malfunction or become overloaded - **Cables** can be cut, corroded, or disconnected (intentionally or accidentally) - **Switches and routers** can crash, reboot, or become congested - **DNS servers** can return stale data or become unreachable - **Load balancers** can misconfigure or fail over incorrectly - **Firewalls** can block traffic based on misconfigured rules - **ISPs and cloud providers** can experience outages affecting regions or the entire internet73:T120e,/** * Example: Resilient HTTP client with timeout, retry, and circuit breaker * This illustrates the patterns needed for network unreliability */ interface RetryConfig { maxRetries: number; baseDelayMs: number; maxDelayMs: number; } interface CircuitBreakerConfig { failureThreshold: number; // Opens after this many failures recoveryTimeMs: number; // Try again after this interval } class ResilientHttpClient { private failureCount = 0; private lastFailureTime = 0; private circuitOpen = false; constructor( private retryConfig: RetryConfig = { maxRetries: 3, baseDelayMs: 100, maxDelayMs: 10000 }, private circuitConfig: CircuitBreakerConfig = { failureThreshold: 5, recoveryTimeMs: 30000 }, private timeoutMs: number = 5000 ) {} async request(url: string, options: RequestInit = {}): Promise { // Check circuit breaker state if (this.isCircuitOpen()) { throw new Error('Circuit breaker open - failing fast'); } let lastError: Error | null = null; for (let attempt = 0; attempt <= this.retryConfig.maxRetries; attempt++) { try { const response = await this.executeWithTimeout(url, options); // Success - reset failure count this.onSuccess(); return response as T; } catch (error) { lastError = error as Error; this.onFailure(); // Don't retry if circuit opened if (this.isCircuitOpen()) { throw new Error('Circuit breaker opened during retries'); } // Don't retry on client errors (4xx) - these won't resolve if (error instanceof HttpError && error.status >= 400 && error.status < 500) { throw error; } // Wait before retry with exponential backoff + jitter if (attempt < this.retryConfig.maxRetries) { const delay = this.calculateBackoff(attempt); await this.sleep(delay); } } } throw lastError || new Error('Request failed after retries'); } private async executeWithTimeout(url: string, options: RequestInit): Promise { const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), this.timeoutMs); try { const response = await fetch(url, { ...options, signal: controller.signal }); if (!response.ok) { throw new HttpError(response.status, await response.text()); } return await response.json(); } finally { clearTimeout(timeoutId); } } private calculateBackoff(attempt: number): number { // Exponential backoff: baseDelay * 2^attempt const exponentialDelay = this.retryConfig.baseDelayMs * Math.pow(2, attempt); // Cap at maximum delay const cappedDelay = Math.min(exponentialDelay, this.retryConfig.maxDelayMs); // Add jitter (±25%) to prevent thundering herd const jitter = cappedDelay * 0.25 * (Math.random() * 2 - 1); return Math.floor(cappedDelay + jitter); } private isCircuitOpen(): boolean { if (!this.circuitOpen) return false; // Check if recovery period has passed const timeSinceFailure = Date.now() - this.lastFailureTime; if (timeSinceFailure >= this.circuitConfig.recoveryTimeMs) { // Half-open state: allow one request to test recovery return false; } return true; } private onSuccess(): void { this.failureCount = 0; this.circuitOpen = false; } private onFailure(): void { this.failureCount++; this.lastFailureTime = Date.now(); if (this.failureCount >= this.circuitConfig.failureThreshold) { this.circuitOpen = true; console.warn(`Circuit breaker opened after ${this.failureCount} failures`); } } private sleep(ms: number): Promise { return new Promise(resolve => setTimeout(resolve, ms)); } } class HttpError extends Error { constructor(public status: number, message: string) { super(`HTTP ${status}: ${message}`); this.name = 'HttpError'; } }74:Tba5,/** * Server-side idempotency implementation * Tracks idempotency keys to prevent duplicate processing */ interface IdempotencyRecord { key: string; response: unknown; createdAt: Date; expiresAt: Date; } class IdempotencyService { // In production: use Redis or database with TTL private records = new Map(); private readonly TTL_MS = 24 * 60 * 60 * 1000; // 24 hours /** * Execute an operation idempotently * Returns cached result for duplicate keys */ async executeOnce( idempotencyKey: string, operation: () => Promise ): Promise { // Check for existing record const existing = this.records.get(idempotencyKey); if (existing) { if (existing.expiresAt > new Date()) { console.log(`Returning cached result for key: ${idempotencyKey}`); return existing.response as T; } else { // Expired - remove and allow re-execution // Be careful: in production, consider if re-execution is safe this.records.delete(idempotencyKey); } } // Execute the operation // NOTE: In production, use distributed locking to prevent // concurrent executions with the same idempotency key const result = await operation(); // Cache the result const now = new Date(); this.records.set(idempotencyKey, { key: idempotencyKey, response: result, createdAt: now, expiresAt: new Date(now.getTime() + this.TTL_MS), }); return result; } } // Example API endpoint using idempotency class PaymentController { constructor(private idempotencyService: IdempotencyService) {} async chargeCustomer(request: { idempotencyKey: string; customerId: string; amount: number; }): Promise { // Same idempotency key = same result, even if called multiple times return this.idempotencyService.executeOnce( request.idempotencyKey, async () => { // Actual payment processing happens here // This will only execute once per unique idempotency key const charge = await this.paymentGateway.charge( request.customerId, request.amount ); 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The request goes out, the response comes back. Networking feels invisible, magical, perfectly dependable. And so developers write code that assumes this reliability extends to production environments with hundreds of services communicating across data centers spanning continents."},"type":"text"},{"data":{"icon":"AlertCircle","title":"The Cost of This Fallacy","content":"This single assumption has caused more distributed system outages than perhaps any other design flaw. Companies have lost billions in revenue, engineers have spent countless sleepless nights debugging phantom failures, and users have experienced data loss—all because someone assumed the network would always be there when needed.","variant":"warning"},"type":"callout"},{"data":{"content":"In this page, we will dissect exactly why networks are unreliable, understand the myriad ways they fail, and develop architectural patterns that embrace—rather than ignore—this fundamental reality of distributed computing."},"type":"text"}]},{"id":"why-networks-unreliable","title":"Why Networks Are Inherently Unreliable","content":[{"data":{"content":"$72"},"type":"text"},{"data":{"rows":[["Packet Loss","Packets dropped by congested routers or faulty hardware","Milliseconds (per packet)","Continuous (0.1-1% typical)"],["Network Partition","Two groups of nodes cannot communicate with each other","Seconds to hours","Rare but catastrophic"],["Timeout","Request sent but no response received within expected time","Seconds","Regular occurrence"],["DNS Failure","Domain name resolution fails or returns incorrect IP","Minutes to hours","Occasional"],["BGP Misconfiguration","Internet routing tables corrupted, traffic misrouted","Minutes to hours","Rare but widespread"],["Cable Cut","Physical infrastructure damage (construction, sharks, etc.)","Hours to days","Uncommon but severe"],["Hardware Failure","NIC, switch, or router fails completely","Minutes to hours","Regular in large deployments"],["Congestion Collapse","Network saturated, most packets dropped","Minutes","Under peak load"]],"caption":"Common Network Failure Modes","headers":["Failure Type","Description","Typical Duration","Frequency"]},"type":"table"},{"data":{"icon":"Info","title":"The Fallacy of the Local Network","content":"Even networks that seem 'local'—machines in the same rack, same data center, same availability zone—can and do fail. 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No timeout gives you certainty; it only tells you that you waited too long."],"title":{"icon":"AlertCircle","text":"The Three States of a Remote Request","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**The unknown state is the central challenge of distributed systems.**\n\nConsider this scenario: You send a request to a payment service to charge a customer $100. Your request times out after 30 seconds. What happened?\n\n1. The request was never received (you should retry)\n2. The request was received but processing failed (you should retry)\n3. The request was received, the customer was charged, but the response was lost (retrying will double-charge)\n4. The request is still processing and will eventually succeed (retrying will double-charge)\n\nWithout additional mechanisms like idempotency keys or exactly-once semantics, you literally cannot know which scenario occurred. 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These case studies illustrate how network unreliability has caused major outages at companies that should—and did—know better."},"type":"text"},{"data":{"items":["**What happened:** A network partition separated GitHub's Redis masters from their slaves for about 10 seconds.","**The assumption:** The Redis failover system assumed network partitions would be short, and slaves would receive all writes before potential promotion.","**The reality:** During the partition, the master continued accepting writes. When network healed, data from both former-master and promoted-slave had to be reconciled—but couldn't be.","**The impact:** Significant data loss and extended downtime as engineers manually reconciled database state.","**The lesson:** Network partitions don't just prevent communication; they create parallel universes that diverge and must somehow be merged."],"title":{"icon":"Target","text":"Case Study: The GitHub Outage (2012)","type":"icon","color":"primary","variant":"outline-circle"},"variant":"glass"},"type":"list"},{"data":{"items":["**What happened:** A routine debugging operation required removing a small number of S3 storage servers from service.","**The assumption:** The removal tool's command syntax would be correctly specified, and the system would handle partial outages gracefully.","**The reality:** A typo caused far more servers than intended to be removed, effectively creating a massive network partition for S3 subsystems.","**The impact:** Major portions of the internet went offline for hours. Services from Quora to Slack to parts of Docker Hub became unavailable.","**The lesson:** Cascading failures from network-related issues don't stop at your service boundary. Your dependencies have dependencies."],"title":{"icon":"Target","text":"Case Study: AWS US-East-1 (2017)","type":"icon","color":"primary","variant":"outline-circle"},"variant":"glass"},"type":"list"},{"data":{"items":["**What happens:** A database cluster consists of multiple nodes. A network partition divides them into two groups, each believing the other has failed.","**The assumption:** Each partition detects the other as failed and promotes itself to primary to maintain availability.","**The reality:** Both partitions accept writes. When network heals, you have two conflicting views of reality.","**The impact:** This exact scenario has caused data loss at MongoDB deployments, MySQL clusters, and essentially every distributed database at some point.","**The lesson:** This is why distributed databases require quorum-based systems and why the CAP theorem forces a choice between consistency and availability during partitions."],"title":{"icon":"Target","text":"Case Study: The Split-Brain Problem","type":"icon","color":"primary","variant":"outline-circle"},"variant":"glass"},"type":"list"},{"data":{"icon":"Lightbulb","title":"Study Post-Mortems","content":"Major tech companies publish detailed post-mortems of their outages. Reading these is one of the most effective ways to internalize the reality of network failures. Every post-mortem teaches lessons that no textbook can match.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"patterns-unreliability","title":"Patterns for Handling Network Unreliability","content":[{"data":{"content":"Since we cannot make networks reliable, we must architect systems that function correctly despite unreliable networks. This requires specific design patterns applied at multiple layers."},"type":"text"},{"data":{"items":["**Timeouts** — Every network call must have a timeout. An operation without a timeout can hang forever, consuming resources and propagating failures. Choose timeout values based on observed latency distributions, not guesses.","**Retries with Exponential Backoff** — Transient failures often resolve quickly. Retry with increasing delays (1s, 2s, 4s, 8s...) to avoid overwhelming recovering services. Add jitter to prevent thundering herds.","**Idempotency** — Design operations to produce the same result whether executed once or multiple times. This allows safe retries when you're in the 'unknown' state.","**Circuit Breakers** — Track failure rates for dependencies. When failures exceed a threshold, 'open' the circuit and fail fast instead of waiting for timeouts. Periodically test if the dependency has recovered.","**Fallbacks and Degradation** — When a dependency fails, provide degraded functionality instead of complete failure. Show cached data. Disable non-essential features. Always prefer partial functionality to full outages."],"title":{"icon":"CheckCircle","text":"Essential Resilience Patterns","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"fileName":"resilient-http-client.ts","languages":[{"code":"$73","label":"TypeScript","value":"typescript"}]},"type":"code"},{"data":{"icon":"Info","title":"Defense in Depth","content":"These patterns work best in combination. Timeouts prevent indefinite waits, retries handle transient failures, idempotency makes retries safe, circuit breakers prevent cascade failures, and fallbacks maintain user experience. Missing any layer leaves gaps in your resilience.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"idempotency-deep-dive","title":"Idempotency: The Key to Safe Retries","content":[{"data":{"content":"When a request times out, you face a dilemma: Do you retry (risking duplicate execution) or give up (risking a failed operation the user believes succeeded)? **Idempotency** resolves this dilemma by making duplicate execution safe.\n\nAn operation is **idempotent** if executing it multiple times produces the same result as executing it once.\n\n**Naturally idempotent operations:**\n- Set user email to 'alice@example.com' (can repeat safely)\n- Delete record with ID 42 (already deleted = still deleted)\n- GET /api/users/123 (pure read, no state change)\n\n**Non-idempotent operations that must be made idempotent:**\n- Increment counter by 1 (each call adds 1)\n- Charge customer $100 (each call charges again)\n- Send notification (each call sends another email)"},"type":"text"},{"data":{"items":["**Idempotency Keys** — Client generates a unique key (UUID) for each logical operation. Server tracks which keys have been processed and returns cached results for duplicates. Stripe, PayPal, and most payment APIs use this pattern.","**Conditional Updates** — Use ETags or version numbers. 'Update only if version is X' becomes idempotent—once X is no longer current, retries have no effect.","**Natural Keys** — Design operations around inherent unique identifiers. 'Create order with ID ord_12345' is idempotent; 'Create order' is not.","**At-Most-Once Delivery** — For messaging, track message IDs and reject duplicates. Combined with retries, this gives exactly-once semantics despite unreliable delivery."],"title":{"icon":"CheckCircle","text":"Patterns for Implementing Idempotency","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"fileName":"idempotency-implementation.ts","languages":[{"code":"$74","label":"TypeScript","value":"typescript"}]},"type":"code"},{"data":{"icon":"AlertCircle","title":"Idempotency Has Subtleties","content":"Idempotency key storage must be durable and consistent. If your idempotency keys are stored in-memory or on a separate database that can diverge from your main data, you can still get duplicates. In distributed systems, idempotency storage becomes a consensus problem itself.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"testing-network-failures","title":"Testing for Network Failures","content":[{"data":{"content":"You cannot build resilient systems without testing failure scenarios. Unfortunately, network failures are inherently non-deterministic and hard to reproduce. Sophisticated teams use **chaos engineering** and **fault injection** to systematically test their assumptions.\n\n**Approaches to testing network unreliability:**"},"type":"text"},{"data":{"rows":[["Fault Injection","Programmatically inject failures (delays, errors, drops)","Toxiproxy, Envoy fault injection","Development, integration tests"],["Chaos Engineering","Randomly inject failures in production","Chaos Monkey, Gremlin, Litmus","Production (with monitoring)"],["Network Simulation","Simulate specific network conditions","tc (Linux), Clumsy (Windows)","Development, CI/CD"],["Partition Testing","Physically or logically isolate network segments","iptables, network namespaces","Pre-production validation"],["Dependency Mocking","Mock external services with controllable failures","WireMock, LocalStack","Unit and integration tests"]],"caption":"Network Failure Testing Techniques","headers":["Technique","Description","Tools","When to Use"]},"type":"table"},{"data":{"items":["**Complete failure** — Dependency returns no response (simulate with iptables DROP)","**Slow responses** — Responses arrive but after unusual delays (simulate with Toxiproxy)","**Partial failure** — Some requests succeed, others fail randomly","**Byzantine failure** — Dependency returns corrupted or incorrect data","**Cascade failure** — One failure triggers failures in other services","**Recovery behavior** — System returns to normal when failure resolves","**Split-brain scenarios** — Different nodes see different views of the network"],"title":{"icon":"Target","text":"Failure Scenarios to Test","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"icon":"Lightbulb","title":"Netflix's Simian Army","content":"Netflix pioneered chaos engineering with their 'Simian Army'—a collection of tools that continuously attack their own production systems. Chaos Monkey kills random instances. Chaos Kong simulates entire AWS region failures. Latency Monkey adds delays. By constantly testing failure modes, Netflix builds systems that handle real outages gracefully.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"architectural-implications","title":"Architectural Implications","content":[{"data":{"content":"Accepting network unreliability as a fundamental constraint has profound implications for how we design systems. These aren't just code patterns—they're architectural philosophies."},"type":"text"},{"data":{"left":[{"data":{"items":["Synchronous chains of service calls","Tight coupling between services","Single points of failure","Operations without timeouts","Unbounded retries","Missing circuit breakers","In-memory session state"],"title":{"icon":"XCircle","text":"Anti-Patterns to Avoid","type":"icon","color":"destructive","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["Async message-driven communication","Loose coupling via events","Redundancy at every layer","Timeouts on all I/O","Bounded retries with backoff","Circuit breakers for dependencies","Externalized session management"],"title":{"icon":"CheckCircle","text":"Resilience Patterns","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"},{"data":{"content":"**Design for failure, not for success:**\n\nThe mindset shift required is profound. Instead of asking, 'What happens when this succeeds?' defensive architects ask, 'What happens when this fails?' and 'How do we know it failed?' and 'What do we do about partial failure?'\n\nThis leads to patterns like:\n\n- **Bulkheads** — Isolate failures so they don't cascade. If the recommendation service fails, search should still work.\n- **Timeouts and deadlines** — Every operation has a budget. If we can't complete in time, fail gracefully.\n- **Health checks and observability** — You can't fix what you can't see. Comprehensive monitoring is essential.\n- **Graceful degradation** — When dependencies fail, provide reduced functionality rather than no functionality.\n- **Self-healing** — Automate recovery wherever possible. Restart crashed processes, route around failures, rebalance load."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Embrace the Chaos","content":"The best distributed systems engineers don't try to prevent all failures—they accept failures as inevitable and design systems that handle them gracefully. A system that works perfectly 99% of the time and fails catastrophically 1% of the time is worse than one that works well 100% of the time including during failures.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"summary","title":"Summary: The Network Is Never Reliable","content":[{"data":{"content":"We've explored the first and most fundamental fallacy of distributed computing: the assumption that the network is reliable. Let's consolidate the key insights:"},"type":"text"},{"data":{"type":"ordered","items":["**Networks fail in myriad ways** — Physical failures, configuration errors, software bugs, and congestion all cause network unreliability at every scale from local to global.","**The unknown state is the central challenge** — When a request times out, you cannot know if it succeeded, failed, or is still in progress. This uncertainty drives much of distributed systems complexity.","**Every network call needs protection** — Timeouts, retries with backoff, circuit breakers, and fallbacks are not optional—they're mandatory for any production-grade distributed system.","**Idempotency enables safe retries** — Design operations to be repeatable without side effects, allowing clients to retry safely when they can't determine if the original request succeeded.","**Test failure scenarios explicitly** — Chaos engineering and fault injection are essential practices for building confidence that your system handles failures gracefully.","**Design for failure, not success** — Assume everything will fail and build systems that maintain acceptable behavior during failures."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**What's next:**\n\nThe first fallacy taught us that networks fail. But even when networks work, they don't work *instantly*. The next fallacy—**Latency Is Zero**—explores why ignoring the time it takes for data to travel leads to equally devastating system failures."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now understand why assuming network reliability is dangerous and how to architect systems that embrace unreliability. The patterns you've learned—timeouts, retries, idempotency, circuit breakers, and chaos engineering—are the foundation of resilient distributed systems.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":8}],"isLocked":false},{"id":"seed_cc3a920ae631620e75ff5fa7bcd7f6c9","title":"Latency Is Zero (It's Not)","order":1,"content":null,"isLocked":true},{"id":"seed_92973315b7bf3cc194890f0b0a5869c1","title":"Bandwidth Is Infinite (It's Not)","order":2,"content":null,"isLocked":true},{"id":"seed_ed46e1168ce55aec6e754a158fc5a4bf","title":"The Network Is Secure (It's Not)","order":3,"content":null,"isLocked":true},{"id":"seed_fb063a68cb4fea6b17ea1e526c8f9aab","title":"All Eight Fallacies and Their Implications","order":4,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792288270471,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"fallacies-of-distributed-computing"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"fallacies-of-distributed-computing\"},\"type\":\"query\"}]"},{"dehydratedAt":1792288270652,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792288270472,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_1e4594477c4f735a717c11f7960f0f60"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_1e4594477c4f735a717c11f7960f0f60\"},\"type\":\"query\"}]"},{"dehydratedAt":1792288270652,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792288270472,"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_cc3a920ae631620e75ff5fa7bcd7f6c9"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_cc3a920ae631620e75ff5fa7bcd7f6c9\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792288270652,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792288270472,"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_cc3a920ae631620e75ff5fa7bcd7f6c9"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_cc3a920ae631620e75ff5fa7bcd7f6c9\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792288270652,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792288270472,"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_cc3a920ae631620e75ff5fa7bcd7f6c9"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_cc3a920ae631620e75ff5fa7bcd7f6c9\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792288270652,"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_cc3a920ae631620e75ff5fa7bcd7f6c9","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_cc3a920ae631620e75ff5fa7bcd7f6c9\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@75"}]},"children":"$L76"}]