An FP Practitioner’s Perspective on the Cloudflare Feature-File Outage

cloudflare

Disclaimer: I am not affiliated with Cloudflare. This analysis is based entirely on publicly available incident reports and technical discussions. The architectural reconstructions and code examples represent my educated interpretation of what likely occurred, informed by functional programming principles and my own experience with similar failure modes. Where I speculate beyond published details, I have tried to make those inferences explicit.

Introduction: When the Internet Went Down — and Rust Took the Blame

When Cloudflare experienced a global outage in November 2025, the technical details spread quickly. So did the reactions. Screenshots of a Rust .unwrap() call circulated widely, accompanied by familiar conclusions: Rust is overhyped, “safe” languages promise too much, one careless line of code brought down a large portion of the internet.

It was an easy story to believe. One dramatic artifact, neatly cropped, offered a simple villain.

But that framing is deeply incomplete.

Not because the code was flawless — it wasn’t. Not because Cloudflare bears no responsibility — they do. The framing fails because it mistakes the symptom for the disease. The outage was not caused by Rust being unsafe. It was caused by a fragile assumption being encoded as an impossibility, then replicated across a global system without a meaningful checkpoint.

Rust did what it is designed to do: force an explicit decision. Either handle the error or declare it impossible. The outage happened because that declaration turned out to be wrong.

I’m writing this not to defend Rust, nor to assign blame after the fact, but because this incident reads like a textbook example of a failure mode that functional programming has been warning about for decades: allowing unverified values to masquerade as verified ones, and allowing boundary failures to escalate into process death.

What Actually Happened: A Precise Reconstruction

Before discussing abstractions, it is important to be concrete about the facts.

Cloudflare operates a global edge network with hundreds of Points of Presence. Among the services running at the edge is bot detection and mitigation. That subsystem relies on a periodically generated feature file: a structured artifact containing metadata used by traffic-handling components.

This feature file is generated upstream from database queries and other internal data sources. Once produced, it is distributed globally so that edge processes can operate independently.

Prior to the outage, a database-related change occurred. According to Cloudflare’s own account, this change altered query behavior in a way that introduced duplicated entries into the dataset used for feature generation. The generation job did not fail. It produced a syntactically valid file — just a much larger one than expected.

This detail matters. No alarms were triggered. No invariants were explicitly violated at generation time. The system implicitly assumed that “valid output” implied “acceptable output.”

The oversized file was then distributed to the entire edge.

When edge processes attempted to load it, an internal limit was exceeded. The relevant function returned an error — an error that was already modeled in the code. At that point, the code invoked .unwrap(), converting the error into a panic. The panic terminated the process. Because the same artifact reached many PoPs simultaneously, the failure manifested globally.

Nothing here was exotic. Each component behaved according to its local contract. The outage emerged from how those contracts composed.

In a robust pipeline, the first alert would not be “edge processes are crashing.” It would be “candidate artifact violated invariants” — file size doubling, feature count jumping unexpectedly, or validation rejection rates spiking during canary deployment. The system needed checks that could reject a candidate before it reached production, not fail during production activation. This missing validation layer is precisely what railway-oriented design addresses: treating artifact ingestion as a multi-phase process where rejection is survivable and informative, not catastrophic.

What unwrap() Means, Semantically

In Rust, operations that may fail return Result<T, E> or Option<T>. This is one of Rust’s defining strengths: failure is explicit.

unwrap() resolves such a value by asserting that it represents success and panicking otherwise:

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match result {
    Ok(v) => v,
    Err(_) => panic!("called unwrap() on an error"),
}

From a functional perspective, unwrap() is a partial function. It collapses a sum type into a single path by asserting that the other path is impossible.

This is not inherently wrong. It is often appropriate when encoding local invariants. For example:

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let ports = vec![80, 443];
let https = ports.iter().find(|&&p| p == 443).unwrap();

If this panics, the program itself is incorrect.

But at system boundaries, unwrap() asserts something very different. It asserts not a property of the code, but a property of reality: that files exist, schemas match, sizes remain bounded, upstream systems behave as expected, and historical assumptions still hold.

Rust developers generally treat unwrap() as a deliberate assertion: this must succeed, and if it doesn’t, panicking is acceptable. The name is short, but the semantic weight is real. Every unwrap() is a loud declaration: “I am asserting this cannot fail.”

In the Cloudflare incident, unwrap() behaved exactly as specified. The failure lies not in its implementation, but in where it was used.

The Failure as a State Space Collapse

Seen through an FP lens, the core failure is architectural rather than syntactic.

In effect, the operational model collapsed into two meaningful states: “accept and run” or “panic and restart.” There wasn’t a first-class, supported mode for “reject candidate and keep serving traffic with last-known-good.”

Consider what the system needed to represent:

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// implicit model: success or crash (crash lives outside the type)
enum ProcessState {
    Running(ActiveConfig),
}

// what the system needed
enum ProcessState {
    FullyOperational(VerifiedConfig),
    DegradedBotDetection { 
        lkg_config: VerifiedConfig, 
        rejection_reason: LoadError,
        rejected_at: Timestamp,
    },
    AwaitingFirstConfig {
        startup_time: Timestamp,
    },
    // only terminate if these states are impossible
}

The distinction matters. When failure has no representation, it has nowhere to go except through the process boundary.

I encountered exactly this pattern while implementing a constrained Maximum Likelihood Estimation algorithm in F#. My code worked perfectly on x86-64 systems but produced wildly different parameter estimates on ARM64 due to platform-dependent RNG behavior in .NET. The issue was a state space mismatch: I had modeled “converged” and “didn’t converge,” but reality added a third state — “converged to different parameters depending on hardware.” The fix wasn’t better error messages; it was expanding the state space to include “reproducibility validated across architectures” as an explicit, type-enforced requirement. The failure mode is identical to Cloudflare’s: reality had more states than my model, and the missing state surfaced only after deployment pressure.

This is what railway-oriented programming actually means in practice. Not “handle errors gracefully” as an aspiration, but the system must have representations for every outcome that can actually occur.

Railway-Oriented Programming as System Architecture

Railway-oriented programming is often introduced with diagrams showing success and failure on parallel tracks. That metaphor is useful, but it undersells the idea’s architectural power.

At scale, railway-oriented design is about containing uncertainty. It ensures that failures remain data that can be reasoned about, logged, aggregated, and acted upon, rather than becoming uncontrolled control-flow events.

Applied to the Cloudflare case, the design principle is straightforward: ingestion of new configuration must remain on the “failure-admitting” track until validation succeeds.

Here’s what the architecture should have looked like:

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// phase 1: parsing - convert bytes to structure
fn parse_feature_file(blob: &[u8]) -> Result<IntermediateConfig, ParseError> {
    // can fail: malformed syntax, schema mismatch
}

// phase 2: validation - verify constraints
fn validate_config(config: IntermediateConfig) -> Result<VerifiedConfig, ValidationError> {
    // can fail: size limits, semantic constraints, resource requirements
    if config.estimated_memory_mb > MAX_EDGE_MEMORY {
        return Err(ValidationError::ExceedsMemoryLimit {
            required: config.estimated_memory_mb,
            available: MAX_EDGE_MEMORY,
        });
    }
    
    if config.feature_count > MAX_FEATURES {
        return Err(ValidationError::TooManyFeatures {
            actual: config.feature_count,
            limit: MAX_FEATURES,
        });
    }
    
    Ok(VerifiedConfig::from_intermediate(config))
}

// phase 3: activation - update runtime state
fn try_activate_new_config(
    blob: &[u8],
    current_state: &ConfigState,
) -> Result<ConfigState, ActivationError> {
    let intermediate = parse_feature_file(blob)?;
    let verified = validate_config(intermediate)?;
    
    Ok(ConfigState::Active {
        config: verified,
        activated_at: Timestamp::now(),
    })
}

So far, this is ordinary error handling. The crucial step is what happens when activation fails. Railway-oriented thinking insists that validation failure be absorbed into system state, not escalated to process termination:

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pub enum ConfigState {
    // normal operation
    Active {
        config: VerifiedConfig,
        activated_at: Timestamp,
    },
    
    // degraded but operational - using last known good
    UsingLastKnownGood {
        lkg: VerifiedConfig,
        lkg_age: Duration,
        last_rejection: ActivationError,
        rejected_at: Timestamp,
        rejection_count: u32,
    },
    
    // startup state - no config loaded yet
    AwaitingFirstConfig {
        startup_time: Timestamp,
        failed_attempts: Vec<(Timestamp, ActivationError)>,
    },
}

impl ConfigState {
    pub fn try_update(&self, new_blob: &[u8]) -> Self {
        match try_activate_new_config(new_blob, self) {
            Ok(new_state) => {
                log::info!("Successfully activated new config");
                new_state
            }
            Err(e) => {
                log::warn!("Failed to activate new config: {:?}", e);
                
                match self {
                    ConfigState::Active { config, activated_at } => {
                        // we have a working config - keep using it
                        ConfigState::UsingLastKnownGood {
                            lkg: config.clone(),
                            lkg_age: Timestamp::now().duration_since(*activated_at),
                            last_rejection: e,
                            rejected_at: Timestamp::now(),
                            rejection_count: 1,
                        }
                    }
                    ConfigState::UsingLastKnownGood { lkg, lkg_age, rejected_at, rejection_count } => {
                        // already degraded - increment counter, age continues growing
                        let age_increment = Timestamp::now().duration_since(*rejected_at);
                        ConfigState::UsingLastKnownGood {
                            lkg: lkg.clone(),
                            lkg_age: *lkg_age + age_increment,
                            last_rejection: e,
                            rejected_at: Timestamp::now(),
                            rejection_count: rejection_count + 1,
                        }
                    }
                    ConfigState::AwaitingFirstConfig { startup_time, failed_attempts } => {
                        // still waiting for first valid config
                        let mut attempts = failed_attempts.clone();
                        attempts.push((Timestamp::now(), e));
                        ConfigState::AwaitingFirstConfig {
                            startup_time: *startup_time,
                            failed_attempts: attempts,
                        }
                    }
                }
            }
        }
    }
    
    // no unwrap() needed - state is always valid
    pub fn get_active_config(&self) -> Option<&VerifiedConfig> {
        match self {
            ConfigState::Active { config, .. } => Some(config),
            ConfigState::UsingLastKnownGood { lkg, .. } => Some(lkg),
            ConfigState::AwaitingFirstConfig { .. } => None,
        }
    }
}

Every possible outcome of ingestion maps to a valid system state. The system cannot fall off the rails.

This is not merely defensive coding. It is an explicit declaration of operational semantics: rejection is expected, survivable, and observable.

Notice what this architecture guarantees:

  1. Process stability: Activation failure never terminates the process
  2. Operational continuity: Traffic keeps flowing using last-known-good config
  3. Observability: Every state transition is logged and queryable
  4. Alert graduation: First failure is a warning; sustained failures become critical
  5. Human-compatible timescales: Operators have time to investigate before impact

The Cloudflare outage happened because none of these guarantees existed. A single bad file could reach the entire edge simultaneously, and every process had only two options: succeed or die.

Boundaries, Trust, and the Decay of Assumptions

One of the most instructive aspects of this outage is that the problematic input was internal. It was generated by Cloudflare’s own systems.

The feature file generation had run thousands of times without incident. The size had been stable for months. The schema was internal — controlled by the same team consuming it. Every signal suggested safety.

This is precisely when systems become vulnerable. Not when dealing with obviously untrusted input, but when dealing with formerly trusted input whose trust has expired unnoticed.

Consider what must have been true for this outage to happen:

  1. The database change that caused duplication was deployed
  2. The feature generation job ran successfully
  3. No size limits were enforced at generation time
  4. No alerts fired on file size anomaly
  5. The oversized file was distributed globally
  6. Every edge process attempted to load it
  7. Every edge process terminated on load failure

Each of these steps involved someone (or something) making a decision based on “this has always worked before.”

In survey statistics, we’d call this specification error — when your model assumptions no longer match the data generating process. The field has evolved sophisticated methods for detecting and correcting this. Balanced Repeated Replication, which I’ve written about before, exists precisely because assuming stable variance structure across time is how surveys become unrepresentative.

Software lacks formal methods for detecting assumption decay. We have types. When verified and unverified values share a type, assumption decay becomes invisible until it fails catastrophically.

Here’s the type system failure:

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// what the code implied
fn load_feature_file(path: &Path) -> FeatureConfig {
    let bytes = std::fs::read(path).unwrap(); // "file must exist"
    let parsed = parse(&bytes).unwrap();       // "must be valid syntax"
    let validated = validate(parsed).unwrap(); // "must meet constraints"
    validated
}

// what the type system should have enforced
fn load_feature_file(path: &Path) -> Result<VerifiedConfig, LoadError> {
    let bytes = std::fs::read(path)
        .map_err(|e| LoadError::ReadFailed(e))?;
    
    let parsed = parse(&bytes)
        .map_err(|e| LoadError::ParseFailed(e))?;
    
    let validated = validate(parsed)
        .map_err(|e| LoadError::ValidationFailed(e))?;
    
    Ok(validated)
}

// and what the caller should have done
match load_feature_file(&config_path) {
    Ok(config) => state.activate(config),
    Err(e) => {
        log::error!("Failed to load new config: {:?}", e);
        state.mark_degraded(e);
        // keep serving traffic with existing config
    }
}

The first version treats three distinct failure modes as impossible. The second version makes them explicit and composable. The third version absorbs them into operational state.

Functional programming treats every boundary as suspect, not because engineers are careless, but because time erodes certainty. Type-driven design gives this realism teeth by making it impossible to accidentally treat unvalidated data as trusted.

When I discovered my ARM64 reproducibility issue, the fix wasn’t just seeding the RNG deterministically. The fix was creating a distinct type for “validated-across-architectures” results:

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type ArchitectureValidation =
  | NotYetValidated of EstimationResult
  | ValidatedConsistent of EstimationResult
  | FailedValidation of x86: EstimationResult * arm: EstimationResult * divergence: float

// the type system prevents using unvalidated results in production
let deploy_model (validated: ValidatedConsistent) : DeploymentResult =
  // can only be called with ValidatedConsistent, not NotYetValidated
  ...

The compiler became a checkpoint. I couldn’t accidentally deploy an unvalidated model because unvalidated and validated results had different types.

This is what “make illegal states unrepresentable” means in practice. Not that you write perfect code, but that the type system prevents assumptions from silently expiring.

Panic, Degradation, and Operational Intent

Here’s the crucial question the Cloudflare incident forces us to confront:

Should failure to load bot mitigation features terminate traffic routing?

Cloudflare’s architecture answered “yes” by making that function call .unwrap(). Not because anyone explicitly decided routing should die, but because no one explicitly decided it shouldn’t.

This is the cost of implicit operational priorities. The system had a clear hierarchy — routing is existential, bot detection is important but secondary — but that hierarchy existed only in people’s heads, not in the code.

Panic says: “continuing would violate a property so fundamental that the program itself is incorrect.” Loading an oversized feature file doesn’t meet that bar. The correct program behavior is: log the rejection, keep serving traffic, use the last-known-good configuration, alert humans.

Railway-oriented design makes this explicit. Not “graceful degradation” as an aspiration, but degraded states as first-class values the system is designed to inhabit:

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pub enum ServiceHealth {
    FullyOperational {
        routing: RoutingEngine,
        bot_detection: BotDetector,
        analytics: AnalyticsEngine,
    },
    
    DegradedBotDetection {
        routing: RoutingEngine,         // CRITICAL - must work
        bot_detection: Option<BotDetector>,  // degraded
        analytics: AnalyticsEngine,     // still operational
        degradation_reason: String,
    },
    
    DegradedAnalytics {
        routing: RoutingEngine,         // CRITICAL - must work
        bot_detection: BotDetector,     // still operational
        analytics: Option<AnalyticsEngine>,  // degraded
        degradation_reason: String,
    },
    
    EmergencyMode {
        routing: RoutingEngine,         // CRITICAL - only essential service
        // everything else disabled
    },
}

impl ServiceHealth {
    pub fn handle_request(&self, req: &Request) -> Response {
        // routing works in ALL states
        let routed = match self {
            ServiceHealth::FullyOperational { routing, .. } => routing.route(req),
            ServiceHealth::DegradedBotDetection { routing, .. } => routing.route(req),
            ServiceHealth::DegradedAnalytics { routing, .. } => routing.route(req),
            ServiceHealth::EmergencyMode { routing } => routing.route(req),
        };
        
        // bot detection is best-effort
        let bot_checked = match self {
            ServiceHealth::FullyOperational { bot_detection, .. } => {
                bot_detection.check(req)
            }
            ServiceHealth::DegradedBotDetection { bot_detection: Some(bd), .. } => {
                bd.check(req) // using stale config
            }
            _ => BotCheckResult::Unavailable, // degraded - skip check
        };
        
        routed.with_bot_check(bot_checked)
    }
}

In this design:

  • Routing failure would justify panic (violates core invariant)
  • Bot detection failure transitions to degraded state (expected, survivable)
  • The type system enforces that all states handle routing
  • Degradation is observable, measurable, and alertable

The hierarchy of operational intent is encoded in the type structure, not scattered across .unwrap() calls.

“Fail gracefully” becomes concrete: the system fails into a valid state rather than failing out of the state space entirely.

Why This Pattern Repeats Everywhere

This failure mode appears everywhere because it emerges from how systems evolve, not from how they’re initially designed.

When you first build a system that consumes generated artifacts, validation feels obvious. The generation code is simple. The schema is small. Failure modes are concrete and enumerable. You write careful checks. You test thoroughly.

Then reality intervenes.

The schema grows. New fields appear. Generation logic becomes distributed across services. Different teams own different parts. The data source changes from SQL to a distributed cache. Volume increases 10x. The generation job gets rewritten in a different language.

“Validation” gradually becomes “hope the upstream team didn’t break anything.”

What was once an explicit trust boundary — “we verify before loading” — becomes an implicit assumption — “it must be valid because it was generated by us.”

The technical name for this is “privilege escalation.” Not in the security sense, but in the semantic sense: unverified data is granted the privileges of verified data without ever crossing an actual verification boundary.

Consider the parallels across domains:

ML Model Deployment:

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# generation side - training pipeline
def train_model(data):
    model = train_complex_network(data)
    save(model, "model.pkl")  # valid pickle file
    
# consumption side - production service  
def load_model():
    model = pickle.load("model.pkl")  # assume valid
    return model  # assume performant, assume safe inputs, assume stable over time

The model file is syntactically valid but semantically unchecked. You haven’t verified:

  • Performance on production data distribution
  • Behavior on edge cases
  • Resource requirements under load
  • Compatibility with current input schema
  • Safety against adversarial inputs

Feature Flag Systems:

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// generation side - admin UI
async function publishFlags(flags) {
    await db.insert('feature_flags', flags);
    await redis.publish('flags_updated', flags);
}

// consumption side - application servers
redis.subscribe('flags_updated', (flags) => {
    global.featureFlags = flags;  // assume valid, assume safe, assume compatible
});

No validation that flags match known flag names, have valid types, don’t create contradictory states, or won’t crash the application.

Configuration Management:

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# generation side - CI/CD pipeline
deploy:
  replicas: 100
  memory: 2Gi
  cpu: 500m
  
# consumption side - orchestrator
kubectl apply -f deployment.yaml  # assume cluster has capacity, assume values are sane

The YAML is well-formed but unchecked against cluster capacity, cost constraints, or operational sanity.

Every ML model deployment faces this. Every configuration management system faces this. Every feature flag service faces this. The moment you have one component generating artifacts another component consumes, you have a trust boundary.

The only question is whether you encode it or assume it.

Here’s what encoding the boundary looks like:

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// make the trust boundary explicit in types
pub struct UnverifiedBlob(Vec<u8>);
pub struct VerifiedConfig { /* fields */ }

// can only construct VerifiedConfig through validation
impl VerifiedConfig {
    pub fn validate(blob: UnverifiedBlob) -> Result<Self, ValidationError> {
        // explicit checks that must pass
        let parsed = parse(&blob.0)?;
        check_size_limits(&parsed)?;
        check_schema_version(&parsed)?;
        check_resource_requirements(&parsed)?;
        check_semantic_constraints(&parsed)?;
        
        Ok(VerifiedConfig { /* ... */ })
    }
    
    // no other way to construct this type
}

// API only accepts verified config
pub fn activate_config(config: VerifiedConfig) -> ActivationResult {
    // guaranteed to be validated - can use safely
}

The type system now prevents privilege escalation. You cannot pass an UnverifiedBlob where a VerifiedConfig is expected. The trust boundary is a type boundary.

Functional programming’s insistence on explicit trust boundaries isn’t ideological purity. It’s organizational realism. When teams scale, assumptions decay. When systems evolve, validation atrophies. Types are how you prevent yesterday’s careful assumptions from becoming tomorrow’s outage.

Why Rust Was Not the Villain

This brings us back to Rust.

Rust did not cause the outage. Rust made it unavoidable.

In many stacks, the same violation might have manifested as silent truncation, partial loading, undefined behavior, or inconsistent internal state. Those failures are often more dangerous precisely because they do not force immediate attention.

Imagine this in Python:

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def load_config(path):
    try:
        with open(path) as f:
            data = json.load(f)
        return data
    except Exception as e:
        logger.warning(f"Failed to load config: {e}")
        return {}  # silent fallback to empty dict

The process continues. The config is “loaded” (empty). Bot detection silently stops working. No alerts fire because no crash occurred. The issue might not be discovered for hours or days.

Or imagine this in C++:

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Config* load_config(const char* path) {
    FILE* f = fopen(path, "r");
    if (!f) return nullptr;  // caller must check
    
    Config* cfg = parse(f);
    fclose(f);
    return cfg;  // might be null, might be partial, might be corrupt
}

// caller
Config* cfg = load_config(config_path);
cfg->apply();  // undefined behavior if cfg is null

Rust refused to proceed under a violated assumption. That is a feature.

The problem was not that Rust panicked, but that panic was placed too close to the system boundary. A boundary failure was treated as a core invariant violation.

Compare these two framings:

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// treating boundary failure as invariant violation
fn main() {
    let config = load_config("features.dat").unwrap();  // PANIC on any load failure
    run_service(config);
}

// treating boundary failure as expected case
fn main() {
    let state = match load_config("features.dat") {
        Ok(cfg) => ServiceState::Active(cfg),
        Err(e) => {
            log::warn!("Failed to load config: {:?}", e);
            ServiceState::AwaitingConfig(e)
        }
    };
    
    run_service(state);  // service handles all states
}

The first treats “config failed to load” as impossible. The second treats it as one of several expected operational states.

The correct response is not to avoid Rust’s strictness, but to align system state models with Rust’s error model so that panics are reserved for truly impossible states — those that indicate programmer error, not environmental variability.

When I debug production systems, I want them to crash loudly on violated assumptions. What I don’t want is for reasonable environmental variation to be classified as a violated assumption.

That’s not a language problem. That’s an architecture problem.

The Question Every System Must Answer

Before this outage, if someone had asked Cloudflare engineers “what happens when the feature file is oversized,” the answer would have been clear: it fails to load.

The correct follow-up — the one that exposes the architectural gap — is: “And then what?”

If the answer is “and then the process terminates,” you’ve found your next outage.

Railway-oriented programming forces you to answer “and then what?” at design time, not incident time. It makes degraded states explicit. It makes fallback strategies mandatory. It makes “fail safely” into a type requirement, not a code review comment.

This is not about Rust. This is not even about functional programming languages. This is about whether your system’s state space matches reality’s state space — and whether failures at the boundary cascade inward or get absorbed into state.

The next time someone says FP is academic or impractical, ask them this:

A global outage happened because a system treated a boundary failure as an impossibility. The fix is straightforward: expand the state space to include degraded operation, make validation explicit, and absorb failures into state transitions rather than process termination.

It’s tempting to treat this as an infrastructure issue, but to me it looks more like a modeling gap: the system’s state space didn’t match reality’s state space.

Functional programming is how you think better about systems that must survive contact with reality.

Because reality always finds the gap between your assumptions and the truth. The only question is whether that gap terminates your process or transitions to a degraded state.

All things considered, here is my final take-home message: Make degradation representable, and reality becomes survivable.