d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
29 KiB
29 KiB
RuVector Postgres v2 - Self-Healing System
The Missing Piece
We built the sensor (mincut integrity monitoring). Now we build the actuator (automated remediation).
Most systems detect problems and alert humans. We detect problems and fix them automatically.
Traditional: Detect → Alert → Human → Diagnose → Fix → Verify
Self-Healing: Detect → Classify → Remediate → Verify → Learn
This completes the control loop and makes RuVector truly autonomous.
Design Principles
- Graduated response — Small problems get small fixes
- Reversible actions — Every remediation can be undone
- Blast radius limits — Never make things worse
- Audit trail — Every action is logged and signed
- Learn from outcomes — Improve remediation over time
Architecture
+------------------------------------------------------------------+
| Integrity Monitor |
| - Computes lambda_cut on contracted graph |
| - Detects state transitions (normal → stress → critical) |
+------------------------------------------------------------------+
|
v
+------------------------------------------------------------------+
| Problem Classifier |
| - Identifies root cause from witness edges |
| - Maps symptoms to remediation strategies |
+------------------------------------------------------------------+
|
v
+------------------------------------------------------------------+
| Remediation Engine |
| - Selects appropriate action |
| - Executes with timeout and rollback |
| - Verifies improvement |
+------------------------------------------------------------------+
|
v
+------------------------------------------------------------------+
| Outcome Tracker |
| - Records success/failure |
| - Updates strategy weights |
| - Feeds learning pipeline |
+------------------------------------------------------------------+
Problem Classification
Witness Edge Analysis
The mincut computation produces witness edges — the edges that would break the graph. These reveal the problem type.
// src/healing/classifier.rs
#[derive(Debug, Clone)]
pub enum ProblemClass {
/// Hot partition overloaded
HotspotCongestion {
partition_ids: Vec<i64>,
load_ratio: f32, // vs average
},
/// Centroid imbalance in IVF index
CentroidSkew {
centroid_ids: Vec<i64>,
skew_factor: f32,
},
/// Replication lag causing consistency issues
ReplicationLag {
replica_ids: Vec<i64>,
lag_seconds: f32,
},
/// Background job contention
MaintenanceContention {
job_types: Vec<String>,
queue_depth: usize,
},
/// Index fragmentation
IndexFragmentation {
index_ids: Vec<i64>,
fragmentation_pct: f32,
},
/// Memory pressure
MemoryPressure {
current_usage_pct: f32,
largest_consumers: Vec<(String, usize)>,
},
/// Unknown (needs human investigation)
Unknown {
witness_summary: String,
},
}
pub fn classify_problem(
witness_edges: &[WitnessEdge],
metrics: &SystemMetrics,
) -> ProblemClass {
// Analyze witness edge patterns
let edge_types = count_edge_types(witness_edges);
let node_types = count_node_types(witness_edges);
// Pattern matching
if edge_types.get("partition_link").unwrap_or(&0) > &3 {
// Multiple partition links weak → hotspot
let hot_partitions = find_hot_partitions(witness_edges, metrics);
return ProblemClass::HotspotCongestion {
partition_ids: hot_partitions,
load_ratio: compute_load_ratio(&hot_partitions, metrics),
};
}
if node_types.get("centroid").unwrap_or(&0) > &5 {
// Centroid nodes in witness → skew
let skewed = find_skewed_centroids(witness_edges, metrics);
return ProblemClass::CentroidSkew {
centroid_ids: skewed,
skew_factor: compute_skew_factor(&skewed, metrics),
};
}
if edge_types.get("replication").unwrap_or(&0) > &0 {
// Replication edges weak
let lagging = find_lagging_replicas(witness_edges, metrics);
return ProblemClass::ReplicationLag {
replica_ids: lagging,
lag_seconds: get_max_lag(&lagging, metrics),
};
}
if edge_types.get("dependency").unwrap_or(&0) > &2 {
// Maintenance dependencies weak
let jobs = find_contending_jobs(witness_edges, metrics);
return ProblemClass::MaintenanceContention {
job_types: jobs,
queue_depth: metrics.maintenance_queue_depth,
};
}
// Check metrics for other patterns
if metrics.memory_usage_pct > 85.0 {
return ProblemClass::MemoryPressure {
current_usage_pct: metrics.memory_usage_pct,
largest_consumers: metrics.top_memory_consumers.clone(),
};
}
if metrics.index_fragmentation_pct > 30.0 {
return ProblemClass::IndexFragmentation {
index_ids: metrics.fragmented_indexes.clone(),
fragmentation_pct: metrics.index_fragmentation_pct,
};
}
ProblemClass::Unknown {
witness_summary: summarize_witnesses(witness_edges),
}
}
Remediation Strategies
Strategy Registry
// src/healing/strategies.rs
pub trait RemediationStrategy: Send + Sync {
/// Human-readable name
fn name(&self) -> &str;
/// Problem classes this strategy handles
fn handles(&self) -> Vec<ProblemClass>;
/// Estimate impact (0-1, higher = more disruptive)
fn impact(&self) -> f32;
/// Estimate time to complete
fn estimated_duration(&self) -> Duration;
/// Can this be reversed?
fn reversible(&self) -> bool;
/// Execute the remediation
fn execute(&self, context: &RemediationContext) -> Result<RemediationResult, Error>;
/// Rollback if needed
fn rollback(&self, context: &RemediationContext) -> Result<(), Error>;
}
/// Registry of all available strategies
pub struct StrategyRegistry {
strategies: Vec<Box<dyn RemediationStrategy>>,
weights: HashMap<String, f32>, // Learned effectiveness weights
}
impl StrategyRegistry {
pub fn new() -> Self {
let mut registry = Self {
strategies: vec![],
weights: HashMap::new(),
};
// Register built-in strategies
registry.register(Box::new(RebalancePartitions));
registry.register(Box::new(RebuildCentroids));
registry.register(Box::new(PauseMaintenanceJobs));
registry.register(Box::new(ThrottleIngestion));
registry.register(Box::new(EvictColdData));
registry.register(Box::new(CompactFragmentedIndexes));
registry.register(Box::new(ScaleReadReplicas));
registry.register(Box::new(DrainHotPartition));
registry
}
/// Select best strategy for a problem
pub fn select(&self, problem: &ProblemClass, context: &SystemContext) -> Option<&dyn RemediationStrategy> {
self.strategies.iter()
.filter(|s| s.handles().iter().any(|h| matches_class(h, problem)))
.filter(|s| s.impact() <= context.max_allowed_impact)
.max_by(|a, b| {
let weight_a = self.weights.get(a.name()).unwrap_or(&1.0);
let weight_b = self.weights.get(b.name()).unwrap_or(&1.0);
weight_a.partial_cmp(weight_b).unwrap()
})
.map(|s| s.as_ref())
}
}
Built-in Strategies
1. Rebalance Partitions
pub struct RebalancePartitions;
impl RemediationStrategy for RebalancePartitions {
fn name(&self) -> &str { "rebalance_partitions" }
fn handles(&self) -> Vec<ProblemClass> {
vec![ProblemClass::HotspotCongestion { .. }]
}
fn impact(&self) -> f32 { 0.3 } // Medium impact
fn reversible(&self) -> bool { true }
fn execute(&self, ctx: &RemediationContext) -> Result<RemediationResult, Error> {
let problem = ctx.problem.as_hotspot()?;
// Find underutilized partitions
let cold_partitions = find_cold_partitions(ctx.metrics);
// Calculate rebalance plan
let plan = compute_rebalance_plan(
&problem.partition_ids,
&cold_partitions,
ctx.metrics,
);
// Execute moves incrementally
for mv in plan.moves {
// Move vectors from hot to cold partition
move_vectors(mv.from_partition, mv.to_partition, mv.vector_ids)?;
// Check if integrity improved
let new_lambda = sample_integrity(ctx.collection_id)?;
if new_lambda > ctx.initial_lambda * 1.1 {
// Good progress, continue
} else if new_lambda < ctx.initial_lambda * 0.9 {
// Made things worse, rollback this move
move_vectors(mv.to_partition, mv.from_partition, mv.vector_ids)?;
break;
}
}
Ok(RemediationResult {
success: true,
actions_taken: plan.moves.len(),
improvement: compute_improvement(ctx),
})
}
fn rollback(&self, ctx: &RemediationContext) -> Result<(), Error> {
// Reverse all recorded moves
for mv in ctx.recorded_moves.iter().rev() {
move_vectors(mv.to_partition, mv.from_partition, mv.vector_ids)?;
}
Ok(())
}
}
2. Pause Maintenance Jobs
pub struct PauseMaintenanceJobs;
impl RemediationStrategy for PauseMaintenanceJobs {
fn name(&self) -> &str { "pause_maintenance" }
fn handles(&self) -> Vec<ProblemClass> {
vec![ProblemClass::MaintenanceContention { .. }]
}
fn impact(&self) -> f32 { 0.1 } // Low impact
fn reversible(&self) -> bool { true }
fn execute(&self, ctx: &RemediationContext) -> Result<RemediationResult, Error> {
let problem = ctx.problem.as_maintenance_contention()?;
// Pause low-priority jobs
let paused = problem.job_types.iter()
.filter(|j| job_priority(j) < Priority::High)
.map(|j| {
pause_job(j)?;
j.clone()
})
.collect::<Vec<_>>();
// Wait for current operations to drain
wait_for_drain(Duration::from_secs(30))?;
// Verify improvement
let new_lambda = sample_integrity(ctx.collection_id)?;
Ok(RemediationResult {
success: new_lambda > ctx.initial_lambda,
actions_taken: paused.len(),
improvement: (new_lambda - ctx.initial_lambda) / ctx.initial_lambda,
metadata: json!({ "paused_jobs": paused }),
})
}
fn rollback(&self, ctx: &RemediationContext) -> Result<(), Error> {
// Resume paused jobs
let paused: Vec<String> = ctx.result.metadata["paused_jobs"].as_array()
.map(|a| a.iter().filter_map(|v| v.as_str().map(String::from)).collect())
.unwrap_or_default();
for job in paused {
resume_job(&job)?;
}
Ok(())
}
}
3. Throttle Ingestion
pub struct ThrottleIngestion;
impl RemediationStrategy for ThrottleIngestion {
fn name(&self) -> &str { "throttle_ingestion" }
fn handles(&self) -> Vec<ProblemClass> {
vec![
ProblemClass::HotspotCongestion { .. },
ProblemClass::MemoryPressure { .. },
]
}
fn impact(&self) -> f32 { 0.4 } // Medium-high (affects writes)
fn reversible(&self) -> bool { true }
fn execute(&self, ctx: &RemediationContext) -> Result<RemediationResult, Error> {
// Calculate throttle percentage based on severity
let throttle_pct = match ctx.state {
IntegrityState::Stress => 50,
IntegrityState::Critical => 90,
_ => return Ok(RemediationResult::noop()),
};
// Apply throttle via shared memory
set_throttle_percentage(ctx.collection_id, "insert", throttle_pct)?;
set_throttle_percentage(ctx.collection_id, "bulk_insert", throttle_pct + 10)?;
// Record for rollback
ctx.record_action(ThrottleAction {
collection_id: ctx.collection_id,
previous_throttle: get_current_throttle(ctx.collection_id),
new_throttle: throttle_pct,
});
Ok(RemediationResult {
success: true,
actions_taken: 1,
improvement: 0.0, // Preventive, not curative
metadata: json!({ "throttle_pct": throttle_pct }),
})
}
fn rollback(&self, ctx: &RemediationContext) -> Result<(), Error> {
// Restore previous throttle level
let action: ThrottleAction = ctx.get_action()?;
set_throttle_percentage(ctx.collection_id, "insert", action.previous_throttle)?;
Ok(())
}
}
4. Scale Read Replicas (Kubernetes)
pub struct ScaleReadReplicas;
impl RemediationStrategy for ScaleReadReplicas {
fn name(&self) -> &str { "scale_replicas" }
fn handles(&self) -> Vec<ProblemClass> {
vec![ProblemClass::HotspotCongestion { .. }]
}
fn impact(&self) -> f32 { 0.2 } // Low impact
fn reversible(&self) -> bool { true }
fn execute(&self, ctx: &RemediationContext) -> Result<RemediationResult, Error> {
// Only available in K8s environment
let k8s = K8sClient::try_new()?;
// Get current replica count
let deployment = k8s.get_deployment("ruvector-read")?;
let current = deployment.spec.replicas;
// Scale up by 50% (capped)
let new_count = (current as f32 * 1.5).ceil() as i32;
let new_count = new_count.min(ctx.config.max_replicas);
if new_count == current {
return Ok(RemediationResult::noop());
}
// Apply scale
k8s.scale_deployment("ruvector-read", new_count)?;
// Wait for pods to be ready
k8s.wait_for_ready("ruvector-read", Duration::from_secs(300))?;
Ok(RemediationResult {
success: true,
actions_taken: 1,
improvement: 0.0, // Measured later
metadata: json!({
"previous_replicas": current,
"new_replicas": new_count,
}),
})
}
fn rollback(&self, ctx: &RemediationContext) -> Result<(), Error> {
let k8s = K8sClient::try_new()?;
let previous: i32 = ctx.result.metadata["previous_replicas"].as_i64()? as i32;
k8s.scale_deployment("ruvector-read", previous)?;
Ok(())
}
}
5. Compact Fragmented Indexes
pub struct CompactFragmentedIndexes;
impl RemediationStrategy for CompactFragmentedIndexes {
fn name(&self) -> &str { "compact_indexes" }
fn handles(&self) -> Vec<ProblemClass> {
vec![ProblemClass::IndexFragmentation { .. }]
}
fn impact(&self) -> f32 { 0.5 } // Higher impact (CPU intensive)
fn reversible(&self) -> bool { false } // Compaction is one-way
fn execute(&self, ctx: &RemediationContext) -> Result<RemediationResult, Error> {
let problem = ctx.problem.as_fragmentation()?;
// Compact most fragmented indexes first
let mut compacted = 0;
for index_id in &problem.index_ids {
// Check if we have time/resources
if ctx.elapsed() > ctx.timeout / 2 {
break;
}
// Run incremental compaction
compact_index_incremental(*index_id, CompactConfig {
max_duration: Duration::from_secs(60),
batch_size: 10000,
})?;
compacted += 1;
// Check improvement
let new_lambda = sample_integrity(ctx.collection_id)?;
if new_lambda > ctx.target_lambda {
break; // Good enough
}
}
Ok(RemediationResult {
success: compacted > 0,
actions_taken: compacted,
improvement: compute_improvement(ctx),
})
}
fn rollback(&self, _ctx: &RemediationContext) -> Result<(), Error> {
// Compaction is not reversible
Err(Error::NotReversible)
}
}
Remediation Engine
Execution Flow
// src/healing/engine.rs
pub struct RemediationEngine {
registry: StrategyRegistry,
config: HealingConfig,
outcome_tracker: OutcomeTracker,
}
impl RemediationEngine {
/// Main healing loop (called when integrity degrades)
pub fn heal(&self, trigger: &IntegrityTrigger) -> HealingOutcome {
let ctx = RemediationContext::new(trigger);
// 1. Classify the problem
let problem = classify_problem(&trigger.witness_edges, &ctx.metrics);
log_problem(&problem);
// 2. Check if we should auto-heal
if !self.should_auto_heal(&problem, &ctx) {
return HealingOutcome::Deferred {
reason: "Problem requires human review",
problem: problem.clone(),
};
}
// 3. Select strategy
let strategy = match self.registry.select(&problem, &ctx.system) {
Some(s) => s,
None => {
return HealingOutcome::NoStrategy {
problem: problem.clone(),
};
}
};
log_strategy_selected(strategy.name(), &problem);
// 4. Execute with timeout and monitoring
let result = self.execute_with_safeguards(strategy, &ctx);
// 5. Verify improvement
let verified = self.verify_improvement(&ctx, &result);
// 6. Rollback if needed
if !verified && strategy.reversible() {
log_rollback(strategy.name());
if let Err(e) = strategy.rollback(&ctx) {
log_rollback_failed(e);
}
}
// 7. Record outcome for learning
self.outcome_tracker.record(OutcomeRecord {
problem: problem.clone(),
strategy: strategy.name().to_string(),
result: result.clone(),
verified,
timestamp: Utc::now(),
});
HealingOutcome::Completed {
problem,
strategy: strategy.name().to_string(),
result,
verified,
}
}
fn should_auto_heal(&self, problem: &ProblemClass, ctx: &RemediationContext) -> bool {
// Don't auto-heal Unknown problems
if matches!(problem, ProblemClass::Unknown { .. }) {
return false;
}
// Check cooldown
if ctx.last_healing_attempt.elapsed() < self.config.min_healing_interval {
return false;
}
// Check max attempts
if ctx.healing_attempts_in_window > self.config.max_attempts_per_window {
return false;
}
// Check if problem is getting worse despite healing
if self.is_healing_ineffective(ctx) {
return false;
}
true
}
fn execute_with_safeguards(
&self,
strategy: &dyn RemediationStrategy,
ctx: &RemediationContext,
) -> RemediationResult {
// Set up timeout
let timeout = strategy.estimated_duration() * 2;
// Execute in separate thread with panic catching
let result = std::panic::catch_unwind(|| {
tokio::time::timeout(timeout, async {
strategy.execute(ctx)
})
});
match result {
Ok(Ok(Ok(r))) => r,
Ok(Ok(Err(e))) => RemediationResult::failed(e.to_string()),
Ok(Err(_)) => RemediationResult::failed("Timeout"),
Err(_) => RemediationResult::failed("Panic during remediation"),
}
}
fn verify_improvement(&self, ctx: &RemediationContext, result: &RemediationResult) -> bool {
if !result.success {
return false;
}
// Wait for system to stabilize
std::thread::sleep(Duration::from_secs(10));
// Sample integrity
let new_lambda = sample_integrity(ctx.collection_id).unwrap_or(0.0);
// Must improve by at least 10%
new_lambda > ctx.initial_lambda * 1.1
}
}
Safety Limits
// src/healing/config.rs
pub struct HealingConfig {
/// Minimum time between healing attempts
pub min_healing_interval: Duration,
/// Maximum attempts per time window
pub max_attempts_per_window: usize,
/// Time window for attempt counting
pub attempt_window: Duration,
/// Maximum impact level for auto-healing
pub max_auto_heal_impact: f32,
/// Problems that require human approval
pub require_approval: Vec<ProblemClass>,
/// Strategies that require human approval
pub require_approval_strategies: Vec<String>,
/// Enable learning from outcomes
pub learning_enabled: bool,
}
impl Default for HealingConfig {
fn default() -> Self {
Self {
min_healing_interval: Duration::from_secs(300), // 5 min
max_attempts_per_window: 3,
attempt_window: Duration::from_secs(3600), // 1 hour
max_auto_heal_impact: 0.5,
require_approval: vec![],
require_approval_strategies: vec!["scale_replicas".to_string()],
learning_enabled: true,
}
}
}
Learning from Outcomes
Outcome Tracking
CREATE TABLE ruvector.healing_outcomes (
id BIGSERIAL PRIMARY KEY,
collection_id INTEGER NOT NULL,
tenant_id TEXT,
-- Problem
problem_class TEXT NOT NULL,
problem_details JSONB NOT NULL,
-- Strategy
strategy_name TEXT NOT NULL,
strategy_params JSONB,
-- Execution
started_at TIMESTAMPTZ NOT NULL,
completed_at TIMESTAMPTZ,
duration_ms INTEGER,
-- Result
success BOOLEAN NOT NULL,
verified BOOLEAN,
actions_taken INTEGER,
improvement_pct REAL,
error_message TEXT,
-- Context
initial_lambda REAL NOT NULL,
final_lambda REAL,
witness_edges JSONB,
system_metrics JSONB,
-- Learning
feedback_score REAL, -- Human feedback if provided
created_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE INDEX idx_healing_outcomes_class ON ruvector.healing_outcomes(problem_class);
CREATE INDEX idx_healing_outcomes_strategy ON ruvector.healing_outcomes(strategy_name);
CREATE INDEX idx_healing_outcomes_success ON ruvector.healing_outcomes(success, verified);
Strategy Weight Updates
// src/healing/learning.rs
pub struct OutcomeTracker {
db: DbPool,
strategy_weights: RwLock<HashMap<String, f32>>,
}
impl OutcomeTracker {
/// Update strategy weights based on outcomes
pub fn update_weights(&self) {
let outcomes = self.get_recent_outcomes(Duration::from_days(30));
let mut new_weights = HashMap::new();
for strategy in self.list_strategies() {
let strategy_outcomes: Vec<_> = outcomes.iter()
.filter(|o| o.strategy_name == strategy)
.collect();
if strategy_outcomes.is_empty() {
continue;
}
// Calculate effectiveness
let success_rate = strategy_outcomes.iter()
.filter(|o| o.success && o.verified.unwrap_or(false))
.count() as f32 / strategy_outcomes.len() as f32;
let avg_improvement = strategy_outcomes.iter()
.filter_map(|o| o.improvement_pct)
.sum::<f32>() / strategy_outcomes.len() as f32;
// Weight = success_rate * (1 + avg_improvement)
let weight = success_rate * (1.0 + avg_improvement);
new_weights.insert(strategy, weight);
}
*self.strategy_weights.write() = new_weights;
}
/// Get effectiveness report
pub fn effectiveness_report(&self) -> EffectivenessReport {
let weights = self.strategy_weights.read();
EffectivenessReport {
strategies: weights.iter()
.map(|(name, weight)| StrategyEffectiveness {
name: name.clone(),
weight: *weight,
recent_outcomes: self.get_recent_outcomes_for(name, 10),
})
.collect(),
overall_success_rate: self.compute_overall_success_rate(),
avg_time_to_recovery: self.compute_avg_recovery_time(),
}
}
}
SQL Interface
Monitoring
-- View healing status
SELECT * FROM ruvector_healing_status();
-- Returns:
-- {
-- "enabled": true,
-- "last_healing": "2024-01-15T10:30:00Z",
-- "total_healings_24h": 3,
-- "success_rate": 0.67,
-- "active_remediations": [],
-- "cooldown_until": null
-- }
-- View recent healing events
SELECT * FROM ruvector_healing_history(
since := NOW() - INTERVAL '24 hours',
limit_ := 20
);
-- View strategy effectiveness
SELECT * FROM ruvector_healing_effectiveness();
Configuration
-- Configure healing behavior
SELECT ruvector_healing_configure('{
"enabled": true,
"min_healing_interval_seconds": 300,
"max_attempts_per_hour": 3,
"max_auto_heal_impact": 0.5,
"require_approval_strategies": ["scale_replicas"],
"learning_enabled": true
}'::jsonb);
-- Manually trigger healing (for testing)
SELECT ruvector_healing_trigger('embeddings');
-- Approve pending healing action
SELECT ruvector_healing_approve(action_id := 123);
-- Abort active healing
SELECT ruvector_healing_abort(action_id := 123);
Manual Remediation
-- Execute specific strategy manually
SELECT ruvector_healing_execute('embeddings', 'rebalance_partitions', '{
"dry_run": false,
"max_moves": 100
}'::jsonb);
-- Rollback last healing action
SELECT ruvector_healing_rollback('embeddings');
Prometheus Metrics
# Healing activity
ruvector_healing_attempts_total{collection="embeddings",strategy="rebalance"} 15
ruvector_healing_success_total{collection="embeddings",strategy="rebalance"} 12
ruvector_healing_duration_seconds{collection="embeddings",strategy="rebalance",quantile="0.99"} 45.2
# Current state
ruvector_healing_active{collection="embeddings"} 0
ruvector_healing_cooldown{collection="embeddings"} 0
# Effectiveness
ruvector_healing_success_rate{collection="embeddings"} 0.80
ruvector_healing_avg_improvement{collection="embeddings"} 0.25
ruvector_healing_time_to_recovery_seconds{collection="embeddings"} 120
Alerting Integration
# Alert when healing is failing
- alert: RuVectorHealingIneffective
expr: ruvector_healing_success_rate < 0.5
for: 1h
labels:
severity: warning
annotations:
summary: "Self-healing is not effective"
description: "Healing success rate is {{ $value }} - human intervention may be required"
# Alert when healing is disabled
- alert: RuVectorHealingDisabled
expr: ruvector_healing_enabled == 0
for: 5m
labels:
severity: info
annotations:
summary: "Self-healing is disabled for {{ $labels.collection }}"
# Alert when in prolonged degraded state
- alert: RuVectorProlongedDegradation
expr: ruvector_integrity_state > 1 and ruvector_healing_attempts_total == 0
for: 30m
labels:
severity: critical
annotations:
summary: "Prolonged degradation without healing attempts"
Testing Requirements
Unit Tests
- Problem classification accuracy
- Strategy selection logic
- Rollback correctness
- Weight update calculations
Integration Tests
- End-to-end healing cycle
- Concurrent healing prevention
- Timeout and panic handling
- Kubernetes scaling (mock)
Chaos Tests
- Random failure injection
- Healing under load
- Cascading failure prevention
- Recovery time objectives
The Complete Loop
+----------------+
| Normal State |
+-------+--------+
|
| (stress detected)
v
+-------+--------+
| Classify |
+-------+--------+
|
v
+-------+--------+
| Remediate |
+-------+--------+
|
| (verify)
v
+-------+--------+
| Learn |
+-------+--------+
|
v
+-------+--------+
| Normal State |<----+
+----------------+ |
|
(automatic recovery)
This is what makes RuVector truly different:
We don't just detect problems early. We fix them automatically before they become incidents.
The system is not just observable. It is self-aware and self-repairing.