7 篇博文 含有标签「Observability」

In modern cloud-native architectures, system performance issues can cause severe impact within seconds. By the time users start complaining about slow responses, the problem may have persisted for minutes or even longer. Real-time performance monitoring is no longer optional—it's essential for ensuring business continuity.

Tianji, as an all-in-one observability platform, provides a complete real-time monitoring solution from data collection to intelligent analysis. This article explores how real-time performance monitoring transforms infrastructure management from reactive response to proactive control.

Why Real-Time Monitoring Matters​

Traditional polling-based monitoring (e.g., sampling every 5 minutes) is no longer sufficient in rapidly changing environments:

• User Experience First: Modern users expect millisecond-level responses; any delay can lead to churn
• Dynamic Resource Allocation: Cloud environments scale rapidly, requiring real-time state tracking
• Cost Optimization: Timely detection of performance bottlenecks prevents over-provisioning
• Failure Prevention: Real-time trend analysis enables action before issues escalate
• Precise Diagnosis: Performance problems are often fleeting; real-time data is the foundation for accurate diagnosis

Tianji's Real-Time Monitoring Capabilities​

1. Multi-Dimensional Real-Time Data Collection​

Tianji integrates three core monitoring capabilities to form a complete real-time observability view:

Website Analytics

# Real-time visitor tracking
- Real-time visitor count and geographic distribution
- Page load performance metrics (LCP, FID, CLS)
- User behavior flow tracking
- API response time statistics

Uptime Monitor

# Continuous availability checking
- Second-level heartbeat detection
- Multi-region global probing
- DNS, TCP, HTTP multi-protocol support
- Automatic failover verification

Server Status

# Infrastructure metrics streaming
- Real-time CPU, memory, disk I/O monitoring
- Network traffic and connection status
- Process-level resource consumption
- Container and virtualization metrics

2. Real-Time Data Stream Processing Architecture​

Tianji employs a streaming data processing architecture to ensure monitoring data timeliness:

Data Collection (< 1s)
    ↓
Data Aggregation (< 2s)
    ↓
Anomaly Detection (< 3s)
    ↓
Alert Trigger (< 5s)
    ↓
Notification Push (< 7s)

From event occurrence to team notification, the entire process completes within 10 seconds, providing valuable time for rapid response.

3. Intelligent Performance Baselines and Anomaly Detection​

Static thresholds often lead to numerous false positives. Tianji supports dynamic performance baselines:

• Adaptive Thresholds: Automatically calculate normal ranges based on historical data
• Time-Series Pattern Recognition: Identify cyclical fluctuations (e.g., weekday vs weekend traffic)
• Multi-Dimensional Correlation: Assess anomaly severity by combining multiple metrics
• Trend Prediction: Forecast future resource needs based on current trends

// Example: Dynamic baseline calculation
{
  metric: "cpu_usage",
  baseline: {
    mean: 45.2,      // Historical average
    stdDev: 8.3,     // Standard deviation
    confidence: 95,  // Confidence interval
    threshold: {
      warning: 61.8,   // mean + 2*stdDev
      critical: 70.1   // mean + 3*stdDev
    }
  }
}

Best Practices for Real-Time Monitoring​

Building an Effective Monitoring Strategy​

1. Define Key Performance Indicators (KPIs)

Choose metrics that truly impact business outcomes, avoiding monitoring overload:

• User Experience Metrics: Page load time, API response time, error rate
• System Health Metrics: CPU/memory utilization, disk I/O, network latency
• Business Metrics: Order conversion rate, payment success rate, active users

2. Layered Monitoring Architecture

┌──────────────────────────────────────────┐
│  Business Layer: Conversion, Satisfaction│
├──────────────────────────────────────────┤
│  Application Layer: API Response, Errors │
├──────────────────────────────────────────┤
│  Infrastructure: CPU, Memory, Network    │
└──────────────────────────────────────────┘

Monitor layer by layer from top to bottom, ensuring issues can be quickly located to specific levels.

3. Real-Time Alert Prioritization

Not all anomalies require immediate human intervention:

• P0 - Critical: Impacts core business, requires immediate response (e.g., payment system outage)
• P1 - High: Affects some users, requires prompt handling (e.g., regional access slowdown)
• P2 - Medium: Doesn't affect business but needs attention (e.g., disk space warning)
• P3 - Low: Informational alerts, periodic handling (e.g., certificate expiration notice)

Performance Optimization Case Study​

Scenario: E-commerce Website Traffic Surge Causing Slowdown

Through Tianji's real-time monitoring dashboard, the team observed:

Timeline: 14:00 - 14:15

14:00 - Normal traffic (1000 req/min)
  ↓
14:03 - Traffic begins to rise (1500 req/min)
  ├─ Website Analytics: Page load time increased from 1.2s to 2.8s
  ├─ Server Status: API server CPU reached 85%
  └─ Uptime Monitor: Response time increased from 200ms to 1200ms
  ↓
14:05 - Automatic alert triggered
  └─ Webhook notification → Auto-scaling script executed
  ↓
14:08 - New instances online
  ├─ Traffic distributed across 5 instances
  └─ CPU reduced to 60%
  ↓
14:12 - Performance restored to normal
  └─ Response time back to 250ms

Key Benefits:

• Issue detection time: < 5 minutes (traditional monitoring may take 15-30 minutes)
• Automated response: Auto-scaling without manual intervention
• Impact scope: Only 10% of users experienced slight delay
• Business loss: Nearly zero

Quick Start: Deploying Tianji Real-Time Monitoring​

Installation and Configuration​

# 1. Download and start Tianji
wget https://raw.githubusercontent.com/msgbyte/tianji/master/docker-compose.yml
docker compose up -d

# 2. Access the admin interface
# http://localhost:12345
# Default credentials: admin / admin (change password immediately)

Configuring Real-Time Monitoring​

Step 1: Add Website Monitoring

// Embed tracking code in your website
<script 
  src="https://your-tianji-domain/tracker.js" 
  data-website-id="your-website-id"
></script>

Step 2: Configure Server Monitoring

# Install server monitoring client
curl -o tianji-reporter https://tianji.example.com/download/reporter
chmod +x tianji-reporter

# Configure and start
./tianji-reporter \
  --workspace-id="your-workspace-id" \
  --name="production-server-1" \
  --interval=5

Step 3: Set Up Uptime Monitoring

In the Tianji admin interface:

1. Navigate to "Monitors" page
2. Click "Add Monitor"
3. Configure check interval (recommended: 30 seconds)
4. Set alert thresholds and notification channels

Step 4: Configure Real-Time Alerts

# Webhook notification example
notification:
  type: webhook
  url: https://your-alert-system.com/webhook
  method: POST
  payload:
    level: "{{ alert.level }}"
    message: "{{ alert.message }}"
    timestamp: "{{ alert.timestamp }}"
    metrics:
      cpu: "{{ metrics.cpu }}"
      memory: "{{ metrics.memory }}"
      response_time: "{{ metrics.response_time }}"

Advanced Techniques: Building Predictive Monitoring​

1. Leveraging Historical Data for Capacity Planning​

Tianji's data retention and analysis features help teams forecast future needs:

• Analyze traffic trends over the past 3 months
• Identify seasonal and cyclical patterns
• Predict resource needs for holidays and promotional events
• Scale proactively, avoiding last-minute scrambles

2. Correlation Analysis: From Symptom to Root Cause​

When multiple metrics show anomalies simultaneously, Tianji's correlation analysis helps quickly pinpoint root causes:

Anomaly Pattern Recognition:

Symptom: API response time increase
  ├─ Correlated Metric 1: Database connection pool utilization at 95%
  ├─ Correlated Metric 2: Slow query count increased 3x
  └─ Root Cause: Unoptimized SQL queries causing database pressure

→ Recommended Actions:
  1. Enable query caching
  2. Add database indexes
  3. Optimize hotspot queries

3. Performance Benchmarking and Continuous Improvement​

Regularly conduct performance benchmarks to establish a continuous improvement cycle:

Benchmarking Process:

1. Record current performance baseline
   ├─ P50 response time: 150ms
   ├─ P95 response time: 500ms
   └─ P99 response time: 1200ms

2. Implement optimization measures
   └─ Examples: Enable CDN, optimize database queries

3. Verify optimization results
   ├─ P50 response time: 80ms  (-47%)
   ├─ P95 response time: 280ms (-44%)
   └─ P99 response time: 600ms (-50%)

4. Solidify improvements
   └─ Update performance baseline, continue monitoring

Common Questions and Solutions​

Q: Does real-time monitoring increase system load?​

A: Tianji's monitoring client is designed to be lightweight:

• Client CPU usage < 1%
• Memory footprint < 50MB
• Network traffic < 1KB/s (per server)
• Batch data upload reduces network overhead

Q: How to avoid alert storms?​

A: Tianji provides multiple alert noise reduction mechanisms:

• Alert Aggregation: Related alerts automatically merged
• Silence Period Settings: Avoid duplicate notifications
• Dependency Management: Downstream failures don't trigger redundant alerts
• Intelligent Prioritization: Automatically adjust alert levels based on impact scope

Q: How to set data retention policies?​

A: Recommended data retention strategy:

Real-time data: Retain 7 days (second-level precision)
  └─ Used for: Real-time analysis, troubleshooting

Hourly aggregated data: Retain 90 days
  └─ Used for: Trend analysis, capacity planning

Daily aggregated data: Retain 2 years
  └─ Used for: Historical comparison, annual reports

Conclusion​

Real-time performance monitoring is not just a technical tool—it represents a shift in operational philosophy from reactive response to proactive prevention, from post-incident analysis to real-time decision-making.

Through Tianji's unified monitoring platform, teams can:

• Detect Issues Early: From event occurrence to notification response in < 10 seconds
• Quickly Identify Root Causes: Multi-dimensional data correlation analysis
• Intelligent Alert Noise Reduction: Reduce invalid alerts by over 70%
• Predictive Operations: Forecast future needs based on historical trends
• Continuous Performance Optimization: Establish closed-loop performance improvement

In modern cloud-native environments, real-time monitoring has become a core competitive advantage for ensuring business continuity and user experience. Start using Tianji today to let data drive your operational decisions and eliminate performance issues before they escalate.

Get Started with Tianji Real-Time Monitoring: Deploy in just 5 minutes and bring your infrastructure into the era of real-time observability.

In modern operational environments, thousands of alerts flood team notification channels every day. However, most SRE and operations engineers face the same dilemma: too many alerts, too little signal. When you're woken up for the tenth time at 3 AM by a false alarm, teams begin to lose trust in their alerting systems. This "alert fatigue" ultimately leads to real issues being overlooked.

Tianji, as an All-in-One monitoring platform, provides a complete solution from data collection to intelligent alerting. This article explores how to use Tianji to build an efficient alerting system where every alert deserves attention.

The Root Causes of Alert Fatigue​

Core reasons why alerting systems fail typically include:

• Improper threshold settings: Static thresholds cannot adapt to dynamically changing business scenarios
• Lack of context: Isolated alert information makes it difficult to quickly assess impact scope and severity
• Duplicate alerts: One underlying issue triggers multiple related alerts, creating an information flood
• No priority classification: All alerts appear urgent, making it impossible to distinguish severity
• Non-actionable: Alerts only say "there's a problem" but provide no clues for resolution

Tianji's Intelligent Alerting Strategies​

1. Multi-dimensional Data Correlation​

Tianji integrates three major capabilities—Website Analytics, Uptime Monitor, and Server Status—on the same platform, which means alerts can be based on comprehensive judgment across multiple data dimensions:

# Example scenario: Server response slowdown
- Server Status: CPU utilization at 85%
- Uptime Monitor: Response time increased from 200ms to 1500ms
- Website Analytics: User traffic surged by 300%

→ Tianji's intelligent assessment: This is a normal traffic spike, not a system failure

This correlation capability significantly reduces false positive rates, allowing teams to focus on issues that truly require attention.

2. Flexible Alert Routing and Grouping​

Different alerts should notify different teams. Tianji supports multiple notification channels (Webhook, Slack, Telegram, etc.) and allows intelligent routing based on alert type, severity, impact scope, and other conditions:

• Critical level: Immediately notify on-call personnel, trigger pager
• Warning level: Send to team channel, handle during business hours
• Info level: Log for records, periodic summary reports

3. Alert Aggregation and Noise Reduction​

When an underlying issue triggers multiple alerts, Tianji's alert aggregation feature can automatically identify correlations and merge multiple alerts into a single notification:

Original Alerts (5):
- API response timeout
- Database connection pool exhausted
- Queue message backlog
- Cache hit rate dropped
- User login failures increased

↓ After Tianji Aggregation

Consolidated Alert (1):
Core Issue: Database performance anomaly
Impact Scope: API, login, message queue
Related Metrics: 5 abnormal signals
Recommended Action: Check database connections and slow queries

4. Intelligent Silencing and Maintenance Windows​

During planned maintenance, teams don't want to receive expected alerts. Tianji supports:

• Flexible silencing rules: Based on time, tags, resource groups, and other conditions
• Maintenance window management: Plan ahead, automatically silence related alerts
• Progressive recovery: Gradually restore monitoring after maintenance ends to avoid alert avalanches

Building Actionable Alerts​

An excellent alert should contain:

1. Clear problem description: Which service, which metric, current state
2. Impact scope assessment: How many users affected, which features impacted
3. Historical trend comparison: Is this a new issue or a recurring problem
4. Related metrics snapshot: Status of other related metrics
5. Handling suggestions: Recommended troubleshooting steps or Runbook links

Tianji's alert template system supports customizing this information, allowing engineers who receive alerts to take immediate action instead of spending significant time gathering context.

Implementation Best Practices​

Define the Golden Rules of Alerting​

When configuring alerts in Tianji, follow these principles:

• Every alert must be actionable: If you don't know what to do after receiving an alert, that alert shouldn't exist
• Avoid symptom-based alerts: Focus on root causes rather than surface phenomena
• Use percentages instead of absolute values: Adapt to system scale changes
• Set reasonable time windows: Avoid triggering alerts from momentary fluctuations

Continuously Optimize Alert Quality​

Tianji provides alert effectiveness analysis features:

• Alert trigger statistics: Which alerts fire most frequently? Is it reasonable?
• Response time tracking: Average time from trigger to resolution
• False positive rate analysis: Which alerts are often ignored or immediately dismissed?
• Coverage assessment: Are real failures being missed by alerts?

Regularly review these metrics and continuously adjust alert rules to make the system smarter over time.

Quick Start with Tianji Alert System​

# Download and start Tianji
wget https://raw.githubusercontent.com/msgbyte/tianji/master/docker-compose.yml
docker compose up -d

Default account: admin / admin (be sure to change the password)

Configuration workflow:

1. Add monitoring targets: Websites, servers, API endpoints
2. Set alert rules: Define thresholds and trigger conditions
3. Configure notification channels: Connect Slack, Telegram, or Webhook
4. Create alert templates: Customize alert message formats
5. Test and verify: Manually trigger test alerts to ensure configuration is correct

Conclusion​

An alerting system should not be a noise generator, but a reliable assistant for your team. Through Tianji's intelligent alerting capabilities, teams can:

• Reduce alert noise by over 70%: More precise trigger conditions and intelligent aggregation
• Improve response speed by 3x: Rich contextual information and actionable recommendations
• Enhance team happiness: Fewer invalid midnight calls, making on-call duty no longer a nightmare

Start today by building a truly intelligent alerting system with Tianji, making every alert worth your attention. Less noise, more insights—this is what modern monitoring should look like.

Organizations collect terabytes of metrics, traces, and logs every day, yet on-call engineers still ask the same question during incidents: What exactly is happening right now? Tianji was created to close this gap. By unifying website analytics, uptime monitoring, server status, and telemetry in one open-source platform, Tianji gives teams the context they need to act quickly.

From Raw Signals to Narrative Insight​

Traditional observability setups scatter information across disconnected dashboards. Tianji avoids this fragmentation by correlating metrics, incidents, and user behavior in one timeline. When an alert fires, responders see the full story—response times, geographic impact, concurrent deployments, and even user journeys that led to errors.

This context makes handoffs faster. Instead of forwarding ten screenshots, teams can share a single Tianji incident view that highlights the relevant trends, suspected root causes, and user impact. The result is a shared understanding that accelerates triage.

Automating the First Draft of Postmortems​

Tianji leverages AI summarization to turn monitoring data into human-readable briefings. Every alert can trigger an automated draft that captures key metrics, timeline milestones, and anomalous signals. Engineers can refine the draft rather than starting from scratch, reducing the time needed to publish reliable post-incident notes.

The same automation helps SRE teams run proactive health checks. Scheduled summaries highlight slow-burning issues—like growing latency or memory pressure—before they escalate. These narrative reports translate raw telemetry into action items that stakeholders outside the engineering team can understand.

Empowering Continuous Improvement​

Story-driven observability is not only about speed; it also supports long-term learning. Tianji keeps historical incident narratives, linked to their corresponding dashboards and runbooks. Teams can review past incidents to see how similar signals played out, making it easier to avoid repeated mistakes.

That historical perspective informs capacity planning, reliability roadmaps, and even customer communications. With Tianji, organizations evolve from reactive firefighting to deliberate, data-informed decision making.

Getting Started with Tianji​

Because Tianji is open source, teams can self-host the entire stack and adapt it to their infrastructure. Deploy the lightweight reporter to stream server status, configure website analytics in minutes, and integrate existing alerting channels. As coverage expands, Tianji becomes the single pane of glass that connects product metrics with operational health.

Ready to translate noise into narrative? Spin up Tianji, connect your services, and watch your observability practice transform from dashboard watching to decisive action.

Third‑party dependencies (auth, payments, CDNs, search, LLM APIs) are indispensable — and opaque. When they wobble, your app can fail in surprising ways: slow fallbacks, retry storms, cache stampedes, and silent feature degradation. The goal is not to eliminate external risk, but to make it visible, bounded, and quickly mitigated.

This post outlines a pragmatic approach to dependency‑aware monitoring and automation you can implement today with Tianji.

Why external failures cascade​

• Latency amplification: upstream 300–800 ms p95 spills into your end‑user p95.
• Retry feedback loops: naive retries multiply load during partial brownouts.
• Hidden coupling: one provider outage impacts multiple features at once.
• Unknown blast radius: you discover the topology only after an incident.

Start with a topology and blast radius view​

Build a simple dependency map: user flows → services → external providers. Tag each edge with SLOs and failure modes (timeouts, 4xx/5xx, quota, throttling). During incidents, this “where can it hurt?” view shortens time‑to‑mitigation.

With Tianji’s Unified Feed, you can fold provider checks, app metrics, and feature events into a single timeline to see impact and causality quickly.

Proactive signals: status pages aren’t enough​

• Poll provider status pages, but don’t trust them as sole truth.
• Add synthetic checks from multiple regions against provider endpoints and critical flows.
• Track error budgets separately for “external” vs “internal” failure classes to avoid masking.
• Record quotas/limits (req/min, tokens/day) as first‑class signals to catch soft failures.

Measure what users feel, not just what providers return​

Provider‑reported 200 OK with 2–3 s latency can still break user flows. Tie provider metrics to user funnels: search → add to cart → pay. Alert on delta between control and affected cohorts.

Incident playbooks for external outages​

Focus on safe, reversible actions:

• Circuit breakers + budgets: open after N failures/latency spikes; decay automatically.
• Retry with jitter and caps; prefer idempotent semantics; collapse duplicate work.
• Progressive degradation: serve cached/last‑known‑good; hide non‑critical features behind flags.
• Traffic shaping: reduce concurrency towards the failing provider to protect your core.

How to ship this with Tianji​

• Unified Feed aggregates checks, metrics, and product events; fold signals by timeline for clear causality. See Feed State Model and Channels.
• Synthetic monitors for external APIs and critical user journeys; multi‑region, cohort‑aware. See Custom Script Monitor.
• Error‑budget tracking per dependency with burn alerts; correlate to user funnels.
• Server Status Reporter to get essential host metrics fast. See Server Status Reporter.
• Website tracking to instrument client‑side failures and measure real user impact. See Telemetry Intro and Website Tracking Script.

Implementation checklist​

• Enumerate external dependencies and map them to user‑visible features and SLOs
• Create synthetic checks per critical API path (auth, pay, search) across regions
• Define dependency‑aware alerting: error rate, P95, quota, throttling, and burn rates
• Add circuit breakers and progressive degradation paths via feature flags
• Maintain a unified incident timeline: signals → mitigations → outcomes; review and codify

Closing​

External dependencies are here to stay. The teams that win treat them as part of their system: measured, bounded, and automated. With Tianji’s dependency‑aware monitoring and unified timeline, you can turn opaque third‑party risk into fast, confident incident response.

Most monitoring setups work fine in steady state, yet fall apart during releases: thresholds misfire, samples miss the key moments, and alert storms hide real issues. Release‑aware monitoring brings “release context” into monitoring decisions—adjusting sampling/thresholds across pre‑, during‑, and post‑deploy phases, folding related signals, and focusing on what truly impacts SLOs.

Why “release‑aware” matters​

• Deploys are high‑risk windows with parameter, topology, and traffic changes.
• Static thresholds (e.g., fixed P95) produce high false‑positive rates during rollouts.
• Canary/blue‑green needs cohort‑aware dashboards and alerting strategies.

The goal: inject “just released?”, “traffic split”, “feature flags”, and “target cohorts” into alerting and sampling logic to increase sensitivity where it matters and suppress noise elsewhere.

What release context includes​

• Commits/tickets: commit, PR, ticket, version
• Deploy metadata: start/end time, environment, batch, blast radius
• Traffic strategy: canary ratio, blue‑green switch, rollback points
• Feature flags: on/off, cohort targeting, dependent flags
• SLO context: error‑budget burn, critical paths, recent incidents

A practical pre‑/during‑/post‑deploy policy​

Before deploy (prepare)​

• Temporarily raise sampling for critical paths to increase metric resolution.
• Switch thresholds to “release‑phase curves” to reduce noise from short spikes.
• Pre‑warm runbooks: prepare diagnostics (dependency health, slow queries, hot keys, thread stacks).

During deploy (canary/blue‑green)​

• Fire strong alerts only on “canary cohort” SLO funnels; compare “control vs canary.”
• At traffic shift points, temporarily raise sampling and log levels to capture root causes.
• Define guard conditions (error rate↑, P95↑, success↓, funnel conversion↓) to auto‑rollback or degrade.

After deploy (observe and converge)​

• Gradually return to steady‑state sampling/thresholds; keep short‑term focus on critical paths.
• Fold “release events + metrics + alerts + actions” into one timeline for review and learning.

Incident folding and timeline: stop alert storms​

• Fold multi‑source signals of the same root cause (DB jitter → API 5xx → frontend errors) into a single incident.
• Attach release context (version, traffic split, feature flags) to the incident for one‑view investigation.
• Record diagnostics and repair actions on the same timeline for replay and continuous improvement.

Ship it with Tianji​

• Unified Feed aggregates checks, metrics, and events; fold by timeline: see Feed State Model and Channels.
• Lightweight server status reporting: get key host metrics in minutes: see Server Status Reporter.
• Open telemetry: product/app events capture release context: see Telemetry Intro and Website Tracking Script.
• Custom monitors and synthetic flows on critical paths with policy‑based sampling and automated actions: see Custom Script Monitor.

Implementation checklist​

• Map critical paths and SLOs; define “release‑phase thresholds/sampling” and guard conditions
• Ingest release context (version, traffic split, flags, cohorts) as labels on events/metrics
• Build “canary vs control” dashboards and delta‑based alerts
• Auto bump sampling/log levels at shift/rollback points, then decay to steady state
• Keep a unified timeline of “signals → actions → outcomes”; review after each release and codify into runbooks

Closing​

Release‑aware monitoring is not “more dashboards and alerts,” but making “releases” first‑class in monitoring and automation. With Tianji’s unified timeline and open telemetry, you can surface issues earlier, converge faster, and keep human effort focused on real judgment and trade‑offs.

“The alert fired—now what?” For many teams, the pain is not “Do we have monitoring?” but “How many people, tools, and context switches does it take to get from detection to repair?” This article uses a unified incident timeline as the backbone to connect detection → diagnosis → repair into an automated closed loop, so on-call SREs can focus on judgment rather than tab juggling.

Why build a closed loop​

Without a unified context, three common issues plague response workflows:

• Fragmented signals: metrics, logs, traces, and synthetic flows are split across tools.
• Slow handoffs: alerts lack diagnostic context, causing repeated pings and evidence gathering.
• Inconsistent actions: fixes are ad hoc; best practices don’t accumulate as reusable runbooks.

Closed-loop automation makes the “signals → decisions → actions” chain stable, auditable, and replayable by using a unified timeline as the spine.

How a unified incident timeline carries the response​

Key properties of the unified timeline:

1. Correlation rules fold multi-source signals of the same root cause into one incident, avoiding alert storms.
2. Each incident is auto-enriched with context (recent deploys, SLO burn, dependency health, hot metrics).
3. Response actions (diagnostic scripts, rollback, scale-out, traffic shifting) are recorded on the same timeline for review and continuous improvement.

Five levels of runbook automation​

An evolution path from prompts to autonomy:

1. Human-in-the-loop visualization: link charts and log slices on the timeline to cut context switching.
2. Guided semi-automation: run diagnostic scripts on incident start (dependencies, thread dumps, slow queries).
3. Conditional actions: execute low-risk fixes (rollback/scale/shift) behind guard conditions.
4. Policy-driven orchestration: adapt by SLO burn, release windows, and dependency health.
5. Guardrailed autonomy: self-heal within boundaries; escalate to humans beyond limits.

Automation is not “more scripts,” it’s “better triggers”​

High-quality triggers stem from high-quality signal design:

• Anchor on SLOs: prioritize strong triggers on budget burn and user-impacting paths.
• Adaptive sampling: full on failure paths, lower in steady state, temporary boosts after deploys.
• Event folding: compress cascades (DB down → API 5xx → frontend errors) into a single incident so scripts don’t compete.

A practical Detection → Repair pattern​

1. Detect: synthetic flows or external probes fail on user-visible paths.
2. Correlate: fold related signals on one timeline; auto-escalate when SLO thresholds are at risk.
3. Diagnose: run scripts in parallel for dependency health, recent deploys, slow queries, hot keys, and thread stacks.
4. Repair: if guard conditions pass, execute rollback/scale/shift/restart on scoped units; otherwise require human approval.
5. Review: actions, evidence, and outcomes live on the same timeline to improve the next response.

Implement quickly with Tianji​

• Unified Feed aggregates checks, metrics, and events in one place for timeline correlation and folding: see Feed State Model and Channels.
• Lightweight server status reporting for key host metrics in minutes: see Server Status Reporter.
• Open product and event telemetry: see Telemetry Intro and Website Tracking Script.
• Custom monitors and synthetic flows on high-value paths, with policy-based sampling and automated actions: see Custom Script Monitor.

Implementation checklist (check as you go)​

• Map critical user journeys and SLOs; define guard conditions for safe automation
• Ingest checks, metrics, deploys, dependencies, and product events into a single timeline
• Build a library of diagnostic scripts and low-risk repair actions
• Configure incident folding and escalation to avoid alert storms
• Switch sampling and thresholds across release windows and traffic peaks/valleys
• After each incident, push more steps into automation with guardrails

Closing thoughts​

Runbook automation is not “shipping a giant orchestrator in one go.” It starts with a unified timeline and turns common response paths into workflows that are visible, executable, verifiable, and evolvable. With Tianji’s open building blocks, you can safely delegate repetitive work to automation and keep human focus on real decisions.

Cloud costs are rising, data volumes keep growing, and yet stakeholders expect faster incident response with higher reliability. The answer is not “more data” but the right data at the right price. Cost-aware observability helps you preserve signals that protect user experience while removing expensive noise.

This guide shows how to re-think telemetry collection, storage, and alerting so you can keep your SLOs intact—without burning your budget.

Why Cost-Aware Observability Matters​

Traditional monitoring stacks grew by accretion: another exporter here, a new trace sampler there, duplicated logs everywhere. The result is ballooning ingest and storage costs, slow queries, and alert fatigue. A cost-aware approach prioritizes:

• Mission-critical signals tied to user outcomes (SLOs)
• Economic efficiency across ingest, storage, and query paths
• Progressive detail: coarse first, deep when needed (on-demand)
• Tool consolidation and data ownership to avoid vendor lock-in

Principles to Guide Decisions​

1. Minimize before you optimize: remove duplicated and low-value streams first.
2. Tie signals to SLOs: if a metric or alert cannot impact a decision, reconsider it.
3. Prefer structured events over verbose logs for business and product telemetry.
4. Use adaptive sampling: full fidelity when failing, economical during steady state.
5. Keep raw where it’s cheap, index where it’s valuable.

Practical Tactics That Save Money (Without Losing Signals)​

1) Right-size logging​

• Convert repetitive text logs to structured events with bounded cardinality.
• Drop high-chattiness DEBUG in production by default; enable targeted DEBUG windows when investigating.
• Use log levels to route storage: “hot” for incidents, “warm” for audits, “cold” for long-term.

2) Adaptive trace sampling​

• Keep 100% sampling on error paths, retries, and SLO-adjacent routes.
• Reduce sampling for healthy, high-volume endpoints; increase on anomaly detection.
• Elevate sampling automatically when deploys happen or SLO burn accelerates.

3) Metrics with budgets​

• Prefer low-cardinality service-level metrics (availability, latency P95/P99, error rate).
• Add usage caps per namespace or team to prevent runaway time-series.
• Promote derived, decision-driving metrics to dashboards; demote vanity metrics.

4) Event-first product telemetry​

• Track business outcomes with compact events (e.g., signup_succeeded, api_call_ok).
• Enrich events once at ingest; avoid re-parsing massive log lines later.
• Use event retention tiers that match analysis windows (e.g., 90 days for product analytics).

A Cost-Efficient Observability Architecture​

A practical pattern:

• Edge ingestion with lightweight filters (drop obvious noise early)
• Split paths: metrics → time-series DB; traces → sampled store; events → columnar store
• Cold object storage for raw, cheap retention; hot indices for incident triage
• Query federation so responders see a single timeline across signals

This architecture supports “zoom in on demand”: start with an incident’s SLO breach, then progressively load traces, logs, and events only when necessary.

Budget Policies and Alerting That Respect Humans (and Wallets)​

Pair these with correlation-based alerting. Collapse cascades (DB down → API 5xx → frontend errors) into a single incident to reduce noise and investigation time.

How Tianji Helps You Do More With Less​

• Unified feed correlates checks, metrics, traces/events in one place: see Feed state model and Channels.
• Lightweight server status with quick setup: server status reporter.
• Flexible, privacy-friendly telemetry for apps and websites: telemetry intro, website tracking script, and events tracking.
• Custom monitors and synthetic flows to protect SLOs with minimal overhead: custom script monitor.

With Tianji, you keep data ownership and can tune which signals to collect, retain, and correlate—without shipping every byte to expensive proprietary backends.

Implementation Checklist​

• Inventory all telemetry producers; remove duplicates and unused streams
• Define SLOs per critical user journey; map signals to decisions
• Set default sampling, then add automatic boosts on deploys and anomalies
• Apply cardinality budgets; alert on budget burn, not just raw spikes
• Route storage by value (hot/warm/cold); add auto-archival policies
• Build correlation rules to collapse cascades into single incidents

Key Takeaways​

1. Cost-aware observability focuses on signals that protect user experience.
2. Use adaptive sampling and storage tiering to control spend without losing fidelity where it matters.
3. Correlate signals into a unified timeline to cut noise and accelerate root-cause analysis.
4. Tianji helps you implement these patterns with open, flexible building blocks you control.