「Monitoring」タグの記事が3件件あります

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.

Alert fatigue happens when teams receive so many notifications that the truly critical ones get buried. The result: slow responses, missed incidents, and burned-out engineers. The goal of a modern alerting system is simple: only wake humans when action is required, include rich context to shorten time to resolution, and suppress everything else.

Why Alert Fatigue Happens​

Most organizations unintentionally create noisy alerting ecosystems. Common causes include:

1. Static thresholds that ignore diurnal patterns and seasonal traffic.
2. Duplicate alerts across tools without correlation or deduplication.
3. Health checks that confirm liveness but not correctness of user flows.
4. Paging for warnings instead of issues requiring immediate human action.
5. Missing maintenance windows and deployment-aware mute rules.

When every blip pages the on-call, people quickly learn to ignore pages—and that is the fastest way to miss real outages.

Start With SLOs and Error Budgets​

Service Level Objectives (SLOs) translate reliability goals into measurable targets. Error budgets (the allowable unreliability) help decide when to slow releases and when to page.

• Define user-centric SLOs: availability for core endpoints, latency at P95/P99, success rates for critical flows.
• Set page conditions based on budget burn rate, not just instantaneous values.
• Prioritize business-critical paths over peripheral features.

Design Principles for Actionable Alerts​

1. Page only for human-actionable issues. Everything else goes to review queues (email/Slack) or is auto-remediated.
2. Use correlation to reduce noise. Group related symptoms (API 5xx, DB latency, queue backlog) into a single incident.
3. Include diagnostic context in the first alert: recent deploy, top failing endpoints, region breakdown, related logs/metrics.
4. Implement escalation policies with rate limiting and cool-downs.
5. Respect maintenance windows and deploy windows automatically.
6. Use multi-signal detection: combine synthetic checks, server metrics, and real user signals (RUM/telemetry).

From Reactive to Proactive: Synthetic + Telemetry​

Reactive alerting waits for failures. Proactive systems combine synthetic monitoring (to test critical paths) and telemetry (to see real user impact).

• Synthetic monitoring validates complete flows: login → action → confirmation.
• Real User Monitoring reveals device/network/browser-specific degradations.
• Cross-region checks detect localized issues (DNS/CDN/regional outages).

With Tianji you can combine these signals in a unified timeline so responders see cause and effect in one place. See: Feed overview, State model, and Channels.

Building a Quiet, Reliable On-Call​

Implement these patterns to cut noise while improving MTTR:

1) Explicit Alert Taxonomy​

• Critical: Page immediately; human action required; data loss/security/major outage.
• High: Notify on-call during business hours; fast follow-up; customer-impacting but contained.
• Info/Review: No page; log to feed; analyzed in post-incident or weekly review.

2) Deploy-Aware Alerting​

• Tag telemetry and alerts with release versions and feature flags.
• Auto-create canary guardrails and roll back on breach.

3) Correlation and Deduplication​

• Collapse cascades (e.g., DB down → API 5xx → frontend errors) into one incident.
• Attach root-cause candidates automatically (change events, infra incidents, quota limits).

4) Context-Rich Notifications​

Include:

• Impacted SLO/SLA and current budget burn rate
• Top failing routes and exemplar traces
• Region/device breakdowns
• Recent changes (deploys/config/infra)
• Runbook link and one-click diagnostics

5) Progressive Escalation​

• Start with Slack/email; escalate to SMS/call only if not acknowledged within target time.
• Apply per-service quiet hours and automatic silences during maintenance.

Practical Metrics to Track​

• Page volume per week (target declining trend)
• Percentage of pages that lead to real actions (>70% is a healthy target)
• Acknowledgement time (TTA) and time to restore (TTR)
• False positive rate and duplication rate
• Budget burn alerts avoided by early correlation

How Tianji Helps​

• Unified feed for events, alerts, and telemetry with a consistent state model and flexible channels.
• Lightweight server status reporting for CPU, memory, disk, and network: server status reporter.
• Correlated timeline across checks, metrics, and user events to surface root causes faster.
• Extensible, open-source architecture so you control data and adapt alerts to your stack.

Key Takeaways​

1. Define SLOs and page on budget burn—not raw spikes.
2. Correlate symptoms into single incidents and include rich context.
3. Page only for human-actionable issues; escalate progressively.
4. Combine synthetic flows with telemetry for proactive detection.
5. Use Tianji to consolidate signals and reduce MTTR.

Quiet paging is achievable. Start by measuring what matters, suppressing the rest, and investing in context so every page moves responders toward resolution.