Since an API Proxy can be loaded into multiple Environments, metrics are queried based on environment. Environment selection must be made from the top of the page.
Features
General Status Monitoring
You can view the general status of API Proxies, total request count, success/error rates
Performance Metrics
You can analyze response times, processing times, and backend performance metrics
Traffic Analysis
You can monitor API traffic over time, detect peak periods
Error Tracking
You can monitor error rates, error types, and problematic endpoints
Automatic Refresh
You can track metrics live by activating the page’s automatic refresh feature
Environment-Based Filtering
You can view separate metrics for different environments
Dashboard Metrics
The dashboard includes the following main metric categories:General Metrics
Traffic Metrics
Request Count Over Time
- Change in request count over time (line chart)
- Detection of peak periods
- Trend analysis
Request Distribution by API Proxy
- Which API Proxies are used how much
- Usage intensity comparison
- Most used APIs
Request Distribution by Endpoint
- Most called endpoints
- Endpoint usage statistics
- Call count ranking
HTTP Method Distribution
- Usage rates of GET, POST, PUT, DELETE methods
- Request counts by method
Performance Metrics
Average Response Time
- Average response time of all requests
- Change in response time over time
- Performance trend analysis
Response Time Distribution
- Response times in different time ranges
- P50, P95, P99 percentile values
- Min/Max response times
Backend Performance
- Response times of backend APIs
- Backend connection times
- Upstream performance metrics
Slowest Endpoints
- Endpoints with highest response times
- APIs requiring optimization
Error Metrics
Status Code Distribution
- 2xx, 4xx, 5xx status code rates
- Success/Error distribution (pie chart)
Error Types
- Authentication errors
- Routing errors
- Policy errors
- Backend errors
APIs with Most Errors
- API Proxies with high error rates
- Problematic endpoints
Error Rate Over Time
- Change in error rate over time
- Detection of error spikes
Client Metrics
Most Active Clients
- Clients sending most requests
- Request counts by client
- Analysis by Client IP/Key
Client Status Distribution
- Success/error rates by client
- Blocked/Throttled clients
Automatic Refresh
The dashboard enables live tracking of metrics with the automatic refresh feature.1
Activate Automatic Refresh Option
Select the refresh interval from the Automatic Refresh dropdown menu at the top of the page
2
Select Refresh Interval
- 10 seconds
- 30 seconds
- 1 minute
- 5 minutes
3
Live Monitoring
Metrics on the screen are automatically refreshed at the selected interval
Environment-Based Querying
Dashboard metrics are queried based on environment. Separate metrics are displayed for each environment.Environment Selection
1
Open Environment Dropdown
Open the environment selector dropdown at the top of the page
2
Select Relevant Environment
Select the environment you want to see metrics for (Development, Test, Production, etc.)
3
Metrics Load
Metrics for the selected environment are automatically loaded and displayed
Different environments may be connected to different Elasticsearch connectors. If multiple Elasticsearch Connectors are defined, data from the first Elasticsearch Connector added in Apinizer Management Console is displayed.
Time Range Filtering
You can select a time range for metrics:Metric Interpretation and Usage
You can monitor the health of your APIs by correctly interpreting dashboard metrics.Success Rate Interpretation
Response Time Interpretation
Traffic Analysis
Peak Periods:- Which hours have heavy traffic?
- Which days receive more requests?
- Should capacity planning be done?
- Are there unexpected traffic increases?
- Is there suspicion of DDoS attack?
- Is rate limiting sufficient?
- Is traffic increasing / decreasing?
- Are there seasonal changes?
- How is the impact of new feature launch?
Problem Detection and Resolution
You can detect and resolve common problems with the dashboard.High Error Rate
Problem Detection:- Error rate above 5%
- High 4xx/5xx rate in Status Code Distribution
- Concentrated errors on specific endpoints
- Check the APIs with Most Errors metric
- Filter failed requests from the API Traffic page
- Perform detailed error analysis with Tracing
- Backend API issues → Inform backend team
- Authentication errors → Check credentials
- Validation errors → Fix request format
Slow Performance
Problem Detection:- Average response time increased
- Problematic APIs in Slowest Endpoints list
- Low backend performance
- Review the Slowest Endpoints metric
- Analyze policy performance with Tracing
- Check backend response times
- Add cache policy
- Optimize database queries
- Adjust backend timeout values
- Optimize or remove slow policies
Abnormal Traffic Increase
Problem Detection:- Sudden and unexpected request increase
- Heavy requests from specific IPs
- Rate limit violations
- Detect spikes in the Request Count Over Time chart
- Check the Most Active Clients metric
- Examine suspicious requests with API Traffic
- Add rate limiting and throttling policies
- Apply IP blacklist/whitelist
- Activate DDoS protection
Use Cases
Scenario 1: Daily Operational Monitoring
Purpose: Check API health daily.1
Perform Morning Check
- General metrics: Success rate, total request count
- Were there any issues overnight?
2
Check Error Rate
- Is error rate at normal levels?
- Are there new error types?
3
Review Performance Metrics
- Are response times normal?
- Are there slowdowns?
4
Detect Abnormal Situations
- Unexpected traffic increases
- New error spikes
- Time Range: Last 24 Hours
- Automatic Refresh: 5 Minutes
Scenario 2: Weekly Performance Analysis
Purpose: Weekly trend analysis and capacity planning.1
Review Traffic Trends
- Did traffic increase / decrease?
- Which days are busier?
2
Analyze Performance Changes
- Did response times change?
- How is backend performance?
3
Identify Most Used APIs
- Which APIs are used most?
- Is optimization needed?
4
Perform Capacity Planning
- Is current capacity sufficient?
- Is scaling necessary?
- Time Range: Last 7 Days
- Comparison: With previous week
Scenario 3: Post-Incident Analysis
Purpose: Root cause analysis after a problem occurs.1
Determine Problem Time
- Select custom time range (incident time)
- Can spike or anomaly be seen?
2
Identify Affected APIs
- Which APIs were affected?
- What was the error type?
3
Root Cause Analysis
- Backend problem?
- Policy error?
- Traffic increase?
4
Detailed Review with Tracing
- Examine problematic requests with Tracing
- At which step did the error occur?
- Time Range: Custom (incident time ±1 hour)
- Focus: Error metrics and affected endpoints
Best Practices
Perform Regular Monitoring
- Daily operational checks
- Weekly trend analyses
- Monthly capacity planning evaluation
Create Baseline
- Record normal performance metrics
- Monitor deviations from baseline
- Set alarm thresholds
Use Automatic Refresh
- Automatic refresh for real-time monitoring
- Appropriate refresh interval selection (1-5 minutes)
- Consider Elasticsearch load
Environment-Based Monitoring
- Separate monitoring for each environment
- Prioritize Production monitoring
- Compare between environments
Learn Metric Interpretation
- Know success rate thresholds
- Set response time expectations
- Quickly detect abnormal situations
Use Detailed Analysis Tools
- Detect problems from Dashboard
- Examine in detail with API Traffic
- Find root cause with Tracing
Limitations and Considerations
Data Freshness Time:
- Metrics are created from data in Elasticsearch
- It may take a few seconds for log records to reach Elasticsearch
- Shows near real-time data, not real-time
Related Resources
API Traffic
Detailed traffic analysis of all API Proxies
Tracing
Detailed trace and debug operations
Query Editor
Advanced query and filter definitions
Reports
Customized analytics reports
API Traffic Log Settings
Log record configurations
Analytics Engine
Analytics architecture and components