Synthetic Monitoring
Synthetic monitoring is a proactive testing approach that simulates user interactions or system behaviors to evaluate performance, availability, and functionality. By generating artificial transactions or requests at regular intervals, organizations can detect issues before they impact real users or critical business operations.
How synthetic monitoring works
Synthetic monitoring operates by executing predefined scripts or transactions that mimic real-world interactions. These automated tests run at scheduled intervals, typically collecting time-series data about system performance and behavior.
The monitoring system records key metrics such as:
- Response times
- Availability percentages
- Transaction success rates
- API endpoint health
- Service level agreement (SLA) compliance
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Applications in financial systems
In financial markets, synthetic monitoring is crucial for validating critical trading infrastructure:
- Order flow simulation
- Market data feed connectivity
- Settlement system availability
- Trading algorithm behavior
For example, a trading system might use synthetic orders to verify:
- Order routing functionality
- Latency measurements
- Price feed accuracy
- Risk check execution
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Benefits and considerations
Key advantages
- Early detection of issues
- Consistent baseline measurements
- Geographic performance insights
- SLA validation
- Regression testing
Implementation challenges
- Test script maintenance
- Resource consumption
- False positives
- Production impact considerations
- Cost of monitoring infrastructure
Best practices for implementation
-
Strategic test placement
- Deploy monitors across relevant geographic regions
- Test all critical business paths
- Monitor from multiple network perspectives
-
Data management
- Store monitoring results in a time-series database
- Implement appropriate data retention policies
- Consider data compression strategies
-
Alert configuration
- Define meaningful thresholds
- Implement progressive alerting
- Avoid alert fatigue through proper tuning
The effectiveness of synthetic monitoring depends on how well it reflects real-world usage patterns while providing actionable insights into system performance and reliability.