Database stability
Continuously monitor database performance to improve service stability and quality.
EZIS for PostgreSQL · Product overview
EZIS for PostgreSQL
PostgreSQL performance monitoring
EZIS for PostgreSQL integrates and analyzes every signal affecting PostgreSQL DBMS performancein real time to ensure optimal performance.
WHY EZIS
Why EZIS for Database Monitoring?
Connect stability, immediacy, efficiency, cost control and scalability in one monitoring system.
Real-time monitoring
Analyze critical health and performance signals live for faster detection and response.
Higher efficiency
Automate recurring checks and performance reviews to improve database operations.
Lower cost
Prevent issues and detect degradation early to reduce incident and operating costs.
Scalability
Expand management through one view as databases and services grow.
What makes EZIS different
Real-time performance analysis
Session monitoring
Timeline-based analysis and tracing
Intuitive SQL tracing
PostgreSQL DMA monitoring
Collects data even during incidents or hangs through Direct Memory Access and handles large volumes quickly.
Oracle USS DMA monitoring
Captures SQL precisely without collection gaps and makes it available in real time.
Vacuum prediction
Uses dead tuples, Auto Vacuum estimates and thresholds to predict and simplify maintenance.
Read View and Purge
Tracks History List Length from long queries and transactions and analyzes Purge impact.
Database replication monitoring
Checks Master/Slave state and transaction gaps to verify healthy replication.
Database security
Audits database access and SQL execution history for stronger control and traceability.
EZIS FOR POSTGRESQL · PRODUCT SCREEN
EZIS operational screens and capabilities
Dashboard
Compare core DBMS and OS health in one view to understand the operational flow quickly.
Performance Monitoring
Review live performance indicators and session status to detect service delays early.
Wait Analysis Monitoring
Connect session waits, locks and transactions to identify bottleneck causes.
Auto Vacuum Schedule
Use dead tuples, Auto Vacuum ratios and thresholds to anticipate Vacuum work.
Performance History
Change the date and interval to compare historical performance, sessions and SQL.
Elapsed Time Analysis
Find SQL degradation points in one-minute blocks covering the full 24-hour day.
Dead Tuple Analysis
Visualize dead-tuple volume over time to narrow down problem intervals and causes.
Event Analysis
Review threshold events and alarm history together to trace incident signals.