Database problems found
before your users find them.
Continuous query performance monitoring, verified backups, schema drift detection across environments, and capacity forecasts before you need them.
Why database problems always seem to surface at the worst time
Slow queries are found by users, not monitoring
A query that took 80ms last week is taking 4 seconds today. You find out when someone files a support ticket or a dashboard stops loading. By then the table has grown, the index is missing, and the problem is in production.
Backup verification is manual and often skipped
Backups run. Whether they restore successfully is another question. The test restore that should happen monthly gets pushed because there's always something more urgent — until there's a recovery incident.
Schema changes create surprises
A migration ran in staging but changed behaviour in production. A column was dropped that an upstream service still queries. Schema drift between environments is discovered at runtime, not before.
Capacity planning is based on intuition
Disk is 70% full. When will it hit 100%? Table growth trends are linear in your head but the actual data says otherwise. You're making infrastructure decisions on guesses.
From continuous monitoring to actionable recommendations
Continuous query performance monitoring
The agent monitors slow query logs and execution statistics across your databases. Queries that cross configurable latency thresholds are captured, analysed for root cause (missing index, stats staleness, lock contention), and surfaced with a specific recommendation — not just a flag.
Backup integrity verification
Automated backup verification runs on a defined schedule: the agent triggers a restore to a test environment, validates row counts and spot-check queries, and reports pass/fail. If a backup fails verification, the alert fires before the next backup cycle — not at recovery time.
Schema drift detection
Schema snapshots taken across environments. Differences between staging and production surfaced automatically — missing columns, type mismatches, dropped indexes, changed constraints. Detected before deployment, not after.
Capacity and anomaly alerting
Growth trends tracked per table, index, and tablespace. Forecasts generated from actual growth curves, not linear extrapolation. Anomalous patterns — sudden row count drops, unexpected replication lag spikes, lock wait time surges — alerted with context.
What the agent monitors and surfaces
Slow query detection
Queries exceeding latency thresholds identified with execution plan analysis and specific index or stats recommendations.
Index recommendations
Missing indexes identified from slow query patterns. Redundant indexes flagged for removal. Recommendations include estimated impact.
Backup verification
Automated restore-and-validate cycle. Every backup tested, not just confirmed as written.
Schema drift detection
Cross-environment schema comparison. Differences surfaced with specific field-level diffs before they cause production failures.
Replication lag monitoring
Primary-replica lag tracked continuously. Threshold breaches alerted with context on the queries driving the lag.
Capacity forecasting
Table and disk growth forecasts based on actual growth curves. Time-to-capacity estimates surfaced before they become urgent.
Lock and contention analysis
Long-running transactions and lock contention patterns detected. Blocking query chains surfaced with the full chain, not just the blocked query.
Anomaly detection
Row count anomalies, unexpected query pattern changes, and throughput drops detected and alerted with historical context.
Works with your databases and alerting stack
Alerts fire to PagerDuty or Slack. Recommendations tracked as Jira tickets. Schema diffs exported to your documentation. Your existing workflow stays intact.
View all integrations →
PostgreSQL
MySQL
Elasticsearch
AWS RDS
Azure SQL
PagerDuty
Slack
JiraPoint it at your slowest queries
Connect the agent to a read-only replica in the demo. We'll show you what it surfaces from your actual query performance data — not synthetic examples.