Database Transaction Monitor

Overview

Monitor active database transactions in real time to detect long-running queries, lock contention, uncommitted transactions, and transaction throughput anomalies across PostgreSQL, MySQL, and MongoDB.

Prerequisites

• Database credentials with access to system catalogs (pg_stat_activity, information_schema.PROCESSLIST, or MongoDB currentOp)
• psql, mysql, or mongosh CLI installed
• Permissions to view other sessions' transactions (PostgreSQL: pg_monitor role; MySQL: PROCESS privilege)
• Baseline metrics for normal transaction duration and throughput
• Alerting infrastructure (email, Slack webhook, or PagerDuty) for notifications

Instructions

1. Query the active transaction view to establish a baseline. For PostgreSQL: SELECT pid, state, query_start, now() - query_start AS duration, query FROM pg_stat_activity WHERE state != 'idle' ORDER BY duration DESC. For MySQL: SELECT id, user, host, db, command, time, state, info FROM information_schema.PROCESSLIST WHERE command != 'Sleep'.

2. Identify long-running transactions by filtering for duration exceeding the application's expected transaction time. Set initial thresholds at 30 seconds for OLTP workloads or 5 minutes for batch/reporting workloads.

3. Detect idle-in-transaction sessions that hold locks without executing queries. For PostgreSQL: SELECT pid, state, query_start, now() - state_change AS idle_duration FROM pg_stat_activity WHERE state = 'idle in transaction' AND now() - state_change > interval '5 minutes'.

4. Monitor lock contention by querying the lock manager. For PostgreSQL: SELECT blocked_locks.pid AS blocked_pid, blocking_locks.pid AS blocking_pid, blocked_activity.query AS blocked_query FROM pg_catalog.pg_locks blocked_locks JOIN pg_catalog.pg_locks blocking_locks ON blocking_locks.locktype = blocked_locks.locktype. For MySQL: SELECT * FROM information_schema.INNODB_LOCK_WAITS.

5. Track transaction throughput by sampling pg_stat_database (xact_commit, xact_rollback) or MySQL Com_commit / Com_rollback status variables at regular intervals. Calculate commits/second and rollback ratio.

6. Create monitoring scripts that run on a cron schedule (every 30-60 seconds) to capture transaction metrics and write to a time-series store or log file.

7. Configure alerting thresholds: transactions exceeding 60 seconds, idle-in-transaction sessions exceeding 5 minutes, lock wait queues exceeding 10 waiters, and rollback ratio exceeding 5%.

8. Build a transaction summary dashboard query that shows: active transaction count, average duration, longest running transaction, lock wait count, and commits-per-second over the last hour.

9. Implement automatic remediation for known-safe scenarios: terminate idle-in-transaction sessions older than 30 minutes using SELECT pg_terminate_backend(pid) (PostgreSQL) or KILL connection_id (MySQL), with logging of terminated sessions.

10. Generate weekly transaction health reports summarizing peak transaction counts, P95/P99 duration percentiles, deadlock occurrences, and long-running transaction incidents.

Output

• Transaction monitoring queries tailored to the specific database engine in use
• Monitoring scripts (shell or Python) for scheduled transaction health checks
• Alert configuration with threshold definitions and notification channel setup
• Dashboard queries showing transaction throughput, duration distribution, and lock metrics
• Weekly health report template with transaction performance trends and anomaly highlights

Error Handling

Examples

Detecting a connection leak in a web application: Transaction count steadily increases over hours while commit rate remains flat. Monitoring reveals hundreds of idle in transaction sessions from the application server. Root cause: missing connection.close() in error handling paths. Resolution: terminate stale sessions and fix application connection management.

Identifying lock contention during peak hours: Dashboard shows lock wait count spiking from 0 to 50+ between 2-4 PM daily. Lock analysis reveals a nightly reporting query overlapping with high-volume order processing. Resolution: reschedule reporting queries to off-peak hours and add NOWAIT hints to critical transaction paths.

Tracking transaction rollback ratio spike: Rollback ratio jumps from 1% to 15% after a deployment. Transaction monitor logs show serialization failures on a frequently updated inventory table. Resolution: reduce transaction isolation level from SERIALIZABLE to READ COMMITTED for non-critical paths and add retry logic for serialization failures.

Resources

• PostgreSQL monitoring views: https://www.postgresql.org/docs/current/monitoring-stats.html
• MySQL performance schema: https://dev.mysql.com/doc/refman/8.0/en/performance-schema.html
• MongoDB currentOp: https://www.mongodb.com/docs/manual/reference/method/db.currentOp/
• pg_stat_statements extension: https://www.postgresql.org/docs/current/pgstatstatements.html
• Lock monitoring best practices: https://wiki.postgresql.org/wiki/Lock_Monitoring