monitoring-database-transactions

# 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

| Error | Cause | Solution |
|-------|-------|---------|
| `pg_stat_activity` returns no rows for other sessions | Missing `pg_monitor` role or `track_activities` disabled | Grant `pg_monitor` role; set `track_activities = on` in postgresql.conf |
| Lock monitoring query times out | Massive lock table during contention storm | Query `pg_locks` with a statement_timeout; reduce monitoring frequency during incidents |
| False positive alerts for long-running transactions | Batch jobs or maintenance operations trigger duration alerts | Create an exclusion list for known batch job PIDs or application users; use separate thresholds for batch vs OLTP |
| Transaction throughput drops to zero | Connection pool exhaustion or database crash | Check `max_connections` usage; verify database process is running; check for full disk or OOM conditions |
| Monitoring queries add overhead | High-frequency polling of system catalogs | Reduce polling interval to every 60 seconds; use `pg_stat_statements` for aggregated stats instead of per-query monitoring |

## 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