The client is an e-commerce analyticsplatform whose production environment runs on Amazon RDS for PostgreSQL. Duringa major promotional sale, traffic peaked and the database came under heavy,sudden load.

Business Challenges

A sudden surge of identical, resource-heavy queries during peak sales pushed the database past its limits, with no connection-level protection in place.

Sudden load spike during peak sales

Connections and database load climbed sharply during a major promotional sale.

Many connections running the same query

A large number of concurrent connections executed the same query at once, driving up CPU, memory and active sessions.

Database restart and downtime

The database became unresponsive and was restarted, causing more than 5 minutes of downtime.

Application slowness and errors

Resource exhaustion degraded service for the application while the incident lasted.

No connection-level protect

Without pooling or timeouts, nothing limited the blast radius, and the risk of a repeat incident remained until the root cause was addressed.

Project Objectives

Geopits was asked to restore stability to the client's Amazon RDS for PostgreSQL database, find the cause of the load spike and reduce the risk of it happening again.

Key Goals:

Monitor thedatabase after the restart and identify the query driving the load
Mitigate theload safely, with client confirmation before any action
Tune the queryand add connection-level safeguards to prevent recurrence

Solution Provided by Geopits

Geopits worked through the incident in order: monitor, identify, confirm with the client, mitigate, then tune.

Post-restart monitoring

The team watched database load (average active sessions) in Performance Insights, connection count against max_connections, and running queries and wait events through pg_stat_activity.

Root cause identification

One query was being executed repeatedly by many concurrent connections. It accounted for the majority of database load and held sessions open long enough for connections to pile up.

Client-validated mitigation

Because terminating a query can affect application behavior, Geopits shared the query details with the client first. After their confirmation, the offending sessions were terminated and the load came down.

Query analysis and recommendations

Top SQL, wait events and execution time were reviewed in Performance Insights, with recommendations on indexing, query rewrites, connection pooling, timeouts and caching.

Results & Business Impact

After the client applied the recommended changes, CPU, connections and database load returned to expected levels, with database load recovering first.

Capabilities

Before

After

CPU utilization

Peaked at 97%

50-60%

Connections

Peaked at 1,070

Below 1,000

Database load (AAS)

Peaked at 400

Below 100

Availability

More than 5 minutes of downtime

Operating normally

Connection protection

No pooling or timeouts

Pooling and timeouts recommended and applied

Key outcomes

Root cause isolated

One unoptimized query was identified as the driver of the majority of database load.

Client-confirmed mitigation

No query was terminated until the client had reviewed and approved the action.

Metrics back to normal

CPU settled at 50-60%, connections returned below 1,000 and database load fell below 100.

Query tuned, guardrails added

The client applied Geopits' recommendations, reducing query execution time and adding connection-level protection.

Prevention built in

Alerting on connection count, CPU and average active sessions, regular top SQL reviews, load testing before release, and a documented runbook for confirming and terminating problem queries with the client.

Conclusion

Geopits helped an e-commerce analytics platform recover from a PostgreSQL load spike on Amazon RDS during a major sale. CPU peaked at 97%, connections reached 1,070 and database load hit 400 before the team identified the single query responsible, confirmed the action with the client and recommended query tuning and connection safeguards. CPU settled at 50–60%, connections returned below 1,000 and database load fell below 100.

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