RunLoyal is a SaaS platform serving the pet services industry, running its application data on Amazon Aurora MySQL in AWS. As usage grew, severely inefficient, long-running queries began consuming excessive CPU resources and creating sustained database-load spikes during peak application usage.

Business Challenges

Query inefficiency at the database layer surfaced as visible slowness across RunLoyal's application for end users.

CPU-intensive long-running queries

Severely inefficient, long-running queries consumed excessive CPU resources, creating sustained database-load spikes during peak usage.

Connection contention

Increased query execution time caused connection contention and slower transaction processing.

Elevated latency across workflows

Response latency rose across critical application workflows as query performance degraded.

Inconsistent user experience

Clients and end users experienced application lag, delayed page loads, slow data retrieval, and an overall inconsistent experience.

Project Objectives

Geopits was brought in to reduce database query latency, eliminate application slowness, and ensure a stable, consistent user experience across RunLoyal's platform.

Key Goals:

Reduce query latency and get rid of CPU load spikes
Optimize database resource utilization without disruptive scaling
Deploy fixes to production with zero downtime

Solution Provided by Geopits

Geopits diagnosed and fixed the highest-impact queries on an isolated replica before rolling optimizations out to production with zero downtime.

Isolated replica for safe testing

An isolated replica of the production Aurora MySQL instance was created to safely investigate and validate performance changes without affecting live users.

Query and execution plan analysis

The highest-impact long-running queries were identified by execution time, frequency, and CPU usage, then analyzed full table scans, inefficient joins, missing indexes, and unnecessary data reads.

Targeted indexing strategy

Single-column and composite indexes were designed and implemented to improve filtering, join operations, sorting, and data retrieval paths.

Validated, zero-downtime rollout

Optimized queries were tested and validated on the replica before a zero-downtime rollout to production, with continuous monitoring of performance, CPU, and response time.

Capabilities

Before

After

Query execution time

Long-running, CPU-intensive

Significantly reduced

CPU and database load

Sustained spikes at peak usage

Lowered, more stable

Application response

Lag, delayed page loads

Faster and consistent across workflows

Infrastructure costs

Risk of unnecessary scaling

Avoided through query-level optimization

Key outcomes

Faster query execution

Query execution time was significantly reduced after implementing targeted indexes and query optimizations.

Lower CPU and database load

CPU spikes and overall Aurora MySQL database load dropped, easing pressure on the platform during peak usage.

Consistent application performance

Critical application workflows responded faster, application slowness was eliminated, and users received a more stable, consistent experience.

Conclusion

Geopits optimized RunLoyal's Aurora MySQL environment through targeted query analysis and indexing, reducing CPU load and query latency, eliminating application slowness, and avoiding unnecessary infrastructure scaling, all with zero downtime.

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