High Performance Moodle

Approach

When Moodle becomes mission-critical (enrollment peaks, simultaneous exams, heavy reporting), performance doesn’t improve with a single tweak—it must be engineered end-to-end. This service optimizes Moodle across the full stack to reduce latency, increase throughput, and prevent outages caused by saturation.

Optimization Strategies

To achieve superior performance at scale, we implement a multi-layer architecture and tuning strategy:

1. Database Layer

• HA/Scale Architecture: Replication and/or clustering depending on the context (Galera / Aurora or equivalent options), offloading reads when applicable.
• Query Tuning: Enable and analyze slow queries, rewrite bottleneck queries (reports, cohorts, enrolments), and optimize indexing.
• Connection Pooling: ProxySQL (or equivalent) to reduce connection overhead and stabilize traffic spikes.
• Controlled maintenance: housekeeping, large-table cleanup, lock/transaction review.

2. Caching and Sessions

• Redis (segmented by purpose): dedicated configuration for Sessions, MUC (Moodle Universal Cache), and application cache—preventing cross-load interference.
• Opcache tuning: optimize PHP Opcache (memory, revalidation, strings) to avoid recompilation and improve response time.
• MUC strategy: store selection and TTL tuning for critical caches (course modinfo, strings, config, etc.).

3. PHP-FPM and Web Tier

• PHP-FPM tuning: pm.*, worker limits, timeouts, queues, and process sizing to maximize concurrency without thrashing.
• Optimized web server: Nginx/Apache compression, keep-alive, limits, and buffers aligned with Moodle traffic patterns.
• Offloading: isolate heavy workloads (cron, adhoc tasks) so user traffic does not compete with batch processing.

4. Frontend and User Experience

• True lazy loading: defer heavy resources, iframes, and media to improve TTFB and LCP on long course pages.
• Asset optimization: minification, cache headers, versioning, and validation to eliminate render-blocking assets.
• Reduce “heavy pages”: identify blocks/widgets that trigger excessive queries (especially home pages and dashboards).

5. Observability and Load Testing

• Metrics + alerting: latency, throughput, error rates, PHP-FPM, Redis, and DB signals to detect degradation before incidents.
• Load testing: representative scenarios (mass login, navigation, concurrent quizzes, reporting) with before/after comparisons.
• Peak-event checklist: runbook for critical windows (enrollment/exams) with parameters and contingency actions.

Expected Outcomes

• Significant latency and load-time reduction on critical routes (login, course, quiz).
• Higher resilience during concurrency spikes without saturation-driven outages.
• An “operable” platform: monitoring, alerting, and faster incident response capability.