Multi-Tenant CRM Platform Cut Query Latency by 40% After an AWS Database Migration

Project Overview

The company operates a SaaS CRM platform used by business teams to manage customer relationships, sales pipelines, and day-to-day operations across multiple tenant organizations. As the platform scaled, its distributed database architecture began struggling to keep pace with production workload demands. The team needed a data layer that could preserve strict multi-tenant isolation while eliminating the operational instability threatening customer-facing reliability.

• 100+ Business Teams Served
• 3 Citus Worker Nodes

The Challenge

The platform’s distributed database architecture had reached a breaking point, creating both stability and operational risks that needed urgent resolution:

• Recurring database crashes: Frequent crashes disrupted production availability, directly affecting customer-facing CRM operations and eroding platform reliability.
• Data chunking overhead: Distributing and routing data chunks across worker nodes created growing overhead as datasets and cross-shard queries increased in complexity.
• WAL file saturation: Excessive Write-Ahead Logging generation and replication between coordinator and worker nodes overwhelmed storage and I/O capacity.
• Elevated Mean Time to Recovery: Diagnosing crashes required deep inspection of distributed metadata and shard placement logs, extending recovery time significantly.
• Infrastructure over-provisioning: Maintaining coordinator and worker nodes added ongoing operational and cost overhead disproportionate to actual workload needs.

The Solution

The engineering team designed a AWS database migration path from PostgreSQL to Aurora migration that would eliminate distributed-database complexity while preserving full application compatibility and tenant data integrity:

• Consolidated database architecture: Replaced the multi-node distributed cluster with a single, highly available Amazon Aurora PostgreSQL cluster to remove coordinator/worker overhead entirely.
• Preserved application connection layer: Kept existing PostgreSQL and Sequelize connection strings intact, redirecting them to an Aurora writer endpoint with no application-layer rework required.
• Strict schema and tenant-isolation retention: Carried forward business-critical columns, composite keys, indexes, and tenant-isolation rules unchanged to avoid any data-integrity risk.
• Complete removal of legacy distributed metadata: Eliminated coordinator/worker metadata, shard placements, and distribution-specific operational commands from the environment.
• Cloud-native storage adoption: Leveraged Aurora’s log-structured storage layer, which pushes log records directly rather than writing full WAL files to disk, resolving the root cause of prior crashes and delivering measurable database performance optimization.
• Built-in high-availability database architecture: Adopted Aurora’s six-way data replication across three Availability Zones to support fast, automatic failover.

The Results

• 99.99% database uptime achieved post-migration with zero crash incidents
• Up to 40% reduction in average query latency for complex, previously cross-shard queries
• 60%+ improvement in P99 latency during peak traffic periods
• 70%+ reduction in database I/O wait times through cloud-native log storage
• 95% faster Mean Time to Recovery, with automatic failover in under 30 seconds
• ~30% reduction in Total Cost of Ownership for the database tier

Key Takeaway

Consolidating a distributed database into a managed, cloud-native architecture proved that stability and performance don’t require added infrastructure complexity. The AWS database migration gives the platform a resilient foundation for multi-tenant database scaling, built to support continued growth without reintroducing the operational risks of the past.