Case Study: Migrating 500GB from Postgres to MongoDB Using Mongoose.Cloud

This case study walks through the migration of a mid-sized SaaS product's primary datastore from PostgreSQL to MongoDB. The project involved a 500GB dataset, complex relational structures, and a requirement of near-zero downtime. We used Mongoose.Cloud to manage schema evolution, orchestrate data transformations, and coordinate deployments across teams.

Background and goals

The client had outgrown some aspects of their relational model — they needed more predictable horizontal scaling for denormalized workloads and finer-grained document-level control for their multi-tenant architecture. Goals included:

• Move 500GB of relational data with minimal downtime
• Maintain strong consistency for critical reads and writes
• Allow iterative schema changes and feature flags for rollout

Approach overview

We split the migration into three phases: analysis & modeling, dual-write & verification, and cutover. Each phase leveraged Mongoose.Cloud features for safety and observability.

Phase 1 — Modeling

We began by mapping relational schemas to document structures. Rather than blindly embedding all relations, we analyzed access patterns and decided which relationships benefited from embedding versus referencing. For example, user profiles and small preference blobs were embedded while large audit logs and invoices were referenced.

Mongoose.Cloud assisted with schema previewing — developers could upload proposed schemas and run a simulation of how a sample dataset would be represented in the new model. This reduced guesswork and surfaced corner cases early.

Phase 2 — Dual-write and ETL

To ensure a smooth transition, we implemented a dual-write strategy. Application services wrote to both Postgres and MongoDB for a configurable period, and a background reconciliation job verified consistency.

• Change data capture (CDC): We used a CDC pipeline to stream updates from Postgres to MongoDB for historical rows, using transformation logic to reshape data into documents.
• Migrations in Mongoose.Cloud: Declarative migration scripts performed index creation and small in-place updates. For heavy ETL, we used batch workers outside of request paths.
• Data validation: Continuous assertions compared checksums and record counts; discrepancies triggered alerts and paused promotion.

Phase 3 — Cutover

Cutover was performed during a low-traffic window. Because we had dual-write and reconciliation in place, switching read traffic to MongoDB was primarily a routing change. We used feature flags to gradually increase traffic and monitor key metrics.

Key technical challenges and solutions

Several challenges required careful strategies:

• Transaction semantics: Some workflows relied on Postgres transactions. We refactored those into idempotent application-level flows or leveraged MongoDB's multi-document transactions with careful ordering during the migration window.
• Large joins: Complex JOIN-heavy reports were replaced with precomputed views and aggregation pipelines that ran asynchronously and stored denormalized snapshots to minimize runtime complexity.
• Index tuning: Creating large indexes on production data is expensive. We staged index builds during off-peak hours with background creation and used throttling to avoid resource contention.

Results

The migration completed with less than two minutes of perceived downtime for end-users and without data loss. Post-migration observations included:

• Average read latency improved by 30% for denormalized endpoints.
• Write throughput increased under horizontal scaling with sharded clusters.
• Operational overhead decreased due to reduced joins and simpler caching strategies.

Lessons learned

Several lessons stand out:

• Prototype early: Small dataset prototypes quickly reveal modeling pitfalls.
• Keep transformation logic simple: Complex transforms are harder to test and reconcile.
• Invest in verification: Dual-write and continuous reconciliation are worth the upfront cost.

Conclusion

Migrations between paradigms are non-trivial, but with disciplined planning and the right tooling — like Mongoose.Cloud's schema previews, migration orchestration, and telemetry — they can be executed safely. If you are evaluating a migration, start with a small proof-of-concept that covers a representative subset of your workload and iterate from there.

If you'd like to discuss your migration, our team offers a migration readiness review that walks through the phases and helps estimate effort and risk.