Mastering Massive Data Migration: Observe.AI’s Zero-Downtime Blueprint

Moving nearly 10TB of legacy data while maintaining uninterrupted service is no small feat. Observe.AI’s recent migration of 1.8 billion records from an overloaded MongoDB v0 to a modern architecture comprising MongoDB v1, Amazon S3, and Snowflake, showcases how planning, precision, and smart engineering can deliver high-throughput migration with zero downtime.

Why Change Was Necessary

The legacy MongoDB v0 system was struggling under the weight of deeply nested schemas and bloated JSON payloads. These issues resulted in performance bottlenecks, making sharding too risky and expensive. The solution? A rearchitected stack: MongoDB v1 for operational data, S3 for heavy unstructured content, and Snowflake for analytics with each chosen for scalability and speed.

Phased Migration: Safety in Stages

• Parallel Writes: Services wrote to both v0 and v1, with rigorous backfills and validation ensuring data parity.
  
  
• Parallel Reads: Feature flags enabled per-tenant migration, allowing performance benchmarking on the new stack.
  
  
• Controlled Cutover: All new writes and reads switched to v1, with v0 kept for safe rollback.
  
  
• Decommissioning: The old stack was fully retired, and v1 became the single source of truth.

This methodical rollout enabled continuous testing, rapid rollback if needed, and kept systems perfectly in sync.

Migration at Scale: Divide and Parallelize

To manage complexity, migration tasks were split by tenant and daily data slices. Each task covering extraction, transformation, loading, and verification was managed through a migration request queue. This granularity allowed high levels of parallelism and made troubleshooting straightforward.

Optimizing Throughput and Reliability

• Multi-threading: Each migration pod used multiple worker threads, assigned by tenant, processing daily tasks sequentially for clarity and speed.
  
  
• Multi-pod Scaling: Deploying multiple pods across Kubernetes clusters enabled horizontal scaling and non-overlapping task processing.
  
  
• Fine-Tuned Performance: Load testing revealed the sweet spot of about 10 threads per pod and 10,000-document batches for peak throughput without straining resources.

Handling Heavy Data and Streaming

• Large fields, such as transcripts, were moved to S3 with efficient, structured keys. Up to 100 threads per pod uploaded in parallel.
  
  
• Bulk inserts into MongoDB v1 used streamlined schemas and minimal indexing. Errors and duplicates were logged and resolved idempotently.
  
  
• Minimal durability settings (w=1, journaling off) maximized speed, with integrity checks post-migration to ensure accuracy.

Real-Time Analytics with Kafka and Snowflake

Data was streamed via Kafka to Snowflake using the same real-time ETL pipelines as live data. This enabled instant analytics on migrated data and doubled as a live stress test for analytics infrastructure. Deduplication and robust error handling maintained consistency without needing massive reloads.

Validation, Resilience, and Lessons Learned

• Initial staging migrations surfaced issues with memory usage and batch sizing.
• Planned interruptions and rigorous dry runs built resilience and ensured idempotency, enabling recovery from failures without data loss.
• Post-migration audits that is comparing counts, running spot checks, and verifying S3 object existence helped ensure even minor discrepancies were addressed promptly.

Strategic Takeaways

• Granular partitioning made the process manageable and recoverable.
• Idempotent, resilient design was vital for safe retries and restarts.
• Streaming pipelines allowed for live validation and seamless cutover.
• Proactive scaling (e.g., NVMe upgrades) supported throughput goals.
• Layered validation including counts, samples, and checksums, guaranteed data integrity.

Looking Forward

With their migration complete, Observe.AI now enjoys a more scalable and analytics-ready platform. Future ambitions include automated scaling, enhanced data quality checks, and exploring even more robust distributed processing. The project’s lessons including partitioning, parallelism, validation, and communication helped set a new standard for large-scale data migrations at Oberseve.ai

Final Thoughts

Observe.AI’s experience demonstrates that even the largest, most complex migrations are achievable with disciplined planning and modern architecture. Their approach not only handled today’s challenges but also paved the way for future growth and innovation.