Developing a Fail-Safe Database Backup and Recovery System for High Availability

About project:

Client overview

A leading technology company needed a robust database recovery solution to protect their critical operations from potential data loss and minimize system downtime. Their existing PostgreSQL backup systems were inadequate for their rapid recovery requirements, putting their business continuity at risk.

Tech Stack:

Kubernetes, Custom Script Management, Basic Logging, Manual Error Handling

Tech stack after migration:

Apache Airflow, Secure Credential Storage (e.g., HashiCorp Vault), Enhanced Logging (e.g., ELK Stack), Python scripts

Time to deliver project:

6-8 Weeks

Problem

  • The client was facing a serious issue: they had no reliable way to quickly restore their database in the event of a problem. This put their operations at significant risk, as any downtime or data loss could lead to major disruptions.

Inspection

  • We identified the need for a tool that could quickly rebuild the entire database structure from scratch and restore critical data rapidly. Standard backup and restore tools in PostgreSQL were insufficient. To address this, we set up a separate database solely for storage, mirroring the main database's structure. A validator sync was implemented to track changes in the main database and replicate them to the backup. We also created dedicated sinks that regularly dump data from the main data warehouse to the backup. In case of a failure, a script was developed to first restore the entire database structure, followed by parallel data backfill scripts that prioritize and restore the most important data within 20-30 minutes.

Recommendation

  • Always implement a robust data recovery mechanism, even if you are confident in the reliability of your storage environment.

Resolution

We implemented a comprehensive data recovery solution, including a backup bucket, backfill scripts, and a full database restore process. This system allows for the complete restoration of critical data within 20-30 minutes of an issue, ensuring minimal downtime and data loss.

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