Open Source DevOps

Stash

Backup Tool for Kubernetes Volumes

DevOps & Platform 2017-2019 AppsCode Inc. Open Source

Project Overview

Stash is a Kubernetes-native backup solution that extends Restic to provide automated, scheduled backups for Kubernetes workloads with support for multiple cloud storage backends and disaster recovery capabilities.

Challenge

As organizations moved critical workloads to Kubernetes, they faced significant challenges in implementing reliable backup and disaster recovery strategies. Traditional backup solutions were not designed for cloud-native, containerized environments. Key challenges included:

  • No native Kubernetes backup solution for persistent volumes
  • Complex manual processes for backing up stateful applications
  • Lack of automation and scheduling for backup operations
  • Difficulty in managing backups across multiple clusters and cloud providers
  • No easy way to restore data in disaster recovery scenarios

Solution

Stash was developed as a Kubernetes Operator that automates backup and recovery operations for Kubernetes volumes. The solution provides:

  • Kubernetes-Native Design: Built as a CRD controller following Kubernetes Operator pattern
  • Restic Integration: Leverages Restic for efficient, incremental, encrypted backups
  • Multi-Backend Support: Works with AWS S3, GCS, Azure Blob, MinIO, and local storage
  • Automated Scheduling: Cron-based backup scheduling with retention policies
  • Application-Aware Backups: Hooks for pre-backup and post-backup operations
  • Easy Recovery: Simple restore operations through Kubernetes resources

Technical Implementation

Stash was architected with enterprise-grade features and production readiness:

Core Architecture

  • Operator Pattern: Kubernetes controller watching custom resources (Restic, Repository, Recovery)
  • Sidecar Injection: Automatic sidecar container injection for backup operations
  • Backup Strategies: Support for both sidecar and job-based backup approaches
  • Incremental Backups: Efficient deduplication and compression using Restic
  • Encryption: AES-256 encryption for all backup data

Key Features

  • Volume Backup: Backup any Kubernetes volume (PVC, hostPath, emptyDir)
  • Database Support: Specialized backup for PostgreSQL, MySQL, MongoDB, Elasticsearch
  • Retention Policies: Flexible retention rules (keep last N, keep weekly, monthly)
  • Monitoring: Prometheus metrics for backup status, duration, and size
  • Notifications: Webhook support for backup success/failure alerts
  • RBAC: Fine-grained access control for backup operations

Impact & Results

3,000+
GitHub Stars
500K+
Docker Pulls
Enterprise
Production Use
Multi-Cloud
Storage Support
Production Proven: Stash is used by enterprises worldwide to protect critical Kubernetes workloads, providing reliable disaster recovery capabilities and meeting compliance requirements for data protection.

Use Cases

Stateful Application Backup

Automated backup of databases, file storage, and other stateful workloads running on Kubernetes.

Disaster Recovery

Quick recovery from data loss, cluster failures, or ransomware attacks with point-in-time restore.

Multi-Cloud Migration

Backup from one cloud provider and restore to another, enabling cloud migration strategies.

Compliance

Meet regulatory requirements for data retention, backup frequency, and disaster recovery testing.

Technical Leadership

Dipta Das was a lead developer on the Stash project at AppsCode, contributing to:

  • Core controller logic and Kubernetes CRD implementation
  • Restic integration and backup workflow orchestration
  • Multi-cloud storage backend implementations
  • Database-specific backup strategies (PostgreSQL, MySQL, MongoDB)
  • Monitoring and alerting integrations
  • Community support and documentation

Technology Highlights

  • Language: Go (Golang) for Kubernetes ecosystem compatibility
  • Backup Engine: Restic for efficient, encrypted, deduplicated backups
  • Storage: S3-compatible, GCS, Azure Blob, local filesystem
  • Scheduling: Kubernetes CronJob-based scheduling
  • Observability: Prometheus metrics, structured logging