Designing Reliable Cross-Cloud Database Disaster Recovery with Volcano Engine

1. Database Disaster Recovery Background and Mainstream Solutions

In the digital era, cross-cloud database disaster recovery is essential to keep data available and consistent during natural disasters, hardware failures, or network attacks, aiming for zero data loss, rapid recovery, and uninterrupted business.

Zero data loss : protect all data without loss.

Fast recovery : restore databases quickly to minimize downtime.

Business continuity : ensure services keep running during failover.

Key challenges include data consistency, network latency and bandwidth limits, cost control, and the difficulty of testing and rehearsing failover procedures.

Typical migration stages progress from multi-cloud split, dual-active applications, unitized dual-active, to unitized multi-cloud multi-active architectures.

2. Implementing Cross-Cloud Database Disaster Recovery

2.1 Overall Business Flow

Assuming a company uses both Cloud A and Volcano Engine, the normal state runs traffic on the source database while Volcano provides standby. In a disaster, two strategies are possible:

Switch only the failed database (minimal fault radius).

Switch all traffic at the entry point to Volcano for global stability.

After recovery, first back-track data to align source and Volcano, then switch traffic back to the original cloud.

2.2 Core Product Capabilities

The Database Transmission Service (DTS) integrates migration, synchronization, and subscription for relational and non-relational sources, simplifying cross-cloud data flow.

Rich scenarios : supports many engines, reduces downtime to minutes, works over public or VPC networks, and offers pure incremental sync.

Operational simplicity : visual UI with wizard-style configuration, real-time progress, rate charts, and dynamic link scaling.

Data security : high-availability instances, automatic fault healing, and breakpoint-resume for interrupted links.

2.2.1 Product Advantages

DTS provides high-performance, secure transmission links that are easier to create and manage than third‑party tools.

2.2.2 Required Capabilities in Disaster Scenarios

To guarantee data availability and integrity, DTS must handle structural, full, and incremental synchronization, and allow dynamic object selection for free data flow.

3. Value-Added Services for Disaster-Recovery Nodes

3.1 Cold-Hot Separation

Cold storage reduces compute costs while keeping hot data accessible. Typical performance: 450 GB table conversion in 20 min; point‑lookup P99 ≈ 15 ms; indexed range query P99 ≈ 50 ms; non-indexed range query P99 ≈ 15 s.

3.2 HTAP Lightweight Analysis

Latency-insensitive workloads can run on Volcano’s disaster side, reusing compute and storage for analytical queries without affecting transactional performance.

Automatic TP/AP traffic splitting at the kernel.

MPP architecture enables elastic scaling.

Supports

INSERT INTO … SELECT FROM …

across transactional and analytical tables.

4. Challenges and Solutions

4.1 Compatibility Issues

Different clouds expose varied APIs and protocols. Using open-source tools and Terraform to unify management mitigates these gaps.

4.2 Multi-Cloud Management Barriers

High technical expertise and cost are required to operate across clouds; a step‑wise migration from disaster recovery to active‑active reduces risk.

5. Overall Business Value

Combining disaster recovery with cold-hot separation and HTAP delivers “cost-down, efficiency-up” benefits. An enterprise case shows a 30 % budget increase yields robust availability and performance, while further optimizations aim to keep costs flat.

The Volcano Engine cloud foundation team leverages large-scale practice to provide secure, high-performance, and cost-effective multi-cloud solutions.