Fast, Accurate, and Cost-Efficient: Opening a New Chapter in Granular Recovery of Virtualization Resources
Author: scutech Published time: 2026-06-25 13:31:00 Views: 79
Distributed deduplication pools and object deduplication pools significantly reduce backup storage costs. However, what truly affects customer experience is not only whether data can be stored economically, but whether it can be retrieved, restored quickly, and located precisely at critical moments.
Dingjia DBackup now fully supports file-level recovery in both distributed deduplication pools and object deduplication pools. This enables backups for virtualization and cloud platforms to evolve from whole-machine restore to on-demand retrieval, opening a new chapter in granular recovery for virtualization resources.
01 The “Recovery Blind Spot” of Deduplication Pools: Economical to Store, Difficult to Retrieve
Distributed deduplication pools and object deduplication pools have substantially reduced backup storage costs. This has become an industry consensus. Yet for file-level recovery, most backup software products still have limited capabilities.
To retrieve a single 10 KB configuration file, traditional methods often require restoring the entire virtual machine from the deduplication pool. This can take several hours and consume a large amount of temporary storage. The result is clear: the data is economical to store, but difficult to retrieve.
Core Contradictions in Traditional Recovery Methods
- Coarse recovery granularity: Even when only one small file is required, the entire virtual machine must be restored.
- Long recovery time: Whole-machine restore often takes hours, making it hard to meet the demands of audit, operations, and emergency response scenarios.
- Heavy temporary resource consumption: Restoring an entire machine requires additional compute, storage, and network resources, amplifying recovery costs.
- Slow response at critical moments: The more urgent the scenario, the stronger the need to directly locate the target file instead of bringing up the entire virtual machine again.
DBackup closes this blind spot: both distributed deduplication pools and object deduplication pools support file-level recovery, enabling cloud-native backups to be not only storage-efficient but also retrieval-precise.
02 Multi-Type Storage Pools: Storing Backup Data According to Requirements
Different business data should not be limited to a single backup storage option. Daily backups emphasize stable performance and rapid recovery, while long-term archiving prioritizes low cost, cloud-native elasticity, and massive scalability.
03 From Distributed Deduplication to Object Deduplication: A Technical Breakthrough in File-Level Recovery
A distributed deduplication pool cuts data into extremely fine-grained fingerprint blocks. At the underlying layer, files are completely fragmented. To locate a single file, traditional methods must first unpack and reassemble the puzzle, fully restoring the entire backup set.
An object deduplication pool is even more challenging on this basis: the same fragmentation is compounded by the high network latency of object storage. Once large-scale object reads are triggered, an I/O storm may form and block the recovery channel.
Breakthrough 1: Real-Time Logical Synthesis in Memory – Overcoming Discrete Fragments
This breaks away from the traditional download -> unpack -> reassemble model. In memory, the system performs real-time logical mapping of full and incremental data blocks and directly outputs a complete and continuous disk view. It requires zero additional storage and consumes no temporary physical space.
Breakthrough 2: Lightweight Sandbox Parsing – Native Parsing of Any File System
Moving from a disk view to a file directory tree requires crossing different file-system formats. The built-in lightweight micro-virtual machine TinyVM serves as a unified parsing engine. By leveraging the host runtime environment, it performs diskless startup and rapidly launches an isolated sandbox. Without a complete operating system, it can natively parse file-system structures.
Breakthrough 3: On-Demand Precise Reading – Overcoming I/O Bottlenecks
After the user issues a recovery command, the system precisely locates the offset of the target file, traverses the deduplication mapping, and pulls only the specific data blocks required by that file from the storage back end. TB-level whole-machine transfer is compressed into the minimum data volume required for the target file.
Breakthrough 4: Dynamic Mounting – The Backup Set Becomes a Network Storage Volume
The synthesized complete disk view is exposed as a network storage volume through standard storage protocols. Once mounted, it behaves like a real disk and can be directly read, written, searched, and verified without any recovery operation.
Technical Value Assessment
The key to file-level recovery is not simply making whole-machine restoration somewhat faster. Rather, it is to equip the backup system with the ability to locate, parse, mount, and extract target files directly from deduplicated fragments.
04 File Recovery Architecture Overview: TinyVM-Driven Multi-Path Retrieval
Architecture Highlights
- Backup data sources: backup sets in distributed deduplication pools and object deduplication pools.
- Core engine: a lightweight TinyVM starts on demand within seconds, does not affect production services, and is automatically released after use.
- Access methods: file-level precise recovery, instant NFS export, and direct browser download, covering different RTO scenarios.
- Key advantages: no whole-machine restore is required; recovery time is reduced by more than 90%; fine-grained recovery and security auditing are supported.
05 Fast, Accurate, and Cost-Efficient Capabilities of File-Level Recovery
Fast: Skip Whole-Machine Restoration and Reach the Target File Directly
Users can directly browse the file directory tree of the backup set and select the target file for recovery. Compared with traditional whole-machine restoration, which often requires several hours of waiting, file-level recovery represents a leap from hours to seconds.
Accurate: Precise Recovery on Demand
Recovery granularity supports a single file, multiple files, and folders. Supported file systems include ext2, ext3, ext4, XFS, btrfs, NTFS, FAT16, FAT32, ReFS, and others. Recovery targets can include a local directory on the backup host, a custom path on a target machine, a network storage share path, or the default browser download folder.
Cost-Efficient: Cost Reduction Across Storage, Archiving, and Recovery
Cost efficiency is not a single dimension of storage compression. It means end-to-end cost reduction across backup storage, archiving, and recovery.
- Storage: cross-VM deduplication eliminates redundancy, allowing operating systems, patches, and images to occupy only one copy of space across multiple backups.
- Archiving: infrequently accessed data is tiered down to object storage. Deduplication and compression further reduce the amount of data written to disk, and long-term retention costs are far lower than with block storage.
- Recovery: target files are extracted precisely without restoring the entire machine, minimizing recovery compute and temporary space consumption.
recovery entry point; it turns backup sets from cold data archives into a unified data source that can be browsed, mounted, searched, distributed, and verified.
06 Typical Business Scenarios: Making Backup Sets Truly Callable
01 Cloud Compliance Audit – Retrieve Historical Files from an Object Deduplication Pool in Seconds
Scenario: a regulatory audit requires retrieval of trans
The value of file-level recovery becomes more intuitive in real business scenarios. It is not merely an additional action system logs from three months earlier.
Traditional method: download a whole-machine backup of hundreds of GB, restore the entire virtual machine, log in and search, and then export the logs. This takes half a day to a full day.
DBackup solution: directly browse the file directory tree of the backup set in the object deduplication pool through the Web interface, locate the logs, and click download. The task is completed in seconds, ensuring audit timeliness.
Cloud-native value: audit data is retained at low cost in object storage. File-level recovery breaks the stored-but-inaccessible dilemma, preserves the low-cost advantage, and sharply improves data accessibility.
02 Multi-Cloud Cross-Region Data Distribution – Using the Object Deduplication Pool as a Unified Data Source
Scenario: development teams across North China, South China, and Southeast Asia frequently need to extract test data from backups.
Traditional method: perform whole-machine restoration across regions. This causes enormous network overhead and often requires several hours of waiting for data.
DBackup solution: use the object deduplication pool as a unified data foundation, and precisely distribute target files to each region through network storage volume mounting or link download. There is no need for whole-machine restoration and no need to transfer the entire backup set across regions.
Cloud-native value: object storage natively supports cross-region access. File-level recovery enables it to become a practical model of unified storage plus on-demand delivery, significantly improving the efficiency of multi-cloud data distribution.
03 Routine Operations Accidental Deletion – Self-Service Recovery of Key Files from the Deduplication Pool
Scenario: an operations engineer accidentally deletes a core configuration file, causing a database service interruption.
Traditional method: submit a request, obtain approval, schedule the task, perform whole-machine restoration, and log in to find the file. From incident occurrence to recovery, the process takes 2 to 4 hours.
DBackup solution: the engineer logs directly into the Web interface, browses the backup set in the distributed deduplication pool, locates the deleted file, and restores it to the target path with one click. The entire process is self-service and completed within 10 minutes.
04 Backup Set as a Security Range – Mounting Backup Sets to Unlock Security Audit and Fault Tracing
Scenario: the security team needs to perform a security scan on backup data from six months earlier to investigate traces of an implanted backdoor.Traditional method: download a whole-machine backup of several hundred GB, fully restore the virtual machine, and search directory by directory. The process takes at least a full day and may also overwrite the existing environment.
DBackup solution: mount the backup set as a network storage volume with one click, directly use native tools such as grep and find to search, and verify scripts in a sandbox-isolated environment. Changes are written to temporary cache and discarded upon unmounting, so the underlying data is not affected.
With one mount operation, search, verification, and remediation can be completed together. The backup set is transformed from a data archive into a callable security range.
07 Platform Compatibility: Covering 20+ Virtualization and Cloud Platforms
DBackup file-level recovery covers more than 20 virtualization and cloud platforms. Regardless of the platform, the file-level recovery capabilities of distributed deduplication pools and object deduplication pools remain consistent.
Deduplication Pools Are No Longer Only About Saving
Distributed deduplication pools must deliver both the storage efficiency of large-capacity deduplication and precise, flexible file-level recovery. Object deduplication pools enable low-cost storage in the cloud to truly support on-demand and precise data recovery.
Space savings, fast recovery, and precise retrieval can now be achieved together, without compromise.
