Cleaning Up Data
When using the Face SDK Web Service for Liveness Assessment and Face Identification, persistent data can accumulate over time:
- For Liveness Assessment, transaction data is stored.
- For Face Search, data on registered Persons is stored.
This can lead to the growth of persistent storage, potentially consuming significant resources.
To manage this, the system includes a HouseKeeper cleaning mechanism that helps remove unnecessary data.
Cloud Storage Considerations
For cloud storage solutions, there are often built-in cleaning mechanisms provided by the cloud provider. Depending on the provider's pricing policy, using the built-in cleaning mechanism within our product may lead to additional costs. Therefore, it may be more cost-effective to use the cleaning mechanisms provided by your cloud provider.
The following articles describe native mechanisms of managing object lifecycles in storages:
How Cleaning Mechanism Works
The cleaning mechanism operates on a sliding window principle, where all data older than a specified time is deleted. This is managed by a component called HouseKeeper, which uses time-to-live (TTL) settings to determine which data to remove.
To configure the cleaning mechanism and the HouseKeeper component, refer to the Configuration settings.
When using the Face Identification Module (1:N), for Person creation, the ttl (Time-To-Live) parameter sets the lifespan of the Person's records in seconds.
Handling Data When Migrating From Previous Versions
Once upgrading from previous versions of the Face SDK Web Service to version 6.3, it's important to be aware of changes in how data is managed. By default, the cleanup feature for Liveness transactions and created Persons is disabled, meaning that the time-to-live (TTL) settings are not applied, and data will not be automatically removed.
If you choose to enable the cleanup feature, data will be deleted according to the specified retention window set in the configuration.