Major Description
In the current cloud deployment, users are limited by the disk space of the cluster's nodes. However, the blob storage services provided by cloud providers (e.g., S3) can virtually store an "unlimited" amount of data. Thus, AsterixDB can provide the means to store beyond what the cluster's local drives can.
In this proposal, we want to extend AsterixDB's capability to allow the local drives to act as a cache, instead of a mirror image of what's stored in the cloud. By "as a cache" we mean files and pages can be retrieved/persited and removed (evicted) from the local drives, according to some policy.
The aim of this proposal is to describe and implement a mechanism called "Weep and Sweep". Those are the names of two phases when the amount of the data in the cloud exceeds the space of the cluster's local disks.
Weep
When the disk is pressured (the pressure size can be configured), the system will start to "weep" and devise a plan to what should be "evicted" according to some statistics and policies, which are not solidified yet and still a work in progress.
Sweep
After "weeping", a sweep operation will take place and start evicting what the weep's plan considers as evictable. Depending on the index type (primary/secondary) and the storage format (row/column), the smallest evictable unit can differ. The following table shows the smallest unit of evictable unit:
| Index Type | Evictable |
| Metadata Indexes (e.g., Dataset, ..etc) | Not evictable |
| Secondary indexes | Evicted as a whole |
| Primary Indexes (Row) | Evicted as a whole |
| Primary Indexes (Columnar) | Columns (or columns’ pages) |
Featured Considerations
- For columnar primary index, they will never be downloaded as a whole
- Instead, columns will be streamed from the cloud (if accessed for the first time) and persisted to local disk if necessary
- We are considering providing a mechanism to prefetch the next columns of the next mega-leaf node. The hope here is to mask any latencies when reading columns from the cloud
- Depending on the disk pressure and the operation, the system can determine if the streamed columns from the cloud are "worthy" to be cached locally. For example, if columns are read in a merge operation, it might not be "wise" to persist these columns as their on-disk component is going to be deleted at the end of the merge operation. Thus, it might be "better" to dedicate the free space on disk for the newly created/merged component.