This section will recommend designs and starting values for VAST DataBase tables and queries: what to choose for a given workload, and how to size for it. Recommendations are a starting point, based on benchmarks and deployments. Always measure with your own data before you rely on them.
What this section will contain
- Choosing a table layout by workload: when to use regular, sorted, partitioned or semi-sorted tables for common access patterns, and what each costs.
- Sizing and parallelism: how many CNodes and endpoints to use, and how to set splits and sub-splits for a given scan rate.
- Bulk ingest at scale: batch sizes, parallel writers and file layouts for large loads.
- Trino and Spark tuning: connector and session settings that matter for VAST DataBase.
- Trino with VAST DataBase (draft): network, endpoints, splits, Trino sizing and table design for Trino workloads.
- Several engines on one copy of the data: patterns for mixing Python ingest, Trino dashboards and Spark jobs on the same tables.
Each page will say which VAST Cluster version and test setup its recommendations come from.
Where to find how it works
The rest of the VAST DataBase docs explain how each feature works, what each setting does and how to measure the result: data modeling, table structures, querying, ingest, performance. Best-practices pages link to those pages rather than repeat them.
Status
This section is being written. Until its pages are published, start with the pages above, and ask your VAST account team for guidance on your workload.