multi‑omics deep dive
This page presents a concrete example of how SynSQL handles large multi‑omics datasets. We describe the test dataset, the structure of the resulting table, and the ingestion performance obtained via single queries.
The test cluster consists of two DELL R6515 servers, each with an AMD Epyc 7302P 16-core processor, 128 GB of RAM and 6 x 2 TB SSDs. This configuration is representative of a modest two‑node research cluster. Even at this scale, SynSQL comfortably handles multiple wide scientific tables.
The dataset used for this benchmark is a multi‑omics matrix with exactly 10 000 lines and 585 010 columns, The raw data was generated from a C program in text format. It was inserted into the multi_omic table row by row. As the dataset was generated, do not expect that the data distribution is realistic.
The multi‑omics table contains one row per patient and several hundred thousand molecular
features spanning multiple modalities. These include:
• 1 patient_id key — a mandatory hash key
• 20 000 gene expression features
• 100 000 SNP‑level features
• 10 000 protein abundance features
• 5 000 metabolite measurements
• 450 000 CpG methylation values
• 10 metadata fields (clinical and technical annotations)
All values are stored using simple SQL types (INT and fixed‑point encoded integers).
No specialized scientific format is required, and the entire dataset fits into a single
wide table.
SynSQL supports both high‑throughput bulk loading and standard SQL mutability (INSERT, UPDATE, DELETE). This combination allows fast ingestion of large datasets while preserving the flexibility of fine‑grained modifications. For the multi_omics case, we considered that bulk data load was not the best way. A multi omics data set will usualy concern up to 10 000 patients. So the the table data was produced line by line.