Earlier quoted context omitted.
I’m not sure I agree with this statement. BigQuery is not in memory, nor is redshift (not super familiar with snowflake). Whereas memsql is and would be prohibitively expensive to operate on datasets that are analytics based, because it is in memory based. It’s my understanding that citus is best suited to analytics workloads and parallel queries, and really isn’t comparable to memsql as it is attempting to provide s…
MemSQL has in-memory rowstores and disk-based columnstores. Every columnstore table has a rowstore to aid in fast data ingest. Rowstores are meant for access of individual rows, lots of data updates, and smaller reference tables while columnstores are for the bulk of data requiring large scans and analysis. We have tables with 20 billion rows that take up BigQuery streaming ingest also uses in-memory buffering into B…
This article had a nice overview. Somewhat outdated but mostly still up to date. https://lnkd.in/g4anCf5
I’ll say it seems precarious to me to mix and match row store in memory data with a columnar store. Is it never a performance issue to have these two systems potentially in contention with one another for resources?