141–150 of 167 posts
Can multiple separate clients access the same single-file database at the same time?
One of the DuckDB authors here. The answer is yes, but only in read-only mode at the same time.
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How would that be different from someone editing something in window.localStorage?
If that's all you need, it wouldn't make sense to include SQLite in the browser. If you're going to include a relational database you might as well use it
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And if you need to run expensive migrations, use a template database. https://www.postgresql.org/docs/current/manage-ag-templatedb...
While PostgreSQL templates allow to scaffold new databases (one per test-spec) quite fast, it's even faster to have a warmed up pool of databases ready: https://github.com/allaboutapps/integresql#background Disclaimer: author.
A suggestion for the web site: It would be good to mention the license more prominently. I had to click through to Github to find out what it was, and I believe this information would matter to more people than just me.
A suggestion for the web site: It would be good to mention the license more prominently. I had to click through to Github to find out what it was, and I believe this information would matter to more people than just me.
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Before the latest optimization, and only using 1 core, vs. SQLite we were seeing 133x performance on a basic group by or join, and about 4x for a pretty complex query. It was roughly even to Pandas in performance, but it can scale to larger than memory data and now it can use multiple cores! As an example, I could build views from 2 Pandas DataFrames with 2 columns and 1 million rows each, join them, and return the 1…
How about the other way? When will SQLite perform better than DuckDB?
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OLAP databases can/are still Relational databases. The difference is that they’re optimised for different workloads. SQLite/MySQL/Postgres/MSSQL etc are all OLTP databases whose primary operation is based around operations on single (or few) rows. OLAP databases like ClickHouse/DuckDB, Monet, Redshift, etc are optimised for operating on columns and performing operations like bulk aggregations, group-bys, pivots, etc…
I gather that there may be some differences in functionality offered, but that it’s probably not much. So I presume performance is the biggest area of difference. In that regard: how big are the differences in performance for each category’s representative workloads?
I could add more indices to MSSQL and do all sorts of query optimisations, but out of the box the OLAP wins hands down for that workload.
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I gather that there may be some differences in functionality offered, but that it’s probably not much. So I presume performance is the biggest area of difference. In that regard: how big are the differences in performance for each category’s representative workloads?
Before the latest optimization, and only using 1 core, vs. SQLite we were seeing 133x performance on a basic group by or join, and about 4x for a pretty complex query. It was roughly even to Pandas in performance, but it can scale to larger than memory data and now it can use multiple cores! As an example, I could build views from 2 Pandas DataFrames with 2 columns and 1 million rows each, join them, and return the 1…
(I made one using Proxy API, called ref-db. The cons is it require manual indexing to speed up lookup by non-primary key)