duckdb
alasql
Our great sponsors
| duckdb | alasql | |
|---|---|---|
| 52 | 4 | |
| 16,576 | 6,888 | |
| 10.7% | 0.5% | |
| 10.0 | 9.1 | |
| 4 days ago | 6 days ago | |
| C++ | JavaScript | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
duckdb
- 🪄 DuckDB sql hack : get things SORTED w/ constraint CHECK
- DuckDB: Move to push-based execution model (2021)
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DuckDB performance improvements with the latest release
I'm not sure if the fix is reassuring or not: https://github.com/duckdb/duckdb/pull/9411/files
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Building a Distributed Data Warehouse Without Data Lakes
It's an interesting question!
The problem is that the data is spread everywhere - no choice about that. So with that in mind, how do you query that data? Today, the idea is that you HAVE to put it into a central location. With tools like Bacalhau[1] and DuckDB [2], you no longer have to - a single query can be sharded amongst all your data - EFFECTIVELY giving you a lot of what you want from a data lake.
It's not a replacement, but if you can do a few of these items WITHOUT moving the data, you will be able to see really significant cost and time savings.
[1] https://github.com/bacalhau-project/bacalhau
[2] https://github.com/duckdb/duckdb
- DuckDB 0.9.0
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Push or Pull, is this a question?
[4] Switch to Push-Based Execution Model by Mytherin · Pull Request #2393 · duckdb/duckdb (github.com)
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Show HN: Hydra 1.0 – open-source column-oriented Postgres
it depends on your query obviously.
In general, I did very deep benchmarking of pg, clickhouse and duckdb, and I sure didn't make stupid mistakes like this: https://news.ycombinator.com/item?id=36990831
My dataset has 50B rows and 2tb of data, and I think columnar dbs are very overhiped and I chose pg because:
- pg performance is acceptable, maybe 2-3x times slower than clickhouse and duckdb on some queries if pg is configured correctly and run on compressed storage
- clickhouse and duckdb start falling apart very fast because they specialized on very narrow type of queries: https://github.com/ClickHouse/ClickHouse/issues/47520 https://github.com/ClickHouse/ClickHouse/issues/47521 https://github.com/duckdb/duckdb/discussions/6696
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🦆 Effortless Data Quality w/duckdb on GitHub ♾️
This action installs duckdb with the version provided in input.
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Using SQL inside Python pipelines with Duckdb, Glaredb (and others?)
Duckdb: https://github.com/duckdb/duckdb - seems pretty popular, been keeping an eye on this for close to a year now.
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CSV or Parquet File Format
The Parquet-Go library is very complex, not yet success to use it. So I ask whether DuckDB can provide API https://github.com/duckdb/duckdb/issues/7776
alasql
- AlaSQL Db for Browser and Node
- Alasql.js – JavaScript SQL database for browser and Node.js
- Let's write a compiler, part 5: A code generator
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Hosting SQLite databases on GitHub Pages (or any static file hoster)
For similar results, you can use SQL with IndexedDB running fully client-side: https://github.com/agershun/alasql
What are some alternatives?
ClickHouse - ClickHouse® is a free analytics DBMS for big data
sql.js-httpvfs - Hosting read-only SQLite databases on static file hosters like Github Pages
sqlite-worker - A simple, and persistent, SQLite database for Web and Workers.
sqlparse - A non-validating SQL parser module for Python
datasette - An open source multi-tool for exploring and publishing data
zetasql - ZetaSQL - Analyzer Framework for SQL
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
go-mysql-server - A MySQL-compatible relational database with a storage agnostic query engine. Implemented in pure Go.
metabase-clickhouse-driver - ClickHouse database driver for the Metabase business intelligence front-end
JSqlParser - JSqlParser parses an SQL statement and translate it into a hierarchy of Java classes. The generated hierarchy can be navigated using the Visitor Pattern
arrow-datafusion - Apache DataFusion SQL Query Engine