duckdb
jq
| duckdb | jq | |
|---|---|---|
| 52 | 306 | |
| 16,749 | 25,063 | |
| 4.5% | - | |
| 10.0 | 0.0 | |
| 7 days ago | 11 months ago | |
| C++ | C | |
| MIT License | GNU General Public License v3.0 or later |
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
jq
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GNU Parallel, where have you been all my life?
That should recursively list directories, counting only the files within each, and output² jsonl that can be further mangled within the shell². You could just as easily populate an associative array for further work, or $whatever. Unlike bash, zsh has reasonable behaviour around quoting and whitespace too.
¹ https://zsh.sourceforge.io/Doc/Release/User-Contributions.ht...
² https://github.com/jpmens/jo
³ https://github.com/stedolan/jq
- How do i edit reputation?
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Jj: JSON Stream Editor
What I miss from jq and what is implemented but unreleased is platform independent line delimiters.
jq on Windows produces \r\n terminated lines which can be annoying when used with Cygwin / MSYS2 / WSL. The '--binary' option to not convert line delimiters is one of those pending improvements.
https://github.com/stedolan/jq/commit/0dab2b18d73e561f511801...
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Building and deploying a web API powered by ChatGPT
If you have jq installed you can use it to make the output look nicer.
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Search in your Jupyter notebooks from the CLI, fast.
It requires jq for JSON processing and GNU parallel for concurrent searches in the notebooks.
- Check the jq manual!
- mkv vs mp4 metadata
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Amazon Begs Employees Not to Leak Corporate Secrets to ChatGPT
jq is your friend.
- Memes are all cool and all. But this is your daily remaining that 10000! =
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How to export/import/externally-edit/whatever WI entries?
The jq command (https://stedolan.github.io/jq/) is useful pulling that information out.
What are some alternatives?
ClickHouse - ClickHouse® is a free analytics DBMS for big data
yq - Command-line YAML, XML, TOML processor - jq wrapper for YAML/XML/TOML documents
sqlite-worker - A simple, and persistent, SQLite database for Web and Workers.
dasel - Select, put and delete data from JSON, TOML, YAML, XML and CSV files with a single tool. Supports conversion between formats and can be used as a Go package.
datasette - An open source multi-tool for exploring and publishing data
gojq - Pure Go implementation of jq
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
json5 - JSON5 — JSON for Humans
metabase-clickhouse-driver - ClickHouse database driver for the Metabase business intelligence front-end
jp - Validate and transform JSON with Bash
datafusion - Apache DataFusion SQL Query Engine
nushell - A new type of shell