textql
jq
textql | jq | |
---|---|---|
15 | 59 | |
9,037 | 29,275 | |
- | 0.8% | |
3.7 | 9.3 | |
8 months ago | 6 days ago | |
Go | C | |
MIT License | GNU General Public License v3.0 or later |
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textql
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Jaq – A jq clone focused on correctness, speed, and simplicity
I like textql [0] better for this use case, as it's simpler in my mind.
[0] https://github.com/dinedal/textql
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Can SQL be used without an RDBMS?
Primarily, you are right. SQL is for working with structured data and that largely covers RDBs. However, there are tools (like textql) that allow you to query CSV files and the effort works with most other text files that have some kind of structure.
- Textql: Execute SQL against structured text like CSV or TSV
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Show HN: ClickHouse-local – a small tool for serverless data analytics
As the author of textql ( https://github.com/dinedal/textql ) - thanks for the shoutout!
Looks great, I love more options in the space for CLI based data analysis tools! Fantastic work!
- Using Commandline To Process CSV files
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sqly - execute SQL against CSV / JSON with shell
Apparently, there were many who thought the same thing; Tools to execute SQL against CSV were trdsql, q, csvq, TextQL. They were highly functional, hoewver, had many options and no input completion. I found it just a little difficult to use.
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Q – Run SQL Directly on CSV or TSV Files
Reminds me of the textQL extension that's available in Asciidoc.
Point it to an external CSV file, enable TextQL, and bam, there's your query returned as a table. Handy for parts lists, inventory, that kind of crap.
https://github.com/dinedal/textql
https://gist.github.com/mojavelinux/8856117
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Beginner interested in learning SQL. Have a few question that I wasn’t able to find on google.
Through more magic, you COULD of course use stuff like Spark, or easier with programs like TextQL, sq, OctoSQL.
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textql VS trdsql - a user suggested alternative
2 projects | 25 Jun 2022
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Xlite: Query Excel, Open Document spreadsheets (.ods) as SQLite virtual tables
Somewhat-kinda related, the textql extension for Asciidoctor is so dang useful it should be in core.
https://gist.github.com/mojavelinux/8856117
I use this as a "centralized parts repository" for big ol' maintenance manuals. Refresh from PDM/PLM/LSA/Whatever. Rebuild for new parts data.
Built on TextQL, natch
https://github.com/dinedal/textql
jq
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Data Science at the Command Line, 2nd Edition (2021)
Thanks, if anyone else is interested there is an explanation of this feature here: https://subtxt.in/library-data/2016/03/28/json_stream_jq And: https://github.com/jqlang/jq/wiki/FAQ#streaming-json-parser
The last time I tried, I think the reason I gave up on JQ for large inputs was that the throughput would max out at 7mb/s whereas the same thing with spark SQL on the same hardware (MacBook) would max out at 250mb/s. So I started looking into using other solutions for big data while I use jq in parallel for small data in multiple files.
I will test it out again cause this was 4-5 years ago when I last tested it, but I believe jaq is still preferred for large inputs. Still I prefer for big data to use Spark/Polars/clickhouse etc.
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Bytecode VMs in Surprising Places
Looks like you are correct https://github.com/jqlang/jq/blob/ed8f7154f4e3e0a8b01e6778de...
- Frawk: An efficient Awk-like programming language. (2021)
- Dehydrated: Letsencrypt/acme client implemented as a shell-script
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I turned my open-source project into a full-time business
I think like you. But also, one does not necessarily know beforehand that they will want to make money.
Like a project could be born out of pure generosity, but after the happy initial phase the project might get too heavy on the maintenance requirements, causing the author to approach burnout, and possibly deciding that they want to make money to continue pulling the cart forward.
However, here's something I do think: if you create something as Open Source, it should be out of a mentality of goodwill and for the greater good, regardless of how it ends up being used. OSS licenses do mean this with their terms. If you later get tired or burned out, you should just retire and allow the community to keep taking care of it. Just like it happened with the Jq tool [1].
[1]: https://github.com/jqlang/jq/releases/tag/jq-1.7
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How to load JSON data in PostgreSQL with the the COPY command
In this blog we'll see how to upload the JSON directly using PostgreSQL COPY command and using an utility called jq!
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How to Recover Locally Deleted Files From Github
And we can then make it easier to find the commit by filtering the response with jq.
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Essential Command Line Tools for Developers
Official Documentation: jqlang.github.io/jq
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Command line tools I always install on Ubuntu servers
To handle JSON files and JSON outputs in a script or format and highlight it, jq can be very handy. Many command line tools provide a json output, so you don't have to write a custom parser for a table a list in a terminal. Instead of that, you can use jq to get a specific value from the output or even modify the output. For more information, you can visit https://jqlang.github.io/jq/
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How I use Nix in my Elm projects
In some projects I've wanted to use HTTPie to test APIs and jq to work with some JSON data. Nix has been really helpful in managing those dependencies that I can't easily get from npm.
What are some alternatives?
q - q - Run SQL directly on delimited files and multi-file sqlite databases
yq - Command-line YAML, XML, TOML processor - jq wrapper for YAML/XML/TOML documents
go-duckdb - go-duckdb provides a database/sql driver for the DuckDB database engine.
jp - Validate and transform JSON with Bash
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
gojq - Pure Go implementation of jq
cq - Query CSVs using SQL
Jolt - JSON to JSON transformation library written in Java.
dsq - Commandline tool for running SQL queries against JSON, CSV, Excel, Parquet, and more.
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.
brackit - Query processor with proven optimizations, ready to use for your JSON store to query semi-structured data with JSONiq. Can also be used as an ad-hoc in-memory query processor.
jmespath.py - JMESPath is a query language for JSON.