octosql
textql
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octosql | textql | |
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34 | 15 | |
4,689 | 9,031 | |
- | - | |
4.3 | 3.7 | |
7 months ago | 6 months ago | |
Go | Go | |
Mozilla Public License 2.0 | 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.
octosql
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Wazero: Zero dependency WebAssembly runtime written in Go
Never got it to anything close to a finished state, instead moving on to doing the same prototype in llvm and then cranelift.
That said, here's some of the wazero-based code on a branch - https://github.com/cube2222/octosql/tree/wasm-experiment/was...
It really is just a very very basic prototype.
- Analyzing multi-gigabyte JSON files locally
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DuckDB: Querying JSON files as if they were tables
This is really cool!
With their Postgres scanner[0] you can now easily query multiple datasources using SQL and join between them (i.e. Postgres table with JSON file). Something I strived to build with OctoSQL[1] before.
It's amazing to see how quickly DuckDB is adding new features.
Not a huge fan of C++, which is right now used for authoring extensions, it'd be really cool if somebody implemented a Rust extension SDK, or even something like Steampipe[2] does for Postgres FDWs which would provide a shim for quickly implementing non-performance-sensitive extensions for various things.
Godspeed!
[0]: https://duckdb.org/2022/09/30/postgres-scanner.html
[1]: https://github.com/cube2222/octosql
[2]: https://steampipe.io
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Show HN: ClickHouse-local – a small tool for serverless data analytics
Congrats on the Show HN!
It's great to see more tools in this area (querying data from various sources in-place) and the Lambda use case is a really cool idea!
I've recently done a bunch of benchmarking, including ClickHouse Local and the usage was straightforward, with everything working as it's supposed to.
Just to comment on the performance area though, one area I think ClickHouse could still possibly improve on - vs OctoSQL[0] at least - is that it seems like the JSON datasource is slower, especially if only a small part of the JSON objects is used. If only a single field of many is used, OctoSQL lazily parses only that field, and skips the others, which yields non-trivial performance gains on big JSON files with small queries.
Basically, for a query like `SELECT COUNT(*), AVG(overall) FROM books.json` with the Amazon Review Dataset, OctoSQL is twice as fast (3s vs 6s). That's a minor thing though (OctoSQL will slow down for more complicated queries, while for ClickHouse decoding the input is and remains the bottleneck).
[0]: https://github.com/cube2222/octosql
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Steampipe – Select * from Cloud;
To add somewhat of a counterpoint to the other response, I've tried the Steampipe CSV plugin and got 50x slower performance vs OctoSQL[0], which is itself 5x slower than something like DataFusion[1]. The CSV plugin doesn't contact any external API's so it should be a good benchmark of the plugin architecture, though it might just not be optimized yet.
That said, I don't imagine this ever being a bottleneck for the main use case of Steampipe - in that case I think the APIs themselves will always be the limiting part. But it does - potentially - speak to what you can expect if you'd like to extend your usage of Steampipe to more than just DevOps data.
[0]: https://github.com/cube2222/octosql
[1]: https://github.com/apache/arrow-datafusion
Disclaimer: author of OctoSQL
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Go runtime: 4 years later
Actually, folks just use gRPC or Yaegi in Go.
See Terraform[0], Traefik[1], or OctoSQL[2].
Although I agree plugins would be welcome, especially for performance reasons, though also to be able to compile and load go code into a running go process (JIT-ish).
[0]: https://github.com/hashicorp/terraform
[1]: https://github.com/traefik/traefik
[2]: https://github.com/cube2222/octosql
Disclaimer: author of OctoSQL
- Run SQL on CSV, Parquet, JSON, Arrow, Unix Pipes and Google Sheet
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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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How I Used DALL·E 2 to Generate The Logo for OctoSQL
The logo was created for OctoSQL and in the article you can find a lot of sample phrase-image combinations, as it describes the whole path (generation, variation, editing) I went down. Let me know what you think!
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How I Used DALL·E 2 to Generate the Logo for OctoSQL
Hey, author here, happy to answer any questions!
The logo was created for OctoSQL[0] and in the article you can find a lot of sample phrase-image combinations, as it describes the whole path (generation, variation, editing) I went down. Let me know what you think!
[0]:https://github.com/cube2222/octosql
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
What are some alternatives?
duckdb - DuckDB is an in-process SQL OLAP Database Management System
q - q - Run SQL directly on delimited files and multi-file sqlite databases
go-duckdb - go-duckdb provides a database/sql driver for the DuckDB database engine.
trdsql - CLI tool that can execute SQL queries on CSV, LTSV, JSON, YAML and TBLN. Can output to various formats.
cq - Query CSVs using SQL
sqlitebrowser - Official home of the DB Browser for SQLite (DB4S) project. Previously known as "SQLite Database Browser" and "Database Browser for SQLite". Website at:
dsq - Commandline tool for running SQL queries against JSON, CSV, Excel, Parquet, and more.
sqlite-utils - Python CLI utility and library for manipulating SQLite databases
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.
lnav - Log file navigator