lnav
octosql
lnav | octosql | |
---|---|---|
78 | 34 | |
6,792 | 4,711 | |
- | - | |
9.6 | 2.7 | |
5 days ago | 8 days ago | |
C++ | Go | |
BSD 2-clause "Simplified" License | Mozilla Public License 2.0 |
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.
lnav
-
Ask HN: Interesting TUIs (text user interfaces), maybe forgotten ones?
The Logfile Navigator (https://lnav.org) is a log file viewer/merger/tailer for the terminal. It has some advanced UX features, like showing previews of operations and displaying context sensitive help. For example, the preview for filtering out logs by regex is to highlight the lines that will be hidden in red. This can make crafting the right regex a bit easier since the preview updates as you type. lnav also has some simple bar charting abilities, so you can visualize the results of SQL queries made against the log messages.
- Lnav: A log file viewer for the terminal
-
Angle-grinder: Slice and dice logs on the command line
See https://lnav.org for a powerful mini-ETL CLI power tool; it embeds SQLite, supports ~every format, has great UX and easily handles a few million rows at a time.
- FLaNK Stack 26 February 2024
- LNAV – The Logfile Navigator
-
Toolong: Terminal application to view, tail, merge, and search log files
The code base seems like a good reference as a small Python project.
My fav option in this class of apps: https://lnav.org/ It lets you use journalctl with pipes as requested here: https://github.com/Textualize/toolong/issues/4
-
Logdy.dev – web based logs viewer UI for local development environment
For local development, I cannot recommend lnav[1] enough. Discovering this tool was a game changer in my day to day life. Adding comments, filtering in/out, prettify and analyse distribution is hard to live without now.
I don't think a browser tool would fit in my workflow. I need to pipe the output to the tool.
[1] https://lnav.org/
- Textanalysistool.net
- Ask HN: What apps have you created for your own use?
octosql
-
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
-
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
-
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
-
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
-
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
-
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.
-
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!
-
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
What are some alternatives?
lightproxy - 💎 Cross platform Web debugging proxy
duckdb - DuckDB is an in-process SQL OLAP Database Management System
dive - A tool for exploring each layer in a docker image
q - q - Run SQL directly on delimited files and multi-file sqlite databases
glow - Render markdown on the CLI, with pizzazz! 💅🏻
trdsql - CLI tool that can execute SQL queries on CSV, LTSV, JSON, YAML and TBLN. Can output to various formats.
GoAccess - GoAccess is a real-time web log analyzer and interactive viewer that runs in a terminal in *nix systems or through your browser.
sqlitebrowser - Official home of the DB Browser for SQLite (DB4S) project. Previously known as "SQLite Database Browser" and "Database Browser for SQLite". Website at:
conio-for-linux - Conio.h for linux
sqlite-utils - Python CLI utility and library for manipulating SQLite databases
nnn - n³ The unorthodox terminal file manager
textql - Execute SQL against structured text like CSV or TSV