miller
zsv
miller | zsv | |
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63 | 25 | |
8,559 | 171 | |
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9.0 | 7.5 | |
9 days ago | 17 days ago | |
Go | C | |
GNU General Public License v3.0 or later | MIT License |
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miller
- Qsv: Efficient CSV CLI Toolkit
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jq 1.7 Released
jq and miller[1] are essential parts of my toolbelt, right up there with awk and vim.
[1]: https://github.com/johnkerl/miller
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Perl first commit: a “replacement” for Awk and sed
> This works really well if your problem can be solved in one or two liners.
My personal comfort threshold is around the 100-line mark. It's even possible to write maintainable shell scripts up to 500 lines, but it mostly depends on the problem you're trying to solve, and the discipline of the programmer to follow best practices (use sane defaults, ShellCheck, etc.).
> It go bad very quickly when, say, you have two CSV files and want to join them the sql-way.
In that case we're talking about structured data, and, yeah, Perl or Python would be easier to work with. That said, depending on the complexity of the CSV, you can still go a long way with plain Bash with IFS/read(1) or tr(1) to split CSV columns. This wouldn't be very robust, but there are tools that handle CSV specifically[1], which can be composed in a shell script just fine.
So it's always a balancing act of being productive quickly with a shell script, or reaching out for a programming language once the tools aren't a good fit, or maintenance becomes an issue.
[1]: https://miller.readthedocs.io/
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Need help on cleaning this data!!
where mlr is from https://github.com/johnkerl/miller
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Running weekly average
if this class of problems (i.e., csv/tsv data) is your main target you may find miller (https://github.com/johnkerl/miller) much more useful in the long run
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GQL: A new SQL like query language for .git files written in Rust
That said, you may be interested in Miller (https://github.com/johnkerl/miller) which provides similar capabilities for CSV, JSON, and XML files. It doesn't use a SQL grammar, but that's just the proverbial lipstick on the thing. I'm not the author, but I have used it and I see some parallels in use cases at the very least.
- johnkerl/miller: Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
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Any cli utility to create ascii/org mode tables?
worth giving Miller a shot
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I wrote this iCalendar (.ics) command-line utility to turn common calendar exports into more broadly compatible CSV files.
CSV utilities (still haven't pick a favorite one...): https://github.com/harelba/q https://github.com/BurntSushi/xsv https://github.com/wireservice/csvkit https://github.com/johnkerl/miller
- Miller: Like Awk, sed, cut, join, and sort for CSV, TSV, and tabular JSON
zsv
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Analyzing multi-gigabyte JSON files locally
If it could be tabular in nature, maybe convert to sqlite3 so you can make use of indexing, or CSV to make use of high-performance tools like xsv or zsv (the latter of which I'm an author).
https://github.com/BurntSushi/xsv
https://github.com/liquidaty/zsv/blob/main/docs/csv_json_sql...
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Show HN: Up to 100x Faster FastAPI with simdjson and io_uring on Linux 5.19
Parsing CSV doesn't have to be slow if you use something like xsv or zsv (https://github.com/liquidaty/zsv) (disclaimer: I'm an author). The speed of CSV parsers is fast enough that unless you are doing something ultra-trivial such as "count rows", your bottleneck will be elsewhere.
The benefits of CSV are:
- human readable
- does not need to be typed (sometimes, data in the raw such as date-formatted data is not amenable to typing without introducing a pre-processing layer that gets you further from the original data)
- accessible to anyone: you don't need to be a data person to dbl-click and open in Excel or similar
The main drawback is that if your data is already typed, CSV does not communicate what the type is. You can alleviate this through various approaches such as is described at https://github.com/liquidaty/zsv/blob/main/docs/csv_json_sql..., though I wouldn't disagree that if you can be assured that your starting data conforms to non-text data types, there are probably better formats than CSV.
The main benefit of Arrow, IMHO, is less as a format for transmitting / communicating but rather as a format for data at rest, that would benefit from having higher performance column-based read and compression
- Yq is a portable yq: command-line YAML, JSON, XML, CSV and properties processor
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csvkit: Command-line tools for working with CSV
I wanted so much to use csvkit and all the features it had, but its horrendous performance made it unscalable and therefore the more I used it, the more technical debt I accumulated.
This was one of the reasons I wrote zsv (https://github.com/liquidaty/zsv). Maybe csvkit could incorporate the zsv engine and we could get the best of both worlds?
Examples (using majestic million csv):
---
- Ask HN: Programs that saved you 100 hours? (2022 edition)
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Show HN: Split CSV into multiple files to avoid the Excel's 1M row limitation
}
```
This of course assumes that each line is a single record, so you'll need some preprocessing if your CSV might contain embedded line-ends. For the preprocessing, you can use something like the `2tsv` command of https://github.com/liquidaty/zsv (disclaimer: I'm its author), which converts CSV to TSV and replaces newline with \n.
You can also use something like `xsv split` (see https://lib.rs/crates/xsv) which frankly is probably your best option as of today (though zsv will be getting its own shard command soon)
- Run SQL on CSV, Parquet, JSON, Arrow, Unix Pipes and Google Sheet
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Ask HN: Best way to find help creating technical doc (open- or closed-source)?
Am looking for one-time help creating documentation (e.g. man pages, tutorials) for open source project (e.g. https://github.com/liquidaty/zsv) as well as product documentation for commercial products, but not enough need for a full-time job. Requires familiarity with, for lack of better term, data janitorial work, and preferably with methods of auto-generating documentation. Any suggestions as to forums or other ways to find folks who might fit the bill for ad-hoc or part-time work of this nature?
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Q – Run SQL Directly on CSV or TSV Files
Nice work. I am a fan of tools like this and look forward to giving this a try.
However, in my first attempted query (version 3.1.6 on MacOS), I ran into significant performance limitations and more importantly, it did not give correct output.
In particular, running on a narrow table with 1mm rows (the same one used in the xsv examples) using the command "select country, count() from worldcitiespop_mil.csv group by country" takes 12 seconds just to get an incorrect error 'no such column: country'.
using sqlite3, it takes two seconds or so to load, and less than a second to run, and gives me the correct result.
Using https://github.com/liquidaty/zsv (disclaimer, I'm one of its authors), I get the correct results in 0.95 seconds with the one-liner `zsv sql 'select country, count() from data group by country' worldcitiespop_mil.csv`.
I look forward to trying it again sometime soon
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A Trillion Prices
All this banter arguing over CSV, JSON, sqlite seems unnecessary when you can just push format X through a pipe and get whichever format Y you want back out: https://github.com/liquidaty/zsv/blob/main/docs/csv_json_sql...
(disclaimer: I'm one of the zsv authors)
What are some alternatives?
visidata - A terminal spreadsheet multitool for discovering and arranging data
xsv - A fast CSV command line toolkit written in Rust.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
jq - Command-line JSON processor [Moved to: https://github.com/jqlang/jq]
lnav - Log file navigator
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.
tsv-utils - eBay's TSV Utilities: Command line tools for large, tabular data files. Filtering, statistics, sampling, joins and more.
csvtk - A cross-platform, efficient and practical CSV/TSV toolkit in Golang
ClickHouse - ClickHouse® is a free analytics DBMS for big data
yq - yq is a portable command-line YAML, JSON, XML, CSV, TOML and properties processor
nio - Low Overhead Numerical/Native IO library & tools