zsv
csvq
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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):
---
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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)
csvq
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Fx – Terminal JSON Viewer
sure can do, if you already use that shell [1], but personally I like specific tools for specific jobs such as jq [2], fx, csvq [3] etc, there's value in decoupling shells from utils (modularity, speed, innovation etc).
[1] I don't but tempted to try, like its data-types concept
[2] https://jqlang.github.io/jq/
[3] https://github.com/mithrandie/csvq
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Tool to interact with CSV
csvq
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Can SQL be used without an RDBMS?
There is a way of running SQL-like queries against CSV files.
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Yq is a portable yq: command-line YAML, JSON, XML, CSV and properties processor
Lately I have had to do a lot of flat file analysis and tools along these lines have been a godsend. Will check this out.
My go to lately has been csvq (https://mithrandie.github.io/csvq/). Really nice to be able run complicated selects right over a CSV file with no setup at all.
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Wie fusioniert man CSV tables?
csvq (https://mithrandie.github.io/csvq/)
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Tool to explore big data sets
I usually do this with awk, my largest target files being half a TB in size for a project last year (and far too large to hold entirely in RAM). There are some other utilities like csvq and csvsql both of which let you write SQL-style queries against CSV files, but I'm not sure how they perform on large files. There's a nice list of CSV manipulation tools too if any of those jog your memory.
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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.
- One-liner for running queries against CSV files with SQLite
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Most efficient way to query .CSV files for Mac?
Please check out this tool https://github.com/mithrandie/csvq
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Looking for: library to turn SQL (or abstracted) to code & execute against custom backend (slice of structs)
If you are looking to query nondb data with sql statements then you may want to check something like https://github.com/mithrandie/csvq (SQL for csv).
What are some alternatives?
visidata - A terminal spreadsheet multitool for discovering and arranging data
querycsv - QueryCSV enables you to load CSV files and manipulate them using SQL queries then after you finish you can export the new values to a CSV file
duckdb - DuckDB is an in-process SQL OLAP Database Management System
q - q - Run SQL directly on delimited files and multi-file sqlite databases
lnav - Log file navigator
yq - yq is a portable command-line YAML, JSON, XML, CSV, TOML and properties processor
tsv-utils - eBay's TSV Utilities: Command line tools for large, tabular data files. Filtering, statistics, sampling, joins and more.
yq - Command-line YAML, XML, TOML processor - jq wrapper for YAML/XML/TOML documents
ClickHouse - ClickHouse® is a free analytics DBMS for big data
miller - Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
nio - Low Overhead Numerical/Native IO library & tools