Everyone is out here making fancy AI projects and talking about high tech stuff. In the mean time, I wrote this plain bash script to convert .ics files (a format from 1998) to CSV files.
This lets you explore your calendar data without needing to setup API access and deal with authorization to your calendar software's API. (common ones all have .ics exports readily available)
Combine this with CSV tooling and you got yourself some quick and easy calendar data analysis / exploration setup.
Hope you like it!
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
In icsp – Command-line iCalendar (.ics) to CSV parserharelba
q
Python2012–2023
harelba / q
q
q - Run SQL directly on delimited files and multi-file sqlite databases
24Distinct people
26Mentions
0Explicit recommenders
10.4KGitHub stars
What to know before exploring
What it is
q - Run SQL directly on delimited files and multi-file sqlite databases
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
- dinedal/textql · linked in 2 of the same discussions
- johnkerl/miller · linked in 2 of the same discussions
- multiprocessio/dsq · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year26 dated mentions
Observed mentions: 2012-02-13 12:40:24 UTC to 2023-03-25 04:53:14 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2012 | 2012-01-01 00:00:00 UTC | 2013-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2013 | 2013-01-01 00:00:00 UTC | 2014-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2014 | 2014-01-01 00:00:00 UTC | 2015-01-01 00:00:00 UTC | 3 | 2 | 1 |
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 4 | 2 | 2 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 6 | 1 | 5 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 3 | 0 | 3 |
From the project README
An excerpt from the repository, fetched Nov 13, 2026.
Read the project’s introduction
q - Text as Data
q's purpose is to bring SQL expressive power to the Linux command line and to provide easy access to text as actual data.
q allows the following:
Performing SQL-like statements directly on tabular text data, auto-caching the data in order to accelerate additional querying on the same file.
Performing SQL statements directly on multi-file sqlite3 databases, without having to merge them or load them into memory
The following table shows the impact of using caching:
Rows
Columns
File Size
Query time without caching
Query time with caching
Speed Improvement
5,000,000
100
4.8GB
4 minutes, 47 seconds
1.92 seconds
x149
1,000,000
100
983MB
50.9 seconds
0.461 seconds
x110
1,000,000
50
477MB
27.1 seconds
0.272 seconds
x99
100,000
100
99MB
5.2 seconds
0.141 seconds
x36
100,000
50
48MB
2.7 seconds
0.105 seconds
x25
Notice that for the current version, caching is not enabled by default, since the caches take disk space. Use -C readwrite or -C read to enable it for a query, or add caching_mode to .qrc to set a new default.
q's web site is https://harelba.github.io/q/ or https://q.textasdata.wiki It contains everything you need to download and use q immediately.
Usage Examples
q treats ordinary files as database tables, and supports all SQL constructs, such as WHERE, GROUP BY, JOINs, etc. It supports automatic column name and type detection, and provides full support for multiple character encodings.
Here are some example commands to get the idea:
$ q "SELECT COUNT(*) FROM ./clicks_file.csv WHERE c3 > 32.3"
$ ps -ef | q -H "SELECT UID, COUNT(*) cnt FROM - GROUP BY UID ORDER BY cnt DESC LIMIT 3"
$ q "select count(*) from some_db.sqlite3:::albums a left join another_db.sqlite3:::tracks t on (a.album_id = t.album_id)"
Detailed examples are in here
Installation.
New Major Version 3.1.6 is out with a lot of significant additions.
Instructions for all OSs are here.
The previous version 2.0.19 Can still be downloaded from here
Contact
Any feedback/suggestions/complaints regarding this tool would be much appreciated. Contributions are most welcome as well, of course.
Linkedin: Harel Ben Attia
Twitter @harelba
Email harelba@gmail.com
q on twitter: #qtextasdata
Patreon: harelba - All the money received is donated to the Center for the Prevention and Treatment of Domestic Violence in my hometown - Ramla, Israel.
Read the full README on GitHubWhat Hacker News says
26 sourcesWhat is the general opinion about Nushell and this tools (or others)?
https://github.com/multiprocessio/dsq
https://github.com/jqnatividad/qsv
https://csvkit.readthedocs.io/en/latest
https://github.com/kellyjonbrazil/jc
https://github.com/TomWright/dasel
https://github.com/johnkerl/miller
https://stedolan.github.io/jq
https://github.com/harelba/q
...
In Nushell.sh ls | where size > 10mb | sort-by modifiedI think they're talking about https://github.com/harelba/q, which is not very fast.
In Show HN: ClickHouse-local – a small tool for serverless data analyticsI like using q for querying CSVs on the command line:
https://github.com/harelba/q
In One-liner for running queries against CSV files with SQLiteIf jq is getting too slow for you (that's never happened for me), it really seems like it's time to put your data in a database like sqlite or duckdb at least.
Incidentally there are many tools that help you do this like dsq [0] (which I develop), q [1], textql [2], etc.
[0] https://github.com/multiprocessio/dsq
[1] https://github.com/harelba/q
[2] https://github.com/dinedal/textql
In Zq: An easier and faster alternative to jq9k stars and 5yrs old:
https://github.com/harelba/q
Why not call it like dnsq or dq like jq (https://stedolan.github.io/jq/) did?
In Show HN: "q", a DNS query tool with support for UDP, TCP, DoT, DoH, DoQ and ODoHdsq references a benchmark done by q (https://github.com/harelba/q/blob/master/test/BENCHMARK.md) that indicates that octosql is significantly slower.
However, octosql's GH repo claims otherwise.
Does anyone have any real world experience that they can share on these tools?
In Dsq: Commandline tool for running SQL queries against JSON, CSV, Parquet, etc.Second this recommendation for the terminal. For my CLI toolbox, VisiData is my favorite.
I find VisiData is great for quickly exploring and querying data from the CLI. It can handle many types of files (SQLite, CSV, TSV, Excel, JSON, YAML, etc). Visidata loads all the data into memory, and so is very responsive when exploring the data. It allows you to quickly do all sorts of of adhoc queries interactively, without having to write a valid SQL query.
I haven't used Q. When I first heard of it, I liked the idea that Q allowed you to run random queries on CSV and TSV files. However, it seemed like it would be slow if you wanted to do follow up queries, since it had to repopulate the in memory SQLite file for each query. Though it looks like the latest version has a way to cache the generated sqlite file. So that seems like it could
Read the rest
help.
Also, if I have some CSV, TSV, JSONL data sqlite-utils is useful for converting them to SQLite, and then exploring with Visidata or SQL queries.
Q: https://github.com/harelba/q
sqlite-utils: https://github.com/simonw/sqlite-utils
Thanks, I'll take your .bzl as inspiration [0].
How did you go about developing against the Starlark API, any IDE support?
[0] https://github.com/harelba/q/blob/master/pyoxidizer.bzl
In Compile Python applications into stand-alone executablesGreat to see SQL being used more for such use cases! (I really like SQL)
This looks nice, especially with the charting!
It's good to know though that there are a few similar open source tools already:
- q[0] - SQL on CSV and TSV
- TextQL[1] - SQL on CSV and TSV
- Datasette[2] - Running SQL on various datasets in various formats, by (if I understand it correctly) importing it to sqlite and running the query there. This one's the closest one to the posted project I think, with interactive data exploration and publishing built in.
- OctoSQL[3] - SQL on files like CSV, JSON as well as non-file databases, endless streams with temporal SQL extensions and joining everything together in a single query.
The performance numbers of this tool however are impressive if they will check out with real data. 1GB in 10 seconds is fast. However, looking at
Read the rest
the documentation it looks like most of the heavy lifting is done by SQLite under the hood. From the docs:
> Under the hood, Superintendent loads CSVs into a SQLite database.
Disclaimer: I'm the author of OctoSQL. Still actively developing a rewrite on the redesign branch.
[0]: https://github.com/harelba/q
[1]: https://github.com/dinedal/textql
[2]: https://datasette.io
[3]: https://github.com/cube2222/octosql
Q – Run SQL Directly on CSV or TSV Files
1 points · Open discussionIs this similar to https://github.com/harelba/q ?
In Show HN: Work with CSV files using SQL. For data scientists and engineers> [...] this is a wrapper that constructs a sqlite db from CSV/TSV (similar to sqlite's '.import') and then runs SQL on it, right?
It appears so: https://github.com/harelba/q/blob/master/bin/q.py#L267
Regardless, simple utilities that expose SQL interfaces over arbitrary data are still incredibly compelling & useful.
In Q – Run SQL Directly on CSV or TSV FilesLooks like it is implemented in python(2) with use of the stdlib sqlite3 module. https://github.com/harelba/q/blob/master/bin/q
In Q: Run SQL Directly on CSV FilesShowing the 15 most recent of 26 sources. Enable JavaScript to search and browse every indexed mention.