q
simdjson
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q | simdjson | |
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46 | 63 | |
10,109 | 18,337 | |
- | 1.1% | |
3.6 | 9.2 | |
3 months ago | 10 days ago | |
Python | C++ | |
GNU General Public License v3.0 only | Apache 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.
q
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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
- Segítség kérés Excel automatizáláshoz
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Show HN: ClickHouse-local – a small tool for serverless data analytics
I think they're talking about https://github.com/harelba/q, which is not very fast.
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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
Hi, author of q here.
Regarding the error you got, q currently does not autodetect headers, so you'd need to add -H as a flag in order to use the "country" column name. You're absolutely correct on failing-fast here - It's a bug which i'll fix.
In general regarding speed - q supports automatic caching of the CSV files (through the "-C readwrite" flag). Once it's activated, it will write the data into another file (with a .qsql extension), and will use it automatically in further queries in order to speed things considerably.
Effectively, the .qsql files are regular sqlite3 files (with some metadata), and q can be used to query them directly (or any regular sqlite3 file), including the ability to seamlessly join between multiple sqlite3 files.
- PostgreSQL alternative for Large amounts of data
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q VS trdsql - a user suggested alternative
2 projects | 25 Jun 2022
- One-liner for running queries against CSV files with SQLite
simdjson
- 1BRC Merykitty's Magic SWAR: 8 Lines of Code Explained in 3k Words
- Training great LLMs from ground zero in the wilderness as a startup
- simdjson: Parsing Gigabytes of JSON per Second
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Use any web browser as GUI, with Zig in the back end and HTML5 in the front end
String parsing is negligible compared to the speed of the DOM which is glacially slow: https://news.ycombinator.com/item?id=38835920
Come on, people, make an effort to learn how insanely fast computers are, and how insanely inefficient our software is.
String parsing can be done at gigabytes per second: https://github.com/simdjson/simdjson If you think that is the slowest operation in the browser, please find some resources that talk about what is actually happening in the browser?
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Cray-1 performance vs. modern CPUs
Thanks for all the detailed information! That answers a bunch of my questions and the implementation of strlen is nice.
The instruction I was thinking of is pshufb. An example ‘weird’ use can be found for detecting white space in simdjson: https://github.com/simdjson/simdjson/blob/24b44309fb52c3e2c5...
This works as follows:
1. Observe that each ascii whitespace character ends with a different nibble.
2. Make some vector of 16 bytes which has the white space character whose final nibble is the index of the byte, or some other character with a different final nibble from the byte (eg first element is space =0x20, next could be eg 0xff but not 0xf1 as that ends in the same nibble as index)
3. For each block where you want to find white space, compute pcmpeqb(pshufb(whitespace, input), input). The rules of pshufb mean (a) non-ascii (ie bit 7 set) characters go to 0 so will compare false, (b) other characters are replaced with an element of whitespace according to their last nibble so will compare equal only if they are that whitespace character.
I’m not sure how easy it would be to do such tricks with vgather.vv. In particular, the length of the input doesn’t matter (could be longer) but the length of white space must be 16 bytes. I’m not sure how the whole vlen stuff interacts with tricks like this where you (a) require certain fixed lengths and (b) may have different lengths for tables and input vectors. (and indeed there might just be better ways, eg you could imagine an operation with a 256-bit register where you permute some vector of bytes by sign-extending the nth bit of the 256-bit register into the result where the input byte is n).
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Codebases to read
Additionally, if you like low level stuff, check out libfmt (https://github.com/fmtlib/fmt) - not a big project, not difficult to understand. Or something like simdjson (https://github.com/simdjson/simdjson).
- Simdjson: Parsing Gigabytes of JSON per Second
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Building a high performance JSON parser
Everything you said is totally reasonable. I'm a big fan of napkin math and theoretical upper bounds on performance.
simdjson (https://github.com/simdjson/simdjson) claims to fully parse JSON on the order of 3 GB/sec. Which is faster than OP's Go whitespace parsing! These tests are running on different hardware so it's not apples-to-apples.
The phrase "cannot go faster than this" is just begging for a "well ackshully". Which I hate to do. But the fact that there is an existence proof of Problem A running faster in C++ SIMD than OP's Probably B scalar Go is quite interesting and worth calling out imho. But I admit it doesn't change the rest of the post.
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New package : lspce - a simple LSP Client for Emacs
I have same question as /u/JDRiverRun : how do you deal with JSON, do you parse json on Rust side or on Emacs side. I see that you are requiring json.el in your lspce.el, but I haven't looked through entire file carefully. If you parse on Rust side, do you use simdjson (there are at least two Rust bindings to it)? If yes, what are your impressions, experiences compared to more "standard" json library?
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Any fresh jvm21 benchmarks ?
I expect a lot of transcoders will be rewritten when the Vector instructions land. You can see speedups when used in other languages, such as simdjson. Please try to be more thoughtful and not disregard other people's hard work so easily.
What are some alternatives?
textql - Execute SQL against structured text like CSV or TSV
RapidJSON - A fast JSON parser/generator for C++ with both SAX/DOM style API
csvq - SQL-like query language for csv
jsoniter - jsoniter (json-iterator) is fast and flexible JSON parser available in Java and Go
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
json - JSON for Modern C++
xsv - A fast CSV command line toolkit written in Rust.
json-schema-validator - JSON schema validator for JSON for Modern C++
InquirerPy - :snake: Python port of Inquirer.js (A collection of common interactive command-line user interfaces)
JsonCpp - A C++ library for interacting with JSON.
ledger - Double-entry accounting system with a command-line reporting interface
json - A C++11 library for parsing and serializing JSON to and from a DOM container in memory.