A faster CSV parser in 5KB (min) (MIT Licensed)
uDSV is a fast JS library for parsing well-formed CSV strings, either from memory or incrementally from disk or network. It is mostly RFC 4180 compliant, with support for quoted values containing commas, escaped quotes, and line breaks¹. The aim of this project is to handle the 99.5% use-case without adding complexity and performance trade-offs to support the remaining 0.5%.
¹ Line breaks (\n,\r,\r\n) within quoted values must match the row separator.
What does uDSV pack into 5KB?
- RFC 4180 compliant
- Incremental or full parsing, with optional accumulation
- Auto-detection and customization of delimiters (rows, columns, quotes, escapes)
- Schema inference and value typing:
string,number,boolean,date,json - Defined handling of
'','null','NaN' - Whitespace trimming of values & skipping empty lines
- Multi-row header skipping and column renaming
- Multiple outputs: arrays (tuples), objects, nested objects, columnar arrays
Of course, most of these are table stakes for CSV parsers :)
Is it Lightning Fast™ or Blazing Fast™?
No, those are too slow! uDSV has Ludicrous Speed™; it's faster than the parsers you recognize and faster than those you've never heard of.
Most CSV parsers have one happy/fast path -- the one without quoted values, without value typing, and only when using the default settings & output format. Once you're off that path, you can generally throw any self-promoting benchmarks in the trash. In contrast, uDSV remains fast with any datasets and all options; its happy path is every path.
On a Ryzen 7 ThinkPad, Linux v6.13.3, and NodeJS v22.14.0, a diverse set of benchmarks show a 1x-5x performance boost relative to the popular, proven-fast, Papa Parse.
For way too many synthetic and real-world benchmarks, head over to /bench...and don't forget your coffee!
┌───────────────────────────────────────────────────────────────────────────────────────────────┐ │ customers-100000.csv (17 MB, 12 cols x 100K rows) (parsing to strings) │ ├────────────────────────┬────────┬─────────────────────────────────────────────────────────────┤ │ Name │ Rows/s │ Throughput (MiB/s) │ ├────────────────────────┼────────┼─────────────────────────────────────────────────────────────┤ │ csv-simple-parser │ 1.45M │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 240 │ │ uDSV │ 1.39M │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 230 │ │ PapaParse │ 1.13M │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 187 │ │ tiddlycsv │ 1.09M │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 180 │ │ ACsv │ 1.07M │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 176 │ │ but-csv │ 1.05M │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 174 │ │ d3-dsv │ 987K │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 163 │ │ csv-rex │ 887K │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 147 │ │ csv42 │ 781K │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 129 │ │ achilles-csv-parser │ 687K │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░ 114 │ │ arquero │ 567K │ ░░░░░░░░░░░░░░░░░░░░░░░ 93.6 │ │ comma-separated-values │ 545K │ ░░░░░░░░░░░░░░░░░░░░░ 90 │ │ node-csvtojson │ 456K │ ░░░░░░░░░░░░░░░░░░ 75.3 │ │ @vanillaes/csv │ 427K │ ░░░░░░░░░░░░░░░░░ 70.5 │ │ SheetJS │ 415K │ ░░░░░░░░░░░░░░░░ 68.5 │ │ csv-parser (neat-csv) │ 307K │ ░░░░░░░░░░░░ 50.7 │ │ CSVtoJSON │ 297K │ ░░░░░░░░░░░░ 49.1 │ │ dekkai │ 221K │ ░░░░░░░░░ 36.5 │ │ csv-js │ 206K │ ░░░░░░░░ 34.1 │ │ @gregoranders/csv │ 202K │ ░░░░░░░░ 33.3 │ │ csv-parse/sync │ 177K │ ░░░░░░░ 29.3 │ │ jquery-csv │ 155K │ ░░░░░░ 25.6 │ │ @fast-csv/parse │ 114K │ ░░░░░ 18.9 │ │ utils-dsv-base-parse │ 74.3K │ ░░░ 12.3 │ └────────────────────────┴────────┴─────────────────────────────────────────────────────────────┘
┌───────────────────────────────────────────────────────────────────────────────────────────────┐ │ customers-100000.csv (17 MB, 12 cols x 100K rows) (parsing with types) │ ├────────────────────────┬────────┬────────────────────────────────────────┬────────────────────┤ │ Name │ Rows/s │ Throughput (MiB/s) │ Types │ ├────────────────────────┼────────┼────────────────────────────────────────┼────────────────────┤ │ uDSV │ 993K │ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 164 │ date,number,string │ │ csv42 │ 686K │ ░░░░░░░░░░░░░░░░░░░░░░░░ 113 │ number,string │ │ csv-simple-parser │ 666K │ ░░░░░░░░░░░░░░░░░░░░░░░ 110 │ date,number,string │ │ csv-rex │ 627K │ ░░░░░░░░░░░░░░░░░░░░░░ 104 │ number,string │ │ comma-separated-values │ 536K │ ░░░░░░░░░░░░░░░░░░░ 88.5 │ number,string │ │ achilles-csv-parser │ 517K │ ░░░░░░░░░░░░░░░░░░ 85.3 │ number,string │ │ arquero │ 478K │ ░░░░░░░░░░░░░░░░░ 79 │ date,number,string │ │ PapaParse │ 463K │ ░░░░░░░░░░░░░░░░ 76.4 │ number,string │ │ d3-dsv │ 389K │ ░░░░░░░░░░░░░░ 64.3 │ date,number,string │ │ @vanillaes/csv │ 312K │ ░░░░░░░░░░░ 51.5 │ NaN,number,string │ │ CSVtoJSON │ 284K │ ░░░░░░░░░░ 46.8 │ number,string │ │ csv-parser (neat-csv) │ 265K │ ░░░░░░░░░░ 43.7 │ number,string │ │ csv-js │ 211K │ ░░░░░░░░ 34.8 │ number,string │ │ dekkai │ 209K │ ░░░░░░░░ 34.6 │ number,string │ │ csv-parse/sync │ 101K │ ░░░░ 16.7 │ date,number,string │ │ SheetJS │ 64.5K │ ░░░ 10.7 │ number,string │ └────────────────────────┴────────┴────────────────────────────────────────┴────────────────────┘
npm i udsv
or
<script src="./dist/uDSV.iife.min.js"></script>A 150 LoC uDSV.d.ts TypeScript def.
import { inferSchema, initParser } from 'udsv';
let csvStr = 'a,b,c\n1,2,3\n4,5,6';
let schema = inferSchema(csvStr);
let parser = initParser(schema);
// native format (fastest)
let stringArrs = parser.stringArrs(csvStr); // [ ['1','2','3'], ['4','5','6'] ]
// typed formats (internally converted from native)
let typedArrs = parser.typedArrs(csvStr); // [ [1, 2, 3], [4, 5, 6] ]
let typedObjs = parser.typedObjs(csvStr); // [ {a: 1, b: 2, c: 3}, {a: 4, b: 5, c: 6} ]
let typedCols = parser.typedCols(csvStr); // [ [1, 4], [2, 5], [3, 6] ]
let stringObjs = parser.stringObjs(csvStr); // [ {a: '1', b: '2', c: '3'}, {a: '4', b: '5', c: '6'} ]
let stringCols = parser.typedCols(csvStr); // [ ['1', '4'], ['2', '5'], ['3', '6'] ]Nested/deep objects can be re-constructed from column naming via .typedDeep():
// deep/nested objects (from column naming)
let csvStr2 = `
_type,name,description,location.city,location.street,location.geo[0],location.geo[1],speed,heading,size[0],size[1],size[2]
item,Item 0,Item 0 description in text,Rotterdam,Main street,51.9280712,4.4207888,5.4,128.3,3.4,5.1,0.9
`.trim();
let schema2 = inferSchema(csvStr2);
let parser2 = initParser(schema2);
let typedDeep = parser2.typedDeep(csvStr2);
/*
[
{
_type: 'item',
name: 'Item 0',
description: 'Item 0 description in text',
location: {
city: 'Rotterdam',
street: 'Main street',
geo: [ 51.9280712, 4.4207888 ]
},
speed: 5.4,
heading: 128.3,
size: [ 3.4, 5.1, 0.9 ],
}
]
*/CSP Note:
uDSV uses dynamically-generated functions (via new Function()) for its .typed*() methods.
These functions are lazy-generated and use JSON.stringify() code-injection guards, so the risk should be minimal.
Nevertheless, if you have strict CSP headers without unsafe-eval, you won't be able to take advantage of the typed methods and will have to do the type conversion from the string tuples yourself.
uDSV has no inherent knowledge of streams.
Instead, it exposes a generic incremental parsing API to which you can pass sequential chunks.
These chunks can come from various sources, such as a Web Stream or Node stream via fetch() or fs, a WebSocket, etc.
Here's what it looks like with Node's fs.createReadStream():
let stream = fs.createReadStream(filePath);
let parser = null;
let result = null;
stream.on('data', (chunk) => {
// convert from Buffer
let strChunk = chunk.toString();
// on first chunk, infer schema and init parser
parser ??= initParser(inferSchema(strChunk));
// incremental parse to string arrays
parser.chunk(strChunk, parser.stringArrs);
});
stream.on('end', () => {
result = parser.end();
});...and Web streams in Node, or Fetch's Response.body:
let stream = fs.createReadStream(filePath);
let webStream = Stream.Readable.toWeb(stream);
let textStream = webStream.pipeThrough(new TextDecoderStream());
let parser = null;
for await (const strChunk of textStream) {
parser ??= initParser(inferSchema(strChunk));
parser.chunk(strChunk, parser.stringArrs);
}
let result = parser.end();The above examples show accumulating parsers -- they will buffer the full result into memory.
This may not be something you need (or want), for example with huge datasets where you're looking to get the sum of a single column, or want to filter only a small subset of rows.
To bypass this auto-accumulation behavior, simply pass your own handler as the third argument to parser.chunk():
// ...same as above
let sum = 0;
// sums fourth column
let reducer = (row) => {
sum += row[3];
};
for await (const strChunk of textStream) {
parser ??= initParser(inferSchema(strChunk));
parser.chunk(strChunk, parser.typedArrs, reducer); // typedArrs + reducer
}
parser.end();Building on the non-accumulating example, Node's Transform stream will be something like:
import { Transform } from "stream";
class ParseCSVTransform extends Transform {
#parser = null;
#push = null;
constructor() {
super({ objectMode: true });
this.#push = parsed => {
this.push(parsed);
};
}
_transform(chunk, encoding, callback) {
let strChunk = chunk.toString();
this.#parser ??= initParser(inferSchema(strChunk));
this.#parser.chunk(strChunk, this.#parser.typedArrs, this.#push);
callback();
}
_flush(callback) {
this.#parser.end();
callback();
}
}- handle #comment rows
- emit empty-row and #comment events?
