Golang – encoding/csv: Reading is slow

https://github.com/golang/go/issues/16791

by chinmaymk • 10 years ago

105 96 10 years ago

Python's csv module uses an internal module _csv which is written in C. So I'm not sure it's all that surprising that a Go implementation is a bit slower.

chrisper3• 10 years ago

You should contribute this knowledge to the GitHub issue.

jlarocco• 10 years ago

A good idea, but I think it's common knowledge.

Lots of modules in the stdlib are written in C.

baq• 10 years ago

ultimately it's of academic interest - the end user doesn't care what precisely is going on under the hood.

0xFFC• 10 years ago

How about Java? It is quite funny Java version is much faster than Python version even when Python version does use C ? Something fishy going on.

Anderkent• 10 years ago

The java one doesn't do as much as the python one: `line.split(',')` doesn't handle quoted commas, escape characters, different csv separators, etc.

andrepd• 10 years ago

Yeah, calling from python introduces a layer of inefficiency.

justin66• 10 years ago

It's as if there's more to software quality than the choice of tools.

jerf• 10 years ago

There's nothing mysterious about the Java version... that's not a CSV parser. The other two things are truly CSV parsers. (Inasmuch as there is such a thing for such an ill-defined format. (No, the RFC is not determinative.))

It's easy to be faster if you do fundamentally less. Not necessarily wrong, depending on your task, but it's not comparable.

__s• 10 years ago

The java source is using str.split(',') which is completely different than Python's offering of dialect/delimiter/etc https://github.com/python/cpython/blob/master/Lib/csv.py#L24

nradov• 10 years ago

The Java code is defective. It's not checking for double quotes. The CSV format allows for commas inside column values by surrounding with double quotes, and then you can also put double quotes within such values by escaping them as double double quotes. Fix those defects and the Java code will be a little slower.

With modern JVMs, Java can occasionally actually be faster than native compiled languages due to dynamic optimization at runtime.

jsiep• 10 years ago

Besides the point that the Java example is not a good one; The JVM is actually a pretty mean piece of software with a lot of optimisation. So while Go could in theory produce faster code then Java I doubt the Go compiler is clever enough to produce faster code then the JVM in a lot scenarios (at the moment).

masklinn10• 10 years ago

The Java version is just reading lines and splitting them on commas, it's not actually a CSV parser.

Cyph0n• 10 years ago

I don't think the answer is that simple. Perhaps there's a key optimization that the Go team didn't consider.

aartur• 10 years ago

I run the benchmark using PyPy (which doesn't have this C extension) and got a result about 20% slower compared to CPython (ie. still faster than Go).

EDIT. I also did a funny thing and replaced the CPython C _csv.so extensions with pure Python version _csv.py, from PyPy. It run about 80 (eighty) times slower. It shows what wonders does JIT do (at least to some code).

jjawssd• 10 years ago

Would be a great experiment to Cythonize PyPy's _csv.so

rdtsc• 10 years ago

But not sure if it matters. Go is free to use a C module to load csv files as well. It can use assembly or other tricks as well perhaps.

weberc210• 10 years ago

There's a performance cost to calling between c and Go, and sharing memory between the two makes for hard to predict GC behavior. I doubt it would be faster than a pure Go implementation.

djur• 10 years ago

It seems pretty common for languages to start out with a relatively unoptimized CSV parser (if they have one at all) and then get a faster one contributed by the community once there's enough interest. Ruby had that happen with FasterCSV.

The Java comparison here seems inapt, because it doesn't do as much as the other two. It's just a naive "split on commas" implementation that wouldn't handle quoted cells. Really, if Go's CSV reader is only 200% slower than that and 50% slower than Python's optimized C implementation, that's pretty good already.

justincormack• 10 years ago

It also seems to support UTF8, which is perhaps common now. (CSV doesnt tell you what encoding it is, in the old days it was 95% ASCII).

masklinn10• 10 years ago

CSV doesnt tell you what encoding it is, in the old days it was 95% ASCII

Or some non-ascii codepage you're not told about and have to guess (e.g. Excel generates CSV in CP1250 by default, with an option to export UTF-16)

coldtea2• 10 years ago

All 3 support UTF8. The difference with the Go implementation is the mostly useless capability to have a utf-8 multibyte item as the delimiter.

wongarsu2• 10 years ago

How would a CSV parser even break UTF8 encoding (by accident)? All CSV control characters (comma, doubleqote and newline) map to the same codepoints in ASCII and UTF8, and no non-ASCII UTF8 character uses any ASCII codepoint in it's encoding.

masklinn10• 10 years ago

ignore this, I somehow missed that the poster specifically mentioned Python 3, which does have an encoding-aware CSV module.

~~It's also unclear which version of Python is being used, the Python 2 csv module is byte-based and encoding-unaware which can lead to unexpected behaviours.~~

Go's CSV package apparently only does UTF-8, and suggestions for speeding it up in the tracker is to just remove that and work on raw bytes (FFS)

stesch• 10 years ago

It says "Python3 equivalent".

deno6• 10 years ago

How do you mean? The first comment specifies it’s Python 3.

geofft3• 10 years ago

suggestions for speeding it up in the tracker is to just remove that and work on raw bytes (FFS)

This is valid, because UTF-8 was designed to make this valid. The UTF-8 encoding of a comma, 0x2C (also the ASCII encoding of a comma), does not appear as a part of any other UTF-8 encodings. Same with the UTF-8 encoding of the double quote, 0x22. So scanning for 0x22 and 0x2C bytes, without stopping to decode other UTF-8 sequences along the way, will produce the correct result for a valid UTF-8 input string. Then you fully decode UTF-8 for the individual fields when needed (and if you're doing a string-compare for some target value that's already UTF-8, you never need to decode UTF-8 for that field at all).

halayli• 10 years ago

I feel you're trying to defend go without much objectivity. Such performance gap needs to be addressed properly instead of saying it's pretty good already.

It doesn't sound right if go takes 5 hours to finish csv parsing job while Python takes 2.5 hrs.

heroprotagonist• 10 years ago

Well, you are free to address it if it bothers you. The typical response to this of _I don't want to_ or _I shouldn't have to_ seems a bit naive when working with open source projects. The issue has only been on the tracker for two weeks, and it has the 'HelpWanted' tag, so it's not like they're opposed to improving the speed here.

If you're going to throw out specific numbers, you should probably get them, or at least the ratios, correct.

Numbers from the tracker are:

Go: avg 1.489 secs Python: avg 0.933 secs

If you'd like to test this on a really large dataset to come up with how long it would take for Python to perform the same operation when Go requires 5 hours, that might be a bit more useful. If we just look at the available data, then the _extrapolation_ for Python would not be 2.5 hours. There's still a gap, but there's no need to exaggerate.

Roboprog3• 10 years ago

Indeed. Flip it around: Go-lang comes with CSV support in the standard library, whereas Java requires you to get something like the Ostermiller utilities or an Apache library.

tmaly• 10 years ago

I wrote my own in Go that is blazing fast using bytes. I know the data is ascii so I was able to use that to my advantage.

piinbinary• 10 years ago

I did the same [0]. It runs almost as fast as the Java implementation.

I'd be interested to see how yours works if you are willing to share it.

Edit: Plus one that is ~2x faster than Java by avoiding allocations [1].

[0] https://gist.github.com/jmikkola/6ac96ad6d6f66e772c33ec41ed2...

[1] https://gist.github.com/jmikkola/7ded8392226b7659c881f5540be...

sorokod• 10 years ago

Note that internally Java represents strings as utf-16

SeanDav• 10 years ago

Effectively one is comparing library performance here and not language performance. Granted, that line can get very blurry indeed, but in this case this says very little about golang the language and far more about a current implementation of one of the golang libraries.

jonlawlor2• 10 years ago

It is kind of both; go doesn't allow some approaches in native go code that can make it slower than other languages. (I love go, but that is my experience.)

In this case the choice to use utf-8 everywhere, including in the csv delimiters, is making it slower.

weberc210• 10 years ago

This isn't what makes CSV parsing slow, there's nothing about Go that requires you to deal in UTF8.

paulddraper4• 10 years ago

The Java code is not a CSV parser.

I added the results of using Apache Commons CSV to the GitHub thread.

After using that, Python was actually by far the fastest. Hooray for performance sensitive code in C :)

paulddraper4• 10 years ago

FYI, I later went back and ran PyPy, which implements csv in pure Python. Even including start-up time, it was nearly at CPython speed.

petters• 10 years ago

Any CSV reader should be limited only be disk access right? I wrapped together a C++ program solving this problem and got 0.124 seconds. But that does not do quotations etc.

_ph_• 10 years ago

I would guess, if you directly translate your program to Go it would not be much slower. On low level code, Go 1.7 gets quite close to GCC. The point is, that doing the quotations right, especially if you allow non-ascii quotes, eats a lot of performance. So the benchmark is less about the languages involved, but rather the exact algorithms used and capabilities offered.

Roboprog3• 10 years ago

Good point. But assume if you run a series of tests on the same file, it's in RAM. (discard the time of the first run or two, assume data source - network, DB, file - makes access time moot)

masklinn10• 10 years ago

Any CSV reader should be limited only be disk access right?

Depends on the speed of your storage subsystem. If you're working from RAM or from a fast PCIe SSD, you'll probably bottleneck in the encoding validation and actual parsing.

endymi0n2• 10 years ago

On a related note, also the Go stdlib regex package is pretty naive and imperformant compared to a full blown and modern backtracking PCRE implementation (at 1/10 the LOC and complexity) - same thing goes for the reflection based JSON package (which is still kinda "fast enough").

The focus wasn't so much on performance but on initial completeness, good interface, versatility, clarity and simplicity - with faster or more specialized implementations left to the community.

There might be different opinions about that, but I personally like the approach of having a solid and ordered programming pocket knife - that also doesn't replace a Katana for cutting.

dsymonds• 10 years ago

The standard regexp package, unlike PCRE, is actually a proper regular expression parser/matcher. Anything doing backtracking is at risk of exponential blowup and isn't safe.

https://swtch.com/~rsc/regexp/regexp1.html

endymi0n2• 10 years ago

which is exactly what I meant with less specialized but safe :)

paulddraper4• 10 years ago

On differing opinions: I prefer having a Katana, an automatic shotgun, and a spy drone, and leaving the pocket knife at home.

In other words....C/C++ :) I just try not to blow off my leg.

deno6• 10 years ago

Better than node.js

    import * as csv from 'csv-parse';
    import * as fs from 'fs';
    
    type Line = [string,string,string,string,string,string];
    
    const parser = new csv.Parser({});
    
    parser.on('data', (line: Line) => { 
    if (line[0] === '42') {
            console.dir(line);
        } 
    });
    
    fs.createReadStream('mock_data.csv').pipe(parser);
    
    $ /usr/bin/time node parse_csv.js
    43.61user 0.85system 0:45.61elapsed 97%CPU (0avgtext+0avgdata 60076maxresident)k
    
    $ node --version
    v6.4.0

Edit: Using fast-csv

    24.28user 0.20system 0:24.58elapsed 99%CPU (0avgtext+0avgdata 91780maxresident)k

masklinn10• 10 years ago

csv-parse is hardly the only CSV parser for node, and it is by far the slowest: https://github.com/phihag/csv-speedtest (csv2json depends on csv-parse, so it's unsurprising that it's even slower)

deno6• 10 years ago

I chose the most popular one on npm because Go and Python are using stdlib.

deno6• 10 years ago

The fastest streaming example on that list (csv-parser) is still 5× slower than Python.

elmigranto2• 10 years ago

Probably has to do with using `try/catch` whis is not omptimized by V8. Different parser is 10 times faster on my machine.

https://www.npmjs.com/package/csv-parser

Edit: `fast-csv` seems to be using a lot of `RegExp`s on each iteration which can't be that fast compared to csv-parser which seems to simply go over each symbol (state machine?).

deno6• 10 years ago

    4.95user 0.19system 0:05.22elapsed 98%CPU (0avgtext+0avgdata 29704maxresident)k

A lot better but that’s still 5× slower than Python.

weberc210• 10 years ago

You don't need to drop quote handling in order to stream, but handling quotes is a pain.

twotwotwo• 10 years ago

As someone notes on the bug, if you were rolling your own, there are some other things you could do--return a [][]byte that's a pointer to its internal buffer, only usable until the next row is read.

Making a version of encoding/csv that retains most of its features (custom delimiters, handling backslashes and quoting and \r) but streams like that would be a fun open source project for someone who likes Making Things Go Fast.

peterwaller• 10 years ago

I did this a couple of months ago and got a >5x speedup. It's at the expense of dropping quoting though, so no commas or newlines can be in the input data.

https://github.com/pwaller/usv

weberc210• 10 years ago

That guy here. I'm also interested in rolling a version that only supports standard delimiters so I can forego rune parsing. Rune parsing accounts for about 30% of the processing; not sure how much a bytes implementation could save, but I'm hopeful.

lcarlson• 10 years ago

This may be a bit off topic but I've found sqlite to be quite a powerful csv parser. Once posted you can manipulate the data in lots of ways. When you're working with reports that need to get back into some sort of table format, it's very intuitive and easy for SQL people.

WestCoastJustin• 10 years ago

Related to this, was a Reddit thread from a few days ago in /r/golang about improving a csv/reader. See: https://www.reddit.com/r/golang/comments/50ncer/implementing...

Roboprog3• 10 years ago

So, where's the Perl (+ CPAN...) version for comparison?

I guess "Nobody cares about your dead religion" :-)

Still, it would probably be faster, at the expense of being unreadable.

burntsushi2• 10 years ago

On the CSV Game benchmark, the Go csv reader is around 10x slower than the fastest: https://bitbucket.org/ewanhiggs/csv-game

Golang – encoding/csv: Reading is slow

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