When I wrote the same kind of article in Nov 2011 [1], I came to similar conculsions; ujson was blowing everyone away.
However, after swapping a fairly large and json-intensive production spider over to ujson, we noticed a large increase in memory use.
When I investigated, I discovered that simplejson reused allocated string objects, so when parsing/loading you basically got string compression for repeated string keys.
The effects were pretty large for our dataset, which was all API results from various popular websites and featured lots of lists of things with repeating keys; on a lot of large documents, the loaded mem object was sometimes 100M for ujson and 50M for simplejson. We ended up switching back because of this.
Hey Jason. Thats pretty interesting. I have also noticed similar things but for my case, we needed faster loading/unloading for some cases, hence ujson.
The problem with all (widely known) the non-standard JSON packages is, they all have their gotchas.
cjson's way of handling unicode is just plain wrong: it uses utf-8 bytes as unicode code points. ujson cannot handle large numbers (somewhat larger than 263, i've seen a service that encodes unsigned 64-bit hash values in JSON this way: ujson fails to parse its payloads). With simplejson (when using speedups module), string's type depends on its value, i.e. it decodes strings as 'str' type if their characters are ascii-only, but as 'unicode' otherwise; strangely enough, it always decodes strings as unicode (like standard json module) when speedups are disables.
Agreed, especially about simplejson. I work on a project that uses simplejson, and it leads to ugly type checking all over the place because you never know what your JSON string got turned into. For example:
There are so many poorly-written JSON decoders out there. I've had the misfortune of fixing two of PHP's to follow JSON's case-sensitivity and whitspace rules properly.
I disagree with the conclusion. How about this: you should use the tool that most of your coworkers already know and which has large community support and adequate performance. In other words, stop foling around and use json library. If (IF!!!) you find performance inadequate, try the other libraries. And most of all, if optimization is your goal: measure, measure and measure! </rant>
I just want to add another library in here which – at least in my world – is replacing json as the number one configuration and serialisation format. It's called libucl and it's main consumer is probably the new package tool in FreeBSD: `pkg`
Its syntax is nginx-like but can also parse strict json. It's pretty fast too.
The automatic array creation feature [1] seems misguided. It means that as a programmer consuming a configuration file, i can't know whether a given field will be a scalar or an array. I recently worked on a JavaScript API that had that behaviour, and it was a pain.
Apart from that, though, this looks like a really good format.
At one point I looked into using it, but there werent any python bindings at the time, and I didnt have the time (for the project) to write any. Are there any good language libs for it these days?
How hard is it to draw a bar graph? I'd imagine it is easier than creating an ASCII table and then turning that into an image, but I've never experimented with the latter.
Depends on the app. My previous job required processing thousands of address book contact records uploaded to the server in a massive list. It was not unsual for some of these objects to exceed 10mb (when serialized to disk).
The default json module took close to 5 seconds to deserialize the payload once it hit the server, while ujson could do the same work in a fraction of the time (less than a second). 5 seconds might not seem like a whole lot when the import process as a whole could take 30 seconds or so, but when the user is stuck staring at their device it makes sense to cut down the response time any way you can.
for the typical AJAX call for some rows of data selected from a datastore and JSON encoded, then no the JSON encoding is not the bottleneck, the network latency and database io time dominate the time it takes to JSON encode the data.
however, consider an alternative kind of task that might, for example, produce a big JSON dump of thousands of records. this is fairly typical of a data export of some kind. the network and database time for this request is the same as for the smaller one, but now instead of JSON encoding 50 records you're encoding 50000 records. it can start to add up. a poorly optimized JSON library will add multiple full seconds to your response time here.
> So I can't serialize things with ultrajson that aren't serializable? I must be missing something in this statement.
This might not be what they're talking about, but I did run into what might be the same issue when looking at ujson before. The builtin JSON module lets you define custom serializations for types that aren't natively JSON-serializable; we had an application that did that with datetime objects, encoding them as ISO 8601 date strings. ujson doesn't support anything like that; you have to make sure everything is one of the JSON types already before encoding.
> keep in mind that ultrajson only works with well defined collections and will not work for un-serializable collections. But if you are dealing with texts, this should not be a problem.
Well-defined collections? As in, serializable? Well sure, that's requisite for the native json package as well as simplejson (as far as I can recall -- haven't used simplejson in some time.)
But does "texts" refer to strings? As in, only one data type? The source code certainly supports other types, so I wonder what this statement refers to.
ujson doesn't error out if you have a collection that isn't serializable so you can lose individual keys. It also has issues with ints and floats that are too big (just fails out)
I disagree with the verdict at the end of the article, it seems like json would be better if you were doing a lot of dumping? And also for the added maintenance guarantee of being an official package.
What about larger dictionaries? With such a small one I would be worried that a significant proportion of the time would be simple overhead.
[Warning: Anecdote] When we were testing out the various JSON libraries we found simplejson much faster than json for dumps. We used large dictionaries.
Was the simplejson package using its optimized C library?
Yeah, this error is made consistently throughout the article and the author should fix it. It serves to inflate the project's performance by as much as two-thirds, and some people will see this as intentionally misleading and write your project off because of it. ultrajson looks to be way faster; don't alienate people by fudging the numbers.
But ujson comes at a price of slightly reduced functionality. For example, you cannot set indent. (And I typically set indent for files <100MB, when working with third-party data, often manual inspection is necessary).
Plus, AFAIK, at least in Python 3 json IS simplejson (but a few version older). So every comparison of these libraries is going to give different results over time (likely, with difference getting smaller). Of course, simpejson is the newer thing of the same, so it's likely to be better.
I'll have to try ultrajson for my use case, but when I benchmarked pickle, simplejson and msgpack, msgpack came out the fastest. I also tried combining all three formats with gzip, but that did not help. Primarily I care about speed when deserializing from disk.
I know it goes against the grain, but I wish that binary json (UBJSON) had much more widespread usage. There's no reason tools can't convert it back to json for us old humans.
The speed deference between working with binary streams and parsing text is night and day.
We took a look at ujson about a year ago and found that it failed loading even json structures that went 3 layers deep. I also recall issues handling unicode data.
It was a big disappointment after seeing these kinds of performance improvements.
It kills me that the default JSON module is so slow, if you're working with large JSON objects you really have no choice but to use a 3rd party module because the default won't cut it.
[+] [-] jmoiron|11 years ago|reply
However, after swapping a fairly large and json-intensive production spider over to ujson, we noticed a large increase in memory use.
When I investigated, I discovered that simplejson reused allocated string objects, so when parsing/loading you basically got string compression for repeated string keys.
The effects were pretty large for our dataset, which was all API results from various popular websites and featured lots of lists of things with repeating keys; on a lot of large documents, the loaded mem object was sometimes 100M for ujson and 50M for simplejson. We ended up switching back because of this.
[1] http://jmoiron.net/blog/python-serialization/
[+] [-] jyotiska|11 years ago|reply
[+] [-] JoshTriplett|11 years ago|reply
[+] [-] borman|11 years ago|reply
cjson's way of handling unicode is just plain wrong: it uses utf-8 bytes as unicode code points. ujson cannot handle large numbers (somewhat larger than 263, i've seen a service that encodes unsigned 64-bit hash values in JSON this way: ujson fails to parse its payloads). With simplejson (when using speedups module), string's type depends on its value, i.e. it decodes strings as 'str' type if their characters are ascii-only, but as 'unicode' otherwise; strangely enough, it always decodes strings as unicode (like standard json module) when speedups are disables.
[+] [-] smerritt|11 years ago|reply
https://github.com/openstack/swift/blob/39c1362a4f5a7df75730...
and https://github.com/openstack/swift/blob/39c1362a4f5a7df75730...
and https://github.com/openstack/swift/blob/39c1362a4f5a7df75730...
and many more just like those.
The worst part is the bugs that appear or disappear depending on whether simplejson's speedups module is in use or not.
[+] [-] TazeTSchnitzel|11 years ago|reply
[+] [-] Drdrdrq|11 years ago|reply
[+] [-] jbergstroem|11 years ago|reply
Its syntax is nginx-like but can also parse strict json. It's pretty fast too.
More info here: https://github.com/vstakhov/libucl
[+] [-] twic|11 years ago|reply
Apart from that, though, this looks like a really good format.
[1] https://github.com/vstakhov/libucl#automatic-arrays-creation
[+] [-] michaelmior|11 years ago|reply
[+] [-] stock_toaster|11 years ago|reply
[+] [-] crdoconnor|11 years ago|reply
[+] [-] wodenokoto|11 years ago|reply
[+] [-] jyotiska|11 years ago|reply
[+] [-] icebraining|11 years ago|reply
[+] [-] fiatjaf|11 years ago|reply
[+] [-] azinman2|11 years ago|reply
[+] [-] chojeen|11 years ago|reply
[+] [-] MagicWishMonkey|11 years ago|reply
The default json module took close to 5 seconds to deserialize the payload once it hit the server, while ujson could do the same work in a fraction of the time (less than a second). 5 seconds might not seem like a whole lot when the import process as a whole could take 30 seconds or so, but when the user is stuck staring at their device it makes sense to cut down the response time any way you can.
[+] [-] est|11 years ago|reply
We ended up using ijson.
[+] [-] metaphorm|11 years ago|reply
for the typical AJAX call for some rows of data selected from a datastore and JSON encoded, then no the JSON encoding is not the bottleneck, the network latency and database io time dominate the time it takes to JSON encode the data.
however, consider an alternative kind of task that might, for example, produce a big JSON dump of thousands of records. this is fairly typical of a data export of some kind. the network and database time for this request is the same as for the smaller one, but now instead of JSON encoding 50 records you're encoding 50000 records. it can start to add up. a poorly optimized JSON library will add multiple full seconds to your response time here.
[+] [-] michaelmior|11 years ago|reply
So I can't serialize things with ultrajson that aren't serializable? I must be missing something in this statement.
> The verdict is pretty clear. Use simplejson instead of stock json in any case...
The verdict seems clear (based solely on the data in the post) that ultrajson is the winner.
[+] [-] apendleton|11 years ago|reply
This might not be what they're talking about, but I did run into what might be the same issue when looking at ujson before. The builtin JSON module lets you define custom serializations for types that aren't natively JSON-serializable; we had an application that did that with datetime objects, encoding them as ISO 8601 date strings. ujson doesn't support anything like that; you have to make sure everything is one of the JSON types already before encoding.
[+] [-] kelseyfrancis|11 years ago|reply
ultrajson isn't a drop-in replacement, though, because it doesn't support sort_keys.
[+] [-] jroseattle|11 years ago|reply
Well-defined collections? As in, serializable? Well sure, that's requisite for the native json package as well as simplejson (as far as I can recall -- haven't used simplejson in some time.)
But does "texts" refer to strings? As in, only one data type? The source code certainly supports other types, so I wonder what this statement refers to.
[+] [-] random567|11 years ago|reply
[+] [-] foota|11 years ago|reply
[+] [-] jkire|11 years ago|reply
What about larger dictionaries? With such a small one I would be worried that a significant proportion of the time would be simple overhead.
[Warning: Anecdote] When we were testing out the various JSON libraries we found simplejson much faster than json for dumps. We used large dictionaries.
Was the simplejson package using its optimized C library?
[+] [-] jkire|11 years ago|reply
I completely failed to read this the first time I went through. I guess this is equivalent to dumping bigger dictionaries.
> [Warning: Anecdote] When we were testing out the various JSON libraries we found simplejson much faster than json for dumps.
Turns out we were using sort_keys=True option, which apparently makes simplejson much faster than json.
[+] [-] ktzar|11 years ago|reply
[+] [-] alexhill|11 years ago|reply
[+] [-] jyotiska|11 years ago|reply
[+] [-] thomasfoster96|11 years ago|reply
[+] [-] anon4|11 years ago|reply
[+] [-] stared|11 years ago|reply
(BTW: I got tempted to try ujson exactly for the original blog post, i.e. http://blog.dataweave.in/post/87589606893/json-vs-simplejson...)
Plus, AFAIK, at least in Python 3 json IS simplejson (but a few version older). So every comparison of these libraries is going to give different results over time (likely, with difference getting smaller). Of course, simpejson is the newer thing of the same, so it's likely to be better.
[+] [-] willvarfar|11 years ago|reply
I leave this here in case it helps others.
We had other focus such as good for both python and java.
At the time we went msgpack. As msgpack is doing much the same work as json, it just shows that the magic is in the code not the format..)
[+] [-] apu|11 years ago|reply
[+] [-] dbenhur|11 years ago|reply
JSON is a data representation, not a data model.
[+] [-] js2|11 years ago|reply
[+] [-] velox_io|11 years ago|reply
The speed deference between working with binary streams and parsing text is night and day.
[+] [-] zapov|11 years ago|reply
http://hperadin.github.io/jvm-serializers-report/report.html
[+] [-] akoumjian|11 years ago|reply
It was a big disappointment after seeing these kinds of performance improvements.
[+] [-] MagicWishMonkey|11 years ago|reply
[+] [-] bpicolo|11 years ago|reply