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https://github.com/daggaz/json-stream

Simple streaming JSON parser and encoder.
https://github.com/daggaz/json-stream

json json-parser parser python

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Simple streaming JSON parser and encoder.

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# json-stream



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Simple streaming JSON parser and encoder.



When [reading](#reading) JSON data, `json-stream` can decode JSON data in 

a streaming manner, providing a pythonic dict/list-like interface, or a

[visitor-based interfeace](#visitor). Can stream from files, [URLs](#urls) 

or [iterators](#iterators).



When [writing](#writing) JSON data, `json-stream` can stream JSON objects 

as you generate them.



These techniques allow you to [reduce memory consumption and 

latency](#standard-json-problems).



#  Reading



`json-stream` is a JSON parser just like the standard library's

 [`json.load()`](https://docs.python.org/3/library/json.html#json.load). It 

 will read a JSON document and convert it into native python types.



```python

import json_stream

data = json_stream.load(f)

```



Features:

* stream all JSON data types (objects, lists and simple types)

* stream nested data

* simple pythonic `list`-like/`dict`-like interface

* stream truncated or malformed JSON data (up to the first error)

* [native code parsing speedups](#rust-tokenizer) for most common platforms 

* pure python fallback if native extensions not available



Unlike `json.load()`, `json-stream` can _stream_ JSON data from any file-like or

[iterable](#iterators) object. This has the following benefits:



* it does not require the whole json document to be read into memory up-front

* it can start producing data before the entire document has finished loading

* it only requires enough memory to hold the data currently being parsed



There are specific integrations for streaming JSON data from [URLs](#urls) using the 

[`requests`](#requests), [`httpx`](#httpx), or [`urllib`](#urllib).



The objects that `json-stream` produces can be [re-output](#encoding-json-stream-objects)

using `json.dump()` with a little work.



## Usage



### `json_stream.load()`



`json_stream.load()` has two modes of operation, controlled by

the `persistent` argument (default false).



It is also possible to "mix" the modes as you consume the data.



#### Transient mode (default)



This mode is appropriate if you can consume the data iteratively. You cannot 

move backwards through the stream to read data that has already been skipped

over. It is the mode you **must** use if you want to process large amounts of

JSON data without consuming large amounts of memory.



In transient mode, only the data currently being read is stored in memory. Any

data previously read from the stream is discarded (it's up to you what to do 

with it) and attempting to access this data results in a

`TransientAccessException`.



```python

import json_stream



# JSON: {"count": 3, "results": ["a", "b", "c"]}

data = json_stream.load(f)  # data is a transient dict-like object 

# stream has been read up to "{"



# use data like a dict

results = data["results"]  # results is a transient list-like object

# stream has been read up to "[", we now cannot read "count"



# iterate transient list

for result in results:

    print(result)  # prints a, b, c

# stream has been read up to "]"



# attempt to read "count" from earlier in stream

count = data["count"]  # will raise exception

# stream is now exhausted



# attempt to read from list that has already been iterated

for result in results:  # will raise exception

    pass

```



#### Persistent mode



In persistent mode all previously read data is stored in memory as

it is parsed. The returned `dict`-like or `list`-like objects

can be used just like normal data structures.



If you request an index or key that has already been read from the stream

then it is retrieved from memory. If you request an index or key that has

not yet been read from the stream, then the request blocks until that item

is found in the stream.



```python

import json_stream



# JSON: {"count": 1, "results": ["a", "b", "c"]}

data = json_stream.load(f, persistent=True)

# data is a streaming  dict-like object 

# stream has been read up to "{"



# use data like a dict

results = data["results"]  # results is a streaming list-like object

# stream has been read up to "["

# count has been stored data



# use results like a list

a_result = results[1]  # a_result = "b"

# stream has been read up to the middle of list

# "a" and "b" have been stored in results



# read earlier data from memory

count = data["count"]  # count = 1



# consume rest of list

results.read_all()

# stream has been read up to "}"

# "c" is now stored in results too

# results.is_streaming() == False



# consume everything

data.read_all()

# stream is now exhausted

# data.is_streaming() == False

```



Persistent mode is not appropriate if you care about memory consumption, but

provides an identical experience compared to `json.load()`.



#### Mixed mode



In some cases you will need to be able to randomly access some part of the 

data, but still only have that specific data taking up memory resources.



For example, you might have a very long list of objects, but you cannot always 

access the keys of the objects in stream order. You want to be able to iterate

the list transiently, but access the result objects persistently.



This can be achieved using the `persistent()` method of all the `list` or

`dict`-like objects json_stream produces. Calling `persistent()` causes the existing

transient object to produce persistent child objects.



Note that the `persistent()` method makes the children of the object it

is called on persistent, not the object it is called on.



```python

import json_stream



# JSON: {"results": [{"x": 1, "y": 3}, {"y": 4, "x": 2}]}

# note that the keys of the inner objects are not ordered 

data = json_stream.load(f)  # data is a transient dict-like object 



# iterate transient list, but produce persistent items

for result in data['results'].persistent():

    # result is a persistent dict-like object

    print(result['x'])  # print x

    print(result['y'])  # print y (error on second result without .persistent())

    print(result['x'])  # print x again (error without .persistent())

```



The opposite is also possible, going from persistent mode to transient mode, though 

the use cases for this are more esoteric.



```python

# JSON: {"a": 1, "x": ["long", "list", "I", "don't", "want", "in", "memory"], "b": 2}

data = load(StringIO(json), persistent=True).transient()

# data is a persistent dict-list object that produces transient children



print(data["a"])  # prints 1

x = data["x"]  # x is a transient list, you can use it accordingly

print(x[0])  # prints long



# access earlier data from memory

print(data["a"])  # this would have raised an exception if data was transient



print(data["b"])  # prints 2



# we have now moved past all the data in the transient list

print(x[0])  # will raise exception

```



### visitor pattern



You can also parse using a visitor-style approach where a function you supply

is called for each data item as it is parsed (depth-first).



This uses a transient parser under the hood, so does not consume memory for

the whole document.



```python

import json_stream



# JSON: {"x": 1, "y": {}, "xxxx": [1,2, {"yyyy": 1}, "z", 1, []]}



def visitor(item, path):

    print(f"{item} at path {path}")



json_stream.visit(f, visitor)

```



Output:

```

1 at path ('x',)

{} at path ('y',)

1 at path ('xxxx', 0)

2 at path ('xxxx', 1)

1 at path ('xxxx', 2, 'yyyy')

z at path ('xxxx', 3)

1 at path ('xxxx', 4)

[] at path ('xxxx', 5)

```



###  Stream a URL



`json_stream` knows how to stream directly from a URL using a variety of packages.

Supported packages include:

- Python's batteries-included [`urllib`](#urllib) package

- The popular [`requests`](#requests) library

- The newer [`httpx`](#httpx) library



####  urllib



[`urllib`](https://docs.python.org/3/library/urllib.html)'s response objects are already

file-like objects, so we can just pass them directly to `json-stream`.



```python

import urllib.request

import json_stream



with urllib.request.urlopen('http://example.com/data.json') as response:

    data = json_stream.load(response)

```



#### requests



To stream JSON data from [`requests`](https://requests.readthedocs.io/en/latest/), you must

pass `stream=True` when making a request, and call `json_stream.requests.load()` passing the response. 



```python

import requests

import json_stream.requests



with requests.get('http://example.com/data.json', stream=True) as response:

    data = json_stream.requests.load(response)

```





Note: these functions use

[`response.iter_content()`](https://requests.readthedocs.io/en/latest/api/#requests.Response.iter_content) under the

hood with a `chunk_size` of 10k bytes. This default allows us to perform effective reads from the response stream and 

lower CPU usage. The drawback to this is that `requests` will buffer each read until up to 10k bytes have been read 

before passing the data back to `json_stream`. If you need to consume data more responsively the only option is to tune

`chunk_size` back to 1 to disable buffering.



####  httpx



To stream JSON data from [`httpx`](https://www.python-httpx.org/), you must call

[`stream()`](https://www.python-httpx.org/quickstart/#streaming-responses) when

making your request, and call `json_stream.httpx.load()` passing the response.



```python

import httpx

import json_stream.httpx



with httpx.Client() as client, client.stream('GET', 'http://example.com/data.json') as response:

    data = json_stream.httpx.load(response)

```



Under the hood, this works similarly to the [`requests`](#requests) version above, including 

the caveat about [`chunk_size`](#requests-chunk-size).



### Stream a URL (with visitor)



The visitor pattern also works with URL streams.



#### urllib



```python

import urllib.request

import json_stream



def visitor(item, path):

    print(f"{item} at path {path}")

    

with urllib.request.urlopen('http://example.com/data.json') as response:

    json_stream.visit(response, visitor)

```



#### requests



```python

import requests

import json_stream.requests



def visitor(item, path):

    print(f"{item} at path {path}")

    

with requests.get('http://example.com/data.json', stream=True) as response:

    json_stream.requests.visit(response, visitor)

```



The [`chunk_size`](#requests-chunk-size) note also applies to `visit()`.



#### httpx



```python

import httpx

import json_stream.httpx



def visitor(item, path):

    print(f"{item} at path {path}")

    

with httpx.Client() as client, client.stream('GET', 'http://example.com/data.json') as response:

    json_stream.httpx.visit(response, visitor)

```



###  Stream an iterable



`json-stream`'s parsing functions can take any iterable object that produces encoded JSON as

`byte` objects.



```python

import json_stream



def some_iterator():

    yield b'{"some":'

    yield b' "JSON"}'



data = json_stream.load(some_iterator())

assert data['some'] == "JSON"

```



This is actually how the [`requests`](#requests) and [`httpx`](#httpx) extensions work, as

both libraries provide methods to iterate over the response content.



###  Encoding json-stream objects



You can re-output (encode) _persistent_ json-stream `dict`-like and `list`-like object back to JSON using the built-in

`json.dump()` or `json.dumps()` functions, but with a little additional work:



```python

import json



import json_stream

from json_stream.dump import JSONStreamEncoder, default



data = json_stream.load(f, persistent=True)



# Option 1: supply json_stream.encoding.default as the default argument

print(json.dumps(data, default=default))



# Option 2: supply json_stream.encoding.JSONStreamEncoder as the cls argument

# This allows you to create your own subclass to further customise encoding

print(json.dumps(data, cls=JSONStreamEncoder))

```



If you are using a library that internally takes data you pass it and encodes

it using `json.dump()`. You can also use JSONStreamEncoder() as a context manager.



It works by monkey-patching the built-in `JSONEncoder.default` method during the

scope of the `with` statement.



```python 

# library code

def some_library_function_out_of_your_control(arg):

    json.dumps(arg)



# your code

with JSONStreamEncoder():

    some_library_function_out_of_your_control(data)

```



### Converting to standard Python types



To convert a json-stream `dict`-like or `list`-like object and all its

descendants to a standard `list` and `dict`, you can use the

`json_stream.to_standard_types` utility:



```python

# JSON: {"round": 1, "results": [1, 2, 3]}

data = json_stream.load(f)

results = data["results"]

print(results)  # prints 

converted = json_stream.to_standard_types(results)

print(converted)  # prints [1, 2, 3]

```



#### Thread safety (experimental)



There is also a thread-safe version of the `json.dump` context manager:



```python

from json_stream.dump.threading import ThreadSafeJSONStreamEncoder



# your code

with ThreadSafeJSONStreamEncoder():

   some_library_function_out_of_your_control(data)

```



The thread-safe implementation will ensure that concurrent uses of the 

context manager will only apply the patch for the first thread entering

the patched section(s) and will only remove the patch when the last

thread exits the patched sections(s)



Additionally, if the patch is somehow called by a thread that is _not_

currently in a patched section (i.e. some other thread calling 

`json.dump`) then that thread will block until the patch has been

removed. While such an un-patched thread is active, any thread attempting

to apply the patch is blocked.



###  Rust tokenizer speedups



By default `json-stream` uses the 

[`json-stream-rs-tokenizer`](https://pypi.org/project/json-stream-rs-tokenizer/)

native extension.



This is a 3rd party Rust-based tokenizer implementations that provides

significant parsing speedup compared to pure python implementation.



`json-stream` will fallback to its pure python tokenizer implementation

if `json-stream-rs-tokenizer` is not available.



### Custom tokenizer



You can supply an alternative JSON tokenizer implementation. Simply pass 

a tokenizer to the `load()` or `visit()` methods.



```python

json_stream.load(f, tokenizer=some_tokenizer)

```



The requests methods also accept a customer tokenizer parameter.



# Writing



The standard library's `json.dump()` function can only accept standard

python types such as `dict`, `list`, `str`.



`json-stream` allows you to write streaming JSON output based on python

generators instead.



For actually encoding and writing to the stream, `json-stream` 

still uses the standard library's `json.dump()` function, but provides

wrappers that adapt python generators into `dict`/`list` subclasses 

that `json.dump()` can use.



The means that you do not have to generate all of your data upfront

before calling `json.dump()`.



## Usage



To use `json-stream` to generate JSON data iteratively, you must first 

write python generators (or use any other iterable).



To output JSON objects, the iterable must yield key/value pairs.



To output JSON lists, the iterable must yield individual items.



The values yielded can be either be standard python types or another other

`Streamable` object, allowing lists and object to be arbitrarily nested.



`streamable_list`/`streamable_dict` can be used to wrap an existing

iterable:

```python

import sys

import json



from json_stream import streamable_list



# wrap existing iterable

data = streamable_list(range(10))



# consume iterable with standard json.dump()

json.dump(data, sys.stdout)

```



Or they can be used as decorators on generator functions:

```python

import json

import sys



from json_stream import streamable_dict



# declare a new streamable dict generator function

@streamable_dict

def generate_dict_of_squares(n):

    for i in range(n):

        # this could be some memory intensive operation

        # or just a really large value of n

        yield i, i ** 2



# data is will already be Streamable because

# of the decorator

data = generate_dict_of_squares(10)

json.dump(data, sys.stdout)

```



## Example



The following example generates a JSON object with a nested JSON list.

It uses `time.sleep()` to slow down the generation and show that the

output is indeed written as the data is created.



```python

import sys

import json

import time



from json_stream.writer import streamable_dict, streamable_list



# define a list data generator that (slowly) yields 

# items that will be written as a JSON list

@streamable_list

def generate_list(n):

    # output n numbers and their squares

    for i in range(n):  # range is itself a generator

        yield i

        time.sleep(1)



# define a dictionary data generator that (slowly) yields 

# key/value pairs that will be written as a JSON dict

@streamable_dict

def generate_dict(n):

    # output n numbers and their squares

    for i in range(n):  # range is itself a generator

        yield i, i ** 2

        time.sleep(1)



    # yield another dictionary item key, with the value

    # being a streamed nested list

    yield "a list", generate_list(n)



# get a streamable generator

data = generate_dict(5)



# use json.dump() to write dict generator to stdout

json.dump(data, sys.stdout, indent=2)



# if you already have an iterable object, you can just

# call streamable_* on it to make it writable

data = streamable_list(range(10))

json.dump(data, sys.stdout)



```



Output:

```json

{

  "0": 0,

  "1": 1,

  "2": 4,

  "3": 9,

  "4": 16,

  "a list": [

    0,

    1,

    2,

    3,

    4

  ]

}

```



#  What are the problems with the standard `json` package?



## Reading with `json.load()`

The problem with the `json.load()` stem from the fact that it must read

the whole JSON document into memory before parsing it.



### Memory usage



`json.load()` first reads the whole document into memory as a string. It

then starts parsing that string and converting the whole document into python

types again stored in memory. For a very large document, this could be more

memory than you have available to your system.



`json_stream.load()` does not read the whole document into memory, it only

buffers enough from the stream to produce the next item of data.



Additionally, in the default transient mode (see below) `json-stream` doesn't store 

up all of the parsed data in memory.



### Latency



`json.load()` produces all the data after parsing the whole document. If you

only care about the first 10 items in a list of 2 million items, then you

have wait until all 2 million items have been parsed first.



`json_stream.load()` produces data as soon as it is available in the stream.



##  Writing



### Memory usage



While `json.dump()` does iteratively write JSON data to the given

file-like object, you must first produce the entire document to be 

written as standard python types (`dict`, `list`, etc). For a very

large document, this could be more memory than you have available 

to your system.



`json-stream` allows you iteratively generate your data one item at

a time, and thus consumes only the memory required to generate that

one item.



### Latency



`json.dump()` can only start writing to the output file once all the

data has been generated up front at standard python types.



The iterative generation of JSON items provided by `json-stream`

allows the data to be written as it is produced.



# Future improvements



* Allow long strings in the JSON to be read as streams themselves

* Allow transient mode on seekable streams to seek to data earlier in

the stream instead of raising a `TransientAccessException`

* A more efficient tokenizer?



# Alternatives



## NAYA



[NAYA](https://github.com/danielyule/naya) is a pure python JSON parser for

parsing a simple JSON list as a stream.



### Why not NAYA?



* It can only stream JSON containing a top-level list 

* It does not provide a pythonic `dict`/`list`-like interface 



## Yajl-Py



[Yajl-Py](https://pykler.github.io/yajl-py/) is a wrapper around the C YAJL JSON library that can be used to 

generate SAX style events while parsing JSON.



### Why not Yajl-Py?



* No pure python implementation

* It does not provide a pythonic `dict`/`list`-like interface 



## jsonslicer



[jsonslicer](https://github.com/AMDmi3/jsonslicer) is another wrapper around the YAJL C library with a

path lookup based interface.



### Why not jsonslicer?



* No pure python implementation

* It does not provide a pythonic `dict`/`list`-like interface

* Must know all data paths lookup in advance (or make multiple passes)



# Contributing



See the project [contribution guide](https://github.com/daggaz/json-stream/blob/master/CONTRIBUTING.md).



# Donations



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OR



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# Acknowledgements



The JSON tokenizer used in the project was taken from the

[NAYA](https://github.com/danielyule/naya) project.