More JSON Tools
This set of modules provides the following benefits:
- Serialize more datastructures into JSON
- More flexibility in what's accepted as "JSON"
- Iterate over massive JSON easily (
mo_json.stream
) - Provide a bijection between strictly typed JSON, and dynamic typed JSON.
Recent Changes
-
Version 6.x.x - Typed encoder no longer encodes to typed multivalues, rather, encodes to array of typed values. For example, instead of
{"a": {"~n~": [1, 2]}}
we get
{"a": {"~a~": [{"~n~": 1},{"~n~": 2}]}}
Usage
Encode using __json__
Add a __json__
method to any class you wish to serialize to JSON. It is incumbent on you to ensure valid JSON is emitted:
class MyClass(object):
def __init__(self, a, b):
self.a = a
self.b = b
def __json__(self):
separator = "{"
for k, v in self.__dict__.items():
yield separator
separator = ","
yield value2json(k)+": "+value2json(v)
yield "}"
With the __json__
function defined, you may use the value2json
function:
from mo_json import value2json
result = value2json(MyClass(a="name", b=42))
Encode using __data__
Add a __data__
method that will convert your class into some JSON-serializable data structures. You may find this easier to implement than emitting pure JSON. If both __data__
and __json__
exist, then __json__
is used.
from mo_json import value2json
class MyClass(object):
def __init__(self, a, b):
self.a = a
self.b = b
def __data__(self):
return self.__dict__
result = value2json(MyClass(a="name", b=42))
Decoding
The json2value
function provides a couple of options
flexible
- will be very forgiving of JSON accepted (see hjson)leaves
- will interpret keys with dots (".
") as dot-delimited paths
from mo_json import json2value
result = json2value(
"http.headers.referer: http://example.com",
flexible=True,
leaves=True
)
assert result=={'http': {'headers': {'referer': 'http://example.com'}}}
Notice the lack of quotes in the JSON (hjson) and the deep structure created by the dot-delimited path name
Running tests
pip install -r tests/requirements.txt
set PYTHONPATH=.
python.exe -m unittest discover tests
Module Details
Method mo_json.scrub()
Remove, or convert, a number of objects from a structure that are not JSON-izable. It is faster to scrub
and use the default (aka c-based) python encoder than it is to use default
serializer that forces the use of an interpreted python encoder.
Module mo_json.stream
A module that supports queries over very large JSON
strings. The overall objective is to make a large JSON document appear like
a hierarchical database, where arrays of any depth, can be queried like
tables.
Limitations
This is not a generic streaming JSON parser. It is only intended to breakdown the top-level array, or object for less memory usage.
- Array values must be the last object property - If you query into a
nested array, all sibling properties found after that array must be ignored
(must not be in the
expected_vars
). The code will raise an exception if
you can not extract all expected variables.
Method mo_json.stream.parse()
Will return an iterator over all objects found in the JSON stream.
Parameters:
- json - a parameter-less function, when called returns some number of
bytes from the JSON stream. It can also be a string.
- path - a dot-delimited string specifying the path to the nested JSON. Use
"."
if your JSON starts with [
, and is a list. - expected_vars - a list of strings specifying the full property names
required (all other properties are ignored)
Common Usage
The most common use of parse()
is to iterate over all the objects in a large, top-level, array:
parse(json, path=".", required_vars=["."]}
For example, given the following JSON:
[
{"a": 1},
{"a": 2},
{"a": 3},
{"a": 4}
]
returns a generator that provides
{"a": 1}
{"a": 2}
{"a": 3}
{"a": 4}
Examples
Simple Iteration
json = {"b": "done", "a": [1, 2, 3]}
parse(json, path="a", required_vars=["a", "b"]}
We will iterate through the array found on property a
, and return both a
and b
variables. It will return the following values:
{"b": "done", "a": 1}
{"b": "done", "a": 2}
{"b": "done", "a": 3}
Bad - Property follows array
The same query, but different JSON with b
following a
:
json = {"a": [1, 2, 3], "b": "done"}
parse(json, path="a", required_vars=["a", "b"]}
Since property b
follows the array we're iterating over, this will raise an error.
Good - No need for following properties
The same JSON, but different query, which does not require b
:
json = {"a": [1, 2, 3], "b": "done"}
parse(json, path="a", required_vars=["a"]}
If we do not require b
, then streaming will proceed just fine:
{"a": 1}
{"a": 2}
{"a": 3}
Complex Objects
This streamer was meant for very long lists of complex objects. Use dot-delimited naming to refer to full name of the property
json = [{"a": {"b": 1, "c": 2}}, {"a": {"b": 3, "c": 4}}, ...
parse(json, path=".", required_vars=["a.c"])
The dot (.
) can be used to refer to the top-most array. Notice the structure is maintained, but only includes the required variables.
{"a": {"c": 2}}
{"a": {"c": 4}}
...
Nested Arrays
Nested array iteration is meant to mimic a left-join from parent to child table;
as such, it includes every record in the parent.
json = [
{"o": 1: "a": [{"b": 1}: {"b": 2}: {"b": 3}: {"b": 4}]},
{"o": 2: "a": {"b": 5}},
{"o": 3}
]
parse(json, path=[".", "a"], required_vars=["o", "a.b"])
The path
parameter can be a list, which is used to indicate which properties
are expected to have an array, and to iterate over them. Please notice if no
array is found, it is treated like a singleton array, and missing arrays still
produce a result.
{"o": 1, "a": {"b": 1}}
{"o": 1, "a": {"b": 2}}
{"o": 1, "a": {"b": 3}}
{"o": 1, "a": {"b": 4}}
{"o": 2, "a": {"b": 5}}
{"o": 3}
Large top-level objects
Some JSON is a single large object, rather than an array of objects. In these cases, you can use the items
operator to iterate through all name/value pairs of an object:
json = {
"a": "test",
"b": 2,
"c": [1, 2]
}
parse(json, {"items": "."}, {"name", "value"})
produces an iterator of
{"name": "a", "value": "test"}
{"name": "b", "value": 2}
{"name": "c", "value": [1,2]}
Module typed_encoder
One reason that NoSQL documents stores are wonderful is their schema can automatically expand to accept new properties. Unfortunately, this flexibility is not limitless; A string assigned to property prevents an object being assigned to the same, or visa-versa. This flexibility is under attack by the strict-typing zealots; who, in their self-righteous delusion, believe explicit types are better. They make the lives of humans worse; as we are forced to toil over endless schema modifications.
This module translates JSON documents into "typed" form; which allows document containers to store both objects and primitives in the same property. This also enables the storage of values with no containing object!
The typed JSON has a different form than the original, and queries into the document store must take this into account. This conversion is intended to be hidden behind a query abstraction layer that can understand this format.
How it works
There are three main conversions:
- Primitive values are replaced with single-property objects, where the property name indicates the data type of the value stored:
{"a": true} -> {"a": {"~b~": true}}
{"a": 1 } -> {"a": {"~n~": 1 }}
{"a": "1" } -> {"a": {"~s~": "1" }}
- JSON objects get an additional property,
~e~
, to mark existence. This allows us to query for object existence, and to count the number of objects.
{"a": {}} -> {"a": {"~e~": 1}, "~e~": 1}
- JSON arrays are contained in a new object, along with
~e~
to count the number of elements in the array:
{"a": [1, 2, 3]} -> {"a": {
"~e~": 3,
"~a~": [
{"~n~": 1},
{"~n~": 2},
{"~n~": 3}
]
}}
Note the sum of a.~e~
works for both objects and arrays; letting us interpret sub-objects as single-value nested object arrays.
Function typed_encode()
Accepts a dict
, list
, or primitive value, and generates the typed JSON that can be inserted into a document store.
Function json2typed()
Converts an existing JSON unicode string and returns the typed JSON unicode string for the same.
Update Mar2016 - PyPy version 5.x appears to have improved C integration to
the point that the C library callbacks are no longer a significant overhead:
This pure Python JSON encoder is no longer faster than a compound C/Python
solution.
Fast JSON encoder used in convert.value2json()
when running in Pypy. Run the
speed test
to compare with default implementation and ujson