mo-json 2.1.17319

More JSON Tools!

This set of modules solves three problems:

• We want to iterate over massive JSON easily (mo_json.stream)

• A bijection between strictly typed JSON, and dynamic typed JSON.

• Flexible JSON parser to handle comments, and other forms

• JSON encoding is slow (mo_json.encode)

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, actually make the lives of humans worse; toiling 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!

How it works

Typed JSON uses $value and $object properties to markup the original JSON:

• All JSON objects are annotated with "$object":".", which makes querying object existence (especially the empty object) easier.

• All primitive values are replaced with an object with a single $value property: So "value" gets mapped to {"$value": "value"}.

Of course, the typed JSON has a different form than the original, and queries into the documents store must take this into account. Fortunately, the use of typed JSON is intended to be hidden behind a query abstraction layer.

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.

---

also see http://tools.ietf.org/id/draft-pbryan-zyp-json-ref-03.html

Module mo_json.encode

Function: mo_json.encode.json_encoder()

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 speedtest to compare with default implementation and ujson