jaf 0.1.0

JSON Array Filter (jaf) is a versatile filter for JSON arrays. It is a domain-specific language (DSL) that allows you to filter JSON arrays using a simple, yet powerful syntax. Programmaticcally, the AST can be used to filter JSON arrays in a more flexible way.

jaf - JSON Array Filter

jaf is a versatile filtering system designed to sift through JSON arrays. It allows users to filter JSON arrays based on complex conditions using a simple and intuitive query language. The query language is designed to be easy to use and understand, while still being powerful enough to handle complex filtering tasks.

Builtins

We refer to the available operators as builtins. These are the functions that are available to the user to use in their queries. The builtins are functions that are used to compare values, perform operations, or combine other functions to create complex queries. The builtins are the core of the filtering system and are used to create queries that can filter JSON arrays based on the specified conditions.

Predicates conanically end with a ?, e.g., eq? (eauals) and lt? (less-than). General operators do not canocially end with a ?, e.g., lower-case and or. The predicates are used to compare values, while the operators are used to combine predicates or perform other operations that make the desired comparison possible or the desired result achievable.

Note: We do not use operators like == or >, but instead use eq? and gt?. The primary reason for this choice is that we provide a command-line tool, and if we used > it would be interpreted as a redirection operator by the shell.

For example, the lower-case operator is used to convert a string to lowercase before comparison, so that the comparison is case-insensitive. Here is an example query that uses the lower-case operator:

['and', ['lower-case', ['path', 'language']], ['eq?', 'python']]

This query will filter repositories where the language field is equal to "python", regardless of the case of the letters.

Note: Depending on the builtins, the query language can be Turing complete. e.g., it would be trivial to add a lambda builtin that allows users to define their own functions. However, this is not a safe practice, as it would allow users to execute arbitrary code. Therefore, we have chosen to limit the default builtins to a safe set of functions that are useful for filtering JSON arrays. If you need additional functionality, you can always extend or provide your own set of builtins to include the functions you need. As a limiting case, a lambda builtin could be added to the builtins to allow users to define their own functions.

Query Language

Queries are represented using an Abstract Syntax Tree (AST) based on nested lists, where each list takes the form of [<expressio>, <arg1>, <arg2>,...].

We also provide a Domain-Specific Language (DSL) that allows users to craft queries using an intuitive infix notation. The DSL is converted into the AST before being evaluated. Here is the EBNF for the query language:

%import common.WS
%import common.ESCAPED_STRING
%import common.SIGNED_NUMBER
%ignore WS

start: expr

expr: bool_expr

?bool_expr: or_expr

?or_expr: and_expr
        | or_expr OR and_expr -> or_operation

?and_expr: primary
        | and_expr AND primary -> and_operation

?primary: operand
       | "(" bool_expr ")"

?operand: condition
       | function_call
       | path
       | bare_path
       | value

condition: operand operator operand

operator: IDENTIFIER

function_call: "(" IDENTIFIER operand+ ")"

path: ":" path_component ("." path_component)*

bare_path: path_component ("." path_component)*

path_component: IDENTIFIER 
             | STAR  
             | DOUBLESTAR

STAR: "*" 
DOUBLESTAR: "**"

value: ESCAPED_STRING
     | NUMBER
     | BOOLEAN

BOOLEAN: "True" | "False"
NUMBER: SIGNED_NUMBER

IDENTIFIER: /[a-zA-Z][a-zA-Z0-9_\-\?]*/

OR: "OR"
AND: "AND"

For example, consider the following query AST:

['and',
    ['eq?', ['lower-case', ['path', 'language']], 'python'],
    ['gt?', ['path', 'stars'], 100],
    ['eq?', ['path','owner.name'], ['path': 'user.name']]]

It has an equivalent DSL given by:

(lower-case :language) eq? "python" AND :stars gt? 100 AND :owner.name eq? :user.name

We see that we have a special notation for path commands: we prefix the field name with a colon: :, such as :language and :owner.name. This is to distinguish field names from other strings in the query. The pathcommand is used to access the value of a field in the JSON array. For example, :owner.name will access the value of the name field in the owner object where as owner.name will be interpreted as a string.

Paths can also include two kinds of wildcards, * and **. The wildcard * matches any fieldname, e.g., a.*.b.c will match a.d.b.c.a (it will return {'c': 'a'}. The wildcard ** will match any fieldname at any depth after the specified path, e.g., a.**.c will match a.b.c.a (it will also return {'c': 'a'}. You can use as many wildcards as you wish in a single query. If any of the objects denoted by a wildcard path satisfy the query, the object satisfies the query.

The DSL is converted into the AST (see the above EBNF) before being evaluated. This query AST is evaluated against each element of the JSON array, and if it returns True, the corresponding index into the JSON array for that element is added to the result. This is how we filter the JSON array. Alternatively, since queries can also specify general functions, the result may be a value rather than a Boolean, e.g., ['lower-case', 'Python'] will return 'python.

Relative Advantages of AST and DSL

Both have their own advantages and can be used interchangeably based on the user's preference. The AST is:

• programmatic
• easily manipulated
• can be generated from a DSL
• easily serialized for storage or transmission
• allows for operators to be queries, facilitating some meta-programming

The DSL is:

• More human-readable, e.g. infix notation for logical operators
• Easier to write and understand
• Compact

Installation

You can install jaf via PyPI:

pip install `jaf`

Or install directly from the source:

git clone https://github.com/queelius/jaf.git
cd jaf
pip install .

Examples

Suppose we have a list of repositories in the following format:

repos = [
    {
        'id': 1,
        'name': 'DataScienceRepo',
        'language': 'Python',
        'stars': 150,
        'forks': 30,
        'description': 'A repository for data science projects.',
        'owner': {
            'name': 'alice',
            'active': True
        }
    },
    # ... other repositories ...
]

AST-Based Query

Filter repositories where the lower-case of language is "python", owner.active is True, and stars are greater than 100:

query = ['and',
    ['eq?',
        ['lower-case', ['path', 'language'], 'Python']],
        ['path', 'owner.active'],
        ['gt?', ['path', 'stars'], 100]]

filtered = jaf(sample_repos, query_ast)
print("Filtered Repositories:")
pprint(filtered)
# Output: [1, ...]

DSL-Based Query

The equivalent query using the DSL:

query = '(lower-case :language) eq? "python" AND :owner.active AND :stars gt? 100'
filtered = jaf(repos, query)
print("Filtered Repositories:")
print(filtered)
# Output: [1, ...]

Complex Queries

Combine multiple conditions with logical operators.

query = ':language neq? "R" AND (:stars gt? 100 OR :forks gt? 50)'
filtered = jaf(repos, query)
print("Filtered Repositories:")

Handling Errors

Catch and handle filtering errors gracefully.

try:
    invalid_query = 'language unknown "Python"'
    jaf(repos, invalid_query)
except FilterError as e:
    print(f"Error: {e}")

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.