pycql

A pure python CQL parser.

Installation

pip install pycql

Usage

The basic functionality parses the input string to an abstract syntax tree (AST) representation. This AST can then be used to build database filters or similar functionality.

>>> import pycql
>>> ast = pycql.parse(filter_expression)

Inspection

The easiest way to inspect the resulting AST is to use the get_repr function, which returns a nice string representation of what was parsed:

>>> ast = pycql.parse('id = 10')
>>> print(pycql.get_repr(ast))
ATTRIBUTE id = LITERAL 10.0
>>>
>>>
>>> filter_expr = '(number BETWEEN 5 AND 10 AND string NOT LIKE "%B") OR INTERSECTS(geometry, LINESTRING(0 0, 1 1))'
>>> print(pycql.get_repr(pycql.parse(filter_expr)))
(
    (
            ATTRIBUTE number BETWEEN LITERAL 5.0 AND LITERAL 10.0
    ) AND (
            ATTRIBUTE string NOT ILIKE LITERAL '%B'
    )
) OR (
    INTERSECTS(ATTRIBUTE geometry, LITERAL GEOMETRY 'LINESTRING(0 0, 1 1)')
)

Evaluation

In order to create useful filters from the resulting AST, it has to be evaluated. For the Django integration, this was done using a recursive descent into the AST, evaluating the subnodes first and constructing a Q object. Consider having a filters API (for an example look at the Django one) which creates the filter. Now the evaluator looks something like this:

from pycql.ast import *
from myapi import filters   # <- this is where the filters are created.
                            # of course, this can also be done in the
                            # evaluator itself
class FilterEvaluator(object):
    def __init__(self, field_mapping=None, mapping_choices=None):
        self.field_mapping = field_mapping
        self.mapping_choices = mapping_choices

    def to_filter(self, node):
        to_filter = self.to_filter
        if isinstance(node, NotConditionNode):
            return filters.negate(to_filter(node.sub_node))
        elif isinstance(node, CombinationConditionNode):
            return filters.combine(
                (to_filter(node.lhs), to_filter(node.rhs)), node.op
            )
        elif isinstance(node, ComparisonPredicateNode):
            return filters.compare(
                to_filter(node.lhs), to_filter(node.rhs), node.op,
                self.mapping_choices
            )
        elif isinstance(node, BetweenPredicateNode):
            return filters.between(
                to_filter(node.lhs), to_filter(node.low),
                to_filter(node.high), node.not_
            )
        elif isinstance(node, BetweenPredicateNode):
            return filters.between(
                to_filter(node.lhs), to_filter(node.low),
                to_filter(node.high), node.not_
            )

        # ... Some nodes are left out for brevity

        elif isinstance(node, AttributeExpression):
            return filters.attribute(node.name, self.field_mapping)

        elif isinstance(node, LiteralExpression):
            return node.value

        elif isinstance(node, ArithmeticExpressionNode):
            return filters.arithmetic(
                to_filter(node.lhs), to_filter(node.rhs), node.op
            )

        return node

As mentionend, the to_filter method is the recursion.

Testing

The basic functionality can be tested using pytest.

python -m pytest

There is a test project/app to test the Django integration. This is tested using the following command:

python manage.py test testapp

Django integration

For Django there is a default bridging implementation, where all the filters are translated to the Django ORM. In order to use this integration, we need two dictionaries, one mapping the available fields to the Django model fields, and one to map the fields that use choices. Consider the following example models:

from django.contrib.gis.db import models


optional = dict(null=True, blank=True)

class Record(models.Model):
    identifier = models.CharField(max_length=256, unique=True, null=False)
    geometry = models.GeometryField()

    float_attribute = models.FloatField(**optional)
    int_attribute = models.IntegerField(**optional)
    str_attribute = models.CharField(max_length=256, **optional)
    datetime_attribute = models.DateTimeField(**optional)
    choice_attribute = models.PositiveSmallIntegerField(choices=[
                                                                 (1, 'ASCENDING'),
                                                                 (2, 'DESCENDING'),],
                                                        **optional)


class RecordMeta(models.Model):
    record = models.ForeignKey(Record, on_delete=models.CASCADE, related_name='record_metas')

    float_meta_attribute = models.FloatField(**optional)
    int_meta_attribute = models.IntegerField(**optional)
    str_meta_attribute = models.CharField(max_length=256, **optional)
    datetime_meta_attribute = models.DateTimeField(**optional)
    choice_meta_attribute = models.PositiveSmallIntegerField(choices=[
                                                                      (1, 'X'),
                                                                      (2, 'Y'),
                                                                      (3, 'Z')],
                                                             **optional)

Now we can specify the field mappings and mapping choices to be used when applying the filters:

FIELD_MAPPING = {
    'identifier': 'identifier',
    'geometry': 'geometry',
    'floatAttribute': 'float_attribute',
    'intAttribute': 'int_attribute',
    'strAttribute': 'str_attribute',
    'datetimeAttribute': 'datetime_attribute',
    'choiceAttribute': 'choice_attribute',

    # meta fields
    'floatMetaAttribute': 'record_metas__float_meta_attribute',
    'intMetaAttribute': 'record_metas__int_meta_attribute',
    'strMetaAttribute': 'record_metas__str_meta_attribute',
    'datetimeMetaAttribute': 'record_metas__datetime_meta_attribute',
    'choiceMetaAttribute': 'record_metas__choice_meta_attribute',
}

MAPPING_CHOICES = {
    'choiceAttribute': dict(Record._meta.get_field('choice_attribute').choices),
    'choiceMetaAttribute': dict(RecordMeta._meta.get_field('choice_meta_attribute').choices),
}

Finally we are able to connect the CQL AST to the Django database models. We also provide factory functions to parse the timestamps, durations, geometries and envelopes, so that they can be used with the ORM layer:

from pycql.integrations.django import to_filter

cql_expr = 'strMetaAttribute LIKE "%parent%" AND datetimeAttribute BEFORE 2000-01-01T00:00:01Z'

ast = pycql.parse(
    cql_expr, GEOSGeometry, Polygon.from_bbox, parse_datetime,
    parse_duration
)
filters = to_filter(ast, mapping, mapping_choices)

qs = Record.objects.filter(**filters)