Smort Query

Lazy evaluated query implementation for searching through Python objects inspired by Django QuerySets.

• GitHub: https://github.com/matiuszka/smort-query
• PyPi: https://pypi.org/project/smort-query

Rationale

In many moments of our programing tasks we have to filter iterables in search of the right objects in right order. I realized that most of the time code looks almost the same, but what kind of interface will be easiest to use ? In that moment I figured out that Django QuerySets implementation is kinda handy and well known.

So I decided to write small query engine that interface will be similar to Django one. But it will work for Python objects. Additional assumption was that it will be lazy evaluated to avoid memory consumption.

Lookup format

Whole idea relays on keywords arguments naming format. Let's consider following qualname attr1.attr2 which can we used to get or set value for attribute. This engine do things similarly but instead of separating by dot(.) we are separating by __ signs. So above example can be converted to keyword argument name like that attr1__attr2. Due to fact that we can't use . in argument names.

For some methods like filter and exclude, we can also specify comparator. By default those methods are comparing against equality ==. But we can easily change it. If we want to compare by using <= we can use __le or __lte postfix. So we will end up with argument name like attr1__attr2__lt.

All supported comparators are described here in supported comparators section.

Installation

pip install smort-query

Importing

from smort_query import ObjectQuery
# or by alias
from smort_query import OQ

How it works ?

Basics

Each method in ObjectQuery produces new query. Which makes chaining very easy. The most important thing is that ObjectQuery instances are unevaluated - it means that they are not loading an objects to the memory even when we are chaining them.

Query sets can be evaluated in several ways:

• Iteration:

  query = ObjectQuery(range(5))
  
  for obj in query:
      print(obj)
  
  """out:
  1
  2
  3
  4
  5
  """

• Checking length:

  query = ObjectQuery(range(10))
  
  len(query)
  """out:
  10
  """

• Reversing query:

  query = ObjectQuery(range(10))
  
  query.reverse()
  """out:
  <ObjectQuery for <reversed object at 0x04E8B460>>
  """
  
  list(list(query.reverse()))
  """out
  [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
  """

• Getting items:

  • Getting by index evaluates query:

    query = ObjectQuery(range(10))
    query[5]
    """out:
    5
    """

  • But slices not! They creates another query.

    query = ObjectQuery(range(10))
    query[5:0:-1]
    """out:
    <ObjectQuery for <generator object islice_extended at 0x0608B338>>
    """
    list(query[5:0:-1])
    """out:
    [5, 4, 3, 2, 1]
    """

• Initializing other objects that used iterators/iterables (it is still almost same mechanism like normal iteration):

  query1 = ObjectQuery(range(10))
  query2 = ObjectQuery(range(10))
  
  list(query1)
  """out:
  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
  """
  tuple(query2)
  """out:
  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
  """

User cases

Let's consider fallowing code for populating faked humans:

from random import randint, choice


class Human:
    def __init__(self, name, age, sex, height, weight):
        self.name = name
        self.age = age
        self.sex = sex
        self.height = height
        self.weight = weight

    def __repr__(self):
        return str(self.__dict__)


def make_random_human(name):
    return Human(
        name=name,
        age=randint(20, 80),
        sex=choice(('female', 'male')),
        height=randint(160, 210),
        weight=randint(60, 80),
    )

Creating 10 random humans:

humans = [make_random_human(i) for i in range(10)]
"""out:
[{'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 5, 'age': 75, 'sex': 'male', 'height': 189, 'weight': 77},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""

Filtering and excluding

Finding peoples from age between [30; 75). To that we will used specialized comparators:

list(ObjectQuery(humans).filter(age__ge=30, age__lt=75))
"""out:
[{'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""

We can also excluding males in similar way:

list(ObjectQuery(humans).exclude(sex="male"))
"""out:
[{'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
 """

Ordering

Ordering by sex attributes in ascending order:

list(ObjectQuery(humans).order_by("sex"))
"""out
[{'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78},
 {'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 5, 'age': 75, 'sex': 'male', 'height': 189, 'weight': 77},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72}]
"""

Ordering by sex attributes in descending order:

list(ObjectQuery(humans).order_by("-sex"))
"""out
[{'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 5, 'age': 75, 'sex': 'male', 'height': 189, 'weight': 77},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""

Ordering by multiple attributes:

list(ObjectQuery(humans).order_by("-sex", "height"))
"""out:
[{'name': 5, 'age': 75, 'sex': 'male', 'height': 189, 'weight': 77},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71}]
"""

Annotate

If some attributes worth of filtering and ordering are not available by hand we can calculate them on the fly:

# Sorry for example if someone feels offended
root_query = ObjectQuery(humans)

only_females = root_query.filter(sex="female")  # reduce objects for annotation calculation
bmi_annotated_females = only_females.annotate(bmi=lambda obj: obj.weight / (obj.height / 100) ** 2)
overweight_females = bmi_annotated_females.filter(bmi__gt=25)
overweight_females_ordered_by_age = overweight_females.order_by("age")
list(overweight_females_ordered_by_age)
"""out:
[{'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71, 'bmi': 27.390918560240728},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75, 'bmi': 25.95155709342561},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78, 'bmi': 26.061679307694877}]
"""

Copying

Each method query is returning copy. Where iteration over newly created ones does not affect object sources.

root_query = ObjectQuery(humans).filter(age__ge=30, age__lt=75)
query1 = root_query.filter(weight__gt=75)
query2 = root_query.filter(weight__in=[78, 62])

list(query1)
"""out:
[{'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""

list(query2)
"""out:
[{'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""

list(root_query)
"""out:
[{'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""

But sometimes evaluating some query in middle of chain may break it, so when you explicitly want to save somewhere copy of query and be sure that further actions on root will not affect on query, you can do:

root_query = ObjectQuery(humans)
copy = root_query.all()

Reversing

You can also reverse query, but remember that it will evaluate query:

root_query = ObjectQuery(humans).reverse()
list(root_query)
"""out:
[{'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 5, 'age': 75, 'sex': 'male', 'height': 189, 'weight': 77},
 {'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71}]
"""

OR

Bitwise OR combines two queries together. Same as union method. Note that after ORing two queries or even more, ordering might be needed:

root_query = ObjectQuery(humans)
males = root_query.filter(sex="male")
females = root_query.filter(sex="female")
both1 = (males | females)
both2 = males.union(females)

list(both1)
"""out:
[{'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 5, 'age': 75, 'sex': 'male', 'height': 189, 'weight': 77},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""
list(both2)
"""out:
[{'name': 4, 'age': 73, 'sex': 'male', 'height': 174, 'weight': 62},
 {'name': 5, 'age': 75, 'sex': 'male', 'height': 189, 'weight': 77},
 {'name': 6, 'age': 64, 'sex': 'male', 'height': 179, 'weight': 63},
 {'name': 8, 'age': 64, 'sex': 'male', 'height': 188, 'weight': 72},
 {'name': 0, 'age': 24, 'sex': 'female', 'height': 161, 'weight': 71},
 {'name': 1, 'age': 33, 'sex': 'female', 'height': 205, 'weight': 67},
 {'name': 2, 'age': 45, 'sex': 'female', 'height': 186, 'weight': 74},
 {'name': 3, 'age': 48, 'sex': 'female', 'height': 173, 'weight': 78},
 {'name': 7, 'age': 35, 'sex': 'female', 'height': 170, 'weight': 75},
 {'name': 9, 'age': 43, 'sex': 'female', 'height': 198, 'weight': 78}]
"""

Supported Comparators

Project supports many comparator that can be chosen as postfix for lookup:

• Default comparator is eq
• eq makes a == b
• exact makes a == b
• in makes a in b
• contains makes b in a
• gt makes a > b
• gte makes a >= b
• ge makes a >= b
• lt makes a < b
• lte makes a <= b
• le makes a <= b

TODOs

• Sphinx documentation.
• The asc() and desc() methods which works same as order_by() but with specified order in advance.
• The unique_justseen() and unique_everseen() methods to remove duplicates. Comparison realized by passed attributes or delegated to objects equality __eq__.
• The intersection() method for finding common objects in two queries. Comparison realized by passed attributes or delegated to objects equality __eq__.
• The __len__ and __getitem__ improvement for evaluating query only once per life cycle.

Contribution

Any form of contribution is appreciated. Finding issues, new ideas, new features. And of course you are welcome to create PR for this project.