Djaq - pronounced “Jack” - provides an instant remote API to your Django
models data with a powerful query language. No server-side code beyond
two lines of config is required. You don’t write backend classes,
serializers or any other code to be able to immediately get whatever
data you want to the client. And it is blazing fast.

Djaq queries are strings. A query string for our example dataset might
look like this:

::

   (b.name as title, b.publisher.name as publisher) Book b

This retrieves a list of book titles with book publisher. But you can
formulate far more sophisticated queries, see below. You can send Djaq
queries from any language, Java, Javascript, golang, etc. to a Django
application and get results as JSON. In contrast to REST frameworks,
like TastyPie or Django Rest Framework (DRF), you have natural access to
the Django ORM from the client.

Djaq is a good fit if you want:

-  Microservice communication where some services don’t have access to
   the Django ORM or are not implemented with Python

-  Fast local UI development

-  Fast development of Proof of Concepts

Djaq sits on top of the Django ORM. It can happily be used alongside
QuerySets and sometimes calling a Djaq query even locally might be
preferable to constructing a Queryset, although Djaq is not a
replacement for QuerySets.

Features you might appreciate:

-  Immediate gratification with zero or minimal server-side code.
   Because there is minimal setup, there is minimal wasted effort if you
   later move to another framework, like GraphQL or DRF. But getting
   started calling your API is much faster than those frameworks.

-  A natural syntax that lets you compose queries using Python-like
   expressions. The query format and syntax is designed to be written by
   hand quickly. Readability is a key goal.

-  Complex expressions let you push computation down to the database
   layer from the client easily.

-  Fast cursor semantics and explicit retrieval. It only gets data you
   asked for.

-  Obvious performance behaviour. It will trigger a query in one obvious
   way through one of the generator methods: ``.dict()``, ``.tuples()``,
   ``.json()``.

-  A ready-to-go CRUD API that is easy to use. You can send requests to
   have an arbitrary number of Create, Read, Write, Delete operations
   done in a single request.

-  Customisable behaviour using your own functions and data validators.

-  A handy user interface for trying out queries on your data models.

Djaq provides whitelisting of apps and models you want to expose. It
also provides a simple permissions scheme via settings.

   Note that Djaq is still in an early phase of development. No
   warranties about reliability, security or that it will work exactly
   as described.

.. figure:: bookshop/screenshots/djaq_ui.png?raw=true
   :alt: Djaq UI

   Djaq UI

Quickstart and Installation
---------------------------

You need Python 3.6 or higher and Django 2.1 or higher.

Install:

::

   pip install Djaq

The bleeding edge experience:

::

   pip install https://github.com/paul-wolf/djaq/archive/master.zip

Use:

.. code:: python

   from djaq.query import DjangoQuery as DQ

   print(list(DQ("(b.name as title, b.publisher.name as publisher) Book b").dicts()))

   [{'title': 'Name grow along.', 'publisher': 'Long, Lewis and Wright'}, {'title': 'We pay single record.', 'publisher': 'Long, Lewis and Wright'}, {'title': 'Natural develop available manager.', 'publisher': 'Long, Lewis and Wright'}, {'title': 'Fight task international.', 'publisher': 'Long, Lewis and Wright'}, {'title': 'Discover floor phone.', 'publisher': 'Long, Lewis and Wright'}]

Providing an API
----------------

We’ll assume below you are installing the Djaq UI. This is not required
to provide an API but is very useful to try things out.

Install the API and UI in settings:

.. code:: python

   INSTALLED_APPS = (
       ...
       djaq.djaq_api,
       djaq.djaq_ui,
   )

Configure urls in urls.py:

.. code:: python

   urlpatterns = [
       ...
       path("dquery/", include("djaq.djaq_ui.urls")),`
       path("djaq/", include("djaq.djaq_api.urls")),`
   ]

You are done. You can start sending requests to:

::

   /djaq/api/request/

The UI will be available at:

::

   /dquery

Note the UI will send requests to the API endpoint so will not work
without that being configured. You send a request in this form to the
api endpoint:

.. code:: python

   {
    "queries": [
     {
      "q": "(b.id,b.name,b.pages,b.price,b.rating,b.publisher,b.alt_publisher,b.pubdate,b.in_print,) books.Book b",
      "context": {},
      "limit": "100",
      "offset": "0"
     }
    ]
   }

The UI will create this JSON for you if you want to avoid typing it.

You can also create objects, update them and delete them:

.. code:: python

   {
      "queries": [
       {
         "q": "(b.id,b.name,b.pages,b.price,b.rating,b.publisher,b.alt_publisher,b.pubdate,b.in_print,) books.Book b",
         "context": {},
         "limit": "100",
         "offset": "0"
       }
      ],
     "creates":[{
        "_model":"Book"
        "name":"my new book",
        }],
     "updates":[{
        "_model":"Book"
        "_pk": 37,
        "name":"my new title",
        }],
     "deletes":[{
        "_model":"Book"
          "_pk": 37,
        }]
   }

You can send multiple ``queries``, ``creates``, ``updates``, ``deletes``
operations in a single request.

Settings
--------

The API and UI will use the following settings:

-  DJAQ_WHITELIST: a list of apps/models that the user is permitted to
   include in queries.

-  DJAQ_PERMISSIONS: permissions required for staff and superuser.

-  DJAQ_VALIDATOR: if using the remote API, you can specify a validator
   class to handle all requests. The value assigned must be a class
   derived from ``djaq.query.ContextValidator``. The ``request`` object
   is always added to the context by default. You can examine this in
   the validator to make decisions like forbidding access to some users,
   etc.

In the following example, we allow the models from ‘books’ to be exposed
as well as the ``User`` model. We also require the caller to be both a
staff member and superuser:

.. code:: python

   DJAQ_WHITELIST = {
       "django.contrib.auth": ["User"],
       "books": [
           "Profile",
           "Author",
           "Consortium",
           "Publisher",
           "Book_authors",
           "Book",
           "Store_books",
           "Store",
       ],
   }
   DJAQ_PERMISSIONS = {
       "creates": True,
       "updates": True,
       "deletes": True,
       "staff": True,
       "superuser": True,
   }

If we want to allow all models for an app, we can leave away the list of
models. This will have the same effect as the setting above.

.. code:: python

   DJAQ_WHITELIST = {
       "django.contrib.auth": ["User"],
       "books": [],
   }

For permissions, you can optionally require any requesting user to be
staff and/or superuser. And you can deny or allow update operations. If
you do not provide explicit permissions for update operations, the API
will respond with 401 if one of those operations is attempted.

Custom API
----------

You can write your own custom API endpoint. Here is what a view function
for your data layer might look like with Djaq:

.. code:: python

   @login_required
   def djaq_view(request):
       data = json.loads(request.body.decode("utf-8"))
       query_string = data.get("q")
       offset = int(data.get("offset", 0))
       limit = int(data.get("limit", 0))
       context = data.get("context", {})
       return JsonResponse(
           {
              "result": list(
                  DQ(query_string)
                  .context(context)
                  .limit(limit)
                  .offset(offset)
                  .dicts()
              )
           }
       )

You can now query any models in your entire Django deployment remotely,
provided the authentication underlying the ``login_required`` is
satisfied. This is a good solution if your endpoint is only available to
trusted clients who hold a valid authentication token or to clients
without authentication who are in your own network and over which you
have complete control. It is a bad solution on its own for any public
access since it exposes Django framework models, like users,
permissions, etc.

Most likely you want to control access in two ways:

-  Allow access to only some apps/models

-  Allow access to only some rows in each table and possibly only some
   fields.

For controlling access to models, use the whitelist parameter in
constructing the DjangoQuery:

.. code:: python

   DQ(query_string, whitelist={"books": ["Book", "Publisher",],})
     .context(context)
     .limit(limit)
     .offset(offset)
     .dicts()

This restricts access to only the ``book`` app models, Book and Publish.

You probably need a couple more things if you want to expose this to a
browser. But this gives an idea of what you can do. The caller now has
access to any authorised model resource. Serialisation is all taken care
of. Djaq comes already with a view similar to the above. You can just
start calling and retrieving any data you wish. It’s an instant API to
your application provided you trust the client or have sufficient access
control in place.

Difference between Djaq and Other Frameworks
--------------------------------------------

The core of Djaq does not actually have anything *specifically* to do
with remote requests. It is primarily a query language for Django
models. You can just as easily use it within another remote API
framework.

The default remote API for Djaq is not a REST framework. It does use
JSON for encoding data and POST to send requests. But it does not adhere
to the prescribed REST verbs. It comes with a very thin wrapper for
remote HTTP(S) requests that is a simple Django view function. It would
be trivial to write your own or use some REST framework to provide this
functionality. Mainly, it provides a way to formulate queries that are
highly expressive, compact and readable.

There is only one endpoint for Djaq on the backend.

Requests for queries, creates, updates, deletes are always POSTed.

Most importantly, the client decides what information to request using a
query language that is much more powerful than what is available from
other REST frameworks and GraphQL.

Conversely, REST frameworks and GraphQL are more useful than Djaq in
providing server-side business rule implementation.

Limitations
-----------

Compared to other frameworks like GraphQL and DRF, you can’t easily
implement complex business rules on the server. This might be a deal
breaker for your application.

Djaq, without any configuration, provides access to *all* your model
data. That is usually not what you want. For instance, you would not
want to expose all user data, session data, or many other kinds of data
to even authenticated clients. It is trivial to prevent access to data
on an app or a model class level. But this might be too coarse-grained
for your application.

Djaq only supports Postgresql at this time.

Performance
-----------

You will probably experience Djaq calls as blazing fast compared to
other remote frameworks. This is just because not much happens
inbetween. Once the query is parsed, it is about as fast as you will
ever get unless you do something fancy in a validator. The simplest
possible serialization is used by default.

Once the query is parsed, it is about the same overhead as calling this:

::

   conn = connections['default']
   cursor = conn.cursor()
   self.cursor = self.connection.cursor()
   self.cursor.execute(sql)

Parsing is pretty fast and might be a negligible factor if you are
parsing during a remote call as part of a view function.

But if you want to iterate over, say, a dictionary of variables locally,
you’ll want to parse once:

.. code:: python

   dq = DQ("(b.name) Book{ilike(b.name, '$(namestart)')} b")
   dq.parse()
   for vars in var_list:
       results = list(dq.context(vars).tuples())
       <do something with results>

Note that each call of ``context()`` causes the cursor to execute again
when ``tuples()`` is iterated.

Query usage guide
-----------------

Throughout, we use models somewhat like those from Django’s bookshop
example:

.. code:: python

   from django.db import models

   class Author(models.Model):
       name = models.CharField(max_length=100)
       age = models.IntegerField()

   class Publisher(models.Model):
       name = models.CharField(max_length=300)

   class Book(models.Model):
       name = models.CharField(max_length=300)
       pages = models.IntegerField()
       price = models.DecimalField(max_digits=10, decimal_places=2)
       rating = models.FloatField()
       authors = models.ManyToManyField(Author)
       publisher = models.ForeignKey(Publisher, on_delete=models.CASCADE)
       pubdate = models.DateField()

   class Store(models.Model):
       name = models.CharField(max_length=300)
       books = models.ManyToManyField(Book)

These examples use auto-generated titles and names and we have a
slightly more complicated set of models than shown above.

Let’s get book title (name), price, discounted price, amount of discount
and publisher name wherever the price is over 50.

.. code:: python

   result = \
     list(DQ("""(b.name,
          b.price as price,
          0.2 as discount,
          b.price * 0.2 as discount_price,
          b.price - (b.price*0.2) as diff,
          Publisher.name
         ) Book{b.price > 50} b""").dicts())

``result`` now contains a list of dicts each of which is a row in the
result set. One example:

.. code:: python

   [{'b_name': 'Address such conference.',
     'price': Decimal('99.01'),
     'discount': Decimal('0.2'),
     'discount_price': Decimal('19.802'),
     'diff': Decimal('79.208'),
     'publisher_name': 'Arnold Inc'}]

Here is the structure of the syntax:

::

   (<field_exp1>, ...) <ModelName>{<filter_expression>} <alias> order by (<field_exp1>, ...)

Whitespace does not matter too much. You could put things on separate
lines:

.. code:: python

   (
      b.name, b.price,
      Publisher.name
   )
   Book{p.price > 50} b

Always start with column expressions you want to return in parens:

::

   (b.name, b.price, Publisher.name)

These expressions can be Django Model fields or arithmetic expressions
or any expression supported by functions of your underlying database
that are also whitelisted by Djaq. Postgresql has thousands of
functions. About 350 of those are available in Djaq.

Columns are automatically given names. But you can give them your own
name:

::

   (b.name as title, b.price as price, Publisher.name as publisher)

Next is the model alias declaration:

::

   Book b

or if we want to filter and get only books over 50 in price:

::

   Book{b.price > 50} b

``Book`` is the Django Model name. ``b`` is an alias we can use as an
abbreviation in the filter or returned column expressions. We put the
filter in curly braces, ``{}``, between the model name and alias. Use
Python syntax to express the filter. Also use Python syntax to express
the data to return. You don’t have access to the Python Standard
Library. This is basically the intersection of SQL and Python:

The following filter:

::

   {b.price > 50 and ilike(Publisher.name, 'A%')}

will be translated to SQL:

::

   b.price > 50 AND publisher.name ILIKE 'A%'

The expressions are fully parsed so they are not subject to SQL
injection. Trying to do so will cause an exception.

You might notice in the above examples, Publisher does not use an alias.
If you wanted an alias for Publisher, you could use a more complicated
syntax:

::

   (b.name, b.price) Book b
   -> (p.name) Publisher.name p

Notice, we use the ``->`` symbol to add another aliased relationship.
This is one of three options: ``->``, ``<-``, ``<>`` that indicate you
want to explicitly join via an SQL LEFT, RIGHT or INNER join
respectively. But you don’t need to do this. LEFT joins will always be
implicit. We did not even need to refer to the Publisher model directly.
We could have done this:

::

   (b.name, b.price, b.publisher.name as publisher)
   Book{p.price > 50} b

Our example model also has an owner model called “Consortium” that is
the owner of the publisher:

.. code:: python

   In [16]: print(list(DQ("(b.name, b.price, b.publisher.name, b.publisher.owner.name) Book b").limit(1).dicts()))
   Out[16]: [{'b_name': 'Range total author impact.', 'b_price': Decimal('12.00'), 'b_publisher_name': 'Wright, Taylor and Fitzpatrick', 'b_publisher_owner_name': 'Publishers Group'}]

To recap, there are three alternative patterns to follow to get the
publisher name in the result set:

.. code:: python

   In [13]: print(list(DQ("(b.name, b.price) Book b -> (p.name)Publisher p").limit(1).dicts()))

   In [14]: print(list(DQ("(b.name, b.price, Publisher.name) Book b").limit(1).dicts()))

   In [15]: print(list(DQ("(b.name, b.price, b.publisher.name) Book b").limit(1).dicts()))

Note that the above will each produce slightly different auto-generated
output names unless you provide your own aliases.

Signal that you want to summarise results using an aggregate function:

.. code:: python

   list(DQ("(b.publisher.name as publisher, count(b.id) as book_count) Book b").dicts())

   [
       {
           "publisher": "Martinez, Clark and Banks",
           "book_count": 6
       },
       {
           "publisher": "Fischer-Casey",
           "book_count": 9
       },
       etc.
   ]

Order by name:

::

   (b.name, b.price, b.publisher.name as publisher)
   Book{p.price > 50} b
   order by (b.name)

The parentheses around the order by expression are required.

Get average, minimum and maximum prices:

.. code:: python

   list(DQ("(avg(b.price) as average, min(b.price) as minimum, max(b.price) as maximum) Book b).dicts())
   [
      {
         "average": "18.5287169247794985",
         "minimum": "3.00",
         "maximum": "99.01"
      }
   ]

Count all books:

.. code:: python

   list(DQ("(count(b.id)) Book b").dicts())

   [
       {
           "countb_id": 149999
       }
   ]

You can qualify model names with the app name or registered app path:

::

   (b.name, b.publisher.name) books.Book b

You’ll need this if you have models from different apps with the same
name.

To pass parameters, use variables in your query, like ``'$(myvar)'``:

.. code:: python

   In [30]: oldest = '2018-12-20'
       ...: list(DQ("(b.name, b.pubdate) Book{b.pubdate >= '$(oldest)'} b").context({"oldest": oldest}).limit(5).tuples())
   Out[30]:
   [('Available exactly blood.', datetime.date(2018, 12, 20)),
    ('Indicate Congress none always.', datetime.date(2018, 12, 24)),
    ('Old beautiful three program.', datetime.date(2018, 12, 25)),
    ('Oil onto mission.', datetime.date(2018, 12, 21)),
    ('Key same effect me.', datetime.date(2018, 12, 23))]

Notice that the variable holder, ``$()``, *must* be in single quotes.

Query UI
~~~~~~~~

You can optionally install a query user interface to try out queries on
your own data set:

-  After installing djaq, add ``djaq.djaq_ui`` to INSTALLED_APPS

-  Add ``path("dquery/", include("djaq.djaq_ui.urls")),`` to
   ``urlpattenrs   in the sites``\ urls.py\`

Navigate to \`/dquery/’ in your app and you should be able to try out
queries.

-  Send: call the API with the query

-  JSON: show the json that will be sent as the request data

-  SQL: show how the request will be sent to the database as sql

-  Schema: render the schema that describe the available fields

-  Whitelist: show the active whitelist. You can use this to generate a
   whilelist and edit it as required.

There is a combo dropdown control, ``apps``. Select the Django app.
Models for the selected app are listed below. If you click once on a
model, the result field will show the schema for that model. If you
double-click the model, it generates a query for you for all fields in
that model. Once you do that, just press “Send” to see the results.

If the query pane has the focus, you can press shift-return to send the
query request to the server.

Functions
---------

If a function is not defined by DjangoQuery, then the function name is
checked with a whitelist of functions. There are approximately 350
functions available.

A user can define new functions at any time by adding to the custom
functions. Here’s an example of adding a regex matching function:

.. code:: python

   DjangoQuery.functions["REGEX"] = "{} ~ {}"

Now find all book names starting with ‘B’:

.. code:: python

   DQ("(b.name) Book{regex(b.name, 'B.*')} b")

We always want to use upper case for the function name when defining the
function. Usage of a function is then case-insensitive. You may wish to
make sure you are not over-writing existing functions. “REGEX” already
exists, for instance.

You can also provide a ``callable`` to ``DjangoQuery.functions``. The
callable needs to take two arguments: the function name and a list of
positional parameters and it must return SQL as a string that can either
represent a column expression or some value expression from the
underlying backend.

In the following:

.. code:: python

   DQ("(b.name) Book{like(upper(b.name), upper('$(name_search)'))} b")

``like()`` is a Djaq-defined function that is converted to
``field LIKE string``. Whereas ``upper()`` is sent to the underlying
database because it’s a common SQL function. Any function can be created
or existing functions mutated by updating the ``DjangoQuery.functions``
dict where the key is the upper case function name and the value is a
template string with ``{}`` placeholders. Arguments are positionally
interpolated.

Above, we provided this example:

.. code:: python

   DQ("""(
      sum(iif(b.rating < 5, b.rating, 0)) as below_5,
      sum(iif(b.rating >= 5, b.rating, 0)) as above_5
   ) Book b""")

We can simplify further by creating a new function. The IIF function is
defined like this:

::

   "CASE WHEN {} THEN {} ELSE {} END"

We can create a ``SUMIF`` function like this:

::

   DjangoQuery.functions['SUMIF'] = "SUM(CASE WHEN {} THEN {} ELSE {} END)"

Now we can rewrite the above like this:

.. code:: python

   DQ("""(
       sumif(b.rating < 5, b.rating, 0) as below_5,
       sumif(b.rating >= 5, b.rating, 0) as above_5
       ) Book b""")

Here’s an example providing a function:

.. code:: python

   def concat(funcname, args):
       """Return args spliced by sql concat operator."""
       return " || ".join(args)

   DjangoQuery.functions['CONCAT'] = concat

Parameters and Validator
------------------------

We call the Django connection cursor approximately like this:

.. code:: python

   from django.db import connections
   cursor = connections['default']
   cursor.execute(sql, context_dict)

When we execute the resulting SQL query, named parameters are used. You
*must* name your parameters. Positional parameters are not passed:

.. code:: python

   oldest = '2000-01-01'
   DQ("(b.id) Book{b.pub_date >= '$(oldest)'} b").context({"oldest": oldest}).tuples()

Notice that any parameterised value must be represented in the query
expression in single quotes:

::

   '$(myparam)'

Therefore, when you add subqueries, their parameters have to be supplied
at the same time.

Note what is happening here:

::

   name_search = 'Bar.*'
   DQ("(b.id) Book{regex(b.name, '%(name_search)')} b").context(locals()).tuples()

To get all books starting with ‘Bar’. Or:

.. code:: python

   DQ("(b.name) Book{like(upper(b.name), upper('$(name_search)'))} b").context(request.POST)

Provided that ``request.POST`` has a ``name_search`` key/value.

You can provide a validation class that will return context variables.
The default class used is called ``ContextValidator()``. You can
override this to provide a validator that raises exceptions if data is
not valid or mutates the context data, like coercing types from ``str``
to ``int``:

.. code:: python

   class MyContextValidator(ContextValidator):
       def get(self, key, value):
           if key == 'order_no':
               return int(value)
           return value

       def context(self):
           if not 'order_no' in self.data:
               raise Exception("Need order no")
           self.super().context()

Then add the validator:

.. code:: python

   order_no = "12345"
   DQ("(o.order_no, o.customer) Orders{o.order_no == '%(order_no)')} b")
       .validator(MyContextValidator)
       .context(locals())
       .tuples()

You can set your own validator class in Django settings:

::

   DJAQ_VALIDATOR = MyValidator

The ``request`` parameter of the API view is added to the context and
will be available to the validator as ``request``.

Column expressions
------------------

Doing column arithmetic is supported directly in the query syntax:

.. code:: python

   DQ("""(b.name,
       b.price as price,
       0.2 as discount,
       b.price*0.2 as discount_price,
       b.price - (b.price*0.2) as diff
       ) Book b""")

You can use constants:

.. code:: python

   In [60]: list(DQ("(b.name, 'great read') Book b").limit(1).tuples())
   Out[60]: [('Range total author impact.', 'great read')]

You can use the common operators and functions of your underlying db.

The usual arithmetic:

.. code:: python

   In [36]: list(DQ("(b.name, 1+1) Book b").limit(1).tuples())
   Out[36]: [('Range total author impact.', 2)]
   In [38]: list(DQ("(b.name, 2.0/4) Book b").limit(1).tuples())
   Out[38]: [('Range total author impact.', Decimal('0.50000000000000000000'))]
   In [44]: list(DQ("(2*3) Book b").limit(1).tuples())
   Out[44]: [(6,)]

Modulo:

.. code:: python

   In [55]: list(DQ("(mod(4.0,3)) Book b").limit(1).tuples())
   Out[55]: [(Decimal('1.0'),)]

Comparison as a boolean expression:

.. code:: python

   In [45]: list(DQ("(2 > 3) Book b").limit(1).tuples())
   Out[45]: [(False,)]

While the syntax has a superficial resemblance to Python, you do not
have access to any functions of the Python Standard Libary.

Subqueries and ``in`` clause
----------------------------

You can reference subqueries within a Djaq expression using

-  Another DjangoQuery
-  A Queryset
-  A list

The two most useful cases are using a subquery in the filter condition:

::

   DQ('(b.id, b.name) Book{b.id in ["(Book.id)"]} b')

And using a subquery in the selected columns expression:

::

   DQ('(p.name, ["(count(b.id)) Book{Publisher.id == b.publisher} b"]) Publisher p')

You can use an IN clause with the keyword ``in`` (note lower case) If
you are writing queries via the Python API. Create one DjangoQuery and
reference it with ``@queryname``:

::

   DQ("(b.id) Book{name == 'B*'} b", name='dq_sub')
   dq = DQ("(b.name, b.price) Book{id in '@dq_sub'} b")

Note that you have to pass a name to the DjangoQuery to reference it
later. We can also use the ``data`` parameter to pass a QuerySet to the
DjangoQuery:

::

   qs = Book.objects.filter(name__startswith="B").only('id')
   dq = DQ("(b.name, b.price) Book{id in '@qs_sub'} b", names={"qs_sub": qs})

   qs = Book.objects.filter(name__startswith="B").only('id')
   ids = [rec.id for rec in qs]
   dq = DQ("(b.name, b.price) Book{id in '@qs_sub'} b", names={"qs_sub": ids})

As with QuerySets it is nearly always faster to generate a sub query
than use an itemised list.

Order by
--------

You can order_by like this:

::

   DQ("(b.id) Book{b.price > 20} b order by (b.name)")

Descending order:

::

   DQ("(b.id) Book{b.price > 20} b order by (-b.name)")

You can have multple order by expressions.

::

   DQ("(b.name, Publisher.name) Book{b.price > 20} b order by (-b.name, b.publisher.name)")

Count
-----

There are a couple ways to count results. These both return the exact
same thing:

::

   DQ("(Book.id)").count()

   DQ("(count(Book.id)) Book").value()

Datetimes
---------

Datetimes are provided as strings in the iso format that your backend
expects, like ‘2019-01-01 18:00:00’.

Constants
---------

``None``, ``True``, ``False`` are replaced in SQL with ``NULL``,
``TRUE``, ``FALSE``. All of the following work:

::

   DQ("(b.id, b.name) Book{in_print is True} b")
   DQ("(b.id, b.name) Book{in_print is not True} b")
   DQ("(b.id, b.name) Book{in_print is False} b")
   DQ("(b.id, b.name) Book{in_print == True} b")

Slicing
-------

You cannot slice a DjangoQuery because this would frustrate a design
goal of Djaq to provide the performance advantages of cursor-like
behaviour.

You can use ``limit()`` and ``offset()``:

::

   DjangoQuery("...").offset(1000).limit(100).tuples()

Which will provide you with the first hundred results starting from the
1000th record.

Rewind cursor
-------------

You can rewind the cursor but this is just executing the SQL again:

::

   list(dq.tuples())

   # now, calling `dq.tuples()` returns nothing

   list(dq.rewind().tuples())

   # you will again see results

If you call ``DjangoQuery.context(data)``, that will effectively rewind
the cursor since an entirely new query is created and the implementation
currently doesn’t care if ``data`` is the same context as previously
supplied.

Schema
------

There is a function to get the schema available to a calling client:

.. code:: python

   from djaq.app_utils import get_schema
   print(get_schema())

Pass the same whitelist you use for exposing the query endpoint:

.. code:: python

   wl = {"books": []}
   print(get_schema(whitelist=wl))

Comparing to Django QuerySets
-----------------------------

Djaq is not a replacement for Querysets. They have different purposes.
The QuerySet API is not a remote API. You can use Djaq queries inside
your Django application and Djaq syntax can be more intuitive and
simpler than QuerySets. But Querysets are highly integrated with Django
and have been developed over 15 years by many developers. Plus you get
code completion in your IDE with QuerySets. It is a very well thought
out framework that is the best choice working within a service based on
Django’s ORM. You could probably write a complete transactional Django
application with Djaq and not use QuerySets at all but you’d be going
against the framework.

This section is intended to highlight differences for users with high
familiarity with the ``QuerySet`` class for the purpose of understanding
capabilities and limitations of DjangoQuery.

Django provides significant options for adjusting query generation to
fit a specific use case, ``only()``, ``select_related()``,
``prefetch_related()`` are all useful for different cases. Here’s a
point-by-point comparison with Djaq:

-  ``only()``: Djaq always works in “only” mode. Only explicitly
   requested fields are returned.

-  ``select_related()``: The select clause only returns those columns
   explicitly defined. This feature makes loading of related fields
   non-lazy. In contrast, queries are always non-lazy in Djaq.

-  ``prefetch_related()``: When you have a m2m field as a column
   expression, the model hosting that field is repeated in results as
   many times as necessary. Another way is to use a separate query for
   the m2m related records. In anycase, ``prefetch_related()`` this is
   not relevant in Djaq.

-  F expressions: These are QuerySet workarounds for not being able to
   write expressions in the query for things like column value
   arithmetic and other expressions you want to have the db calculate.
   Djaq lets you write these directly and naturally as part of its
   syntax.

-  To aggregate with Querysets, you use ``aggregate()``, whereas Djaq
   aggregates results implicitly whenever an aggregate function appears
   in the column expressions.

-  Model instances with QuerySets exactly represent the corresponding
   Django model. Djaq’s usual return formats, like ``dicts()``,
   ``tuples()``, etc. are more akin to ``QuerySet.value_list()``.

Let’s look at some direct query comparisons:

Get the average price of books:

::

   DQ("(avg(b.price)) Book b")

compared to QuerySet:

::

   Book.objects.all().aggregate(Avg('price'))

Get the difference from the average off the maximum price:

::

   DQ("(Publisher.name, max(Book.price) - avg(Book.price) as price_diff) Book b")

compared to QuerySet:

::

   Book.objects.aggregate(price_diff=Max('price', output_field=FloatField()) - Avg('price'))

Count books per publisher:

::

   DQ("(Publisher.name, count(Book.id) as num_books) Book b")

compared to QuerySet:

::

   Publisher.objects.annotate(num_books=Count("book"))

Count books with ratings up to and over 5:

.. code:: python

   DQ("""(sum(iif(b.rating < 5, b.rating, 0)) as below_5,
       sum(iif(b.rating >= 5, b.rating, 0)) as above_5)
       Book b""")

compared to QuerySet:

.. code:: python

   above_5 = Count('book', filter=Q(book__rating__gt=5))
   below_5 = Count('book', filter=Q(book__rating__lte=5))
   Publisher.objects.annotate(below_5=below_5).annotate(above_5=above_5)

Get average, maximum, minimum price of books:

.. code:: python

   DQ("(avg(b.price), max(b.price), min(b.price)) Book b")

compared to QuerySet:

::

   Book.objects.aggregate(Avg('price'), Max('price'), Min('price'))

Just as there is a ModelInstance class in Django, we have a DQResult
class:

``objs()``: return a DQResult for each result row, basically a namespace
for the object:

.. code:: python

   dq = DQ("(b.id, b.name, Publisher.name as publisher) Book b")
   for book in dq.objs():
       title = book.name
       publisher = book.publisher
       ...

Note that by default, you iterate using a generator. You cannot slice a
generator.

Simple counts:

``DjangoQuery.value()``: when you know the result is a single row with a
single value, you can immediately access it without further iterations:

.. code:: python

   DQ("(count(b.id)) Book b").value()

will return a single integer value representing the count of books.

Django Subquery and OuterRef
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The following do pretty much the same thing:

::

   # QuerySet
   pubs = Publisher.objects.filter(pk=OuterRef('publisher')).only('pk')
   Book.objects.filter(publisher__in=Subquery(pubs))

   # Djaq
   DQ("(p.id) Publisher p", name='pubs')
   DQ("(b.name) Book{publisher in '@pubs'} b")

Obviously, in both cases, you would be filtering Publisher to make it
actually useful, but the effect and verbosity can be extrapolated from
the above.

Most importantly, sending a query request over the wire, you can
reference the outer scope:

::

   DQ('(p.name, ["(count(b.id)) Book{Publisher.id == b.publisher} b"]) Publisher p')

the subquery output expression references the outer scope. It evaluates
to the following SQL:

.. code:: sql

   SELECT
      "books_publisher"."name",
      (SELECT count("books_book"."id") FROM books_book WHERE "books_publisher"."id" = "books_book"."publisher_id")
   FROM books_publisher

There are some constraints on using subqueries like this. For instance,
the subquery cannot contain any joins.

Sample Project
--------------

If you want to use Djaq right away in your own test project and you feel
confident, crack on. In that case skip the following instructions for
using the sample Bookshop project. Or, if you want to try the sample
project, clone the django repo:

::

   git clone git@github.com:paul-wolf/djaq.git
   cd djaq/bookshop

If you clone the repo and use the sample project, you don’t need to
include Djaq as a requirement because it’s included as a module by a
softlink. Create the virtualenv:

::

   python -m venv .venv

Activate the virtual environment:

::

   source .venv/bin/activate

The module itself does not install Django and there are no further
requirements. To install dependencies for the sample application:

::

   pip install -r requirements.txt

Now make sure there is a Postgresql instance running. The settings are
like this:

.. code:: python

   DATABASES = {
       'default': {
           'ENGINE': 'django.db.backends.postgresql_psycopg2',
           'NAME': 'bookshop',
       },

So, it assumes peer authentication. Change to suit your needs. Now you
can migrate. Make sure the virtualenv is activated!

::

   ./manage.py migrate

We provide a script to create some sample data:

::

   ./manage.py build_data --book-count 2000

This creates 2000 books and associated data.

The example app comes with a management command to run queries:

::

   ./manage.py djaq "(Publisher.name, max(Book.price) - round(avg(Book.price)) as diff) Book b"  --format json

Output of the command should look like this:

::

   ▶ ./manage.py djaq "(Publisher.name, max(Book.price) - round(avg(Book.price)) as diff) Book b"  --format json
   SELECT books_publisher.name, (max(books_book.price) - round(avg(books_book.price))) FROM books_book LEFT JOIN books_publisher ON (books_book.publisher_id = books_publisher.id)  GROUP BY books_publisher.name LIMIT 10
   {"publisher_name": "Avila, Garza and Ward", "diff": 14.0}
   {"publisher_name": "Boyer-Clements", "diff": 16.0}
   {"publisher_name": "Clark, Garza and York", "diff": 15.0}
   {"publisher_name": "Clarke PLC", "diff": 14.0}
   {"publisher_name": "Griffin-Blake", "diff": 16.0}
   {"publisher_name": "Hampton-Davis", "diff": 13.0}
   {"publisher_name": "Jones LLC", "diff": 15.0}
   {"publisher_name": "Lane-Kim", "diff": 15.0}
   {"publisher_name": "Norris-Bennett", "diff": 14.0}
   {"publisher_name": "Singleton-King", "diff": 17.0}

Notice the SQL used to retrieve data is printed first.

The best approach now would be to trial various queries using the Djaq
UI as explained above.

Finally, checkout the settings for the bookshop. You will notice that
many admin models are not accessible. In a real application we’d want to
prevent access to user data and other data on perhaps a finer grained
level.
