Quibble, a Damn Small Query Langauge (DSQL) Using Python

This intermediate-level article will demonstrate how do use the filter idiom, delegation tables, list generators and the operator module to create a compact but expandable query langauge for querying data.

When many people hear the word ‘query’, their minds jump to Structured Query Language (SQL).  Now I love SQL as much as anyone[1].  Using SQL for queries is wonderful when one’s data is already loaded into a SQL database[2].  Sometimes the Real World (TM) conspires against this, since:

  • the data might be heterogeneous
  • the data might be easy to express in Python terms, but tedious to refector into a normalized form.  As a quick example, consider a dict of sets, which would require a join and a foreign key and actual *gasp* schema design.
  • one might not have access to a database (though with SQLite being embedded in Python from 2.5 onward, this is less an issue)
  • one might have irrational biases against schemas and the straightjacketing that they impose on agile development, and programmer whimsy.  I suffer from this bias myself, and attend regular SQL indoctination meetings, but so far it’s not sticking!  NoSQL Forever!
  • SQL is enterprisey, but not Web2.0, man!

That said, SQL has lots of advantages:

  • Exteremely flexible, complex querying
  • Widely deployed
  • (etc, etc.)

Let’s begin by building a list of dictionaries to query against.  These could be any list of object that support a dictionary interface.  Note that these objects are heterogeneous.  Also note they are quite contrived, and rather boring.

# a list of dicts to query against
data = [
    dict(a=None, b=1, c=[1,2,3]),
    dict(a=13, d=dict(a=1,b=2)),
    dict(c=13,e="some string"),
    dict(c=10,e="some other string"),
    dict(a=10,e="some other string"),
    {('author','email'): ('Gregg Lind','gregg.lind at fakearoo.com')},

Now that we have some data, we’re going to build a simple query language called Quibble [3] to search against it.  We will be using the filter/pipeline idiom.  The filter idiom is quite simple:  if the an object matches some condition, keep it; else continue on.  On Unix, this is a very simple type of pipeline; when one wants venture capital, call it “map-reduce”.  While Python has a filter function (http://docs.python.org/library/functions.html#filter), the list comprehension builtin will be quite a bit simpler to use for our dumb purposes.

Next we will build a delegation table.  This simple mapping maps names like “<=” to functions.  When people talk about the power of ‘functions are first-class objects’, which is part of what they’re on about.  We can make this mapping of function shorthand names mapped to *unevaluated functions*.

To make our lives easier, Quibble will use a simple convention for defining what is a valid query operator.  An ‘operator function’ must take exactly two argument, following this format:

     my_operator(some_dict[key], value)

Luckily for us [4], the functions in the python operator module http://docs.python.org/library/operator.html mostly take this form.  Having this same calling convention will make it possible to just drop the ‘right’ function in.

import operator
operators = {
    "<" : operator.lt,
    "<=" : operator.le,
    "==" : operator.eq,
    "!=" : operator.ne,
    ">=" : operator.ge,
    ">"  : operator.gt,
    "in" : operator.contains,
    "nin" : lambda x,y: not operator.contains(x,y),

Note that with ‘nin’, we had to wrap it.   Python’s lambda statement makes this easy, and the resulting code is still easy-to-read.  We could also use a true named function here, like this:

def nin_(x,y):
    return x not in y

Or a simpler lambda:

"nin" :  lambda x,y:  x not in y,
def query(D,key,val,operator="=="):
    D:  a dictionary
    key:  the key to query
    val:  the value
    operator:  "==", ">=", "in", et all.

    Returns elements in D such that operator(D.get(key,None), val) is true
        op = operators[operator]
    except KeyError:
        raise ValueError, "operator must be one of %r" % operators
    return [x for x in D if op(x.get(key,None),val)]

print "1st version"
print query(data,'a',1)
print query(data,'c',None,'!=')

Excellent.  Time to retire to a private island.  Oh wait, you want to define new functions?  Chain these queries together?  It should handle exceptions?  We can fix those.

A more fundamental problem with this filter approach is that defining “or” conditions is quite awkward, since filters reduce the input set at each stage, but we will clean this up as well (but it will be ugly).

Let’s add some functionality.

  • operator can be any two argument function
  • return an iterator instead of a list
  • tee the original input, just in case it too is an iterator, we don’t want to exhaust it.
  • adds a keynotfound argument, to change what happens if the key isn't found in the dict
import itertools
import inspect
def _can_take_at_least_n_args(f,n=2):
    ''' helper to check that a function can take at least two unnamed args'''
    (pos, args,kwargs, defaults) = inspect.getargspec(f)
    if args is not None or len(pos) >= n:
        return True
        return False

def query(D,key,val,operator="==", keynotfound=None):
    D:  a list of dictionaries
    key:  the key to query
    val:  the value
    operator:  "==", ">=", "in", et all, or any two-arg function
    keynotfound:  value if key is not found

    Returns elements in D such that operator(D.get(key,None), val) is true
    D = itertools.tee(D,2)[1]  # take a teed copy

    # let's let operator be any two argument callable function, *then*
    # fall back on the delegation table.
    if callable(operator):
        if not _can_take_at_least_n_args(operator,2):
            raise ValueError ("operator must take at least 2 arguments")
            # alternately, we could wrap it in a lambda, like:
            # op = lambda(x,y): operator(x),
            # but we have to check to see how many args it really wants (inc. 0!)
        op = operator
        op = operators.get(operator,None)
    if not op:
        raise ValueError, "operator must be one of %r, or a two-argument function" % operators

    def try_op(f,x,y):
            ans = f(x,y)
            return f(x,y)
        except Exception, exc:
            return False

    return (x for x in D if try_op(op, x.get(key,keynotfound),val))

print "2nd version"
print list(query(data,'a',1))
print list(query(data,'c',None,'!='))
at_fakaroo = lambda k,v:  "fakearoo" in k[1] # v will be irrelevant
print list(query(data, ('author','email'), None, at_fakaroo, keynotfound=('','')))

That is looking quite a bit more powerful!  It still has lots of problems:

  • ‘or’ isn’t well supported.
  • we handle all errors in the function equivalently — by eating them!  This will make it really hard to debug, since none of us writes perfect code.
  • chaining queries is doable via nesting, but it’s ugly (see below).
  • relies on the dictionary interface
  • awkward to peer inside nested components
  • doesn’t handle attribute lookup easily (but could be modified to, using getattr http://docs.python.org/library/functions.html#getattr)

Let’s try to make a “Queryable” object that chains operations via method calls (something like
SQLAlchememy generative selects http://www.sqlalchemy.org/docs/05/sqlexpression.html#intro-to-generative-selects-and-transformations):

class Queryable(object):
    def __init__(self,D):
        self.D = itertools.tee(D,2)[1]

    def tolist(self):
        return list(itertools.tee(self.D,2)[1])

    def query(self,*args,**kwargs):
        return Queryable(query(self.D,*args,**kwargs))

    q = query

print "3rd version, Queryable"
# c > 10 and "other" in e
Q = Queryable(data).q('c',8,'>')
print Q.tolist()
Q = Q.q('e', 'other', 'in')
print Q.tolist()

This is OKAY, and but it still has plenty of codesmell.

  • lots of tee madness
  • ugly “tolist” method
  • we’re the query optimizer… we’re guaranteed that at least one pass will be O(n), since there is no indexing, and no smarts at all in the querying.

Next steps / alternatives:

Knowing when to give up!

Like any domain specific language, Quibble (as written here) walks a very fine line between functionality and complexity (okay it stumbles over the line drunkenly, but not by too much!) If we need much more complexity in our queries (or object model) then we’re back to writing python, and investigating a proper solution (SQL, Mongo, etc.) is probably worthwhile!  For a simple reporting language, or debugging, or a simple command line interface, this might be plenty.

Happy Yule!


1. Not true, I hate it.

2. Unless it’s super complex to query, involves lots of joins, or the query optimizer is off drunk at the pub, or stars are poorly aligned.

3. Quibble — from Query Bibble, Bibble being an ancient Etruscan word for a teething ring.

4. Well, actually, not lucky at all.  Like most scientific papers, this article pretends that inquiry is orderly.  I knew that I wanted to talk about the operator module, and most of the functions in operator take this form, so it seems like a sensible first-approximation convention.