How hard is parsing a context-free1 language? In this post, I will try to provide an overview of one of the simplest parsing techniques of all – recusrive descent parsing by hand.

This type of parsing uses mutually recursive procedures to parse a subset of context-free languages using a top-down approach. Hence, this kind of parsing is also called a top-down recursive descent parser. The grammar is restricted to only those rules that can be expressed without recursion in the left-most term.

Here is a simple grammar for a language that can parse nested expressions, with the restriction that the expressions elements can only be 1 and only addition is supported for simplicity.

E = T "+" E
| T
T = "1"
| "(" E ")"


This grammar can parse expressions such as 1, 1+1, 1+1+1+1 etc.

To start parsing, we need a bit of infrastructure. In particular, we need the ability to tell where we are currently (cur_position), the ability to backtrack to a previous position, and the ability to tell when the input is complete. I use global variables for ease of discussion, and to avoid having to commit too much to Python syntax and semantics. Use the mechanisms available in your language to modularize your parser.

my_input = None
cur_position = 0

def pos_cur():
return cur_position

def pos_set(i):
global cur_position
cur_position = i

def pos_eof():
return pos_cur() == len(my_input)


We also need the ability to extract the next token (in this case, the next element in the input array).

def next_token():
i = pos_cur()
if i+1 > len(my_input): return None
pos_set(i+1)
return my_input[i]


Another convenience we use is the ability to match a token to a given symbol.

def match(t):
return next_token() == t


Once we have all these, the core part of parsing is two procedures. The first tries to match a sequence of terms one by one. If the match succeeds, then we return success. If not, then we signal failure and exit.

def do_seq(seq_terms):
for t in seq_terms:
if not t(): return False
return True


The other corresponds to the alternatives for each production. If any alternative succeeds, then the parsing succeeds.

def do_alt(alt_terms):
for t in alt_terms:
o_pos = pos_cur()
if t(): return True
pos_set(o_pos)
return False


With this, we are now ready to write our parser. Since we are writing a top-down recursive descent parser, we start with the axiom rule E which contains two alternatives.

# E = ...
#   | ...
def E():
return do_alt([E_1, E_2])


Both E_1 and E_2 are simple sequential rules

# E = T + E
def E_1():
return do_seq([T, PLUS, E])
# E = T
def E_2():
return do_seq([T])


Defining T is similar

# T = ...
#   | ...
def T():
return do_alt([T_1, T_2])


And each alternative in T gets defined correspondingly.

# T = 1
def T_1():
return match('1')
# T = ( E )
def T_2():
return do_seq([P_OPEN,E,P_CLOSE])


We also need terminals, which is again simple enough

def PLUS():
return match('+')
def P_OPEN():
return match('(')
def P_CLOSE():
return match(')')


The only thing that remains is to define the parser

def parse(i):
global my_input
my_input = i
assert E()
assert pos_eof()


Using it:

>>> import tdrd
>>> tdrd.parse('1+1')
>>> tdrd.parse('1+(1+1)+1')


The interesting part is that, our infrastructure can be readily turned to parse much more complex grammars, with almost one-to-one rewriting of each rule. For example, here is a slightly more complex grammar:

term = fact mul_op term
| fact

fact =  digits
| "(" expr ")"

digits = digit digits
| digit

digit = 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
mul_op = "*" | "/"


Its conversion is almost automatic

def expr():     return do_alt([expr_1, expr_2])
def expr_1():   return do_seq([term, add_op, expr])
def expr_2():   return do_seq([term])

def term():     return do_alt([term_1, term_2])
def term_1():   return do_seq([fact, mul_op, term])
def term_2():   return do_seq([fact])

def fact():     return do_alt([fact_1, fact_2])
def fact_1():   return do_seq([digits])
def fact_2():   return do_seq([lambda: match('('), expr, lambda: match(')')])

def digits():   return do_alt([digits_1, digits_2])
def digits_1(): return do_seq([digit, digits])
def digits_2(): return do_seq([digit])

def add_op():   return do_alt([lambda: match('+'), lambda: match('-')])
def mul_op():   return do_alt([lambda: match('*'), lambda: match('/')])

# note that list comprehensions will not work here due to closure of i
def digit():    return do_alt(map(lambda i: lambda: match(str(i)), range(10)))


Using it:

>>> import tdrd
>>> tdrd.parse('12*3+(12/13)')


Indeed, it is close enough to automatic, that we can make it fully automatic. We first define the grammar as a datastructure for convenience. I hope I dont need to convince you that I could have easily loaded it as a JSON file, or even parsed the BNF myself if necessary from an external file.

grammar = {
"term": [["fact", "mul_op", "term"], ["fact"]],
"fact": [["digits"], ["(", "expr", ")"]],
"digits": [["digit", "digits"], ["digit"]],
"digit": [[str(i)] for i in list(range(10))],
"mul_op": [["*"], ["/"]]
}


Using the grammar just means that we have to slightly modify our core procedures.

def do_seq(seq_terms):
for t in seq_terms:
if not do_alt(t): return False
return True

def do_alt(key):
if key not in grammar: return match(key)
alt_terms = grammar[key]
for ts in alt_terms:
o_pos = pos_cur()
if do_seq(ts): return True
pos_set(o_pos)
return False

def parse(i):
global my_input
my_input = i
do_alt('expr')
assert pos_eof()


Using it is same as before:

>>> import tdrd
>>> tdrd.parse('12*3+(12/13)')


Briging it all together, place these in tdrd.py

class g_parse:
def __init__(self, g): self._g = g

def remain(self): return self._len - self._i

def next_token(self):
try: return None if self._i + 1 > self._len else self._str[self._i]
finally: self._i += 1

def match(self, t): return self.next_token() == t

def is_nt(self, key): return key in self._g

def do_seq(self, seq_terms): return all(self.do_alt(t) for t in seq_terms)

def _try(self, fn):
o_pos = self._i
if fn(): return True
self._i = o_pos

def do_alt(self, key):
if not self.is_nt(key): return self.match(key)
return any(self._try(lambda: self.do_seq(ts)) for ts in self._g[key])

def parse(self, i):
self._str, self._len, self._i = i, len(i), 0
self.do_alt('expr')
assert self.remain() == 0

if __name__ == '__main__':
my_grammar = {
"expr": [
["term"]],
"term": [
["fact", "mul_op", "term"],
["fact"]],
"fact": [
["digits"],
["(", "expr", ")"]],
"digits": [
["digit", "digits"],
["digit"]],
"digit": [[str(i)] for i in list(range(10))],
"mul_op": [["*"], ["/"]]}

import sys
g_parse(my_grammar).parse(sys.argv[1])


Which can be used as

\$ python3 tdrd.py '123+11+(3*(2))+1'


Of course, one usually wants to do something with the parsed output. However, given that the procedures are organized in a top-down fashion, saving the resulting expressions is relatively trivial.

1: The parser we create is not really interpreting the grammar as a Context-Free Grammar. Rather, it uses the grammar as if it is written using another formalism called Parsing Expression Grammar. However, an important subclass of context-free languages in real world – LL(*) – can be completely represented using PEG. Hence, the title is not completely wrong. $\hookleftarrow$