我的语法是Python语法的扩展。小程序在Macbook Pro上解析约2秒钟。我采用了SLL技巧并应用它：
# Set up the lexer
inputStream = InputStream(s)
lexer = CustomLexer(inputStream)
stream = CommonTokenStream(lexer)
# Set up the error handling stuff
error_handler = CustomErrorStrategy()
error_listener = CustomErrorListener()
buffered_errors = BufferedErrorListener()
error_listener.addDelegatee(buffered_errors)
# Set up the fast parser
parser = PythonQLParser(stream)
parser._interp.predictionMode = PredictionMode.SLL
parser.removeErrorListeners()
parser.errHandler = BailErrorStrategy()
try:
tree = parser.file_input()
return (tree,parser)
但它没有做到这一点，时间并没有显着改变。关于该做什么的任何提示？
我使用Python3和antlr4-python3-runtime-4.5.3
语法文件在这里：Grammar File
项目github页面在这里：Github
我还运行了一个分析器，这里有来自解析器的重要条目：
ncalls tottime percall cumtime percall filename:lineno(function)
21 0.000 0.000 0.094 0.004 PythonQLParser.py:7483(argument)
8 0.000 0.000 0.195 0.024 PythonQLParser.py:7379(arglist)
9 0.000 0.000 0.196 0.022 PythonQLParser.py:6836(trailer)
5/3 0.000 0.000 0.132 0.044 PythonQLParser.py:6765(testlist_comp)
1 0.000 0.000 0.012 0.012 PythonQLParser.py:6154(window_end_cond)
1 0.000 0.000 0.057 0.057 PythonQLParser.py:6058(sliding_window)
1 0.000 0.000 0.057 0.057 PythonQLParser.py:5941(window_clause)
1 0.000 0.000 0.004 0.004 PythonQLParser.py:5807(for_clause_entry)
1 0.000 0.000 0.020 0.020 PythonQLParser.py:5752(for_clause)
2/1 0.000 0.000 0.068 0.068 PythonQLParser.py:5553(query_expression)
48/10 0.000 0.000 0.133 0.013 PythonQLParser.py:5370(atom)
48/7 0.000 0.000 0.315 0.045 PythonQLParser.py:5283(power)
48/7 0.000 0.000 0.315 0.045 PythonQLParser.py:5212(factor)
48/7 0.000 0.000 0.331 0.047 PythonQLParser.py:5132(term)
47/7 0.000 0.000 0.346 0.049 PythonQLParser.py:5071(arith_expr)
47/7 0.000 0.000 0.361 0.052 PythonQLParser.py:5010(shift_expr)
47/7 0.000 0.000 0.376 0.054 PythonQLParser.py:4962(and_expr)
47/7 0.000 0.000 0.390 0.056 PythonQLParser.py:4914(xor_expr)
47/7 0.000 0.000 0.405 0.058 PythonQLParser.py:4866(expr)
44/7 0.000 0.000 0.405 0.058 PythonQLParser.py:4823(star_expr)
43/7 0.000 0.000 0.422 0.060 PythonQLParser.py:4615(not_test)
43/7 0.000 0.000 0.438 0.063 PythonQLParser.py:4563(and_test)
43/7 0.000 0.000 0.453 0.065 PythonQLParser.py:4509(or_test)
43/7 0.000 0.000 0.467 0.067 PythonQLParser.py:4293(old_test)
43/7 0.000 0.000 0.467 0.067 PythonQLParser.py:4179(try_catch_expr)
43/7 0.000 0.000 0.482 0.069 PythonQLParser.py:3978(test)
1 0.000 0.000 0.048 0.048 PythonQLParser.py:2793(import_from)
1 0.000 0.000 0.048 0.048 PythonQLParser.py:2702(import_stmt)
7 0.000 0.000 1.728 0.247 PythonQLParser.py:2251(testlist_star_expr)
4 0.000 0.000 1.770 0.443 PythonQLParser.py:2161(expr_stmt)
5 0.000 0.000 1.822 0.364 PythonQLParser.py:2063(small_stmt)
5 0.000 0.000 1.855 0.371 PythonQLParser.py:1980(simple_stmt)
5 0.000 0.000 1.859 0.372 PythonQLParser.py:1930(stmt)
1 0.000 0.000 1.898 1.898 PythonQLParser.py:1085(file_input)
176 0.002 0.000 0.993 0.006 Lexer.py:127(nextToken)
420 0.000 0.000 0.535 0.001 ParserATNSimulator.py:1120(closure)
705 0.003 0.000 1.642 0.002 ParserATNSimulator.py:315(adaptivePredict)
我正在解析的PythonQL程序就是这个：
# This example illustrates the window query in PythonQL
from collections import namedtuple
trade = namedtuple('Trade', ['day','ammount', 'stock_id'])
trades = [ trade(1, 15.34, 'APPL'),
trade(2, 13.45, 'APPL'),
trade(3, 8.34, 'APPL'),
trade(4, 9.87, 'APPL'),
trade(5, 10.99, 'APPL'),
trade(6, 76.16, 'APPL') ]
# Maximum 3-day sum
res = (select win
for sliding window win in ( select t.ammount for t in trades )
start at s when True
only end at e when (e-s == 2))
print (res)