more complex visit_Call to parse chained command (#5677)

* more complex visit_Call

* add changelog

* traversing all of the tree

.changes/unreleased/Fixes-20220817-163642.yaml

@@ -0,0 +1,7 @@
+kind: Fixes
+body: Fix Unexpected behavior when chaining methods on dbt-ref'ed/sourced dataframes
+time: 2022-08-17T16:36:42.678275-07:00
+custom:
+  Author: ChenyuLInx
+  Issue: "5646"
+  PR: "5677"

core/dbt/parser/models.py

@@ -32,6 +32,10 @@
 from dbt.exceptions import ParsingException, validator_error_message, UndefinedMacroException
 
 
+dbt_function_key_words = set(["ref", "source", "config", "get"])
+dbt_function_full_names = set(["dbt.ref", "dbt.source", "dbt.config", "dbt.config.get"])
+
+
 class PythonValidationVisitor(ast.NodeVisitor):
     def __init__(self):
         super().__init__()
@@ -108,13 +112,40 @@ def _get_call_literals(self, node):
         return arg_literals, kwarg_literals
 
     def visit_Call(self, node: ast.Call) -> None:
-
-        func_name = self._flatten_attr(node.func)
-        if func_name in ["dbt.ref", "dbt.source", "dbt.config", "dbt.config.get"]:
-            # drop the dot-dbt prefix
-            func_name = func_name.split(".")[-1]
-            args, kwargs = self._get_call_literals(node)
-            self.dbt_function_calls.append((func_name, args, kwargs))
+        # check weather the current call could be a dbt function call
+        if isinstance(node.func, ast.Attribute) and node.func.attr in dbt_function_key_words:
+            func_name = self._flatten_attr(node.func)
+            # check weather the current call really is a dbt function call
+            if func_name in dbt_function_full_names:
+                # drop the dot-dbt prefix
+                func_name = func_name.split(".")[-1]
+                args, kwargs = self._get_call_literals(node)
+                self.dbt_function_calls.append((func_name, args, kwargs))
+
+        # no matter what happened above, we should keep visiting the rest of the tree
+        # visit args and kwargs to see if there's call in it
+        for obj in node.args + [kwarg.value for kwarg in node.keywords]:
+            if isinstance(obj, ast.Call):
+                self.visit_Call(obj)
+            # support dbt.ref in list args, kwargs
+            elif isinstance(obj, ast.List) or isinstance(obj, ast.Tuple):
+                for el in obj.elts:
+                    if isinstance(el, ast.Call):
+                        self.visit_Call(el)
+            # support dbt.ref in dict args, kwargs
+            elif isinstance(obj, ast.Dict):
+                for value in obj.values:
+                    if isinstance(value, ast.Call):
+                        self.visit_Call(value)
+        # visit node.func.value if we are at an call attr
+        if isinstance(node.func, ast.Attribute):
+            self.attribute_helper(node.func)
+
+    def attribute_helper(self, node: ast.Attribute) -> None:
+        while isinstance(node, ast.Attribute):
+            node = node.value  # type: ignore
+        if isinstance(node, ast.Call):
+            self.visit_Call(node)
 
     def visit_Import(self, node: ast.Import) -> None:
         for n in node.names:

test/unit/test_parser.py

@@ -549,9 +549,16 @@ def model(dbt, session):
     from torch import b
     import textblob.text
     import sklearn
-    df = dbt.ref("my_sql_model")
-    df = dbt.ref("my_sql_model")
-    df = dbt.ref("my_sql_model_2")
+    df0 = pandas(dbt.ref("a_model"))
+    df1 = dbt.ref("my_sql_model").task.limit(2)
+    df2 = dbt.ref("my_sql_model_1")
+    df3 = dbt.ref("my_sql_model_2")
+    df4 = dbt.source("test", 'table1').limit(max = [max(dbt.ref('something'))])
+    df5 = [dbt.ref('test1')]
+    
+    a_dict = {'test2' : dbt.ref('test2')}
+    df5 = anotherfunction({'test2' : dbt.ref('test3')})
+    df6 = [somethingelse.ref(dbt.ref("test4"))]
 
     df = df.limit(2)
     return df   
@@ -582,7 +589,8 @@ def model(dbt, session):
             checksum=block.file.checksum,
             unrendered_config={'materialized': 'table', 'packages':python_packages},
             config_call_dict={'materialized': 'table', 'packages':python_packages},
-            refs=[['my_sql_model'], ['my_sql_model'], ['my_sql_model_2']]
+            refs=[['a_model'], ['my_sql_model'], ['my_sql_model_1'], ['my_sql_model_2'], ['something'], ['test1'], ['test2'], ['test3'], ['test4']],
+            sources = [['test', 'table1']],
         )
         assertEqualNodes(node, expected)
         file_id = 'snowplow://' + normalize('models/nested/py_model.py')