Error Handling + replace module_name list with str

Author: Michael

README.md

@@ -39,33 +39,27 @@ In order to poll the substream, you will need to call the `poll()` function on t
 print(sb.output_modules)
 
 # Poll the module and return a list of SubstreamOutput objects in the order of the specified modules
-result = sb.poll(["store_swap_events"], start_block=10000835, end_block=10000835+20000)
+result = sb.poll(["map_swap_events"], start_block=10000835, end_block=10000835+20000)
 ```
 
 With the default inputs, this function outputs Pandas Dataframes after streaming all blocks between the start_block and end_block. However depending on how this function is called, a dict object is returned. The `poll()` function has a number of inputs
 
 - output_modules
-    - List of strings of output modules to stream 
+    - String of the output module to stream 
 - start_block
     - Integer block number to start the polling
 - end_block
     - Integer block number to end the polling. In theory, there is no max block number as any block number past chain head will stream the blocks in real time. Its recommended to use an end_block far off into the future if building a data app that will be streaming datain real time as blocks finalize, such as block 20,000,000
-- stream_callback
-    - An optional callback function to be passed into the polling function to execute when valid streamed data is received 
 - return_first_result
     - Boolean value that if True will return data on the first block after the start block to have an applicable TX/Event.
     - Can be called recursively on the front end while incrementing the start_block to return data as its streamed rather than all data at once after streaming is completed
     - Defaults to False
-    - If True, the data is returned in the format {"data": [], "module_name": String, "data_block": int}
 - initial_snapshot
     - Boolean value, defaults to False
-- highest_processed_block
-    - Integer block number that is used in measuring indexing and processing progress, in cases where return_progress is True
-    - Defaults to 0
-- return_progress: bool = False,
-    - Boolean value that if True returns progress in back processing
-    - Defaults to False
-
+- return_type
+    - Specifies the type of value to return
+    - Passing "df" returns the data in a pandas DataFrame
+    - Passing "dict" returns in the format {"data": [], "module_name": String, "data_block": int, error: str | None}
 
 The result here is the default `SubstreamOutput` object, you can access both the `data` and `snapshots` dataframes by doing:
 

substreams/substream.py

@@ -125,11 +125,10 @@ def _parse_snapshot_deltas(self, snapshot: dict) -> list[dict]:
             for x in snapshot["deltas"].get("deltas", list())
         ]
 
-    def _parse_data_outputs(self, data: dict, module_names: list[str]) -> list[dict]:
+    def _parse_data_outputs(self, data: dict, module_name: str) -> list[dict]:
         outputs = list()
-        module_set = set(module_names)
         for output in data["outputs"]:
-            if "mapOutput" not in output or output["name"] not in module_set:
+            if "mapOutput" not in output or output["name"] != module_name:
                 continue
             map_output = output["mapOutput"]
             for key, items in map_output.items():
@@ -163,43 +162,41 @@ def proto_file_map(self) -> dict[str, DescriptorProto]:
 
     def poll(
         self,
-        output_modules: list[str],
+        output_module: str,
         start_block: int,
         end_block: int,
-        stream_callback: Optional[callable] = None,
         return_first_result: bool = False,
         initial_snapshot: bool = False,
         return_type: str = "df"
     ):
 
-        module_name = ""
-        has_error = False
-        return_dict_interface = {"data": [], "module_name": module_name, "data_block": start_block, "error": None}
+        return_dict_interface = {"data": [], "module_name": output_module, "data_block": str(start_block), "error": None}
         valid_return_types = ["dict", "df"]
         results = []
         raw_results = defaultdict(lambda: {"data": list(), "snapshots": list()})
 
         try:
+            if isinstance(output_module, str) is False:
+                raise Exception("The 'output_module' parameter passed into the poll() function is not a string.")
             return_type = return_type.lower()
             if return_type not in valid_return_types:
                 return_type = "df"
 
             from sf.substreams.v1.substreams_pb2 import STEP_IRREVERSIBLE, Request
-            for module in output_modules:
-                if module not in self.output_modules:
-                    raise Exception(f"module '{module}' is not supported for {self.name}")
-                if self.output_modules[module].get('is_map') is False:
-                    raise Exception(f"module '{module}' is not a map module")
-                self._class_from_module(module)
+            if output_module not in self.output_modules:
+                raise Exception(f"module '{output_module}' is not supported for {self.name}")
+            if self.output_modules[output_module].get('is_map') is False:
+                raise Exception(f"module '{output_module}' is not a map module")
+            self._class_from_module(output_module)
 
             stream = self.service.Blocks(
                 Request(
                     start_block_num=start_block,
                     stop_block_num=end_block,
                     fork_steps=[STEP_IRREVERSIBLE],
                     modules=self.pkg.modules,
-                    output_modules=output_modules,
-                    initial_store_snapshot_for_modules=output_modules
+                    output_modules=[output_module],
+                    initial_store_snapshot_for_modules=[output_module]
                     if initial_snapshot
                     else None,
                 )
@@ -214,43 +211,41 @@ def poll(
                     continue
 
                 if snapshot:
-                    module_name = snapshot["moduleName"]
                     snapshot_deltas = self._parse_snapshot_deltas(snapshot)
-                    raw_results[module_name]["snapshots"].extend(snapshot_deltas)
+                    raw_results[output_module]["snapshots"].extend(snapshot_deltas)
 
                 if data:
-                    parsed = self._parse_data_outputs(data, output_modules)
-                    module_name = data["outputs"][0]["name"]
-                    raw_results[module_name]["data"].extend(parsed)
+                    parsed = self._parse_data_outputs(data, output_module)
+                    raw_results[output_module]["data"].extend(parsed)
                     return_dict_interface["data_block"] = data["clock"]["number"]
                     if len(parsed) > 0:
                         parsed = [dict(item, **{'block':data["clock"]["number"]}) for item in parsed]
                         if return_first_result is True:
                             break
-                        if callable(stream_callback):
-                            stream_callback(module_name, parsed)
+                    elif int(return_dict_interface["data_block"]) + 1 == end_block:
+                        results = return_dict_interface
 
             if return_first_result is True and parsed:
                 return_dict_interface["data"] = parsed
                 if return_type == "dict":
                     results = return_dict_interface
                 if return_type == "df":
                     results = pd.DataFrame(parsed)
-            elif raw_results:
-                result = SubstreamOutput(module_name=output_modules[0])
-                data_dict: dict = raw_results.get(output_modules[0])
+            if return_first_result is False and raw_results:
+                result = SubstreamOutput(module_name=output_module)
+                data_dict: dict = raw_results.get(output_module)
                 for k, v in data_dict.items():
                     df = pd.DataFrame(v)
-                    df["output_module"] = output_modules[0]
+                    df["output_module"] = output_module
                     setattr(result, k, df)
                 results.append(result)
                 if return_type == "dict":
                     return_dict_interface["data"] = results.to_dict()
                     results = return_dict_interface
-            else:
-                raise Exception("No Valid Data Results Returned by Substream")
         except Exception as err:
-            has_error = True
-            return_dict_interface["error"] = err
+            error_to_pass = err
+            if isinstance(err, Exception):
+                error_to_pass = str(err)
+            return_dict_interface["error"] = error_to_pass
             results = return_dict_interface
         return results