From 9e2fa4cdfd902e9b37efa2b973b57381925dc334 Mon Sep 17 00:00:00 2001 From: Anthony Mahanna Date: Fri, 15 Dec 2023 21:04:26 -0500 Subject: [PATCH] cleanup: `benchmark` --- benchmark/compare.py | 50 +- benchmark/traces/head/arangodb_to_pyg.json | 2293 ----------- benchmark/traces/{head => main}/README.md | 2 +- benchmark/traces/main/arangodb_to_pyg.json | 3395 +++++++++++++++++ .../{head => main}/pyg_to_arangodb.json | 188 +- benchmark/write.py | 178 +- 6 files changed, 3668 insertions(+), 2438 deletions(-) delete mode 100644 benchmark/traces/head/arangodb_to_pyg.json rename benchmark/traces/{head => main}/README.md (55%) create mode 100644 benchmark/traces/main/arangodb_to_pyg.json rename benchmark/traces/{head => main}/pyg_to_arangodb.json (73%) diff --git a/benchmark/compare.py b/benchmark/compare.py index 7a1c277..d01b868 100644 --- a/benchmark/compare.py +++ b/benchmark/compare.py @@ -5,18 +5,16 @@ class DiffSpan: def __init__( self, - spanID: str, - operationName: str, - duration_head: int, - duration_branch: int, - tags: list[dict[str, str]], + span_id: str, + operation_name: str, + main_duration: int, + branch_duration: int, ): - self.spanID = spanID - self.operationName = operationName - self.duration_head = duration_head - self.duration_branch = duration_branch - self.improvement = f"{round((1 - duration_branch / duration_head) * 100)}%" - self.tags = tags + self.span_id = span_id + self.operation_name = operation_name + self.main_span_duration = main_duration + self.branch_span_duration = branch_duration + self.improvement = f"{round((1 - main_duration / branch_duration) * 100)}%" self.children: dict[str, "DiffSpan"] = {} def add_child(self, span_id: str, child: "DiffSpan"): @@ -24,12 +22,11 @@ def add_child(self, span_id: str, child: "DiffSpan"): def to_dict(self, include_children: bool = True): res = { - "spanID": self.spanID, - "operationName": self.operationName, - "duration_head": self.duration_head, - "duration_branch": self.duration_branch, + "span_id": self.span_id, + "operation_name": self.operation_name, + "main_duration": self.main_span_duration, + "branch_duration": self.branch_span_duration, "improvement": self.improvement, - "tags": self.tags, } if include_children: @@ -39,21 +36,22 @@ def to_dict(self, include_children: bool = True): class DiffTree: - def __init__(self, head_trace: dict, branch_trace: dict): - self.root_span = self.__build_diff_tree(head_trace, branch_trace) + def __init__(self, main_trace: dict, branch_trace: dict): + self.root_span = self.__build_diff_tree(main_trace, branch_trace) + + def __build_diff_tree(self, main_trace: dict, branch_trace: dict): + assert main_trace["operationName"] == branch_trace["operationName"] - def __build_diff_tree(self, head_trace: dict, branch_trace: dict): diff_span = DiffSpan( - head_trace["spanID"], - head_trace["operationName"], - head_trace["duration"], + main_trace["spanID"], + main_trace["operationName"], + main_trace["duration"], branch_trace["duration"], - head_trace["tags"], ) # Recursively build the tree for child spans for head_child_span, branch_child_span in zip( - head_trace["children"], branch_trace["children"] + main_trace["children"], branch_trace["children"] ): child_span = self.__build_diff_tree(head_child_span, branch_child_span) diff_span.add_child(head_child_span["spanID"], child_span) @@ -73,10 +71,10 @@ def main(): current_dir = pathlib.Path(__file__).parent.absolute() for operation in ["pyg_to_arangodb", "arangodb_to_pyg"]: - head_trace = json.load(open(f"{current_dir}/traces/head/{operation}.json")) + main_trace = json.load(open(f"{current_dir}/traces/main/{operation}.json")) branch_trace = json.load(open(f"{current_dir}/traces/branch/{operation}.json")) - diff_tree = DiffTree(head_trace, branch_trace) + diff_tree = DiffTree(main_trace, branch_trace) diff_tree.to_json_file(operation) print("-" * 50) diff --git a/benchmark/traces/head/arangodb_to_pyg.json b/benchmark/traces/head/arangodb_to_pyg.json deleted file mode 100644 index 2f94ecf..0000000 --- a/benchmark/traces/head/arangodb_to_pyg.json +++ /dev/null @@ -1,2293 +0,0 @@ -{ - "spanID": "935ddd725129fb7c", - "operationName": "arangodb_to_pyg", - "duration": 812573, - "tags": { - "name": "FakeHeteroGraphBenchmark" - }, - "children": [ - { - "spanID": "4a5308cc3dfabc08", - "operationName": "__process_adb_v_col", - "duration": 46005, - "tags": { - "v_col": "v0", - "v_col_size": 1008 - }, - "children": [ - { - "spanID": "307bf3262f120554", - "operationName": "__fetch_adb_docs", - 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a/benchmark/traces/head/pyg_to_arangodb.json +++ b/benchmark/traces/main/pyg_to_arangodb.json @@ -1,7 +1,7 @@ { "spanID": "40212ef7cca5a5a1", "operationName": "pyg_to_arangodb", - "duration": 1199797, + "duration": 1149783, "tags": { "name": "FakeHeteroGraphBenchmark" }, @@ -9,27 +9,27 @@ { "spanID": "e8e5216afcbd04c3", "operationName": "__get_node_and_edge_types", - "duration": 11, + "duration": 18, "tags": {}, "children": [] }, { "spanID": "fb97d43588561712", "operationName": "__create_adb_graph", - "duration": 22600, + "duration": 7667, "tags": {}, "children": [ { "spanID": "cf6a659eb4862b21", "operationName": "__etypes_to_edefinitions", - "duration": 18, + "duration": 17, "tags": {}, "children": [] }, { "spanID": "e6f4590b9a164106", "operationName": "__ntypes_to_ocollections", - "duration": 8, + "duration": 7, "tags": {}, "children": [] } @@ -38,7 +38,7 @@ { "spanID": "4f65d4d9259f4329", "operationName": "__process_pyg_n_type", - "duration": 128536, + "duration": 129517, "tags": { "n_type": "v0", "n_type_size": 1008 @@ -47,7 +47,7 @@ { "spanID": "bad640fb19488dec", "operationName": "__process_pyg_node_batch", - "duration": 16962, + "duration": 10941, "tags": { "start_index": 0, "end_index": 1008 @@ -56,21 +56,29 @@ { "spanID": "e61a441c12e0c8b2", "operationName": "__set_adb_data", - "duration": 13736, - "tags": {}, + "duration": 8076, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "af19922ad9b8a714", "operationName": "__build_dataframe_from_tensor", - "duration": 8669, - "tags": {}, + "duration": 3497, + "tags": { + "meta_key": "y", + "meta_val": "y" + }, "children": [] }, { "spanID": "78de58575487ce1e", "operationName": "__build_dataframe_from_tensor", - "duration": 533, - "tags": {}, + "duration": 1515, + "tags": { + "meta_key": "x", + "meta_val": "x" + }, "children": [] } ] @@ -80,7 +88,7 @@ { "spanID": "19c78df48f4ff31e", "operationName": "__insert_adb_docs", - "duration": 111367, + "duration": 118460, "tags": { "col": "v0", "size": 1008 @@ -92,7 +100,7 @@ { "spanID": "6f25e2a25a921187", "operationName": "__process_pyg_n_type", - "duration": 78057, + "duration": 77546, "tags": { "n_type": "v1", "n_type_size": 821 @@ -101,7 +109,7 @@ { "spanID": "9c6316b950f24455", "operationName": "__process_pyg_node_batch", - "duration": 2157, + "duration": 2240, "tags": { "start_index": 0, "end_index": 821 @@ -110,14 +118,19 @@ { "spanID": "e9bb17bca3f2c9bf", "operationName": "__set_adb_data", - "duration": 1266, - "tags": {}, + "duration": 1327, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "f77383c13458a748", "operationName": "__build_dataframe_from_tensor", - "duration": 966, - "tags": {}, + "duration": 1000, + "tags": { + "meta_key": "x", + "meta_val": "x" + }, "children": [] } ] @@ -127,7 +140,7 @@ { "spanID": "7a1d50068d723104", "operationName": "__insert_adb_docs", - "duration": 75805, + "duration": 75209, "tags": { "col": "v1", "size": 821 @@ -139,7 +152,7 @@ { "spanID": "dd84f39e71545a13", "operationName": "__process_pyg_n_type", - "duration": 74717, + "duration": 73123, "tags": { "n_type": "v2", "n_type_size": 894 @@ -148,7 +161,7 @@ { "spanID": "42af9fc385776e9a", "operationName": "__process_pyg_node_batch", - "duration": 2129, + "duration": 2076, "tags": { "start_index": 0, "end_index": 894 @@ -157,14 +170,19 @@ { "spanID": "ce164dba0ff18e02", "operationName": "__set_adb_data", - "duration": 1201, - "tags": {}, + "duration": 1175, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "8c778ea6eb2083e6", "operationName": "__build_dataframe_from_tensor", - "duration": 892, - "tags": {}, + "duration": 869, + "tags": { + "meta_key": "x", + "meta_val": "x" + }, "children": [] } ] @@ -174,7 +192,7 @@ { "spanID": "03983ca8ea7e9d49", "operationName": "__insert_adb_docs", - "duration": 72499, + "duration": 70960, "tags": { "col": "v2", "size": 894 @@ -186,7 +204,7 @@ { "spanID": "b83e90ec17e0aa3c", "operationName": "__process_pyg_e_type", - "duration": 144376, + "duration": 135382, "tags": { "e_type": "[\"v2\",\"e0\",\"v1\"]", "e_type_size": 8895 @@ -195,7 +213,7 @@ { "spanID": "66194cb1d71037d1", "operationName": "__process_pyg_edge_batch", - "duration": 6604, + "duration": 6889, "tags": { "start_index": 0, "end_index": 8895 @@ -204,14 +222,19 @@ { "spanID": "d3290a4cb5d32b16", "operationName": "__set_adb_data", - "duration": 1962, - "tags": {}, + "duration": 1731, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "ab0c1681c8f8e3d0", "operationName": "__build_dataframe_from_tensor", - "duration": 1527, - "tags": {}, + "duration": 1314, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -221,7 +244,7 @@ { "spanID": "004ae545a0116be5", "operationName": "__insert_adb_docs", - "duration": 137652, + "duration": 128374, "tags": { "col": "e0", "size": 8895 @@ -233,7 +256,7 @@ { "spanID": "7e5b1e7f9ca5499d", "operationName": "__process_pyg_e_type", - "duration": 123071, + "duration": 116710, "tags": { "e_type": "[\"v1\",\"e0\",\"v2\"]", "e_type_size": 8161 @@ -242,7 +265,7 @@ { "spanID": "de1b372ad3fbf47a", "operationName": "__process_pyg_edge_batch", - "duration": 4637, + "duration": 4913, "tags": { "start_index": 0, "end_index": 8161 @@ -251,14 +274,19 @@ { "spanID": "3e70f16a55485822", "operationName": "__set_adb_data", - "duration": 1552, - "tags": {}, + "duration": 1661, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "534097cabaf3897a", "operationName": "__build_dataframe_from_tensor", - "duration": 1176, - "tags": {}, + "duration": 1252, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -268,7 +296,7 @@ { "spanID": "ded733e8b421eaeb", "operationName": "__insert_adb_docs", - "duration": 118335, + "duration": 111671, "tags": { "col": "e0", "size": 8161 @@ -280,7 +308,7 @@ { "spanID": "30e9c5cc101fbccc", "operationName": "__process_pyg_e_type", - "duration": 201707, + "duration": 199804, "tags": { "e_type": "[\"v0\",\"e0\",\"v1\"]", "e_type_size": 10022 @@ -289,7 +317,7 @@ { "spanID": "9148624feac1c14f", "operationName": "__process_pyg_edge_batch", - "duration": 5772, + "duration": 5482, "tags": { "start_index": 0, "end_index": 10022 @@ -298,14 +326,19 @@ { "spanID": "3d15eef738c1962e", "operationName": "__set_adb_data", - "duration": 1806, - "tags": {}, + "duration": 1820, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "f7b0b7d2cda8056c", "operationName": "__build_dataframe_from_tensor", - "duration": 1388, - "tags": {}, + "duration": 1439, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -315,7 +348,7 @@ { "spanID": "cd9d2b7d247a8333", "operationName": "__insert_adb_docs", - "duration": 195821, + "duration": 194210, "tags": { "col": "e0", "size": 10022 @@ -327,7 +360,7 @@ { "spanID": "72ae22448b0163c1", "operationName": "__process_pyg_e_type", - "duration": 135807, + "duration": 124746, "tags": { "e_type": "[\"v1\",\"e0\",\"v0\"]", "e_type_size": 8179 @@ -336,7 +369,7 @@ { "spanID": "149818d11759edc3", "operationName": "__process_pyg_edge_batch", - "duration": 4828, + "duration": 4521, "tags": { "start_index": 0, "end_index": 8179 @@ -345,14 +378,19 @@ { "spanID": "51ef1922fe43c49e", "operationName": "__set_adb_data", - "duration": 1587, - "tags": {}, + "duration": 1521, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "820865d6e005b860", "operationName": "__build_dataframe_from_tensor", - "duration": 1199, - "tags": {}, + "duration": 1164, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -362,7 +400,7 @@ { "spanID": "eece328bff7b118e", "operationName": "__insert_adb_docs", - "duration": 130868, + "duration": 120125, "tags": { "col": "e0", "size": 8179 @@ -374,7 +412,7 @@ { "spanID": "1beb37117d41e602", "operationName": "__process_pyg_e_type", - "duration": 127700, + "duration": 120860, "tags": { "e_type": "[\"v1\",\"e0\",\"v1\"]", "e_type_size": 8159 @@ -383,7 +421,7 @@ { "spanID": "8d1fd9b74d2b9deb", "operationName": "__process_pyg_edge_batch", - "duration": 4847, + "duration": 5168, "tags": { "start_index": 0, "end_index": 8159 @@ -392,14 +430,19 @@ { "spanID": "b4e1357d4a84eb03", "operationName": "__set_adb_data", - "duration": 1672, - "tags": {}, + "duration": 1667, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "8c25166a1ff39849", "operationName": "__build_dataframe_from_tensor", - "duration": 1251, - "tags": {}, + "duration": 1240, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -409,7 +452,7 @@ { "spanID": "d080e66e552f233a", "operationName": "__insert_adb_docs", - "duration": 122735, + "duration": 115575, "tags": { "col": "e0", "size": 8159 @@ -421,7 +464,7 @@ { "spanID": "8a5006c1ec188efb", "operationName": "__process_pyg_e_type", - "duration": 147005, + "duration": 148804, "tags": { "e_type": "[\"v0\",\"e0\",\"v2\"]", "e_type_size": 10034 @@ -430,7 +473,7 @@ { "spanID": "f6be1f723405095c", "operationName": "__process_pyg_edge_batch", - "duration": 5611, + "duration": 5613, "tags": { "start_index": 0, "end_index": 10034 @@ -439,14 +482,19 @@ { "spanID": "9a6a5f92cca74147", "operationName": "__set_adb_data", - "duration": 1833, - "tags": {}, + "duration": 1902, + "tags": { + "meta": "{}" + }, "children": [ { "spanID": "966e12778c1745a7", "operationName": "__build_dataframe_from_tensor", - "duration": 1442, - "tags": {}, + "duration": 1509, + "tags": { + "meta_key": "edge_attr", + "meta_val": "edge_attr" + }, "children": [] } ] @@ -456,7 +504,7 @@ { "spanID": "71eacd0549a3e80e", "operationName": "__insert_adb_docs", - "duration": 141286, + "duration": 143086, "tags": { "col": "e0", "size": 10034 diff --git a/benchmark/write.py b/benchmark/write.py index 6176aa3..60dbaff 100644 --- a/benchmark/write.py +++ b/benchmark/write.py @@ -3,14 +3,19 @@ import pathlib import random import time +from collections import defaultdict from typing import Any, Dict, List import numpy as np import requests import torch from arango import ArangoClient +from retry import retry from torch_geometric.datasets import FakeHeteroDataset +# import uuid + + try: from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter except ImportError: @@ -38,23 +43,28 @@ def __init__( duration: int, tags: list[dict[str, str]], ): - self.spanID = spanID - self.operationName = operationName + self.span_id = spanID + self.operation_name = operationName self.duration = duration self.tags = { tag["key"]: tag["value"] for tag in tags if tag["key"] not in ["span.kind", "internal.span.format"] } + self.children: dict[str, "JaegerSpan"] = {} + self.parent: "JaegerSpan" = None def add_child(self, span_id: str, child: "JaegerSpan"): self.children[span_id] = child - def to_dict(self): + def set_parent(self, parent: "JaegerSpan"): + self.parent = parent + + def to_dict(self) -> dict[str, Any]: return { - "spanID": self.spanID, - "operationName": self.operationName, + "spanID": self.span_id, + "operationName": self.operation_name, "duration": self.duration, "tags": self.tags, "children": [child.to_dict() for child in self.children.values()], @@ -62,37 +72,108 @@ def to_dict(self): class JaegerSpanTree: - def __init__(self, jaeger_json_data: Dict[str, Any]): - self.root_span = self.__build_span_tree(jaeger_json_data) - - def __build_span_tree(self, jaeger_json_data: Dict[str, Any]): - sorted_spans = sorted( - jaeger_json_data["data"][0]["spans"], key=lambda span: span["startTime"] - ) - - root_spans: List[JaegerSpan] = [] - span_dict: Dict[str, JaegerSpan] = {} - span: Dict[str, Any] - for span in sorted_spans: - span_id = span["spanID"] - span_dict[span["spanID"]] = JaegerSpan( - span_id, span["operationName"], span["duration"], span["tags"] + def __init__( + self, + jaeger_endpoint: str, + service_name: str, + operation_name: str, + start_time: str, + tags: Dict[str, Any] = {}, + ) -> None: + self.jaeger_endpoint = jaeger_endpoint + self.service_name = service_name + self.operation_name = operation_name + self.start_time = start_time + self.tags = tags + + self.root_span: JaegerSpan = None + self.span_id_to_span: Dict[str, JaegerSpan] = {} + self.operation_name_to_span: Dict[str, List[JaegerSpan]] = defaultdict(list) + + self.__build_span_tree() + print(f"Built span tree for {self.service_name}-{self.operation_name}") + + def get_spans_by_operation_name(self, operation_name: str) -> List[JaegerSpan]: + return self.operation_name_to_span[operation_name] + + def get_span_by_span_id(self, span_id: str) -> JaegerSpan: + return self.span_id_to_span[span_id] + + def get_span_tag_value(self, span_id: str, tag_key: str) -> str: + return self.span_id_to_span[span_id].tags[tag_key] + + def __build_span_tree(self) -> None: + for span in self.__fetch_sorted_spans(): + span_id: str = span["spanID"] + operation_name: str = span["operationName"] + + span_object = JaegerSpan( + span_id, + operation_name, + span["duration"], + span["tags"], ) + self.span_id_to_span[span_id] = span_object + self.operation_name_to_span[operation_name].append(span_object) + references = span.get("references", []) if len(references) == 0: - root_spans.append(span_dict[span_id]) + if self.root_span is not None: + m = f"Found multiple root spans: {self.root_span.span_id} and {span_id}" + print(m) + raise Exception(m) + + self.root_span = self.span_id_to_span[span_id] continue for ref in references: if ref["refType"] == "CHILD_OF": - parent_span = span_dict[ref["spanID"]] - parent_span.add_child(span_id, span_dict[span_id]) + parent_span_id = ref["spanID"] + parent_span = self.span_id_to_span[parent_span_id] + child_span = self.span_id_to_span[span_id] + + parent_span.add_child(span_id, child_span) + child_span.set_parent(parent_span) + + def __fetch_sorted_spans(self) -> List[Dict[str, Any]]: + params = { + "service": self.service_name, + "operation": self.operation_name, + "tag": [f"{k}:{v}" for k, v in self.tags.items()], + "start": self.start_time, + } + + traces = self.__get_jaeger_traces(f"{self.jaeger_endpoint}/api/traces", params) + + if len(traces) > 1: + m = f"Found multiple traces for {params}" + print(m) + raise Exception(m) + + spans = traces[0]["spans"] + return sorted(spans, key=lambda span: span["startTime"]) - assert len(root_spans) == 1 - return root_spans[0] + @retry(tries=6, delay=2, backoff=2) + def __get_jaeger_traces( + self, url: str, params: dict[str, Any] + ) -> List[dict[str, Any]]: + response = requests.get(url, params=params) - def to_dict(self): + if response.status_code != 200: + m = f"Failed to fetch traces for {params}: {response.status_code}" + print(m) + raise Exception(m) + + traces = response.json()["data"] + if len(traces) == 0: + m = f"No traces found for {params}" + print(m) + raise Exception(m) + + return traces + + def to_dict(self) -> dict[str, Any]: return self.root_span.to_dict() def to_json_file(self, output: str): @@ -108,9 +189,10 @@ def parse_args(): parser.add_argument("--dbName", type=str, default="_system") parser.add_argument("--username", type=str, default="root") parser.add_argument("--password", type=str, default="") - parser.add_argument("--otlp_endpoint", type=str, default="localhost:4317") + parser.add_argument("--jaeger_endpoint", type=str, default="http://localhost:16686") + parser.add_argument("--otlp_endpoint", type=str, default="http://localhost:4317") parser.add_argument( - "--output_dir", type=str, default="branch", choices=["branch", "head"] + "--output_dir", type=str, choices=["branch", "head"], required=True ) # Parse the arguments @@ -119,13 +201,13 @@ def parse_args(): return args -def get_adapter(args) -> ADBPyG_Adapter: +def get_adapter(args, service_name: str) -> ADBPyG_Adapter: db = ArangoClient(hosts=args.url).db( args.dbName, username=args.username, password=args.password, verify=True ) tracer = create_tracer( - "adbpyg-adapter-benchmark", + service_name, enable_console_tracing=False, span_exporters=[OTLPSpanExporter(endpoint=args.otlp_endpoint, insecure=True)], ) @@ -155,27 +237,14 @@ def run_arangodb_to_pyg(adapter: ADBPyG_Adapter, name: str) -> None: ) -def get_span_tree(operation: str, start_time: str) -> JaegerSpanTree: - url = "http://localhost:16686/api/traces" - params = { - "service": "adbpyg-adapter-benchmark", - "operation": operation, - "tag": "name:FakeHeteroGraphBenchmark", - "start": start_time, - } - - response = requests.get(url, params=params) - assert response.status_code == 200 - - return JaegerSpanTree(response.json()) - - def main(): + service_name = f"adbpyg-adapter-benchmark" + # 1. Parse the arguments args = parse_args() # 2. Get the adapter - adbpyg_adapter = get_adapter(args) + adbpyg_adapter = get_adapter(args, service_name) # 3. Run the benchmark # TODO: Figure out why Jaeger is reporting the traces @@ -189,8 +258,21 @@ def main(): time.sleep(5) # 4. Get the span trees - pyg_to_arangodb_span_tree = get_span_tree("pyg_to_arangodb", start_time) - arangodb_to_pyg_span_tree = get_span_tree("arangodb_to_pyg", start_time) + pyg_to_arangodb_span_tree = JaegerSpanTree( + args.jaeger_endpoint, + service_name, + "pyg_to_arangodb", + start_time, + {"name": name}, + ) + + arangodb_to_pyg_span_tree = JaegerSpanTree( + args.jaeger_endpoint, + service_name, + "arangodb_to_pyg", + start_time, + {"name": name}, + ) # 5. Write the span trees to disk pyg_to_arangodb_span_tree.to_json_file(f"{args.output_dir}/pyg_to_arangodb.json")