Datasets:

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saferoutevn-snapshots

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Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
accepted_trajectory: struct<domain_sha256: string, forecast: bool, job_id: string, profile: string, vehicle_routes: list<item: struct<actions: list<item: struct<distance_m: double, edge_id: string, end_us: int64, exposure: double, feature_payload: struct<baseSpeedKph: double, baseTravelTimeHours: double, contextVersion: string, edgeId: string, edgeProxy: double, fallbackUsed: bool, fromNodeId: int64, graphVersion: string, lengthKm: double, missingFlags: list<item: string>, relativeExposure: double, riskModelVersion: string, roadFactor: double, sourceType: string, timeBucket: string, timeFactor: double, toNodeId: int64, trafficFactor: double, travelMultiplier: double, travelSourceType: string, travelTimeHours: double, weatherFactor: double, weatherFallbackUsed: bool, weatherRegionId: string, weatherTravelMultiplier: double, weatherValidAt: timestamp[s], widthM: null>, fraction_end: double, fraction_start: double, fraction_start_exact: string, from_node: int64, geometry: list<item: list<item: double>>, incoming_edge: string, kind: string, start_us: int64, to_node: int64, continuation: bool, load_after_kg: double, node_id: int64, order_id: string>>, end_node: int64, order_sequence: list<item: string>, return_load_kg: double, return_us: int64, route_column_id: string, start_node: int64, start_us: int64, total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_soft_lateness_s: int64, total_travel_time_s: double, vehicle_id: string>>>
active_job_id: string
basis: struct<build_sha256: string, context_version: string, generation: string, head_sha256: string, head_version: string, overlay_sha256: null, root_sha256: string, session_id: string, source_sha256: string>
current_time: string
delivered_prefix: list<item: string>
execution_mode: string
metric_scope: string
observed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>
order_ids: list<item: string>
pending_event_ids: list<item: null>
planned_served_suffix: list<item: null>
planned_suffix_metrics: struct<total_cost_vnd: int64, total_distance_m: double, total_exposure: int64, total_travel_time_s: int64>
projected_whole_metrics: struct<total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_travel_time_s: double>
real_world_observation: bool
schema_version: string
unserved: list<item: struct<order_id: string, owner_vehicle_id: string, reason: string>>
vehicles: list<item: struct<active_commitment: null, activity: string, availability: string, capacity_kg: double, current_load_kg: double, executed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>, explicit_committed_stop_id: null, onboard_order_ids: list<item: string>, planned_suffix: list<item: null>, position: struct<coordinates: list<item: double>, incoming_edge: string, kind: string, node_id: int64, position_source: string>, position_timestamp: string, remaining_range_m: double, vehicle_id: string>>
vs
accepted_trajectory: struct<domain_sha256: string, forecast: bool, job_id: string, profile: string, vehicle_routes: list<item: struct<actions: list<item: struct<distance_m: double, edge_id: string, end_us: int64, exposure: double, feature_payload: struct<baseSpeedKph: double, baseTravelTimeHours: double, contextVersion: string, edgeId: string, edgeProxy: double, fallbackUsed: bool, fromNodeId: int64, graphVersion: string, lengthKm: double, missingFlags: list<item: string>, relativeExposure: double, riskModelVersion: string, roadFactor: double, sourceType: string, timeBucket: string, timeFactor: double, toNodeId: int64, trafficFactor: double, travelMultiplier: double, travelSourceType: string, travelTimeHours: double, weatherFactor: double, weatherFallbackUsed: bool, weatherRegionId: string, weatherTravelMultiplier: double, weatherValidAt: timestamp[s], widthM: null>, fraction_end: double, fraction_start: double, fraction_start_exact: string, from_node: int64, geometry: list<item: list<item: double>>, incoming_edge: string, kind: string, start_us: int64, to_node: int64, continuation: bool, load_after_kg: double, node_id: int64, order_id: string>>, end_node: int64, order_sequence: list<item: string>, return_load_kg: double, return_us: int64, route_column_id: string, start_node: int64, start_us: int64, total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_soft_lateness_s: int64, total_travel_time_s: double, vehicle_id: string>>>
active_job_id: string
basis: struct<build_sha256: string, context_version: string, generation: string, head_sha256: string, head_version: string, overlay_sha256: null, root_sha256: string, session_id: string, source_sha256: string>
current_time: timestamp[s]
delivered_prefix: list<item: string>
execution_mode: string
metric_scope: string
observed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>
order_ids: list<item: string>
pending_event_ids: list<item: null>
planned_served_suffix: list<item: null>
planned_suffix_metrics: struct<total_cost_vnd: double, total_distance_m: int64, total_exposure: int64, total_travel_time_s: int64>
projected_whole_metrics: struct<total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_travel_time_s: double>
real_world_observation: bool
schema_version: string
unserved: list<item: struct<order_id: string, owner_vehicle_id: string, reason: string>>
vehicles: list<item: struct<active_commitment: null, activity: string, availability: string, capacity_kg: double, current_load_kg: double, executed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>, explicit_committed_stop_id: null, onboard_order_ids: list<item: string>, planned_suffix: list<item: string>, position: struct<coordinates: list<item: double>, edge_entry_us: int64, edge_id: string, expected_exit_us: int64, from_node: int64, incoming_edge: string, kind: string, position_source: string, progress: double, progress_exact: struct<denominator: string, numerator: string>, to_node: int64, node_id: int64>, position_timestamp: timestamp[s], remaining_range_m: double, suspended_commitment: struct<edge_id: string, kind: string, to_node: int64, until_us: int64>, suspension_reason: string, vehicle_id: string>>
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1868, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 803, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 662, in write_rows_on_file
                  table = pa.concat_tables(self.current_rows)
                File "pyarrow/table.pxi", line 6268, in pyarrow.lib.concat_tables
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              accepted_trajectory: struct<domain_sha256: string, forecast: bool, job_id: string, profile: string, vehicle_routes: list<item: struct<actions: list<item: struct<distance_m: double, edge_id: string, end_us: int64, exposure: double, feature_payload: struct<baseSpeedKph: double, baseTravelTimeHours: double, contextVersion: string, edgeId: string, edgeProxy: double, fallbackUsed: bool, fromNodeId: int64, graphVersion: string, lengthKm: double, missingFlags: list<item: string>, relativeExposure: double, riskModelVersion: string, roadFactor: double, sourceType: string, timeBucket: string, timeFactor: double, toNodeId: int64, trafficFactor: double, travelMultiplier: double, travelSourceType: string, travelTimeHours: double, weatherFactor: double, weatherFallbackUsed: bool, weatherRegionId: string, weatherTravelMultiplier: double, weatherValidAt: timestamp[s], widthM: null>, fraction_end: double, fraction_start: double, fraction_start_exact: string, from_node: int64, geometry: list<item: list<item: double>>, incoming_edge: string, kind: string, start_us: int64, to_node: int64, continuation: bool, load_after_kg: double, node_id: int64, order_id: string>>, end_node: int64, order_sequence: list<item: string>, return_load_kg: double, return_us: int64, route_column_id: string, start_node: int64, start_us: int64, total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_soft_lateness_s: int64, total_travel_time_s: double, vehicle_id: string>>>
              active_job_id: string
              basis: struct<build_sha256: string, context_version: string, generation: string, head_sha256: string, head_version: string, overlay_sha256: null, root_sha256: string, session_id: string, source_sha256: string>
              current_time: string
              delivered_prefix: list<item: string>
              execution_mode: string
              metric_scope: string
              observed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>
              order_ids: list<item: string>
              pending_event_ids: list<item: null>
              planned_served_suffix: list<item: null>
              planned_suffix_metrics: struct<total_cost_vnd: int64, total_distance_m: double, total_exposure: int64, total_travel_time_s: int64>
              projected_whole_metrics: struct<total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_travel_time_s: double>
              real_world_observation: bool
              schema_version: string
              unserved: list<item: struct<order_id: string, owner_vehicle_id: string, reason: string>>
              vehicles: list<item: struct<active_commitment: null, activity: string, availability: string, capacity_kg: double, current_load_kg: double, executed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>, explicit_committed_stop_id: null, onboard_order_ids: list<item: string>, planned_suffix: list<item: null>, position: struct<coordinates: list<item: double>, incoming_edge: string, kind: string, node_id: int64, position_source: string>, position_timestamp: string, remaining_range_m: double, vehicle_id: string>>
              vs
              accepted_trajectory: struct<domain_sha256: string, forecast: bool, job_id: string, profile: string, vehicle_routes: list<item: struct<actions: list<item: struct<distance_m: double, edge_id: string, end_us: int64, exposure: double, feature_payload: struct<baseSpeedKph: double, baseTravelTimeHours: double, contextVersion: string, edgeId: string, edgeProxy: double, fallbackUsed: bool, fromNodeId: int64, graphVersion: string, lengthKm: double, missingFlags: list<item: string>, relativeExposure: double, riskModelVersion: string, roadFactor: double, sourceType: string, timeBucket: string, timeFactor: double, toNodeId: int64, trafficFactor: double, travelMultiplier: double, travelSourceType: string, travelTimeHours: double, weatherFactor: double, weatherFallbackUsed: bool, weatherRegionId: string, weatherTravelMultiplier: double, weatherValidAt: timestamp[s], widthM: null>, fraction_end: double, fraction_start: double, fraction_start_exact: string, from_node: int64, geometry: list<item: list<item: double>>, incoming_edge: string, kind: string, start_us: int64, to_node: int64, continuation: bool, load_after_kg: double, node_id: int64, order_id: string>>, end_node: int64, order_sequence: list<item: string>, return_load_kg: double, return_us: int64, route_column_id: string, start_node: int64, start_us: int64, total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_soft_lateness_s: int64, total_travel_time_s: double, vehicle_id: string>>>
              active_job_id: string
              basis: struct<build_sha256: string, context_version: string, generation: string, head_sha256: string, head_version: string, overlay_sha256: null, root_sha256: string, session_id: string, source_sha256: string>
              current_time: timestamp[s]
              delivered_prefix: list<item: string>
              execution_mode: string
              metric_scope: string
              observed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>
              order_ids: list<item: string>
              pending_event_ids: list<item: null>
              planned_served_suffix: list<item: null>
              planned_suffix_metrics: struct<total_cost_vnd: double, total_distance_m: int64, total_exposure: int64, total_travel_time_s: int64>
              projected_whole_metrics: struct<total_cost_vnd: double, total_distance_m: double, total_exposure: double, total_travel_time_s: double>
              real_world_observation: bool
              schema_version: string
              unserved: list<item: struct<order_id: string, owner_vehicle_id: string, reason: string>>
              vehicles: list<item: struct<active_commitment: null, activity: string, availability: string, capacity_kg: double, current_load_kg: double, executed_metrics: struct<cost_vnd: double, distance_m: double, relative_exposure_proxy: double, service_time_us: int64, travel_time_us: int64, waiting_time_us: int64>, explicit_committed_stop_id: null, onboard_order_ids: list<item: string>, planned_suffix: list<item: string>, position: struct<coordinates: list<item: double>, edge_entry_us: int64, edge_id: string, expected_exit_us: int64, from_node: int64, incoming_edge: string, kind: string, position_source: string, progress: double, progress_exact: struct<denominator: string, numerator: string>, to_node: int64, node_id: int64>, position_timestamp: timestamp[s], remaining_range_m: double, suspended_commitment: struct<edge_id: string, kind: string, to_node: int64, until_us: int64>, suspension_reason: string, vehicle_id: string>>
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset