# Copyright 2023 Cognite AS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from datetime import datetime
from types import TracebackType
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
from cognite.client import CogniteClient
from cognite.client.data_classes import (
Sequence,
SequenceData,
SequenceRows,
TimeSeries,
)
from cognite.client.exceptions import CogniteDuplicatedError, CogniteNotFoundError
from cognite.extractorutils.threading import CancellationToken
from cognite.extractorutils.uploader._base import (
RETRIES,
RETRY_BACKOFF_FACTOR,
RETRY_DELAY,
RETRY_MAX_DELAY,
AbstractUploadQueue,
)
from cognite.extractorutils.uploader._metrics import (
SEQUENCES_UPLOADER_POINTS_QUEUED,
SEQUENCES_UPLOADER_POINTS_WRITTEN,
SEQUENCES_UPLOADER_QUEUE_SIZE,
TIMESERIES_UPLOADER_POINTS_DISCARDED,
TIMESERIES_UPLOADER_POINTS_QUEUED,
TIMESERIES_UPLOADER_POINTS_WRITTEN,
TIMESERIES_UPLOADER_QUEUE_SIZE,
)
from cognite.extractorutils.util import EitherId, cognite_exceptions, retry
MIN_DATAPOINT_TIMESTAMP = -2208988800000
MAX_DATAPOINT_STRING_LENGTH = 255
MAX_DATAPOINT_VALUE = 1e100
MIN_DATAPOINT_VALUE = -1e100
TimeStamp = Union[int, datetime]
DataPoint = Union[Tuple[TimeStamp, float], Tuple[TimeStamp, str]]
DataPointList = List[DataPoint]
[docs]
def default_time_series_factory(external_id: str, datapoints: DataPointList) -> TimeSeries:
"""
Default time series factory used when create_missing in a TimeSeriesUploadQueue is given as a boolean.
Args:
external_id: External ID of time series to create
datapoints: The list of datapoints that were tried to be inserted
Returns:
A TimeSeries object with external_id set, and the is_string automatically detected
"""
is_string = (
isinstance(datapoints[0].get("value"), str) # type: ignore # input might be dict to keep compatibility
if isinstance(datapoints[0], dict)
else isinstance(datapoints[0][1], str)
)
return TimeSeries(external_id=external_id, is_string=is_string)
[docs]
class TimeSeriesUploadQueue(AbstractUploadQueue):
"""
Upload queue for time series
Args:
cdf_client: Cognite Data Fusion client to use
post_upload_function: A function that will be called after each upload. The function will be given one argument:
A list of dicts containing the datapoints that were uploaded (on the same format as the kwargs in
datapoints upload in the Cognite SDK).
max_queue_size: Maximum size of upload queue. Defaults to no max size.
max_upload_interval: Automatically trigger an upload each m seconds when run as a thread (use start/stop
methods).
trigger_log_level: Log level to log upload triggers to.
thread_name: Thread name of uploader thread.
create_missing: Create missing time series if possible (ie, if external id is used). Either given as a boolean
(True would auto-create a time series with nothing but an external ID), or as a factory function taking an
external ID and a list of datapoints about to be inserted and returning a TimeSeries object.
data_set_id: Data set id passed to create_missing. Does nothing if create_missing is False.
If a custom timeseries creation method is set in create_missing, this is used as fallback if
that method does not set data set id on its own.
"""
def __init__(
self,
cdf_client: CogniteClient,
post_upload_function: Optional[Callable[[List[Dict[str, Union[str, DataPointList]]]], None]] = None,
max_queue_size: Optional[int] = None,
max_upload_interval: Optional[int] = None,
trigger_log_level: str = "DEBUG",
thread_name: Optional[str] = None,
create_missing: Union[Callable[[str, DataPointList], TimeSeries], bool] = False,
data_set_id: Optional[int] = None,
cancellation_token: Optional[CancellationToken] = None,
):
# Super sets post_upload and threshold
super().__init__(
cdf_client,
post_upload_function,
max_queue_size,
max_upload_interval,
trigger_log_level,
thread_name,
cancellation_token,
)
self.missing_factory: Callable[[str, DataPointList], TimeSeries]
if isinstance(create_missing, bool):
self.create_missing = create_missing
self.missing_factory = default_time_series_factory
else:
self.create_missing = True
self.missing_factory = create_missing
self.upload_queue: Dict[EitherId, DataPointList] = {}
self.points_queued = TIMESERIES_UPLOADER_POINTS_QUEUED
self.points_written = TIMESERIES_UPLOADER_POINTS_WRITTEN
self.queue_size = TIMESERIES_UPLOADER_QUEUE_SIZE
self.data_set_id = data_set_id
def _verify_datapoint_time(self, time: Union[int, float, datetime, str]) -> bool:
if isinstance(time, int) or isinstance(time, float):
return not math.isnan(time) and time >= MIN_DATAPOINT_TIMESTAMP
elif isinstance(time, str):
return False
else:
return time.timestamp() * 1000.0 >= MIN_DATAPOINT_TIMESTAMP
def _verify_datapoint_value(self, value: Union[int, float, datetime, str]) -> bool:
if isinstance(value, float):
return not (
math.isnan(value) or math.isinf(value) or value > MAX_DATAPOINT_VALUE or value < MIN_DATAPOINT_VALUE
)
elif isinstance(value, str):
return len(value) <= MAX_DATAPOINT_STRING_LENGTH
elif isinstance(value, datetime):
return False
else:
return True
def _is_datapoint_valid(
self,
dp: DataPoint,
) -> bool:
if isinstance(dp, dict):
return self._verify_datapoint_time(dp["timestamp"]) and self._verify_datapoint_value(dp["value"])
elif isinstance(dp, tuple):
return self._verify_datapoint_time(dp[0]) and self._verify_datapoint_value(dp[1])
else:
return True
[docs]
def add_to_upload_queue(
self, *, id: Optional[int] = None, external_id: Optional[str] = None, datapoints: Optional[DataPointList] = None
) -> None:
"""
Add data points to upload queue. The queue will be uploaded if the queue size is larger than the threshold
specified in the __init__.
Args:
id: Internal ID of time series. Either this or external_id must be set.
external_id: External ID of time series. Either this or external_id must be set.
datapoints: List of data points to add
"""
datapoints = datapoints or []
old_len = len(datapoints)
datapoints = list(filter(self._is_datapoint_valid, datapoints))
new_len = len(datapoints)
if old_len > new_len:
diff = old_len - new_len
self.logger.warning(f"Discarding {diff} datapoints due to bad timestamp or value")
TIMESERIES_UPLOADER_POINTS_DISCARDED.inc(diff)
either_id = EitherId(id=id, external_id=external_id)
with self.lock:
if either_id not in self.upload_queue:
self.upload_queue[either_id] = []
self.upload_queue[either_id].extend(datapoints)
self.points_queued.inc(len(datapoints))
self.upload_queue_size += len(datapoints)
self.queue_size.set(self.upload_queue_size)
self._check_triggers()
[docs]
def upload(self) -> None:
"""
Trigger an upload of the queue, clears queue afterwards
"""
@retry(
exceptions=cognite_exceptions(),
cancellation_token=self.cancellation_token,
tries=RETRIES,
delay=RETRY_DELAY,
max_delay=RETRY_MAX_DELAY,
backoff=RETRY_BACKOFF_FACTOR,
)
def _upload_batch(upload_this: List[Dict], retries: int = 5) -> List[Dict]:
if len(upload_this) == 0:
return upload_this
try:
self.cdf_client.time_series.data.insert_multiple(upload_this)
except CogniteNotFoundError as ex:
if not retries:
raise ex
if not self.create_missing:
self.logger.error("Could not upload data points to %s: %s", str(ex.not_found), str(ex))
# Get IDs of time series that exists, but failed because of the non-existing time series
retry_these = [EitherId(**id_dict) for id_dict in ex.failed if id_dict not in ex.not_found]
if self.create_missing:
# Get the time series that can be created
create_these_ids = set(
[id_dict["externalId"] for id_dict in ex.not_found if "externalId" in id_dict]
)
datapoints_lists: Dict[str, DataPointList] = {
ts_dict["externalId"]: ts_dict["datapoints"]
for ts_dict in upload_this
if ts_dict["externalId"] in create_these_ids
}
self.logger.info(f"Creating {len(create_these_ids)} time series")
to_create: List[TimeSeries] = [
self.missing_factory(external_id, datapoints_lists[external_id])
for external_id in create_these_ids
]
if self.data_set_id is not None:
for ts in to_create:
if ts.data_set_id is None:
ts.data_set_id = self.data_set_id
self.cdf_client.time_series.create(to_create)
retry_these.extend([EitherId(external_id=i) for i in create_these_ids])
if len(ex.not_found) != len(create_these_ids):
missing = [id_dict for id_dict in ex.not_found if id_dict.get("externalId") not in retry_these]
missing_num = len(ex.not_found) - len(create_these_ids)
self.logger.error(
f"{missing_num} time series not found, and could not be created automatically:\n"
+ str(missing)
+ "\nData will be dropped"
)
# Remove entries with non-existing time series from upload queue
upload_this = [
entry
for entry in upload_this
if EitherId(id=entry.get("id"), external_id=entry.get("externalId")) in retry_these
]
# Upload remaining
_upload_batch(upload_this, retries - 1)
return upload_this
if len(self.upload_queue) == 0:
return
with self.lock:
upload_this = _upload_batch(
[
{either_id.type(): either_id.content(), "datapoints": datapoints}
for either_id, datapoints in self.upload_queue.items()
if len(datapoints) > 0
]
)
for _either_id, datapoints in self.upload_queue.items():
self.points_written.inc(len(datapoints))
try:
self._post_upload(upload_this)
except Exception as e:
self.logger.error("Error in upload callback: %s", str(e))
self.upload_queue.clear()
self.logger.info(f"Uploaded {self.upload_queue_size} datapoints")
self.upload_queue_size = 0
self.queue_size.set(self.upload_queue_size)
def __enter__(self) -> "TimeSeriesUploadQueue":
"""
Wraps around start method, for use as context manager
Returns:
self
"""
self.start()
return self
def __exit__(
self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
) -> None:
"""
Wraps around stop method, for use as context manager
Args:
exc_type: Exception type
exc_val: Exception value
exc_tb: Traceback
"""
self.stop()
def __len__(self) -> int:
"""
The size of the upload queue
Returns:
Number of data points in queue
"""
return self.upload_queue_size
[docs]
class SequenceUploadQueue(AbstractUploadQueue):
def __init__(
self,
cdf_client: CogniteClient,
post_upload_function: Optional[Callable[[List[Any]], None]] = None,
max_queue_size: Optional[int] = None,
max_upload_interval: Optional[int] = None,
trigger_log_level: str = "DEBUG",
thread_name: Optional[str] = None,
create_missing: bool = False,
cancellation_token: Optional[CancellationToken] = None,
):
"""
Args:
cdf_client: Cognite Data Fusion client to use
post_upload_function: A function that will be called after each upload. The function will be given one
argument: A list of the events that were uploaded.
max_queue_size: Maximum size of upload queue. Defaults to no max size.
max_upload_interval: Automatically trigger an upload each m seconds when run as a thread (use start/stop
methods).
trigger_log_level: Log level to log upload triggers to.
thread_name: Thread name of uploader thread.
create_missing: Create missing sequences if possible (ie, if external id is used)
"""
# Super sets post_upload and threshold
super().__init__(
cdf_client,
post_upload_function,
max_queue_size,
max_upload_interval,
trigger_log_level,
thread_name,
cancellation_token,
)
self.upload_queue: Dict[EitherId, SequenceRows] = {}
self.sequence_metadata: Dict[EitherId, Dict[str, Union[str, int, float]]] = {}
self.sequence_asset_external_ids: Dict[EitherId, str] = {}
self.sequence_dataset_external_ids: Dict[EitherId, str] = {}
self.sequence_names: Dict[EitherId, str] = {}
self.sequence_descriptions: Dict[EitherId, str] = {}
self.column_definitions: Dict[EitherId, List[Dict[str, str]]] = {}
self.asset_ids: Dict[str, int] = {}
self.dataset_ids: Dict[str, int] = {}
self.create_missing = create_missing
self.points_queued = SEQUENCES_UPLOADER_POINTS_QUEUED
self.points_written = SEQUENCES_UPLOADER_POINTS_WRITTEN
self.queue_size = SEQUENCES_UPLOADER_QUEUE_SIZE
[docs]
def set_sequence_column_definition(
self, col_def: List[Dict[str, str]], id: Optional[int] = None, external_id: Optional[str] = None
) -> None:
"""
Set sequence column definition
Args:
col_def: Sequence column definition
id: Sequence internal ID
Use if external_id is None
external_id: Sequence external ID
Us if id is None
"""
either_id = EitherId(id=id, external_id=external_id)
self.column_definitions[either_id] = col_def
[docs]
def add_to_upload_queue(
self,
rows: Union[
Dict[int, List[Union[int, float, str]]],
List[Tuple[int, Union[int, float, str]]],
List[Dict[str, Any]],
SequenceData,
SequenceRows,
],
column_external_ids: Optional[List[dict]] = None,
id: Optional[int] = None,
external_id: Optional[str] = None,
) -> None:
"""
Add sequence rows to upload queue. Mirrors implementation of SequenceApi.insert. Inserted rows will be
cached until uploaded
Args:
rows: The rows to be inserted. Can either be a list of tuples, a list of ["rownumber": ..., "values": ...]
objects, a dictionary of rowNumber: data, or a SequenceData object.
column_external_ids: List of external id for the columns of the sequence
id: Sequence internal ID
Use if external_id is None
external_id: Sequence external ID
Us if id is None
"""
if len(rows) == 0:
pass
either_id = EitherId(id=id, external_id=external_id)
if isinstance(rows, SequenceRows):
# Already in the desired format
pass
elif isinstance(rows, (dict, list)):
rows_raw: List[Dict[str, Any]]
if isinstance(rows, dict):
rows_raw = [{"rowNumber": row_number, "values": values} for row_number, values in rows.items()]
elif isinstance(rows, list) and rows and isinstance(rows[0], (tuple, list)):
rows_raw = [{"rowNumber": row_number, "values": values} for row_number, values in rows]
else:
rows_raw = rows # type: ignore[assignment]
rows = SequenceRows.load(
{
"rows": rows_raw,
"columns": column_external_ids,
"id": id,
"externalId": external_id,
}
)
else:
raise TypeError("Unsupported type for sequence rows: {}".format(type(rows)))
with self.lock:
seq = self.upload_queue.get(either_id)
if seq is not None:
# Update sequence
seq.rows.extend(rows.rows) # type: ignore[attr-defined]
self.upload_queue[either_id] = seq
else:
self.upload_queue[either_id] = rows
self.upload_queue_size = sum([len(rows) for rows in self.upload_queue.values()])
self.queue_size.set(self.upload_queue_size)
self.points_queued.inc()
[docs]
def upload(self) -> None:
"""
Trigger an upload of the queue, clears queue afterwards
"""
@retry(
exceptions=cognite_exceptions(),
cancellation_token=self.cancellation_token,
tries=RETRIES,
delay=RETRY_DELAY,
max_delay=RETRY_MAX_DELAY,
backoff=RETRY_BACKOFF_FACTOR,
)
def _upload_single(either_id: EitherId, upload_this: SequenceData) -> SequenceData:
self.logger.debug("Writing {} rows to sequence {}".format(len(upload_this.values), either_id))
try:
self.cdf_client.sequences.data.insert(
id=either_id.internal_id,
external_id=either_id.external_id,
rows=upload_this,
column_external_ids=None,
)
except CogniteNotFoundError as ex:
if self.create_missing:
# Create missing sequence
self._create_or_update(either_id)
# Retry
self.cdf_client.sequences.data.insert(
id=either_id.internal_id,
external_id=either_id.external_id,
rows=upload_this,
column_external_ids=None,
)
else:
raise ex
return upload_this
if len(self.upload_queue) == 0:
return
with self.lock:
if self.create_missing:
self._resolve_asset_ids()
self._resolve_dataset_ids()
for either_id, upload_this in self.upload_queue.items():
_upload_single(either_id, upload_this)
self.points_written.inc()
try:
self._post_upload([seqdata for _, seqdata in self.upload_queue.items()])
except Exception as e:
self.logger.error("Error in upload callback: %s", str(e))
self.logger.info(f"Uploaded {self.upload_queue_size} sequence rows")
self.upload_queue.clear()
self.upload_queue_size = 0
self.queue_size.set(self.upload_queue_size)
def _create_or_update(self, either_id: EitherId) -> None:
"""
Create or update sequence, based on provided metadata and column definitions
Args:
either_id: Id/External Id of sequence to be updated
"""
column_def = self.column_definitions.get(either_id)
if column_def is None:
self.logger.error(f"Can't create sequence {str(either_id)}, no column definitions provided")
try:
seq = self.cdf_client.sequences.create(
Sequence(
id=either_id.internal_id,
external_id=either_id.external_id,
name=self.sequence_names.get(either_id, None),
description=self.sequence_descriptions.get(either_id, None),
metadata=self.sequence_metadata.get(either_id, None),
asset_id=self.asset_ids.get(self.sequence_asset_external_ids.get(either_id, None), None), # type: ignore
data_set_id=self.dataset_ids.get(self.sequence_dataset_external_ids.get(either_id, None), None), # type: ignore
columns=column_def, # type: ignore # We already checked for None, mypy is wrong
)
)
except CogniteDuplicatedError:
self.logger.info("Sequnce already exist: {}".format(either_id))
seq = self.cdf_client.sequences.retrieve( # type: ignore [assignment]
id=either_id.internal_id,
external_id=either_id.external_id,
)
# Update definition of cached sequence
cseq = self.upload_queue[either_id]
cseq.columns = seq.columns # type: ignore[assignment]
def _resolve_asset_ids(self) -> None:
"""
Resolve id of assets if specified, for use in sequence creation
"""
assets = set(self.sequence_asset_external_ids.values())
assets.discard(None) # type: ignore # safeguard, remove Nones if any
if len(assets) > 0:
try:
self.asset_ids = {
asset.external_id: asset.id
for asset in self.cdf_client.assets.retrieve_multiple(
external_ids=list(assets), ignore_unknown_ids=True
)
if asset.id is not None and asset.external_id is not None
}
except Exception as e:
self.logger.error("Error in resolving asset id: %s", str(e))
self.asset_ids = {}
def _resolve_dataset_ids(self) -> None:
"""
Resolve id of datasets if specified, for use in sequence creation
"""
datasets = set(self.sequence_dataset_external_ids.values())
datasets.discard(None) # type: ignore # safeguard, remove Nones if any
if len(datasets) > 0:
try:
self.dataset_ids = {
dataset.external_id: dataset.id
for dataset in self.cdf_client.data_sets.retrieve_multiple(
external_ids=list(datasets), ignore_unknown_ids=True
)
if dataset.id is not None and dataset.external_id is not None
}
except Exception as e:
self.logger.error("Error in resolving dataset id: %s", str(e))
self.dataset_ids = {}
def __enter__(self) -> "SequenceUploadQueue":
"""
Wraps around start method, for use as context manager
Returns:
self
"""
self.start()
return self
def __exit__(
self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType]
) -> None:
"""
Wraps around stop method, for use as context manager
Args:
exc_type: Exception type
exc_val: Exception value
exc_tb: Traceback
"""
self.stop()
def __len__(self) -> int:
"""
The size of the upload queue
Returns:
Number of data points in queue
"""
return self.upload_queue_size