Package reference

configtools - Utilities for reading, parsing and validating config files

The configtools module exists of tools for loading and verifying config files for extractors.

Extractor configurasions are conventionally written in hyphen-cased YAML. These are typically loaded and serialized as dataclasses in Python.

Config loader

cognite.extractorutils.configtools.load_yaml(source: Union[TextIO, str], config_type: Type[T], case_style: str = 'hyphen', expand_envvars=True) → T[source]

Read a YAML file, and create a config object based on its contents.

Parameters:
  • source – Input stream (as returned by open(…)) or string containing YAML.
  • config_type – Class of config type (i.e. your custom subclass of BaseConfig).
  • case_style – Casing convention of config file. Valid options are ‘snake’, ‘hyphen’ or ‘camel’. Should be ‘hyphen’.
  • expand_envvars – Substitute values with the pattern ${VAR} with the content of the environment variable VAR
Returns:

An initialized config object.
Raises:

InvalidConfigError – If any config field is given as an invalid type, is missing or is unknown

Base classes

The configtools module contains several prebuilt config classes for many common parameters. The class BaseConfig is intended as a starting point for a custom configuration schema, containing parameters for config version, CDF connection and logging.

Example:

@dataclass
class ExtractorConfig:
    state_store: Optional[StateStoreConfig]
    ...

@dataclass
class SourceConfig:
    ...


@dataclass
class MyConfig(BaseConfig):
    extractor: ExtractorConfig
    source: SourceConfig
class cognite.extractorutils.configtools.BaseConfig(version: Union[str, int, None], cognite: cognite.extractorutils.configtools.CogniteConfig, logger: cognite.extractorutils.configtools.LoggingConfig)[source]

Basis for an extractor config, containing config version, CogniteConfig and LoggingConfig

class cognite.extractorutils.configtools.CogniteConfig(project: str, api_key: Optional[str], idp_authentication: Optional[cognite.extractorutils.authentication.AuthenticatorConfig], data_set_id: Optional[int], external_id_prefix: str = '', host: str = 'https://api.cognitedata.com')[source]

Configuration parameters for CDF connection, such as project name, host address and API key

class cognite.extractorutils.configtools.LoggingConfig(console: Optional[cognite.extractorutils.configtools._ConsoleLoggingConfig], file: Optional[cognite.extractorutils.configtools._FileLoggingConfig])[source]

Logging settings, such as log levels and path to log file

class cognite.extractorutils.configtools.MetricsConfig(push_gateways: Optional[List[cognite.extractorutils.configtools._PushGatewayConfig]], cognite: Optional[cognite.extractorutils.configtools._CogniteMetricsConfig])[source]

Destination(s) for metrics, including options for one or several Prometheus push gateways, and pushing as CDF Time Series.

class cognite.extractorutils.configtools.RawDestinationConfig(database: str, table: str)[source]
class cognite.extractorutils.configtools.StateStoreConfig(raw: Union[cognite.extractorutils.configtools.RawStateStoreConfig, NoneType] = None, local: Union[cognite.extractorutils.configtools.LocalStateStoreConfig, NoneType] = None)[source]
class cognite.extractorutils.configtools.RawStateStoreConfig(database: str, table: str, upload_interval: int = 30)[source]
class cognite.extractorutils.configtools.LocalStateStoreConfig(path: str, save_interval: int = 30)[source]

Exceptions

exception cognite.extractorutils.configtools.InvalidConfigError(message: str)[source]

Exception thrown from load_yaml if config file is invalid. This can be due to

  • Missing fields
  • Incompatible types
  • Unkown fields

metrics - Automatic pushers of performance metrics

Module containing tools for pushers for metric reporting.

The classes in this module scrape the default Prometheus registry and uploads it periodically to either a Prometheus push gateway, or to CDF as time series.

The BaseMetrics class forms the basis for a metrics collection for an extractor, containing some general metrics that all extractors should report. To create your own set of metrics, subclass this class and populate it with extractor-specific metrics, as such:

class MyMetrics(BaseMetrics):
    def __init__(self):
        super().__init__(extractor_name="my_extractor", extractor_version=__version__)

        self.a_counter = Counter("my_extractor_example_counter", "An example counter")
        ...
class cognite.extractorutils.metrics.AbstractMetricsPusher(push_interval: Optional[int] = None, thread_name: Optional[str] = None)[source]

Bases: abc.ABC

Base class for metric pushers. Metric pushers spawns a thread that routinely pushes metrics to a configured destination.

Contains all the logic for starting and running threads.

Parameters:
  • push_interval – Seconds between each upload call
  • thread_name – Name of thread to start. If omitted, a standard name such as Thread-4 will be generated.
start() → None[source]

Starts a thread that pushes the default registry to the configured gateway at certain intervals.

stop() → None[source]

Stop the push loop.

class cognite.extractorutils.metrics.BaseMetrics(extractor_name: str, extractor_version: str, process_scrape_interval: float = 15)[source]

Bases: object

Base collection of extractor metrics. The class also spawns a collector thread on init that regularly fetches process information and update the process_* gauges.

To create a set of metrics for an extractor, create a subclass of this class.

Note that only one instance of this class (or any subclass) can exist simultaneously

The collection includes the following metrics:
  • startup: Startup time (unix epoch)
  • finish: Finish time (unix epoch)
  • process_num_threads Number of active threads. Set automatically.
  • process_memory_bytes Memory usage of extractor. Set automatically.
  • process_cpu_percent CPU usage of extractor. Set automatically.
Parameters:
  • extractor_name – Name of extractor, used to prefix metric names
  • process_scrape_interval – Interval (in seconds) between each fetch of data for the process_* gauges
class cognite.extractorutils.metrics.CognitePusher(cdf_client: cognite.client._cognite_client.CogniteClient, external_id_prefix: str, push_interval: int, asset: Optional[cognite.client.data_classes.assets.Asset] = None, thread_name: Optional[str] = None)[source]

Bases: cognite.extractorutils.metrics.AbstractMetricsPusher

Pusher to CDF. Creates time series in CDF for all Gauges and Counters in the default Prometheus registry.

Optional contextualization with an Asset to make the time series observable in Asset Data Insight. The given asset will be created at root level in the tenant if it doesn’t already exist.

Parameters:
  • cdf_client – The CDF tenant to upload time series to
  • external_id_prefix – Unique external ID prefix for this pusher.
  • push_interval – Seconds between each upload call
  • asset – Optional contextualization.
  • thread_name – Name of thread to start. If omitted, a standard name such as Thread-4 will be generated.
start() → None

Starts a thread that pushes the default registry to the configured gateway at certain intervals.

stop() → None

Stop the push loop.

class cognite.extractorutils.metrics.PrometheusPusher(job_name: str, url: str, push_interval: int, username: Optional[str] = None, password: Optional[str] = None, thread_name: Optional[str] = None)[source]

Bases: cognite.extractorutils.metrics.AbstractMetricsPusher

Pusher to a Prometheus push gateway.

Parameters:
  • job_name – Prometheus job name
  • username – Push gateway credentials
  • password – Push gateway credentials
  • url – URL (with portnum) of push gateway
  • push_interval – Seconds between each upload call
  • thread_name – Name of thread to start. If omitted, a standard name such as Thread-4 will be generated.
clear_gateway() → None[source]

Delete metrics stored at the gateway (reset gateway).

start() → None

Starts a thread that pushes the default registry to the configured gateway at certain intervals.

stop() → None

Stop the push loop.

cognite.extractorutils.metrics.safe_get(cls: Type[T]) → T[source]

A factory for instances of metrics collections.

Since Prometheus doesn’t allow multiple metrics with the same name, any subclass of BaseMetrics must never be created more than once. This function creates an instance of the given class on the first call and stores it, any subsequent calls with the same class as argument will return the same instance.

>>> a = safe_get(MyMetrics)  # This will create a new instance of MyMetrics
>>> b = safe_get(MyMetrics)  # This will return the same instance
>>> a is b
True
Parameters:cls – Metrics class to either create or get a cached version of
Returns:An instance of given class

statestore - Storing extractor state between runs locally or remotely

The statestore module contains classes for keeping track of the extraction state of individual items, facilitating incremental load and speeding up startup times.

At the beginning of a run the extractor typically calls the initialize method, which loads the states from the remote store (which can either be a local JSON file or a table in CDF RAW), and during and/or at the end of a run, the synchronize method is called, which saves the current states to the remote store.

class cognite.extractorutils.statestore.AbstractStateStore[source]

Bases: abc.ABC

Base class for a state store.

delete_state(external_id: str) → None[source]

Delete an external ID from the state store.

Parameters:external_id – External ID to remove
expand_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None[source]

Like set_state, but only sets state if the proposed state is outside the stored state. That is if e.g. low is lower than the stored low.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
get_state(external_id: Union[str, List[str]]) → Union[Tuple[Any, Any], List[Tuple[Any, Any]]][source]

Get state(s) for external ID(s)

Parameters:external_id – An external ID or list of external IDs to get states for
Returns:A tuple with (low, high) watermarks, or a list of tuples
initialize(force: bool = False) → None[source]

Get states from remote store

outside_state(external_id: str, new_state: Any) → bool[source]

Check if a new proposed state is outside state interval (ie, if a new datapoint should be processed).

Returns true if new_state is outside of stored state or if external_id is previously unseen.

Parameters:
  • external_id – External ID to test
  • new_state – Proposed new state to test
Returns:

True if new_state is higher than the stored high watermark or lower than the low watermark.
post_upload_handler() → Callable[[List[Dict[str, Union[str, List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]]]]], None][source]

Get a callable suitable for passing to a time series upload queue as post_upload_function, that will automatically update the states in this state store when that upload queue is uploading.

Returns:A function that expands the current states with the values given
set_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None[source]

Set/update state of a singe external ID.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
synchronize() → None[source]

Upload states to remote store

class cognite.extractorutils.statestore.LocalStateStore(file_path: str)[source]

Bases: cognite.extractorutils.statestore.AbstractStateStore

An extractor state store using a local JSON file as backend.

Parameters:file_path – File path to JSON file to use
delete_state(external_id: str) → None

Delete an external ID from the state store.

Parameters:external_id – External ID to remove
expand_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None

Like set_state, but only sets state if the proposed state is outside the stored state. That is if e.g. low is lower than the stored low.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
get_state(external_id: Union[str, List[str]]) → Union[Tuple[Any, Any], List[Tuple[Any, Any]]]

Get state(s) for external ID(s)

Parameters:external_id – An external ID or list of external IDs to get states for
Returns:A tuple with (low, high) watermarks, or a list of tuples
initialize(force: bool = False) → None[source]

Load states from specified JSON file

Parameters:force – Enable re-initialization, ie overwrite when called multiple times
outside_state(external_id: str, new_state: Any) → bool

Check if a new proposed state is outside state interval (ie, if a new datapoint should be processed).

Returns true if new_state is outside of stored state or if external_id is previously unseen.

Parameters:
  • external_id – External ID to test
  • new_state – Proposed new state to test
Returns:

True if new_state is higher than the stored high watermark or lower than the low watermark.
post_upload_handler() → Callable[[List[Dict[str, Union[str, List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]]]]], None]

Get a callable suitable for passing to a time series upload queue as post_upload_function, that will automatically update the states in this state store when that upload queue is uploading.

Returns:A function that expands the current states with the values given
set_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None

Set/update state of a singe external ID.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
synchronize() → None[source]

Save states to specified JSON file

class cognite.extractorutils.statestore.NoStateStore[source]

Bases: cognite.extractorutils.statestore.AbstractStateStore

A state store that only keeps states in memory and never stores or initializes from external sources.

delete_state(external_id: str) → None

Delete an external ID from the state store.

Parameters:external_id – External ID to remove
expand_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None

Like set_state, but only sets state if the proposed state is outside the stored state. That is if e.g. low is lower than the stored low.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
get_state(external_id: Union[str, List[str]]) → Union[Tuple[Any, Any], List[Tuple[Any, Any]]]

Get state(s) for external ID(s)

Parameters:external_id – An external ID or list of external IDs to get states for
Returns:A tuple with (low, high) watermarks, or a list of tuples
initialize(force: bool = False) → None[source]

Get states from remote store

outside_state(external_id: str, new_state: Any) → bool

Check if a new proposed state is outside state interval (ie, if a new datapoint should be processed).

Returns true if new_state is outside of stored state or if external_id is previously unseen.

Parameters:
  • external_id – External ID to test
  • new_state – Proposed new state to test
Returns:

True if new_state is higher than the stored high watermark or lower than the low watermark.
post_upload_handler() → Callable[[List[Dict[str, Union[str, List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]]]]], None]

Get a callable suitable for passing to a time series upload queue as post_upload_function, that will automatically update the states in this state store when that upload queue is uploading.

Returns:A function that expands the current states with the values given
set_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None

Set/update state of a singe external ID.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
synchronize() → None[source]

Upload states to remote store

class cognite.extractorutils.statestore.RawStateStore(cdf_client: cognite.client._cognite_client.CogniteClient, database: str, table: str)[source]

Bases: cognite.extractorutils.statestore.AbstractStateStore

An extractor state store based on CDF RAW.

Parameters:
  • cdf_client – Cognite client to use
  • database – Name of CDF database
  • table – Name of CDF table
delete_state(external_id: str) → None

Delete an external ID from the state store.

Parameters:external_id – External ID to remove
expand_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None

Like set_state, but only sets state if the proposed state is outside the stored state. That is if e.g. low is lower than the stored low.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
get_state(external_id: Union[str, List[str]]) → Union[Tuple[Any, Any], List[Tuple[Any, Any]]]

Get state(s) for external ID(s)

Parameters:external_id – An external ID or list of external IDs to get states for
Returns:A tuple with (low, high) watermarks, or a list of tuples
initialize(force: bool = False) → None[source]

Get states from remote store

outside_state(external_id: str, new_state: Any) → bool

Check if a new proposed state is outside state interval (ie, if a new datapoint should be processed).

Returns true if new_state is outside of stored state or if external_id is previously unseen.

Parameters:
  • external_id – External ID to test
  • new_state – Proposed new state to test
Returns:

True if new_state is higher than the stored high watermark or lower than the low watermark.
post_upload_handler() → Callable[[List[Dict[str, Union[str, List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]]]]], None]

Get a callable suitable for passing to a time series upload queue as post_upload_function, that will automatically update the states in this state store when that upload queue is uploading.

Returns:A function that expands the current states with the values given
set_state(external_id: str, low: Optional[Any] = None, high: Optional[Any] = None) → None

Set/update state of a singe external ID.

Parameters:
  • external_id – External ID of e.g. time series to store state of
  • low – Low watermark
  • high – High watermark
synchronize() → None[source]

Upload states to remote store

uploader - Batching upload queues with automatic upload triggers

Module containing upload queue classes. The UploadQueue classes chunks together items and uploads them together to CDF, both to minimize the load on the API, and also to speed up uploading as requests can be slow.

Each upload queue comes with some configurable conditions that, when met, automatically triggers an upload.

Note: You cannot assume that an element is uploaded when it is added to the queue, since the upload may be delayed. To ensure that everything is uploaded you should set the post_upload_function callback to verify. For example, for a time series queue you might want to check the latest time stamp, as such (assuming incremental time stamps and using timestamp-value tuples as data point format):

state_store = LocalStateStore("states.json")

queue = TimeSeriesUploadQueue(
    cdf_client=my_cognite_client,
    post_upload_function=state_store.post_upload_handler(),
    max_upload_interval=1
)
class cognite.extractorutils.uploader.AbstractUploadQueue(cdf_client: cognite.client._cognite_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)[source]

Bases: abc.ABC

Abstract uploader class.

Parameters:
  • 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 elements 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.
add_to_upload_queue(*args) → None[source]

Adds an element to the upload queue. The queue will be uploaded if the queue byte size is larger than the threshold specified in the config.

start() → None[source]

Start upload thread if max_upload_interval is set, this called the upload method every max_upload_interval seconds.

stop(ensure_upload: bool = True) → None[source]

Stop upload thread if running, and ensures that the upload queue is empty if ensure_upload is True.

Parameters:ensure_upload (bool) – (Optional). Call upload one last time after shutting down thread to ensure empty upload queue.
upload() → None[source]

Uploads the queue.

class cognite.extractorutils.uploader.EventUploadQueue(cdf_client: cognite.client._cognite_client.CogniteClient, post_upload_function: Optional[Callable[[List[cognite.client.data_classes.events.Event]], None]] = None, max_queue_size: Optional[int] = None, max_upload_interval: Optional[int] = None, trigger_log_level: str = 'DEBUG', thread_name: Optional[str] = None)[source]

Bases: cognite.extractorutils.uploader.AbstractUploadQueue

Upload queue for events

Parameters:
  • 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.
add_to_upload_queue(event: cognite.client.data_classes.events.Event) → None[source]

Add event to upload queue. The queue will be uploaded if the queue size is larger than the threshold specified in the __init__.

Parameters:event – Event to add
start() → None

Start upload thread if max_upload_interval is set, this called the upload method every max_upload_interval seconds.

stop(ensure_upload: bool = True) → None

Stop upload thread if running, and ensures that the upload queue is empty if ensure_upload is True.

Parameters:ensure_upload (bool) – (Optional). Call upload one last time after shutting down thread to ensure empty upload queue.
upload() → None[source]

Trigger an upload of the queue, clears queue afterwards

class cognite.extractorutils.uploader.FileUploadQueue(cdf_client: cognite.client._cognite_client.CogniteClient, post_upload_function: Optional[Callable[[List[cognite.client.data_classes.events.Event]], None]] = None, max_queue_size: Optional[int] = None, max_upload_interval: Optional[int] = None, trigger_log_level: str = 'DEBUG', thread_name: Optional[str] = None, overwrite_existing: bool = False)[source]

Bases: cognite.extractorutils.uploader.AbstractUploadQueue

Upload queue for files

Parameters:
  • 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.
add_to_upload_queue(file_meta: cognite.client.data_classes.files.FileMetadata, file_name: Union[str, os.PathLike] = None) → None[source]

Add file to upload queue. The queue will be uploaded if the queue size is larger than the threshold specified in the __init__.

Parameters:
  • file_meta – File metadata-object
  • file_name – Path to file to be uploaded. If none, the file object will still be created, but no data is uploaded
start() → None

Start upload thread if max_upload_interval is set, this called the upload method every max_upload_interval seconds.

stop(ensure_upload: bool = True) → None

Stop upload thread if running, and ensures that the upload queue is empty if ensure_upload is True.

Parameters:ensure_upload (bool) – (Optional). Call upload one last time after shutting down thread to ensure empty upload queue.
upload() → None[source]

Trigger an upload of the queue, clears queue afterwards

class cognite.extractorutils.uploader.RawUploadQueue(cdf_client: cognite.client._cognite_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)[source]

Bases: cognite.extractorutils.uploader.AbstractUploadQueue

Upload queue for RAW

Parameters:
  • 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 rows 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.
add_to_upload_queue(database: str, table: str, raw_row: cognite.client.data_classes.raw.Row) → None[source]

Adds a row to the upload queue. The queue will be uploaded if the queue size is larger than the threshold specified in the __init__.

Parameters:
  • database – The database to upload the Raw object to
  • table – The table to upload the Raw object to
  • raw_row – The row object
start() → None

Start upload thread if max_upload_interval is set, this called the upload method every max_upload_interval seconds.

stop(ensure_upload: bool = True) → None

Stop upload thread if running, and ensures that the upload queue is empty if ensure_upload is True.

Parameters:ensure_upload (bool) – (Optional). Call upload one last time after shutting down thread to ensure empty upload queue.
upload() → None[source]

Trigger an upload of the queue, clears queue afterwards

class cognite.extractorutils.uploader.SequenceUploadQueue(cdf_client: cognite.client._cognite_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=False)[source]

Bases: cognite.extractorutils.uploader.AbstractUploadQueue

add_to_upload_queue(rows: Union[Dict[int, List[Union[int, str, float]]], List[Tuple[int, Union[int, float, str]]], List[Dict[str, Any]], cognite.client.data_classes.sequences.SequenceData], column_external_ids: Optional[List[str]] = None, id: int = None, external_id: str = None) → None[source]

Add sequence rows to upload queue. Mirrors implementation of SequenceApi.insert. Inserted rows will be cached until uploaded

Parameters:
  • 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
set_sequence_column_definition(col_def: List[Dict[str, str]], id: int = None, external_id: str = None) → None[source]

Set sequence column definition

Parameters:
  • 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
set_sequence_metadata(metadata: Dict[str, Union[str, int, float]], id: int = None, external_id: str = None, asset_external_id: str = None, dataset_external_id: str = None) → None[source]

Set sequence metadata. Metadata will be cached until the sequence is created. The metadata will be updated if the sequence already exists

Parameters:
  • metadata – Sequence metadata
  • id – Sequence internal ID Use if external_id is None
  • external_id – Sequence external ID Us if id is None
  • asset_external_id – Sequence asset ID
  • dataset_external_id – Sequence dataset id
start() → None

Start upload thread if max_upload_interval is set, this called the upload method every max_upload_interval seconds.

stop(ensure_upload: bool = True) → None

Stop upload thread if running, and ensures that the upload queue is empty if ensure_upload is True.

Parameters:ensure_upload (bool) – (Optional). Call upload one last time after shutting down thread to ensure empty upload queue.
upload() → None[source]

Trigger an upload of the queue, clears queue afterwards

class cognite.extractorutils.uploader.TimeSeriesUploadQueue(cdf_client: cognite.client._cognite_client.CogniteClient, post_upload_function: Optional[Callable[[List[Dict[str, Union[str, List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]]]]], 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, Union[List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]]], cognite.client.data_classes.time_series.TimeSeries], bool] = False)[source]

Bases: cognite.extractorutils.uploader.AbstractUploadQueue

Upload queue for time series

Parameters:
  • 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.
add_to_upload_queue(*, id: int = None, external_id: str = None, datapoints: Union[List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]] = []) → None[source]

Add data points to upload queue. The queue will be uploaded if the queue size is larger than the threshold specified in the __init__.

Parameters:
  • 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
start() → None

Start upload thread if max_upload_interval is set, this called the upload method every max_upload_interval seconds.

stop(ensure_upload: bool = True) → None

Stop upload thread if running, and ensures that the upload queue is empty if ensure_upload is True.

Parameters:ensure_upload (bool) – (Optional). Call upload one last time after shutting down thread to ensure empty upload queue.
upload() → None[source]

Trigger an upload of the queue, clears queue afterwards

cognite.extractorutils.uploader.default_time_series_factory(external_id: str, datapoints: Union[List[Dict[Union[int, float, datetime.datetime], Union[int, float, str]]], List[Tuple[Union[int, float, datetime.datetime], Union[int, float, str]]]]) → cognite.client.data_classes.time_series.TimeSeries[source]

Default time series factory used when create_missing in a TimeSeriesUploadQueue is given as a boolean.

Parameters:
  • 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

util - Miscellaneous utilities

The util package contains miscellaneous functions and classes that can some times be useful while developing extractors.

class cognite.extractorutils.util.EitherId(**kwargs)[source]

Bases: object

Class representing an ID in CDF, which can either be an external or internal ID. An EitherId can only hold one ID type, not both.

Parameters:
  • id – Internal ID
  • or external_id (externalId) – external ID
Raises:

TypeError – If none of both of id types are set.
content() → Union[int, str][source]

Get the value of the ID

Returns:The ID
type() → str[source]

Get the type of the ID

Returns:‘id’ if the EitherId represents an internal ID, ‘externalId’ if the EitherId represents an external ID
cognite.extractorutils.util.ensure_time_series(cdf_client: cognite.client._cognite_client.CogniteClient, time_series: Iterable[cognite.client.data_classes.time_series.TimeSeries]) → None[source]

Ensure that all the given time series exists in CDF.

Searches through the tenant after the external IDs of the time series given, and creates those that are missing.

Parameters:
  • cdf_client – Tenant to create time series in
  • time_series – Time series to create
cognite.extractorutils.util.set_event_on_interrupt(stop_event: threading.Event) → None[source]

Set given event on SIGINT (Ctrl-C) instead of throwing a KeyboardInterrupt exception.

Parameters:stop_event – Event to set