elasticsearch/_async/client/cat.py [1126:1482]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
self,
*,
job_id: t.Optional[str] = None,
allow_no_match: t.Optional[bool] = None,
bytes: t.Optional[
t.Union[str, t.Literal["b", "gb", "kb", "mb", "pb", "tb"]]
] = None,
error_trace: t.Optional[bool] = None,
filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
format: t.Optional[str] = None,
h: t.Optional[
t.Union[
t.Sequence[
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
]
],
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
],
]
] = None,
help: t.Optional[bool] = None,
human: t.Optional[bool] = None,
pretty: t.Optional[bool] = None,
s: t.Optional[
t.Union[
t.Sequence[
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
]
],
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
],
]
] = None,
time: t.Optional[
t.Union[str, t.Literal["d", "h", "m", "micros", "ms", "nanos", "s"]]
] = None,
v: t.Optional[bool] = None,
) -> t.Union[ObjectApiResponse[t.Any], TextApiResponse]:
"""
.. raw:: html
Get anomaly detection jobs.
Get configuration and usage information for anomaly detection jobs.
This API returns a maximum of 10,000 jobs.
If the Elasticsearch security features are enabled, you must have monitor_ml,
monitor, manage_ml, or manage cluster privileges to use this API.
IMPORTANT: CAT APIs are only intended for human consumption using the Kibana
console or command line. They are not intended for use by applications. For
application consumption, use the get anomaly detection job statistics API.
``_
:param job_id: Identifier for the anomaly detection job.
:param allow_no_match: Specifies what to do when the request: * Contains wildcard
expressions and there are no jobs that match. * Contains the `_all` string
or no identifiers and there are no matches. * Contains wildcard expressions
and there are only partial matches. If `true`, the API returns an empty jobs
array when there are no matches and the subset of results when there are
partial matches. If `false`, the API returns a 404 status code when there
are no matches or only partial matches.
:param bytes: The unit used to display byte values.
:param format: Specifies the format to return the columnar data in, can be set
to `text`, `json`, `cbor`, `yaml`, or `smile`.
:param h: Comma-separated list of column names to display.
:param help: When set to `true` will output available columns. This option can't
be combined with any other query string option.
:param s: Comma-separated list of column names or column aliases used to sort
the response.
:param time: The unit used to display time values.
:param v: When set to `true` will enable verbose output.
"""
__path_parts: t.Dict[str, str]
if job_id not in SKIP_IN_PATH:
__path_parts = {"job_id": _quote(job_id)}
__path = f'/_cat/ml/anomaly_detectors/{__path_parts["job_id"]}'
else:
__path_parts = {}
__path = "/_cat/ml/anomaly_detectors"
__query: t.Dict[str, t.Any] = {}
if allow_no_match is not None:
__query["allow_no_match"] = allow_no_match
if bytes is not None:
__query["bytes"] = bytes
if error_trace is not None:
__query["error_trace"] = error_trace
if filter_path is not None:
__query["filter_path"] = filter_path
if format is not None:
__query["format"] = format
if h is not None:
__query["h"] = h
if help is not None:
__query["help"] = help
if human is not None:
__query["human"] = human
if pretty is not None:
__query["pretty"] = pretty
if s is not None:
__query["s"] = s
if time is not None:
__query["time"] = time
if v is not None:
__query["v"] = v
__headers = {"accept": "text/plain,application/json"}
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
elasticsearch/_sync/client/cat.py [1126:1482]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
self,
*,
job_id: t.Optional[str] = None,
allow_no_match: t.Optional[bool] = None,
bytes: t.Optional[
t.Union[str, t.Literal["b", "gb", "kb", "mb", "pb", "tb"]]
] = None,
error_trace: t.Optional[bool] = None,
filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None,
format: t.Optional[str] = None,
h: t.Optional[
t.Union[
t.Sequence[
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
]
],
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
],
]
] = None,
help: t.Optional[bool] = None,
human: t.Optional[bool] = None,
pretty: t.Optional[bool] = None,
s: t.Optional[
t.Union[
t.Sequence[
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
]
],
t.Union[
str,
t.Literal[
"assignment_explanation",
"buckets.count",
"buckets.time.exp_avg",
"buckets.time.exp_avg_hour",
"buckets.time.max",
"buckets.time.min",
"buckets.time.total",
"data.buckets",
"data.earliest_record",
"data.empty_buckets",
"data.input_bytes",
"data.input_fields",
"data.input_records",
"data.invalid_dates",
"data.last",
"data.last_empty_bucket",
"data.last_sparse_bucket",
"data.latest_record",
"data.missing_fields",
"data.out_of_order_timestamps",
"data.processed_fields",
"data.processed_records",
"data.sparse_buckets",
"forecasts.memory.avg",
"forecasts.memory.max",
"forecasts.memory.min",
"forecasts.memory.total",
"forecasts.records.avg",
"forecasts.records.max",
"forecasts.records.min",
"forecasts.records.total",
"forecasts.time.avg",
"forecasts.time.max",
"forecasts.time.min",
"forecasts.time.total",
"forecasts.total",
"id",
"model.bucket_allocation_failures",
"model.by_fields",
"model.bytes",
"model.bytes_exceeded",
"model.categorization_status",
"model.categorized_doc_count",
"model.dead_category_count",
"model.failed_category_count",
"model.frequent_category_count",
"model.log_time",
"model.memory_limit",
"model.memory_status",
"model.over_fields",
"model.partition_fields",
"model.rare_category_count",
"model.timestamp",
"model.total_category_count",
"node.address",
"node.ephemeral_id",
"node.id",
"node.name",
"opened_time",
"state",
],
],
]
] = None,
time: t.Optional[
t.Union[str, t.Literal["d", "h", "m", "micros", "ms", "nanos", "s"]]
] = None,
v: t.Optional[bool] = None,
) -> t.Union[ObjectApiResponse[t.Any], TextApiResponse]:
"""
.. raw:: html
Get anomaly detection jobs.
Get configuration and usage information for anomaly detection jobs.
This API returns a maximum of 10,000 jobs.
If the Elasticsearch security features are enabled, you must have monitor_ml,
monitor, manage_ml, or manage cluster privileges to use this API.
IMPORTANT: CAT APIs are only intended for human consumption using the Kibana
console or command line. They are not intended for use by applications. For
application consumption, use the get anomaly detection job statistics API.
``_
:param job_id: Identifier for the anomaly detection job.
:param allow_no_match: Specifies what to do when the request: * Contains wildcard
expressions and there are no jobs that match. * Contains the `_all` string
or no identifiers and there are no matches. * Contains wildcard expressions
and there are only partial matches. If `true`, the API returns an empty jobs
array when there are no matches and the subset of results when there are
partial matches. If `false`, the API returns a 404 status code when there
are no matches or only partial matches.
:param bytes: The unit used to display byte values.
:param format: Specifies the format to return the columnar data in, can be set
to `text`, `json`, `cbor`, `yaml`, or `smile`.
:param h: Comma-separated list of column names to display.
:param help: When set to `true` will output available columns. This option can't
be combined with any other query string option.
:param s: Comma-separated list of column names or column aliases used to sort
the response.
:param time: The unit used to display time values.
:param v: When set to `true` will enable verbose output.
"""
__path_parts: t.Dict[str, str]
if job_id not in SKIP_IN_PATH:
__path_parts = {"job_id": _quote(job_id)}
__path = f'/_cat/ml/anomaly_detectors/{__path_parts["job_id"]}'
else:
__path_parts = {}
__path = "/_cat/ml/anomaly_detectors"
__query: t.Dict[str, t.Any] = {}
if allow_no_match is not None:
__query["allow_no_match"] = allow_no_match
if bytes is not None:
__query["bytes"] = bytes
if error_trace is not None:
__query["error_trace"] = error_trace
if filter_path is not None:
__query["filter_path"] = filter_path
if format is not None:
__query["format"] = format
if h is not None:
__query["h"] = h
if help is not None:
__query["help"] = help
if human is not None:
__query["human"] = human
if pretty is not None:
__query["pretty"] = pretty
if s is not None:
__query["s"] = s
if time is not None:
__query["time"] = time
if v is not None:
__query["v"] = v
__headers = {"accept": "text/plain,application/json"}
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -