in elasticsearch_serverless/_sync/client/cat.py [0:0]
def ml_jobs(
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,