roboto.domain.metrics.record

The names of the class variables defined on the model.

Metadata about the private attributes of the model.

The synthesized __init__ [Signature][inspect.Signature] of the model.

Whether model building is completed, or if there are still undefined fields.

The core schema of the model.

Whether the model has a custom __init__ function.

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

A dictionary containing metadata about generic Pydantic models. The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

Parent namespace of the model, used for automatic rebuilding of models.

The name of the post-init method for the model, if defined.

Whether the model is a [RootModel][pydantic.root_model.RootModel].

The pydantic-core SchemaSerializer used to dump instances of the model.

The pydantic-core SchemaValidator used to validate instances of the model.

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

The names of fields explicitly set during instantiation.

Values of private attributes set on the model instance.

Exclusive end of this period bucket, in Unix-epoch nanoseconds (UTC).

Identifier of the aggregated metric definition.

Name of the aggregated metric.

Calendar bucket size used for this aggregation.

Inclusive start of this period bucket, in Unix-epoch nanoseconds (UTC).

Number of raw observations that contributed to this bucket.

Aggregation function to apply to the values in each bucket.

Condition, or nested group of conditions, narrowing which data points are aggregated.

Applied to individual data points rather than to bucket results, so it changes each bucket’s value and total. A period whose data points are all filtered out yields no bucket at all, so a filtered aggregation can return fewer buckets than an unfiltered one over the same window. See condition for the accepted fields and the treatment of data points published without a device.

Exclusive end of the aggregation window, in Unix-epoch nanoseconds (UTC). Built from aggregate()’s end_time parameter the same way.

Field to split the aggregation by, in addition to the period bucket: one NumericAggregateMetricRecord per (period, distinct value) pair, each carrying the value it aggregated under group_key.

None aggregates every matching data point of a period into one bucket. Accepts device.device_id and String, Enum, or Boolean custom fields on sessions and devices (session.custom.<name>, device.custom.<name>); every other field of the vocabulary condition accepts is rejected, since a group key must be single-valued and low-cardinality to be a series. Data points carrying no value for the field are grouped under a null group_key rather than dropped, and the response is not capped: every distinct value with data in the window comes back.

Filter to observations from specific device IDs, None for null device_id only.

Filter to observations from specific invocation IDs, None for null invocation_id only.

Filter to observations for specific session IDs. None is not a valid value: metrics.session_id is non-nullable, so there is no “null session” subset to filter on. Omit (leave as NotSet) for no filter, or pass a list of IDs.

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Name of the metric to aggregate.

Calendar bucket size to group observations by.

Inclusive start of the aggregation window, in Unix-epoch nanoseconds (UTC). Built by aggregate() from its start_time parameter via to_epoch_nanoseconds().

Whether to filter by session start time or end time.

One bucket per calendar day.

One bucket per calendar month.

One bucket per calendar quarter (three months).

One bucket per calendar week.

One bucket per calendar year.

Human-readable description of what the metric measures.

Unique metric name.

Unit of measure for values recorded under this metric, e.g. "%", "ms". Capped at 63 characters. None means unitless.

Timestamp when this metric definition was created.

User or service account that created this metric definition.

Human-readable description of what the metric measures.

Unique identifier for this metric definition.

Timestamp when this metric definition was last modified.

User or service account that last modified this metric definition.

Unique name for this metric.

Organization that owns this metric definition.

Unit of measure for every value recorded under this metric, e.g. "%", "ms", "m/s". Free-form and unvalidated; None means unitless.

Name of the metric definition to record a value for. If the definition does not exist, it is auto-created.

Observed numeric value.

Device that produced the data.

Value of the field named by QueryMetricsRequest.group_by that this data point carries, rendered as text whatever the field’s type. None on every data point of an ungrouped query, and on a data point that carries no value for that field — one published without a device, or whose session or device has never been given a value for the custom field. Mirrors NumericAggregateMetricRecord.group_key, which splits buckets the same way.

Action invocation that produced this data point, if any.

Upper bound of the source session’s aggregate timestamps, in Unix-epoch nanoseconds. None until the session has at least one file contribution. Mirrors max_timestamp_ns.

Identifier of the metric definition this data point belongs to.

Lower bound of the source session’s aggregate timestamps, in Unix-epoch nanoseconds. None until the session has at least one file contribution. Mirrors min_timestamp_ns.

Human-readable name of the metric definition this data point belongs to. Resolved server-side from the parent MetricDefinitionRecord so callers do not need a second lookup to display the metric name alongside the value.

Organization that owns this metric data point.

Timestamp when this data point was published to the platform.

User or service account that published this data point.

Session this metric is associated with.

Unit of measure for value. Resolved server-side from the parent MetricDefinitionRecord, like name, so callers can label a value without a second lookup. None means unitless.

Observed numeric value.

Aggregation function that was applied to produce this record.

Value of the field named by AggregateMetricsRequest.group_by that this bucket’s data points share, rendered as text whatever the field’s type. None on every bucket of an ungrouped aggregation, and on the bucket collecting the grouped data points that carry no value for that field — a data point published without a device, or a session or device that has never been given a value for the custom field.

Unit of measure for value, resolved from the aggregated metric’s definition alongside name. None means unitless.

Aggregated result for this bucket.

Aggregation function that was applied.

Period buckets returned by the aggregation, sorted by start_time ascending.

Count of observations in the bucket.

Maximum value observed in the bucket.

Arithmetic mean of all values in the bucket.

Minimum value observed in the bucket.

Sum of all values in the bucket.

Device that produced the data. When absent (NotSet), the server infers the device from the session’s attached devices: the request succeeds if exactly one device is attached and is rejected otherwise. Pass an explicit device ID or None to skip inference.

Metric data points to insert.

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Session all metrics in this batch will be attached to.

Condition, or nested group of conditions, narrowing which data points are returned.

Every field must be prefixed with the entity it filters on, singular or plural; a bare field name such as name is rejected. The available fields are:

• session.<field>: session_id (alias id), name, min_timestamp_ns (alias start_time), max_timestamp_ns (alias end_time), duration, created, created_by, modified, modified_by, tags. The two timestamp bounds accept anything to_epoch_nanoseconds() converts; duration takes an integer count of nanoseconds.
• device.<field>: device_id (alias id), tags, created, created_by, modified, modified_by, metadata (including dotted paths beneath it). Reads the device that published the data point, not the devices attached to its session.
• session.custom.<name> / device.custom.<name> / collection.custom.<name>: a custom field in the Ready state.
• collection.collection_id (alias collection.id): the data point’s session belongs to that collection. Equals and NotEquals only.

A session belongs to any number of collections, so a collection.* condition quantifies over that set: a data point matches when its session belongs to at least one collection satisfying the condition. A negated comparator (NotEquals, NotContains, NotLike) means the session belongs to no collection satisfying the positive form, so a session in no collection at all matches every negated collection condition. IsNull and NotExists likewise mean no collection the session belongs to carries a value for the field.

A data point published without a device matches a device.* condition only under IsNull and NotExists. Every other comparator asks what the device’s field holds, NotEquals, NotContains, and NotLike included, so a data point with no device, or a device carrying no value for the field, is excluded. Write device.<field> NotEquals x OR device.<field> IsNull to match both.

Any other field, a comparator the field’s type does not accept, a value the field cannot convert, or a Not group raises RobotoIllegalArgumentException.

Order data points from the largest sort_by value to the smallest, instead of smallest first.

With the default sort_by, this returns the most recent data points first.

Exclusive end of the query window, in Unix-epoch nanoseconds (UTC). Built from query()’s end_time parameter the same way. Defaults to None (now).

Field whose value each returned data point should carry, under MetricRecord.group_key.

Unlike AggregateMetricsRequest.group_by, this does not change which rows come back or how many: a raw query already returns one data point per session, so there is nothing to split. It projects the field’s value onto each one, which is what lets a caller separate the points into a series per distinct value without resolving the field itself.

None leaves MetricRecord.group_key null on every data point. Accepts the same vocabulary the aggregation does — device.device_id and String, Enum, or Boolean custom fields on sessions and devices (session.custom.<name>, device.custom.<name>) — and rejects every other field of condition’s vocabulary with RobotoIllegalArgumentException. A data point carrying no value for the field gets a null group_key rather than being dropped.

Filter to observations from specific device IDs, None for null device_id only.

Filter to observations from specific invocation IDs, None for null invocation_id only.

Filter to observations for specific session IDs. None is not a valid value: metrics.session_id is non-nullable, so there is no “null session” subset to filter on. Omit (leave as NotSet) for no filter, or pass a list of IDs.

Maximum number of data points to return. Must be between 1 and MAX_METRIC_LIST_RESULTS (10,000).

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Name of the metric to query.

Field to order data points by, with session_id as a deterministic tiebreaker.

One of time (the session time selected by time_filter), value, published, device_id, session_id or invocation_id; any other field is rejected with RobotoInvalidRequestException. Data points with no device_id or invocation_id sort after every other value. Defaults to time.

Inclusive start of the query window, in Unix-epoch nanoseconds (UTC). Built by query() from its start_time parameter via to_epoch_nanoseconds(). Defaults to None (the Unix epoch).

Whether to filter by session start time or end time.

New description, None to clear, or NotSet to leave unchanged.

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

New unit of measure (max 63 characters), None to clear, or NotSet to leave unchanged.