// Copyright 2022 Google LLC
//
// 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.

syntax = "proto3";

package google.cloud.timeseriesinsights.v1;

import "google/api/annotations.proto";
import "google/api/client.proto";
import "google/api/field_behavior.proto";
import "google/api/resource.proto";
import "google/protobuf/duration.proto";
import "google/protobuf/empty.proto";
import "google/protobuf/timestamp.proto";
import "google/rpc/status.proto";

option cc_enable_arenas = true;
option go_package = "cloud.google.com/go/timeseriesinsights/apiv1/timeseriesinsightspb;timeseriesinsightspb";
option java_multiple_files = true;
option java_outer_classname = "TimeseriesInsightsProto";
option java_package = "com.google.cloud.timeseriesinsights.v1";

service TimeseriesInsightsController {
  option (google.api.default_host) = "timeseriesinsights.googleapis.com";
  option (google.api.oauth_scopes) = "https://www.googleapis.com/auth/cloud-platform";

  // Lists [DataSets][google.cloud.timeseriesinsights.v1.DataSet] under the project.
  //
  // The order of the results is unspecified but deterministic. Newly created
  // [DataSets][google.cloud.timeseriesinsights.v1.DataSet] will not necessarily be added to the end
  // of this list.
  rpc ListDataSets(ListDataSetsRequest) returns (ListDataSetsResponse) {
    option (google.api.http) = {
      get: "/v1/{parent=projects/*/locations/*}/datasets"
      additional_bindings {
        get: "/v1/{parent=projects/*}/datasets"
      }
    };
    option (google.api.method_signature) = "parent";
  }

  // Create a [DataSet][google.cloud.timeseriesinsights.v1.DataSet] from data stored on Cloud
  // Storage.
  //
  // The data must stay immutable while we process the
  // [DataSet][google.cloud.timeseriesinsights.v1.DataSet] creation; otherwise, undefined outcomes
  // might result.  For more information, see [DataSet][google.cloud.timeseriesinsights.v1.DataSet].
  rpc CreateDataSet(CreateDataSetRequest) returns (DataSet) {
    option (google.api.http) = {
      post: "/v1/{parent=projects/*/locations/*}/datasets"
      body: "dataset"
      additional_bindings {
        post: "/v1/{parent=projects/*}/datasets"
        body: "dataset"
      }
    };
    option (google.api.method_signature) = "parent,dataset";
  }

  // Delete a [DataSet][google.cloud.timeseriesinsights.v1.DataSet] from the system.
  //
  // **NOTE**: If the [DataSet][google.cloud.timeseriesinsights.v1.DataSet] is still being
  // processed, it will be aborted and deleted.
  rpc DeleteDataSet(DeleteDataSetRequest) returns (google.protobuf.Empty) {
    option (google.api.http) = {
      delete: "/v1/{name=projects/*/locations/*/datasets/*}"
      additional_bindings {
        delete: "/v1/{name=projects/*/datasets/*}"
      }
    };
    option (google.api.method_signature) = "name";
  }

  // Append events to a `LOADED` [DataSet][google.cloud.timeseriesinsights.v1.DataSet].
  rpc AppendEvents(AppendEventsRequest) returns (AppendEventsResponse) {
    option (google.api.http) = {
      post: "/v1/{dataset=projects/*/locations/*/datasets/*}:appendEvents"
      body: "*"
      additional_bindings {
        post: "/v1/{dataset=projects/*/datasets/*}:appendEvents"
        body: "*"
      }
    };
    option (google.api.method_signature) = "dataset,events";
  }

  // Execute a Timeseries Insights query over a loaded
  // [DataSet][google.cloud.timeseriesinsights.v1.DataSet].
  rpc QueryDataSet(QueryDataSetRequest) returns (QueryDataSetResponse) {
    option (google.api.http) = {
      post: "/v1/{name=projects/*/locations/*/datasets/*}:query"
      body: "*"
      additional_bindings {
        post: "/v1/{name=projects/*/datasets/*}:query"
        body: "*"
      }
    };
  }

  // Evaluate an explicit slice from a loaded [DataSet][google.cloud.timeseriesinsights.v1.DataSet].
  rpc EvaluateSlice(EvaluateSliceRequest) returns (EvaluatedSlice) {
    option (google.api.http) = {
      post: "/v1/{dataset=projects/*/locations/*/datasets/*}:evaluateSlice"
      body: "*"
      additional_bindings {
        post: "/v1/{dataset=projects/*/datasets/*}:evaluateSlice"
        body: "*"
      }
    };
  }

  // Evaluate an explicit timeseries.
  rpc EvaluateTimeseries(EvaluateTimeseriesRequest) returns (EvaluatedSlice) {
    option (google.api.http) = {
      post: "/v1/{parent=projects/*/locations/*}/datasets:evaluateTimeseries"
      body: "*"
      additional_bindings {
        post: "/v1/{parent=projects/*}/datasets:evaluateTimeseries"
        body: "*"
      }
    };
  }
}

// Mapping of BigQuery columns to timestamp, group_id and dimensions.
message BigqueryMapping {
  // The column which should be used as the event timestamps. If not specified
  // 'Timestamp' is used by default. The column may have TIMESTAMP or INT64
  // type (the latter is interpreted as microseconds since the Unix epoch).
  string timestamp_column = 1;

  // The column which should be used as the group ID (grouping events into
  // sessions). If not specified 'GroupId' is used by default, if the input
  // table does not have such a column, random unique group IDs are
  // generated automatically (different group ID per input row).
  string group_id_column = 2;

  // The list of columns that should be translated to dimensions. If empty,
  // all columns are translated to dimensions. The timestamp and group_id
  // columns should not be listed here again. Columns are expected to have
  // primitive types (STRING, INT64, FLOAT64 or NUMERIC).
  repeated string dimension_column = 3;
}

// A data source consists of multiple [Event][google.cloud.timeseriesinsights.v1.Event] objects stored on
// Cloud Storage.  Each Event should be in JSON format, with one Event
// per line, also known as JSON Lines format.
message DataSource {
  // Data source URI.
  //
  // 1) Google Cloud Storage files (JSON) are defined in the following form.
  // `gs://bucket_name/object_name`. For more information on Cloud Storage URIs,
  // please see https://cloud.google.com/storage/docs/reference-uris.
  string uri = 1;

  // For BigQuery inputs defines the columns that should be used for dimensions
  // (including time and group ID).
  BigqueryMapping bq_mapping = 2;
}

// A collection of data sources sent for processing.
message DataSet {
  option (google.api.resource) = {
    type: "timeseriesinsights.googleapis.com/Dataset"
    pattern: "projects/{project}/datasets/{dataset}"
    pattern: "projects/{project}/locations/{location}/datasets/{dataset}"
  };

  // DataSet state.
  enum State {
    // Unspecified / undefined state.
    STATE_UNSPECIFIED = 0;

    // Dataset is unknown to the system; we have never seen this dataset before
    // or we have seen this dataset but have fully GC-ed it.
    UNKNOWN = 1;

    // Dataset processing is pending.
    PENDING = 2;

    // Dataset is loading.
    LOADING = 3;

    // Dataset is loaded and can be queried.
    LOADED = 4;

    // Dataset is unloading.
    UNLOADING = 5;

    // Dataset is unloaded and is removed from the system.
    UNLOADED = 6;

    // Dataset processing failed.
    FAILED = 7;
  }

  // The dataset name, which will be used for querying, status and unload
  // requests. This must be unique within a project.
  string name = 1;

  // [Data dimension names][google.cloud.timeseriesinsights.v1.EventDimension.name] allowed for this `DataSet`.
  //
  // If left empty, all dimension names are included. This field works as a
  // filter to avoid regenerating the data.
  repeated string data_names = 2;

  // Input data.
  repeated DataSource data_sources = 3;

  // Dataset state in the system.
  State state = 4;

  // Dataset processing status.
  google.rpc.Status status = 5;

  // Periodically we discard dataset [Event][google.cloud.timeseriesinsights.v1.Event] objects that have
  // timestamps older than 'ttl'.  Omitting this field or a zero value means no
  // events are discarded.
  google.protobuf.Duration ttl = 6;
}

// Represents an event dimension.
message EventDimension {
  // Dimension name.
  //
  // **NOTE**: `EventDimension` names must be composed of alphanumeric
  // characters only, and are case insensitive. Unicode characters are *not*
  // supported. The underscore '_' is also allowed.
  string name = 1;

  // Dimension value.
  //
  // **NOTE**: All entries of the dimension `name` must have the same `value`
  // type.
  oneof value {
    // String representation.
    //
    // **NOTE**: String values are case insensitive. Unicode characters are
    // supported.
    string string_val = 2;

    // Long representation.
    int64 long_val = 3;

    // Bool representation.
    bool bool_val = 4;

    // Double representation.
    double double_val = 5;
  }
}

// Represents an entry in a data source.
//
// Each Event has:
//
// * A timestamp at which the event occurs.
// * One or multiple dimensions.
// * Optionally, a group ID that allows clients to group logically related
//   events (for example, all events representing payments transactions done by
//   a user in a day have the same group ID).  If a group ID is not provided, an
//   internal one will be generated based on the content and `eventTime`.
//
// **NOTE**:
//
// * Internally, we discretize time in equal-sized chunks and we assume an
//   event has a 0
//   [TimeseriesPoint.value][google.cloud.timeseriesinsights.v1.TimeseriesPoint.value]
//   in a chunk that does not contain any occurrences of an event in the input.
// * The number of Events with the same group ID should be limited.
// * Group ID *cannot* be queried.
// * Group ID does *not* correspond to a user ID or the like. If a user ID is of
//   interest to be queried, use a user ID `dimension` instead.
message Event {
  // Event dimensions.
  repeated EventDimension dimensions = 1;

  // Event group ID.
  //
  // **NOTE**: JSON encoding should use a string to hold a 64-bit integer value,
  // because a native JSON number holds only 53 binary bits for an integer.
  int64 group_id = 2;

  // Event timestamp.
  google.protobuf.Timestamp event_time = 3;
}

// Appends events to an existing DataSet.
message AppendEventsRequest {
  // Events to be appended.
  //
  // Note:
  //
  // 0. The [DataSet][google.cloud.timeseriesinsights.v1.DataSet] must be shown in a `LOADED` state
  //    in the results of `list` method; otherwise, all events from
  //    the append request will be dropped, and a `NOT_FOUND` status will be
  //    returned.
  // 0. All events in a single request must have the same
  //    [groupId][google.cloud.timeseriesinsights.v1.Event.group_id] if set; otherwise, an
  //    `INVALID_ARGUMENT` status will be returned.
  // 0. If [groupId][google.cloud.timeseriesinsights.v1.Event.group_id] is not set (or 0), there
  //    should be only 1 event; otherwise, an `INVALID_ARGUMENT` status will be
  //    returned.
  // 0. The events must be newer than the current time minus
  //    [DataSet TTL][google.cloud.timeseriesinsights.v1.DataSet.ttl] or they will be dropped.
  repeated Event events = 1;

  // Required. The DataSet to which we want to append to in the format of
  // "projects/{project}/datasets/{dataset}"
  string dataset = 2 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "timeseriesinsights.googleapis.com/Dataset"
    }
  ];
}

// Response for an AppendEvents RPC.
message AppendEventsResponse {
  // Dropped events; empty if all events are successfully added.
  repeated Event dropped_events = 1;
}

// Create a DataSet request.
message CreateDataSetRequest {
  // Required. Client project name which will own this DataSet in the format of
  // 'projects/{project}'.
  string parent = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "cloudresourcemanager.googleapis.com/Project"
    }
  ];

  // Required. Dataset to be loaded.
  DataSet dataset = 2 [(google.api.field_behavior) = REQUIRED];
}

// Unload DataSet request from the serving system.
message DeleteDataSetRequest {
  // Required. Dataset name in the format of "projects/{project}/datasets/{dataset}"
  string name = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "timeseriesinsights.googleapis.com/Dataset"
    }
  ];
}

// List the DataSets created by the current project.
message ListDataSetsRequest {
  // Required. Project owning the DataSet in the format of "projects/{project}".
  string parent = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "cloudresourcemanager.googleapis.com/Project"
    }
  ];

  // Number of results to return in the list.
  int32 page_size = 2;

  // Token to provide to skip to a particular spot in the list.
  string page_token = 3;
}

// Created DataSets list response.
message ListDataSetsResponse {
  // The list of created DataSets.
  repeated DataSet datasets = 1;

  // Token to receive the next page of results.
  string next_page_token = 2;
}

// A categorical dimension fixed to a certain value.
message PinnedDimension {
  // The name of the dimension for which we are fixing its value.
  string name = 1;

  // Dimension value.
  //
  // **NOTE**: The `value` type must match that in the data with the same
  // `dimension` as name.
  oneof value {
    // A string value. This can be used for [dimensions][google.cloud.timeseriesinsights.v1.EventDimension], which
    // have their value field set to [string_val][google.cloud.timeseriesinsights.v1.EventDimension.string_val].
    string string_val = 2;

    // A bool value. This can be used for [dimensions][google.cloud.timeseriesinsights.v1.EventDimension], which
    // have their value field set to [bool_val][google.cloud.timeseriesinsights.v1.EventDimension.bool_val].
    bool bool_val = 3;
  }
}

// Parameters that control the sensitivity and other options for the time series
// forecast.
message ForecastParams {
  // A time period of a fixed interval.
  enum Period {
    // Unknown or simply not given.
    PERIOD_UNSPECIFIED = 0;

    // 1 hour
    HOURLY = 5;

    // 24 hours
    DAILY = 1;

    // 7 days
    WEEKLY = 2;

    // 30 days
    MONTHLY = 3;

    // 365 days
    YEARLY = 4;
  }

  // Optional. Penalize variations between the actual and forecasted values smaller than
  // this. For more information about how this parameter affects the score, see
  // the [anomalyScore](EvaluatedSlice.anomaly_score) formula.
  //
  // Intuitively, anomaly scores summarize how statistically significant the
  // change between the actual and forecasted value is compared with what we
  // expect the change to be (see
  // [expectedDeviation](EvaluatedSlice.expected_deviation)). However, in
  // practice, depending on the application, changes smaller than certain
  // absolute values, while statistically significant, may not be important.
  //
  // This parameter allows us to penalize such low absolute value changes.
  //
  // Must be in the (0.0, inf) range.
  //
  // If unspecified, it defaults to 0.000001.
  optional double noise_threshold = 12 [(google.api.field_behavior) = OPTIONAL];

  // Optional. Specifying any known seasonality/periodicity in the time series
  // for the slices we will analyze can improve the quality of the results.
  //
  // If unsure, simply leave it unspecified by not setting a value for this
  // field.
  //
  // If your time series has multiple seasonal patterns, then set it to the most
  // granular one (e.g. if it has daily and weekly patterns, set this to DAILY).
  Period seasonality_hint = 10 [(google.api.field_behavior) = OPTIONAL];

  // Optional. The length of the returned [forecasted
  // timeseries][EvaluatedSlice.forecast].
  //
  // This duration is currently capped at 100 x
  // [granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity].
  //
  // Example: If the detection point is set to "2020-12-27T00:00:00Z", the
  // [granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity] to "3600s" and the
  // horizon_duration to "10800s", then we will generate 3 time
  // series points (from "2020-12-27T01:00:00Z" to "2020-12-27T04:00:00Z"), for
  // which we will return their forecasted values.
  //
  // Note: The horizon time is only used for forecasting not for anormaly
  // detection. To detect anomalies for multiple points of time,
  // simply send multiple queries with those as
  // [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time].
  google.protobuf.Duration horizon_duration = 13 [(google.api.field_behavior) = OPTIONAL];
}

// A point in a time series.
message TimeseriesPoint {
  // The timestamp of this point.
  google.protobuf.Timestamp time = 1;

  // The value for this point.
  //
  // It is computed by aggregating all events in the associated slice that are
  // in the `[time, time + granularity]` range (see
  // [granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity]) using the specified
  // [metric][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric].
  optional double value = 2;
}

// A time series.
message Timeseries {
  // The points in this time series, ordered by their timestamp.
  repeated TimeseriesPoint point = 1;
}

// Forecast result for a given slice.
message EvaluatedSlice {
  // Values for all categorical dimensions that uniquely identify this slice.
  repeated PinnedDimension dimensions = 1;

  // The actual value at the detection time (see
  // [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time]).
  //
  // **NOTE**: This value can be an estimate, so it should not be used as a
  // source of truth.
  optional double detection_point_actual = 11;

  // The expected value at the detection time, which is obtained by forecasting
  // on the historical time series.
  optional double detection_point_forecast = 12;

  // How much our forecast model expects the detection point actual will
  // deviate from its forecasted value based on how well it fit the input time
  // series.
  //
  // In general, we expect the `detectionPointActual` to
  // be in the `[detectionPointForecast - expectedDeviation,
  // detectionPointForecast + expectedDeviation]` range. The more the actual
  // value is outside this range, the more statistically significant the
  // anomaly is.
  //
  // The expected deviation is always positive.
  optional double expected_deviation = 16;

  // Summarizes how significant the change between the actual and forecasted
  // detection points are compared with the historical patterns observed on the
  // [history][google.cloud.timeseriesinsights.v1.EvaluatedSlice.history] time series.
  //
  // Defined as *|a - f| / (e + nt)*, where:
  //
  // - *a* is the [detectionPointActual][google.cloud.timeseriesinsights.v1.EvaluatedSlice.detection_point_actual].
  // - *f* is the [detectionPointForecast][google.cloud.timeseriesinsights.v1.EvaluatedSlice.detection_point_forecast].
  // - *e* is the [expectedDeviation][google.cloud.timeseriesinsights.v1.EvaluatedSlice.expected_deviation].
  // - *nt` is the [noiseThreshold][google.cloud.timeseriesinsights.v1.ForecastParams.noise_threshold].
  //
  // Anomaly scores between different requests and datasets are comparable. As
  // a guideline, the risk of a slice being an anomaly based on the anomaly
  // score is:
  //
  // - **Very High** if `anomalyScore` > 5.
  // - **High** if the `anomalyScore` is in the [2, 5] range.
  // - **Medium** if the `anomalyScore` is in the [1, 2) range.
  // - **Low** if the `anomalyScore` is < 1.
  //
  // If there were issues evaluating this slice, then the anomaly score will be
  // set to -1.0 and the [status][google.cloud.timeseriesinsights.v1.EvaluatedSlice.status] field will contain details on what
  // went wrong.
  optional double anomaly_score = 17;

  // The actual values in the `[`
  // [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] `-`
  // [forecastHistory][google.cloud.timeseriesinsights.v1.TimeseriesParams.forecast_history]`,`
  // [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] `]` time
  // range.
  //
  // **NOTE**: This field is only populated if
  // [returnTimeseries][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.return_timeseries] is true.
  Timeseries history = 5;

  // The forecasted values in the `[`
  // [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] `+`
  // [granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity]`,`
  // [forecastParams.horizonTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.forecast_params] `]` time
  // range.
  //
  // **NOTE**: This field is only populated if
  // [returnTimeseries][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.return_timeseries] is true.
  Timeseries forecast = 10;

  // Evaluation status. Contains an error message if the `anomalyScore` is < 0.
  //
  // Possible error messages:
  //
  // - **"Time series too sparse"**: The returned time series for this slice did
  // not contain enough data points (we require a minimum of 10).
  // - **"Not enough recent time series points"**: The time series contains the
  // minimum of 10 points, but there are not enough close in time to the
  // detection point.
  // - **"Missing detection point data"**: There were not events to be
  // aggregated within the `[detectionTime, detectionTime + granularity]` time
  // interval, so we don't have an actual value with which we can compare our
  // prediction.
  // - **"Data retrieval error"**: We failed to retrieve the time series data
  // for this slice and could not evaluate it successfully. Should be a
  // transient error.
  // - **"Internal server error"**: Internal unexpected error.
  google.rpc.Status status = 18;
}

// Parameters that control how we slice the dataset and, optionally, filter
// slices that have some specific values on some dimensions (pinned dimensions).
message SlicingParams {
  // Required. Dimensions over which we will group the events in slices. The names
  // specified here come from the
  // [EventDimension.name][google.cloud.timeseriesinsights.v1.EventDimension.name] field. At least
  // one dimension name must be specified. All dimension names that do not exist
  // in the queried `DataSet` will be ignored.
  //
  // Currently only dimensions that hold string values can be specified here.
  repeated string dimension_names = 1 [(google.api.field_behavior) = REQUIRED];

  // Optional. We will only analyze slices for which
  // [EvaluatedSlice.dimensions][google.cloud.timeseriesinsights.v1.EvaluatedSlice.dimensions] contain all of the
  // following pinned dimensions. A query with a pinned dimension `{ name: "d3"
  // stringVal: "v3" }` will only analyze events which contain the dimension `{
  // name: "d3" stringVal: "v3" }`.
  // The [pinnedDimensions][google.cloud.timeseriesinsights.v1.SlicingParams.pinned_dimensions] and
  // [dimensionNames][google.cloud.timeseriesinsights.v1.SlicingParams.dimension_names] fields can **not**
  // share the same dimension names.
  //
  // Example a valid specification:
  //
  // ```json
  // {
  //   dimensionNames: ["d1", "d2"],
  //   pinnedDimensions: [
  //     { name: "d3" stringVal: "v3" },
  //     { name: "d4" stringVal: "v4" }
  //   ]
  // }
  // ```
  //
  // In the previous example we will slice the dataset by dimensions "d1",
  // "d2", "d3" and "d4", but we will only analyze slices for which "d3=v3" and
  // "d4=v4".
  //
  // The following example is **invalid** as "d2" is present in both
  // dimensionNames and pinnedDimensions:
  //
  // ```json
  // {
  //   dimensionNames: ["d1", "d2"],
  //   pinnedDimensions: [
  //     { name: "d2" stringVal: "v2" },
  //     { name: "d4" stringVal: "v4" }
  //   ]
  // }
  // ```
  repeated PinnedDimension pinned_dimensions = 2 [(google.api.field_behavior) = OPTIONAL];
}

// Parameters that control how we construct the time series for each slice.
message TimeseriesParams {
  // Methods by which we can aggregate multiple events by a given
  // [metric][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric].
  enum AggregationMethod {
    // Unspecified.
    AGGREGATION_METHOD_UNSPECIFIED = 0;

    // Aggregate multiple events by summing up the values found in the
    // [metric][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric] dimension.
    SUM = 1;

    // Aggregate multiple events by averaging out the values found in the
    // [metric][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric] dimension.
    AVERAGE = 2;
  }

  // Required. How long should we go in the past when fetching the timeline used for
  // forecasting each slice.
  //
  // This is used in combination with the
  // [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] parameter.
  // The time series we construct will have the following time range:
  // `[detectionTime - forecastHistory, detectionTime + granularity]`.
  //
  // The forecast history might be rounded up, so that a multiple of
  // `granularity` is used to process the query.
  //
  // Note: If there are not enough events in the
  // `[detectionTime - forecastHistory, detectionTime + granularity]` time
  // interval, the slice evaluation can fail. For more information, see
  // [EvaluatedSlice.status][google.cloud.timeseriesinsights.v1.EvaluatedSlice.status].
  google.protobuf.Duration forecast_history = 1 [(google.api.field_behavior) = REQUIRED];

  // Required. The time granularity of the time series (on the x-axis). Each time series
  // point starting at time T will aggregate all events for a particular slice
  // in *[T, T + granularity)* time windows.
  //
  // Note: The aggregation is decided based on the
  // [metric][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric] parameter.
  //
  // This granularity defines the query-time aggregation windows and is not
  // necessarily related to any event time granularity in the raw data (though
  // we do recommend that the query-time granularity is not finer than the
  // ingestion-time one).
  //
  // Currently, the minimal supported granularity is 10 seconds.
  google.protobuf.Duration granularity = 2 [(google.api.field_behavior) = REQUIRED];

  // Optional. Denotes the [name][google.cloud.timeseriesinsights.v1.EventDimension.name] of a numerical
  // dimension that will have its values aggregated to compute the y-axis of the
  // time series.
  //
  // The aggregation method must also be specified by setting the
  // [metricAggregationMethod][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric_aggregation_method]
  // field.
  //
  // Note: Currently, if the aggregation method is unspecified, we will
  // default to SUM for backward compatibility reasons, but new implementations
  // should set the
  // [metricAggregationMethod][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric_aggregation_method]
  // explicitly.
  //
  // If the metric is unspecified, we will use the number of events that each
  // time series point contains as the point value.
  //
  // Example: Let's assume we have the following three events in our dataset:
  // ```json
  // {
  //   eventTime: "2020-12-27T00:00:00Z",
  //   dimensions: [
  //     { name: "d1" stringVal: "v1" },
  //     { name: "d2" stringVal: "v2" }
  //     { name: "m1" longVal: 100 }
  //     { name: "m2" longVal: 11 }
  //   ]
  // },
  // {
  //   eventTime: "2020-12-27T00:10:00Z",
  //   dimensions: [
  //     { name: "d1" stringVal: "v1" },
  //     { name: "d2" stringVal: "v2" }
  //     { name: "m1" longVal: 200 }
  //     { name: "m2" longVal: 22 }
  //   ]
  // },
  // {
  //   eventTime: "2020-12-27T00:20:00Z",
  //   dimensions: [
  //     { name: "d1" stringVal: "v1" },
  //     { name: "d2" stringVal: "v2" }
  //     { name: "m1" longVal: 300 }
  //     { name: "m2" longVal: 33 }
  //   ]
  // }
  // ```
  //
  // These events are all within the same hour, spaced 10 minutes between each
  // of them. Assuming our [QueryDataSetRequest][google.cloud.timeseriesinsights.v1.QueryDataSetRequest] had set
  // [slicingParams.dimensionNames][google.cloud.timeseriesinsights.v1.SlicingParams.dimension_names] to ["d1",
  // "d2"] and [timeseries_params.granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity] to
  // "3600s", then all the previous events will be aggregated into the same
  // [timeseries point][google.cloud.timeseriesinsights.v1.TimeseriesPoint].
  //
  // The time series point that they're all part of will have the
  // [time][google.cloud.timeseriesinsights.v1.TimeseriesPoint.time] set to "2020-12-27T00:00:00Z" and the
  // [value][google.cloud.timeseriesinsights.v1.TimeseriesPoint.value] populated based on this metric field:
  //
  // - If the metric is set to "m1" and metric_aggregation_method to SUM, then
  // the value of the point will be 600.
  // - If the metric is set to "m2" and metric_aggregation_method to SUM, then
  // the value of the point will be 66.
  // - If the metric is set to "m1" and metric_aggregation_method to AVERAGE,
  // then the value of the point will be 200.
  // - If the metric is set to "m2" and metric_aggregation_method to AVERAGE,
  // then the value of the point will be 22.
  // - If the metric field is "" or unspecified, then the value of the point
  // will be 3, as we will simply count the events.
  optional string metric = 4 [(google.api.field_behavior) = OPTIONAL];

  // Optional. Together with the [metric][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric] field, specifies how
  // we will aggregate multiple events to obtain the value of a time series
  // point. See the [metric][google.cloud.timeseriesinsights.v1.TimeseriesParams.metric] documentation for more
  // details.
  //
  // If the metric is not specified or "", then this field will be ignored.
  AggregationMethod metric_aggregation_method = 5 [(google.api.field_behavior) = OPTIONAL];
}

// Request for performing a query against a loaded DataSet.
message QueryDataSetRequest {
  // Required. Loaded DataSet to be queried in the format of
  // "projects/{project}/datasets/{dataset}"
  string name = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "timeseriesinsights.googleapis.com/Dataset"
    }
  ];

  // Required. This is the point in time that we want to probe for anomalies.
  //
  // The corresponding [TimeseriesPoint][google.cloud.timeseriesinsights.v1.TimeseriesPoint] is referred to as the
  // detection point.
  //
  // **NOTE**: As with any other time series point, the value is given by
  // aggregating all events in the slice that are in the
  // [detectionTime, detectionTime + granularity) time interval, where
  // the granularity is specified in the
  // [timeseriesParams.granularity][google.cloud.timeseriesinsights.v1.TimeseriesParams.granularity] field.
  google.protobuf.Timestamp detection_time = 11 [(google.api.field_behavior) = REQUIRED];

  // How many slices are returned in
  // [QueryDataSetResponse.slices][google.cloud.timeseriesinsights.v1.QueryDataSetResponse.slices].
  //
  // The returned slices are tentatively the ones with the highest
  // [anomaly scores][google.cloud.timeseriesinsights.v1.EvaluatedSlice.anomaly_score] in the dataset that match
  // the query, but it is not guaranteed.
  //
  // Reducing this number will improve query performance, both in terms of
  // latency and resource usage.
  //
  // Defaults to 50.
  optional int32 num_returned_slices = 13;

  // Parameters controlling how we will split the dataset into the slices that
  // we will analyze.
  SlicingParams slicing_params = 9;

  // Parameters controlling how we will build the time series used to predict
  // the [detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time] value for each slice.
  TimeseriesParams timeseries_params = 10;

  // Parameters that control the time series forecasting models, such as the
  // sensitivity of the anomaly detection.
  ForecastParams forecast_params = 5;

  // If specified, we will return the actual and forecasted time for all
  // returned slices.
  //
  // The time series are returned in the
  // [EvaluatedSlice.history][google.cloud.timeseriesinsights.v1.EvaluatedSlice.history] and
  // [EvaluatedSlice.forecast][google.cloud.timeseriesinsights.v1.EvaluatedSlice.forecast] fields.
  bool return_timeseries = 8;
}

// Response for a query executed by the system.
message QueryDataSetResponse {
  // Loaded DataSet that was queried.
  string name = 1;

  // Slices sorted in descending order by their
  // [anomalyScore][google.cloud.timeseriesinsights.v1.EvaluatedSlice.anomaly_score].
  //
  // At most [numReturnedSlices][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.num_returned_slices]
  // slices are present in this field.
  repeated EvaluatedSlice slices = 3;
}

// Request for evaluateSlice.
message EvaluateSliceRequest {
  // Required. Loaded DataSet to be queried in the format of
  // "projects/{project}/datasets/{dataset}"
  string dataset = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "timeseriesinsights.googleapis.com/Dataset"
    }
  ];

  // Required. Dimensions with pinned values that specify the slice for which we will
  // fetch the time series.
  repeated PinnedDimension pinned_dimensions = 2 [(google.api.field_behavior) = REQUIRED];

  // Required. This is the point in time that we want to probe for anomalies.
  //
  // See documentation for
  // [QueryDataSetRequest.detectionTime][google.cloud.timeseriesinsights.v1.QueryDataSetRequest.detection_time].
  google.protobuf.Timestamp detection_time = 3 [(google.api.field_behavior) = REQUIRED];

  // Parameters controlling how we will build the time series used to predict
  // the [detectionTime][google.cloud.timeseriesinsights.v1.EvaluateSliceRequest.detection_time] value for this slice.
  TimeseriesParams timeseries_params = 4;

  // Parameters that control the time series forecasting models, such as the
  // sensitivity of the anomaly detection.
  ForecastParams forecast_params = 5;
}

// Request for evaluateTimeseries.
message EvaluateTimeseriesRequest {
  // Required. Client project name in the format of 'projects/{project}'.
  string parent = 1 [
    (google.api.field_behavior) = REQUIRED,
    (google.api.resource_reference) = {
      type: "cloudresourcemanager.googleapis.com/Project"
    }
  ];

  // Evaluate this time series without requiring it was previously loaded in
  // a data set.
  //
  // The evaluated time series point is the last one, analogous to calling
  // evaluateSlice or query with
  // [detectionTime][google.cloud.timeseriesinsights.v1.EvaluateSliceRequest.detection_time] set to
  // `timeseries.point(timeseries.point_size() - 1).time`.
  //
  // The length of the time series must be at least 10.
  //
  // All points must have the same time offset relative to the granularity. For
  // example, if the [granularity][google.cloud.timeseriesinsights.v1.EvaluateTimeseriesRequest.granularity] is "5s", then the following
  // point.time sequences are valid:
  // - "100s", "105s", "120s", "125s" (no offset)
  // - "102s", "107s", "122s", "127s" (offset is "2s")
  // However, the following sequence is invalid as it has inconsistent offsets:
  // - "100s", "105s", "122s", "127s" (offsets are either "0s" or "2s")
  Timeseries timeseries = 2;

  // The granularity of the time series (time distance between two consecutive
  // points).
  google.protobuf.Duration granularity = 3;

  // The forecast parameters.
  ForecastParams forecast_params = 4;
}