Query Protocol

extend type Query {
    # Normal service related meta info 
    getAllServices(duration: Duration!, group: String): [Service!]!
    searchServices(duration: Duration!, keyword: String!): [Service!]!
    searchService(serviceCode: String!): Service

    # Fetch all services of Browser type
    getAllBrowserServices(duration: Duration!): [Service!]!
    searchBrowserServices(duration: Duration!, keyword: String!): [Service!]!
    searchBrowserService(serviceCode: String!): Service

    # Service instance query
    getServiceInstances(duration: Duration!, serviceId: ID!): [ServiceInstance!]!

    # Endpoint query
    # Consider there are huge numbers of endpoint,
    # must use endpoint owner's service id, keyword and limit filter to do query.
    searchEndpoint(keyword: String!, serviceId: ID!, limit: Int!): [Endpoint!]!
    getEndpointInfo(endpointId: ID!): EndpointInfo

    # Process query
    # Read process list.
    listProcesses(duration: Duration!, instanceId: ID!): [Process!]!
    # Find process according to given ID. Return null if not existing.
    getProcess(processId: ID!): Process
    # Get the number of matched processes through serviceId, labels
    # Labels: the matched process should contain all labels
    #
    # The return is not a precise number, the process has its lifecycle, as it reboots and shutdowns with time.
    # The return number just gives an abstract of the scale of profiling that would be applied.
    estimateProcessScale(serviceId: ID!, labels: [String!]!): Long!

    # Database related meta info.
    getAllDatabases(duration: Duration!): [Database!]!
    getTimeInfo: TimeInfo
}

extend type Query {
    # Query the global topology
    getGlobalTopology(duration: Duration!): Topology
    # Query the topology, based on the given service
    getServiceTopology(serviceId: ID!, duration: Duration!): Topology
    # Query the topology, based on the given services.
    # `#getServiceTopology` could be replaced by this.
    getServicesTopology(serviceIds: [ID!]!, duration: Duration!): Topology
    # Query the instance topology, based on the given clientServiceId and serverServiceId
    getServiceInstanceTopology(clientServiceId: ID!, serverServiceId: ID!, duration: Duration!): ServiceInstanceTopology
    # Query the topology, based on the given endpoint
    getEndpointTopology(endpointId: ID!, duration: Duration!): Topology
    # v2 of getEndpointTopology
    getEndpointDependencies(endpointId: ID!, duration: Duration!): EndpointTopology
}

extend type Query {
    # Metrics definition metadata query. Response the metrics type which determines the suitable query methods.
    typeOfMetrics(name: String!): MetricsType!
    # Get the list of all available metrics in the current OAP server.
    # Param, regex, could be used to filter the metrics by name.
    listMetrics(regex: String): [MetricDefinition!]!

    # Read metrics single value in the duration of required metrics
    readMetricsValue(condition: MetricsCondition!, duration: Duration!): Long!
    # Read time-series values in the duration of required metrics
    readMetricsValues(condition: MetricsCondition!, duration: Duration!): MetricsValues!
    # Read entity list of required metrics and parent entity type.
    sortMetrics(condition: TopNCondition!, duration: Duration!): [SelectedRecord!]!
    # Read value in the given time duration, usually as a linear.
    # labels: the labels you need to query.
    readLabeledMetricsValues(condition: MetricsCondition!, labels: [String!]!, duration: Duration!): [MetricsValues!]!
    # Heatmap is bucket based value statistic result.
    readHeatMap(condition: MetricsCondition!, duration: Duration!): HeatMap
    # Deprecated since 9.3.0, replaced by readRecords defined in record.graphqls
    # Read the sampled records
    # TopNCondition#scope is not required.
    readSampledRecords(condition: TopNCondition!, duration: Duration!): [SelectedRecord!]!
}

extend type Query {
    getValues(metric: BatchMetricConditions!, duration: Duration!): IntValues
    getLinearIntValues(metric: MetricCondition!, duration: Duration!): IntValues
    # Query the type of metrics including multiple values, and format them as multiple lines.
    # The seq of these multiple lines base on the calculation func in OAL
    # Such as, should us this to query the result of func percentile(50,75,90,95,99) in OAL,
    # then five lines will be responded, p50 is the first element of return value.
    getMultipleLinearIntValues(metric: MetricCondition!, numOfLinear: Int!, duration: Duration!): [IntValues!]!
    getThermodynamic(metric: MetricCondition!, duration: Duration!): Thermodynamic
}

# The aggregation query is different with the metric query.
# All aggregation queries require backend or/and storage do aggregation in query time.
extend type Query {
    # TopN is an aggregation query.
    getServiceTopN(name: String!, topN: Int!, duration: Duration!, order: Order!): [TopNEntity!]!
    getAllServiceInstanceTopN(name: String!, topN: Int!, duration: Duration!, order: Order!): [TopNEntity!]!
    getServiceInstanceTopN(serviceId: ID!, name: String!, topN: Int!, duration: Duration!, order: Order!): [TopNEntity!]!
    getAllEndpointTopN(name: String!, topN: Int!, duration: Duration!, order: Order!): [TopNEntity!]!
    getEndpointTopN(serviceId: ID!, name: String!, topN: Int!, duration: Duration!, order: Order!): [TopNEntity!]!
}

extend type Query {
    # Query collected records with given metric name and parent entity conditions, and return in the requested order.
    readRecords(condition: RecordCondition!, duration: Duration!): [Record!]!
}

extend type Query {
    # Return true if the current storage implementation supports fuzzy query for logs.
    supportQueryLogsByKeywords: Boolean!
    queryLogs(condition: LogQueryCondition): Logs

    # Test the logs and get the results of the LAL output.
    test(requests: LogTestRequest!): LogTestResponse!
}

extend type Query {
    queryBasicTraces(condition: TraceQueryCondition): TraceBrief
    queryTrace(traceId: ID!): Trace
}

extend type Query {
    getAlarmTrend(duration: Duration!): AlarmTrend!
    getAlarm(duration: Duration!, scope: Scope, keyword: String, paging: Pagination!, tags: [AlarmTag]): Alarms
}

extend type Query {
    queryEvents(condition: EventQueryCondition): Events
}

extend type Mutation {
    # crate new profile task
    createProfileTask(creationRequest: ProfileTaskCreationRequest): ProfileTaskCreationResult!
}
extend type Query {
    # query all task list, order by ProfileTask#startTime descending
    getProfileTaskList(serviceId: ID, endpointName: String): [ProfileTask!]!
    # query all task logs
    getProfileTaskLogs(taskID: String): [ProfileTaskLog!]!
    # query all task profiled segment list
    getProfileTaskSegmentList(taskID: String): [BasicTrace!]!
    # query profiled segment
    getProfiledSegment(segmentId: String): ProfiledSegment
    # analyze profiled segment, start and end time use timestamp(millisecond)
    getProfileAnalyze(segmentId: String!, timeRanges: [ProfileAnalyzeTimeRange!]!): ProfileAnalyzation!
}

extend type Mutation {
    # create a new eBPF fixed time profiling task
    createEBPFProfilingFixedTimeTask(request: EBPFProfilingTaskFixedTimeCreationRequest!): EBPFProfilingTaskCreationResult!

    # create a new eBPF network profiling task
    createEBPFNetworkProfiling(request: EBPFProfilingNetworkTaskRequest!): EBPFProfilingTaskCreationResult!
    # keep alive the eBPF profiling task
    keepEBPFNetworkProfiling(taskId: ID!): EBPFNetworkKeepProfilingResult!
}
extend type Query {
    # query eBPF profiling data for prepare create task
    queryPrepareCreateEBPFProfilingTaskData(serviceId: ID!): EBPFProfilingTaskPrepare!
    # query eBPF profiling task list
    queryEBPFProfilingTasks(serviceId: ID, serviceInstanceId: ID, targets: [EBPFProfilingTargetType!]): [EBPFProfilingTask!]!
    # query schedules from profiling task
    queryEBPFProfilingSchedules(taskId: ID!): [EBPFProfilingSchedule!]!
    # analyze the profiling schedule
    # aggregateType is "EBPFProfilingAnalyzeAggregateType#COUNT" as default. 
    analysisEBPFProfilingResult(scheduleIdList: [ID!]!, timeRanges: [EBPFProfilingAnalyzeTimeRange!]!, aggregateType: EBPFProfilingAnalyzeAggregateType): EBPFProfilingAnalyzation!
}

# The Duration defines the start and end time for each query operation.
# Fields: `start` and `end`
#   represents the time span. And each of them matches the step.
#   ref https://www.ietf.org/rfc/rfc3339.txt
#   The time formats are
#       `SECOND` step: yyyy-MM-dd HHmmss
#       `MINUTE` step: yyyy-MM-dd HHmm
#       `HOUR` step: yyyy-MM-dd HH
#       `DAY` step: yyyy-MM-dd
#       `MONTH` step: yyyy-MM
# Field: `step`
#   represents the accurate time point.
# e.g.
#   if step==HOUR , start=2017-11-08 09, end=2017-11-08 19
#   then
#       metrics from the following time points expected
#       2017-11-08 9:00 -> 2017-11-08 19:00
#       there are 11 time points (hours) in the time span.
input Duration {
    start: String!
    end: String!
    step: Step!
}

enum Step {
    MONTH
    DAY
    HOUR
    MINUTE
    SECOND
}