Query Protocol

Query Protocol defines a set of APIs in GraphQL grammar to provide data query and interactive capabilities with SkyWalking native visualization tool or 3rd party system, including Web UI, CLI or private system.

Query protocol official repository, https://github.com/apache/skywalking-query-protocol.


Metadata contains concise information on all services and their instances, endpoints, etc. under monitoring. You may query the metadata in different ways.

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


The topology and dependency graphs among services, instances and endpoints. Includes direct relationships or global maps.

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


Metrics query targets all objects defined in OAL script and MAL. You may obtain the metrics data in linear or thermodynamic matrix formats based on the aggregation functions in script.


Provide Metrics V2 query APIs since 8.0.0, including metadata, single/multiple values, heatmap, and sampled records metrics.

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!]!


3 types of metrics can be queried. V1 APIs were introduced since 6.x. Now they are a shell to V2 APIs.

  1. Single value. Most default metrics are in single value. getValues and getLinearIntValues are suitable for this purpose.
  2. Multiple value. A metric defined in OAL includes multiple value calculations. Use getMultipleLinearIntValues to obtain all values. percentile is a typical multiple value function in OAL.
  3. Heatmap value. Read Heatmap in WIKI for details. thermodynamic is the only OAL function. Use getThermodynamic to get the values.
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

Metrics are defined in the config/oal/*.oal files.


Aggregation query means that the metrics data need a secondary aggregation at query stage, which causes the query interfaces to have some different arguments. A typical example of aggregation query is the TopN list of services. Metrics stream aggregation simply calculates the metrics values of each service, but the expected list requires ordering metrics data by their values.

Aggregation query is for single value metrics only.

# 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!]!


Record is a general and abstract type for collected raw data. In the observability, traces and logs have specific and well-defined meanings, meanwhile, the general records represent other collected records. Such as sampled slow SQL statement, HTTP request raw data(request/response header/body)

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!

Log implementations vary between different database options. Some search engines like ElasticSearch and OpenSearch can support full log text fuzzy queries, while others do not due to considerations related to performance impact and end user experience.

test API serves as the debugging tool for native LAL parsing.


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

Trace query fetches trace segment lists and spans of given trace IDs.


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

Alarm query identifies alarms and related events.


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

Event query fetches the event list based on given sources and time range conditions.


SkyWalking offers two types of profiling, in-process and out-process, allowing users to create tasks and check their execution status.

In-process profiling

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!

Out-process profiling

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!



Duration is a widely used parameter type as the APM data is time-related. See the following for more details. Step relates to precision.

# 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 {