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.

All deprecated APIs are moved here.


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


Provide Metadata V2 query APIs since 9.0.0, including Layer concept.

extend type Query {
    # Read all available layers
    # UI could use this list to determine available dashboards/panels
    # The available layers would change with time in the runtime, because new service could be detected in any time.
    # This list should be loaded periodically.
    listLayers: [String!]!

    # Read the service list according to layer.
    listServices(layer: String): [Service!]!
    # Find service according to given ID. Return null if not existing.
    getService(serviceId: String!): Service
    # Search and find service according to given name. Return null if not existing.
    findService(serviceName: String!): Service

    # Read service instance list.
    listInstances(duration: Duration!, serviceId: ID!): [ServiceInstance!]!
    # Search and find service instance according to given ID. Return null if not existing.
    getInstance(instanceId: String!): ServiceInstance

    # Search and find matched endpoints according to given service and keyword(optional)
    # If no keyword, randomly choose endpoint based on `limit` value.
    findEndpoint(keyword: String, serviceId: ID!, limit: Int!): [Endpoint!]!
    getEndpointInfo(endpointId: ID!): EndpointInfo

    # 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!

    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
    # When layer is specified, the topology of this layer would be queried
    getGlobalTopology(duration: Duration!, layer: String): 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
    # Query the topology, based on the given instance
    getProcessTopology(serviceInstanceId: ID!, duration: Duration!): ProcessTopology


Metrics query targets all objects defined in OAL script and MAL.


Provide Metrics V3 query APIs since 9.5.0, including metadata and MQE. SkyWalking Metrics Query Expression(MQE) is an extension query mechanism. MQE allows users to do simple query-stage calculation like well known PromQL through GraphQL. The expression’s syntax can refer to here.

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!]!
    execExpression(expression: String!, entity: Entity!, duration: Duration!): ExpressionResult!
type ExpressionResult {
    type: ExpressionResultType!
    # When the type == TIME_SERIES_VALUES, the results would be a collection of MQEValues.
    # In other legal type cases, only one MQEValues is expected in the array.
    results: [MQEValues!]!
    # When type == ExpressionResultType.UNKNOWN,
    # the error message includes the expression resolving errors.
    error: String
enum ExpressionResultType {
    # Can't resolve the type of the given expression.
    # A single value
    # A collection of time-series values.
    # The value could have labels or not.
    # A collection of aggregated values through metric sort function
    # A collection of sampled records.
    # When the original metric type is sampled records


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!
    # Read the list of searchable keys
    queryLogTagAutocompleteKeys(duration: Duration!):[String!]
    # Search the available value options of the given key.
    queryLogTagAutocompleteValues(tagKey: String! , duration: Duration!):[String!]

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 {
    # Search segment list with given conditions
    queryBasicTraces(condition: TraceQueryCondition): TraceBrief
    # Read the specific trace ID with given trace ID
    queryTrace(traceId: ID!): Trace
    # Read the list of searchable keys
    queryTraceTagAutocompleteKeys(duration: Duration!):[String!]
    # Search the available value options of the given key.
    queryTraceTagAutocompleteValues(tagKey: String! , duration: Duration!):[String!]

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
    getProfileTaskSegments(taskID: ID!): [ProfiledTraceSegments!]!
    # analyze multiple profiled segments, start and end time use timestamp(millisecond)
    getSegmentsProfileAnalyze(queries: [SegmentProfileAnalyzeQuery!]!): 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
    # query `triggerType == FIXED_TIME` when triggerType is absent
    queryEBPFProfilingTasks(serviceId: ID, serviceInstanceId: ID, targets: [EBPFProfilingTargetType!], triggerType: EBPFProfilingTriggerType, duration: Duration): [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!

On-Demand Pod Logs

Provide APIs to query on-demand pod logs since 9.1.0.

extend type Query {
    listContainers(condition: OndemandContainergQueryCondition): PodContainers
    ondemandPodLogs(condition: OndemandLogQueryCondition): Logs


Provide Hierarchy query APIs since 10.0.0, including service and instance hierarchy.

extend type Query {
    # Query the service hierarchy, based on the given service. Will recursively return all related layers services in the hierarchy.
    getServiceHierarchy(serviceId: ID!, layer: String!): ServiceHierarchy!
    # Query the instance hierarchy, based on the given instance. Will return all direct related layers instances in the hierarchy, no recursive.
    getInstanceHierarchy(instanceId: ID!, layer: String!): InstanceHierarchy!
    # List layer hierarchy levels. The layer levels are defined in the `hierarchy-definition.yml`.
    listLayerLevels: [LayerLevel!]!



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 {