Docs
DocumentationQuery ReferenceAPI Reference
Open Console→→
DocumentationQuery ReferenceAPI Reference

Platform overview

What is Axiom?QuickstartArchitectureFeatures
Fundamentals
Datasets
Edge deployments
Limits
Performance
Optimize usage
Requirements
Semantic conventions
Glossary
Tour
SecurityRoadmap

Send data

Reference architecturesMethods

Understand data

Console
Query
Builder
Editor
Query results
Visualize
Traces
Metrics
Correlations
Save queries
Stream
Dashboard
Create
Elements
Create
Configure
Element types
Gauge
Heatmap
Log stream
Monitor list
Note
Pie chart
Scatter plot
Statistic
Table
Time series
Sections
Configure
Filter
Annotate
Monitor
Overview
View status
Configure
Examples
Monitor types
Anomaly
Match
Threshold
Alerting
Overview
Configure
Notifier types
Custom Webhook
Discord
Email
Microsoft Teams
Opsgenie
PagerDuty
Slack
Manage
Datasets
Overview
Views
Virtual fields
Access
RBAC
Tokens
CLI
Organization
Audit log
Settings
Usage and billing
Profile
Extend
Overview
AWS Lambda
AWS PrivateLink
Cloudflare Workers
Cloudflare Logpush
Convex
Grafana
Hex
Netlify
Supabase
Tailscale
Terraform
Unkey
Vercel
Intelligence
Overview
Spotlight
AI agents
Overview
MCP Server
Query cost limits
Agent-created orgs
Skills
Overview
Axiom alerting
Build dashboards
Control costs
Query metrics
SRE
Translate SPL to APL
Splunk
Overview
Splunk app
Install and configure
Commands
Examples
Portal
How it works
Set up standard mode
Set up transparent mode
Observability Cloud
SPL command support
Examples
Monitor and troubleshoot

Use cases

ObservabilityProduct analytics
LLM observability
Overview
Use Axiom AI SDK
Manual instrumentation
GenAI attributes
Redaction policies

Miscellaneous

LLMs
Overview
List of docs pages
Full docs
Query reference
FAQs
Legal
Acceptable use policy
Cookies
Data processing
HIPAA
Partner agreement
Partner program guide
Privacy policy
SLA
Terms of service
Terms of use
Understand data/Console

Explore traces

Learn how to observe how requests propagate through your distributed systems, understand the interactions between microservices, and trace the life of the request through your app’s architecture.

Distributed tracing in Axiom allows you to observe how requests propagate through your distributed systems. This could involve a user request going through several microservices, and resources until the requested information is retrieved and returned. By tracing these requests, you’re able to understand the interactions between these microservices, pinpoint issues, understand latency, and trace the life of the request through your app’s architecture.

Traces and spans

A trace is a representation of a single operation or transaction as it moves through a system. A trace is made up of multiple spans.

A span represents a logical unit of work in the system with a start and end time. For example, an HTTP request handling process might be a span. Each span includes metadata like unique identifiers (trace_id and span_id), start and end times, parent-child relationships with other spans, and optional events, logs, or other details to help describe the span’s operation.

Trace schema overview

FieldTypeDescription
trace_idStringUnique identifier for a trace
span_idStringUnique identifier for a span within a trace
parent_span_idStringIdentifier of the parent span
nameStringName of the span for example, the operation
kindStringType of the span (for example, client, server, producer)
durationTimespanDuration of the span
errorBooleanWhether this span contains an error
status.codeStringStatus of the span (for example, null, OK, error)
status.messageStringStatus message of the span
attributesObjectKey-value pairs providing additional metadata
eventsArrayTimestamped events associated with the span
linksArrayLinks to related spans or external resources
resourceObjectInformation about the source of the span

This guide explains how you can use Axiom to analyze and interrogate your trace data from simple overviews to complex queries.

Browse traces with the OpenTelemetry app

The Axiom OpenTelemetry app automatically detects any OpenTelemetry trace data flowing into your datasets and publishes an OpenTelemetry Traces dashboard to help you browse your trace data.

Info

The following fields are expected to display the OpenTelemetry Traces dashboard: duration, kind, name, parent_span_id, service.name, span_id, and trace_id.

Navigate the app

  • Use the Filter Bar at the top of the app to narrow the charts to a specific service or operation.
  • Use the Search Input to find a trace ID in the selected time period.
  • Use the Slowest Operations chart to identify performance issues across services and traces.
  • Use the Top Errors list to quickly identify the worst-offending causes of errors.
  • Use the Results table to get an overview and navigate between services, operations, and traces.

View a trace

Click a trace ID in the results table to show the waterfall view. This view allows you to see that span in the context of the entire trace from start to finish.

Customize the app

To customize the app, use the fork button to create an editable duplicate for you and your team.

Query traces

In Axiom, trace events are just like any other events inside datasets. This means they’re directly queryable in the UI. While this is can be a powerful experience, it’s important to note some important details to consider before querying:

  • Directly aggregating upon the duration field produces aggregate values across every span in the dataset. This is usually not the desired outcome when you want to inspect a service’s performance or robustness.

  • For request, rate, and duration aggregations, it’s best to only include the root span using isnull(parent_span_id).

Waterfall view of traces

To see how spans in a trace are related to each other, explore the trace in a waterfall view. In this view, each span in the trace is correlated with its parent and child spans.

Traces in OpenTelemetry Traces dashboard

To explore spans within a trace using the OpenTelemetry Traces app, follow these steps:

  1. Click the Dashboards tab.
  2. Click OpenTelemetry Traces.
  3. In the Slowest Operations chart, click the service that contains the trace.
  4. In the list of trace IDs, click the trace you want to explore.
  5. Explore how spans within the trace are related to each other in the waterfall view. To reveal additional options such as collapsing and expanding child spans, right-click a span.

To try out this example, go to the Axiom Playground.

Run in Playground

Traces in Query tab

To access the waterfall view from the Query tab, follow these steps:

  1. Ensure the dataset you work with has trace data.

  2. Click the Query tab.

  3. Run a query that returns the _time and trace_id fields. For example, the following query returns the number of spans in each trace:

    APL
    ['otel-demo-traces']
    | summarize count() by trace_id
  4. In the list of trace IDs, click the trace you want to explore. To reveal additional options such as copying the trace ID, right-click a trace.

  5. Explore how spans within the trace are related to each other in the waterfall view. To reveal additional options such as collapsing and expanding child spans, right-click a span. Event names are displayed on the timeline for each span.

To try out this example, go to the Axiom Playground.

Run in Playground

Customize waterfall view

To toggle the display of the span details on the right, click Span details.

To resize the width of the waterfall view and the span details panel, drag the border.

Span duration histogram

In the waterfall view of traces, Axiom warns you about slow and fast spans. These spans are outliers because they’re at least a standard deviation over or under the average duration of spans that have the same span name and service name. Hold the pointer over the SLOW or FAST label to see additional information about the span type such as average and maximum duration. In addition, Axiom displays a histogram about the durations of spans that have the same span name and service name as the span you selected. By default, the histogram shows a one-hour window around the selected span.

The span duration histogram can be useful in the following cases, among others:

  • You look at a span and you’re not familiar with the typical behavior of the service that created it. You want to know if you look at something normal in terms of duration or an outlier. The histogram helps you determine if you look at an outlier and might drill down further.
  • You’ve found an outlier. You want to investigate and look at other outliers. The histogram shows you what the baseline is and what’s not normal in terms of duration. You want to filter for the outliers and see what they have in common.
  • You want to see if there was a recent change in the typical duration for the selected span type.

To narrow the time range of the histogram, click and select an area in the histogram.

Example queries

Below are a collection of queries that can help get you started with traces inside Axiom. Queries are all executable on the Axiom Play sandbox.

Number of requests, average response

APL
['otel-demo-traces']
| where isnull(parent_span_id)
| summarize count(),
            avg(duration),
            percentiles_array(duration, 95, 99, 99.9)
  by bin_auto(_time)

Top five slowest services by operation

APL
['otel-demo-traces']
| summarize count(), avg(duration) by name
| sort by avg_duration desc
| limit 5

Top five errors per service and operation

APL
['otel-demo-traces']
| summarize topk(['status.message'], 5) by ['service.name'], name
| limit 5

Semantic conventions

OpenTelemetry semantic conventions specify standard attribute names and values for different kinds of operations and data. For more information on Axiom’s support for OTel semantic conventions and what it means for your data, see Semantic conventions.

Span links

Span links allow you to associate one span with one or more other spans, establishing a relationship between them that indicates the operation of one span depends on the other. Span links can connect spans within the same trace or across different traces.

Span links are useful for representing asynchronous operations or batch-processing scenarios. For example, an initial operation triggers a subsequent operation, but the subsequent operation may start at some unknown later time or even in a different trace. By linking the spans, you can capture and preserve the relationship between these operations, even if they’re not directly connected in the same trace.

How it works

Span links in Axiom are based on the OpenTelemetry specification. When instrumenting your code, you create span links using the OpenTelemetry API by passing the SpanContext (containing trace_id and span_id) of the span to which to link. Links are specified when starting a new span by providing them in the span configuration. The OpenTelemetry SDK includes the link information when exporting spans to Axiom. Links are recorded at span creation time so that sampling decisions can consider them.

View span links

  1. Run the following APL query to find traces with span links, for example:

    APLRun in Playground
    ['dataset'] 
    | where isnotempty(links)
  2. Click on a trace in the results and select the trace_id.

  3. In the trace details view, find the links section. This displays the trace_id and span_id associated with each linked span, as well as other attributes of the link.

  4. Click View span to navigate to a linked span, either in the same trace or a different trace.

Was this page helpful?
Suggest edits on GitHub
PreviousVisualizeNextMetrics
On this page
Traces and spansTrace schema overviewBrowse traces with the OpenTelemetry appNavigate the appView a traceCustomize the appQuery tracesWaterfall view of tracesTraces in OpenTelemetry Traces dashboardTraces in Query tabCustomize waterfall viewSpan duration histogramExample queriesSemantic conventionsSpan linksHow it worksView span links