Key Highlights
Each query your code executes, how long it took, and which service and trace triggered it. Database Activity Capturing traces any database call within the application trace that triggered it, with one-click access to correlated query execution, latency, and error data, no manual query required.
The primary framing for APM's database visibility: trace-correlated database call instrumentation within the application.
Database Operations Tracking captures query execution, latency, throughput, and error rates within trace context.
Each database call is a span within the application trace that triggered it.
It shows the timing your application experienced, tied to the causing request.
Real-Time Database Insights correlate trace data with database activity as it happens.
Directly discovered databases link into APM traces and service topology (8.2.4). Correlation no longer depends on how the database was discovered.
Database timing is always presented in the context of the application trace that explains it.
Correlate trace data with database activity for rapid identification and resolution.
Correlate trace data with database activity for rapid identification and resolution.
See which operations are slowing application responses as they occur.
From any elevated database metric, one-click trace access opens the Trace Explorer.
It arrives pre-filtered to the requests that triggered that activity.
Faster issue resolution by connecting the application trace to the database call in one view.
Database behavior is visible in the same workspace as traces, errors, host metrics, and JVM insights.
Move directly from database activity to traces, errors, logs, and JVM insights, in one workflow.
From the database activity view, one-click trace access gives immediate context.
No manual queries or platform switching required.
Determine whether an issue originates in application code, JVM, database, or infrastructure.
Aligned visual evidence in the same workspace makes the source clear.
Navigate from a slow database call span into the surrounding service call sequence.
The query's full application context is immediately visible.
The Single Investigation Workspace keeps database, trace, and host evidence in one correlated view from start to finish.
Align database call evidence with the broader application and infrastructure picture.
Connection of database activity with application trace spans, JVM runtime behavior, and host metrics in one visual surface.
Dashboards and widgets aligned for database, application, and infrastructure signals give complete operational context.
Database slowdown cause identification: the query, the application’s call pattern, JVM resource pressure, or host conditions.
Single correlated view of query, application, and infrastructure signals for faster diagnosis.
Unified evidence throughout the stack removes the need to switch between an APM tool, a DBA console, and an infrastructure platform.
Database Activity Capturing works for any deployment target that APM supports.
Database call tracing available for Kubernetes, Docker, Host/VM, OpenShift, EKS, AWS EKS Fargate, OKE, GKE, and Rancher deployments.
The same capture behavior applies whether the application is containerized, host-based, or in a managed Kubernetes environment.
Auto-instrumentation via MotaAgent captures database calls. No code changes required for Java, .NET, Python, Node.js, and PHP applications.
Go database tracing requires QueryContext/ExecContext calls with the otelsql wrapper. Query-level span capture in Go is not automatic.
Intelligence
Database problems present as application slowdowns. A slow query under production load shows up as a lagging API response, and volume-driven bottlenecks appear as timeouts. Diagnosing this lacking trace correlation means switching between an APM platform, a query profiler, and a DBA console under time pressure.
Database Activity Capturing automates that connection by correlating each application trace to its database call, capturing query execution, latency, throughput, and error rate in trace context. Real-Time Database Insights lets a slow trace lead directly to its database call, where one click on an elevated error rate opens the Trace Explorer filtered to those requests.
How It Works
Instrument database calls via MotaAgent. Capture query execution, latency, throughput, and error rates.
Record each database call as a trace span, preserving full timing and outcome in the parent trace context.
Present database activity correlated with application traces, JVM insights, and host metrics in one view.
Apply Real-Time Database Insights to link application performance directly to database behavior as it occurs.
Access traces in one click from database activity views, opening Trace Explorer pre-filtered to selected calls.
Display database error rates alongside application KPIs to reveal database issues and their application impact.
Display database error rates alongside application KPIs to reveal database issues and their application impact.
Role-Based Value
Direct database performance investments with application-side evidence rather than server-side capacity estimates.
Direct database performance investments with application-side evidence rather than server-side capacity estimates.
Reduce investigation time for incidents that originate in the database tier.
Reduce investigation time for incidents that originate in the database tier.
Reduce investigation time for incidents that originate in the database tier.
Reduce investigation time for incidents that originate in the database tier.
Understand how application code changes affect database call behavior in production.
Understand how application code changes affect database call behavior in production.
From Visibility to Control
Database Activity Capturing: each database call becomes a span in the trace that triggered it.
Real-Time Database Insights correlate trace and database activity. One click reaches any elevated metric.
Single Investigation Workspace: one click from any database view reaches traces, errors, JVM, and host metrics.
Dashboards and widgets align database evidence with trace spans, JVM behavior, and infrastructure signals.
Deployment coverage: Kubernetes, Docker, Host/VM, OpenShift, EKS, AWS EKS Fargate, OKE, GKE, and Rancher.
Auto-instrumented: Java, .NET, Python, Node.js, PHP. Go needs QueryContext/ExecContext plus the otelsql wrapper.
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