Why Are APM and Database Observability Needed?

APM traces requests as they move through application components, capturing latency, errors, and runtime metrics. The database layer often becomes a bottleneck, impacting overall application responsiveness. Without insight into database queries and their relation to application traces, teams struggle to identify the causes of slowdowns or failures.

Database Observability enhances APM by offering detailed query-level insights, including execution times, query profiling, and execution plans. When these tools operate separately, engineers and DBAs waste time manually correlating issues across systems, leading to blame-shifting.

An integrated APM + DBO approach provides:

  • Complete full-stack visibility from frontend requests to backend queries.
  • Instant correlation between application traces and problematic database queries.
  • Improved collaboration between the developer and DBA teams.
  • Faster root cause analysis and performance tuning by contextualizing query issues within application workflows.

Real-World Use Case: E-Commerce Performance Troubleshooting

Consider an e-commerce platform facing slow checkout experiences during heavy traffic spikes. With the integrated APM and DBO capabilities of SolarWinds Observability SaaS:

  • Developers trace user transactions across application services.
  • They identify latency spikes linked to database calls.
  • By drilling into these traces, they find specific, resource-intensive SQL queries causing delays.
  • DBAs analyze query samples and execution plans to pinpoint a costly unindexed join.
  • The team swiftly resolves the query inefficiency and verifies improvements through updated trace and query metrics.

This process reduces downtime, enhances user experience, and promotes collaboration between application and database teams.

How SolarWinds Helps Address These Use Cases

SolarWinds Observability SaaS provides an integrated platform that features:

  • Unified dashboards displaying APM traces alongside database query data.
  • Seamless trace-to-query linking for easy navigation from slow application traces to query profiles and execution plans.
  • A rich Query Profiler that visualizes high-cost queries, allowing filtering by query type, resource usage, time ranges, and more.
  • The Query Plan Explorer tool for visualizing SQL Server execution plans, highlighting costly operations, index usage, and detailed runtime statistics.
  • Correlation between query samples and trace spans to analyze query behavior within distributed transactions.
  • Alerts and anomaly detection to provide early warnings about problematic queries or trace anomalies.

These features enable teams to quickly diagnose and resolve performance issues in both application and database layers.

How OpenTelemetry Enables This Integration

OpenTelemetry (OTEL) is the open-source, vendor-neutral standard for telemetry data instrumentation, collection, and transmission. SolarWinds leverages OTEL extensively by:

  • Using OTEL SDKs and agents to instrument a wide range of application languages and services.
  • Collecting distributed traces, including trace IDs and span context propagated through application and database calls.
  • Enriching spans with database-specific attributes, e.g., SQL query text, execution duration, and database client info, per OTEL semantic conventions.
  • Correlating APM spans with database queries based on trace and span IDs for end-to-end observability.

OTEL’s open standards foundation enables a consistent, extensible mechanism to unify application tracing with database telemetry in SolarWinds.

How Does the Integration Work?

  • Trace and Span Generation: As user requests flow through the application, OTEL creates traces composed of spans representing distinct operations, including database queries.
  • Trace ID Propagation: Trace IDs and span context propagate seamlessly across service calls and database client libraries instrumented with OTEL.
  • Span Enrichment for DB Calls: Database client spans capture attributes like query text, execution duration, and row counts.
  • Ingestion in SolarWinds Observability: OTEL traces and spans stream into the SolarWinds platform alongside database metrics collected by DBO agents.
  • Contextual Correlation: The platform correlates application traces, spans, and query metrics by matching trace and span IDs, providing unified visibility.
  • Unified Interface: Users explore correlated traces with embedded query profiles, query samples, and graphical execution plans, streamlining root cause analysis.

Key Features Highlighted by This Integration

Query Profiler

  • Visualizes top resource-consuming queries ranked by execution time, frequency, and impact.
  • Supports advanced filtering (by database, client IP, query verb, and time range).
  • Compares performance trends to detect regressions or improvements.

Query Execution Plan Insight

  • Offers graphical SQL query execution plans highlighting expensive operations and index usage.
  • Presents detailed runtime statistics unavailable in many native tools.
  • Provides actionable insights to optimize and tune queries effectively.

Query Samples and Trace Correlation

  • Links traced application requests directly to query executions.
  • Displays samples of actual queries with execution metadata.
  • Facilitates investigation of problematic queries in production contexts.

Explore More About SolarWinds Observability SaaS

By tightly coupling APM tracing with deep database observability via OpenTelemetry standards, SolarWinds Observability SaaS equips teams with comprehensive, correlated insights to identify and fix performance issues faster, reduce downtime, and deliver superior user experiences across distributed applications and databases.