Tracing with OpenTelemetry on OpenSearch®

Aiven for OpenSearch supports distributed tracing through Trace Analytics, which uses OpenTelemetry to instrument applications and collect spans, Data Prepper to process them, and OpenSearch indexes and Dashboards to investigate service performance across complete request paths. The pipeline typically sends application telemetry to an OpenTelemetry Collector, which can batch, sample, and forward data to Data Prepper; Data Prepper then creates searchable span and service-map indexes that enable users to identify regressions, visualize dependencies, inspect individual trace timelines, and correlate spans with logs carrying matching trace or span IDs. Data Prepper is generally preferred over the Collector’s alpha OpenSearch exporter because it also generates service maps, while secure ingestion requires a self-managed Data Prepper deployment with HTTPS credentials for Aiven and appropriate certificate configuration for its own endpoint. Kafka can be placed between collectors and Data Prepper to absorb incident-related traffic surges, retain traces for replay after pipeline changes, and keep each trace together through partitioning by trace ID. Since storing every span can become costly, tail sampling can preserve errors and slow traces while retaining only a portion of routine traffic, though this makes Trace Analytics metrics representative of the stored sample rather than all requests; index lifecycle policies can further manage retention. Because the components communicate through the OTLP standard, tracing infrastructure can be adjusted, including adding Kafka, without modifying application instrumentation.

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