OpenTelemetry and OpenObserve Integration

Input and output integration overview

This plugin receives traces, metrics, and logs from OpenTelemetry clients and agents via gRPC, enabling comprehensive observability of applications.

This configuration pairs Telegraf’s HTTP output with OpenObserve’s native JSON ingestion API, turning any Telegraf agent into a first-class OpenObserve collector.

Integration details

OpenTelemetry

The OpenTelemetry plugin is designed to receive telemetry data such as traces, metrics, and logs from clients and agents implementing OpenTelemetry via gRPC. This plugin initiates a gRPC service that listens for incoming telemetry data, making it distinct from standard plugins that collect metrics at defined intervals. The OpenTelemetry ecosystem aids developers in observing and understanding their applications’ performance by providing a vendor-neutral way to instrument, generate, collect, and export telemetry data. Key features of this plugin include customizable connection timeouts, adjustable maximum message sizes for incoming data, and options for specifying span, log, and profile dimensions to tag the incoming metrics. With this flexibility, organizations can tailor their telemetry collection to meet precise observability requirements and ensure seamless data integration into systems like InfluxDB.

OpenObserve

OpenObserve is an open source observability platform written in Rust that stores data cost-effectively on object storage or local disk. It exposes REST endpoints such as /api/{org}/ingest/metrics/_json that accept batched metric documents conforming to a concise JSON schema, making it an attractive drop-in replacement for Loki or Elasticsearch stacks. The Telegraf HTTP output plugin streams metrics to arbitrary HTTP targets; when the "data_format = "json"" serializer is selected, Telegraf batches its metric objects into a payload that matches OpenObserve’s ingestion contract. The plugin supports configurable batch size, custom headers, TLS, and compression, allowing operators to authenticate with Basic or Bearer tokens and to enforce back-pressure without additional collectors. By reusing existing Telegraf agents already collecting system, application, or SNMP data, organizations can funnel rich telemetry into OpenObserve dashboards and SQL-like analytics with minimal overhead, enabling unified observability, long-term retention, and real-time alerting without vendor lock-in.

Configuration

OpenTelemetry

[[inputs.opentelemetry]]
  ## Override the default (0.0.0.0:4317) destination OpenTelemetry gRPC service
  ## address:port
  # service_address = "0.0.0.0:4317"

  ## Override the default (5s) new connection timeout
  # timeout = "5s"

  ## gRPC Maximum Message Size
  # max_msg_size = "4MB"

  ## Override the default span attributes to be used as line protocol tags.
  ## These are always included as tags:
  ## - trace ID
  ## - span ID
  ## Common attributes can be found here:
  ## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
  # span_dimensions = ["service.name", "span.name"]

  ## Override the default log record attributes to be used as line protocol tags.
  ## These are always included as tags, if available:
  ## - trace ID
  ## - span ID
  ## Common attributes can be found here:
  ## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
  ## When using InfluxDB for both logs and traces, be certain that log_record_dimensions
  ## matches the span_dimensions value.
  # log_record_dimensions = ["service.name"]

  ## Override the default profile attributes to be used as line protocol tags.
  ## These are always included as tags, if available:
  ## - profile_id
  ## - address
  ## - sample
  ## - sample_name
  ## - sample_unit
  ## - sample_type
  ## - sample_type_unit
  ## Common attributes can be found here:
  ## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
  # profile_dimensions = []

  ## Override the default (prometheus-v1) metrics schema.
  ## Supports: "prometheus-v1", "prometheus-v2"
  ## For more information about the alternatives, read the Prometheus input
  ## plugin notes.
  # metrics_schema = "prometheus-v1"

  ## Optional TLS Config.
  ## For advanced options: https://github.com/influxdata/telegraf/blob/v1.18.3/docs/TLS.md
  ##
  ## Set one or more allowed client CA certificate file names to
  ## enable mutually authenticated TLS connections.
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
  ## Add service certificate and key.
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"

OpenObserve

[[outputs.http]]
  ## OpenObserve JSON metrics ingestion endpoint
  url = "https://api.openobserve.ai/api/default/ingest/metrics/_json"

  ## Use POST to push batches
  method = "POST"

  ## Basic auth header (base64 encoded "username:password")
  headers = { Authorization = "Basic dXNlcjpwYXNzd29yZA==" }

  ## Timeout for HTTP requests
  timeout = "10s"

  ## Override Content-Type to match OpenObserve expectation
  content_type = "application/json"

  ## Force Telegraf to batch and serialize metrics as JSON
  data_format = "json"

  ## JSON serializer specific options
  json_timestamp_units = "1ms"

  ## Uncomment to restrict batch size
  # batch_size = 5000

Input and output integration examples

OpenTelemetry

1. Unified Monitoring Across Services: Use the OpenTelemetry plugin to collect and consolidate telemetry data from various microservices within a Kubernetes environment. By instrumenting each service with OpenTelemetry, you can utilize this plugin to gather a holistic view of application performance and dependencies in real-time, enabling faster troubleshooting and improved reliability of complex systems.

2. Enhanced Debugging with Traces: Implement this plugin to capture end-to-end traces of requests flowing through multiple services. For instance, when a user initiates a transaction that triggers several backend services, the OpenTelemetry plugin can record detailed traces that highlight performance bottlenecks, giving developers the necessary insights to debug issues and optimize their code.

3. Dynamic Load Testing and Performance Monitoring: Leverage the capabilities of this plugin during load testing phases by collecting live metrics and traces under simulated higher loads. This approach helps to evaluate the resilience of the application components and identify potential performance degradations preemptively, ensuring a smooth user experience in production.

4. Integrated Logging and Metrics for Real-Time Monitoring: Combine the OpenTelemetry plugin with logging frameworks to gather real-time logs alongside metric data, creating a powerful observability platform. For example, integrate it within a CI/CD pipeline to monitor builds and deployments, while collecting logs that help diagnose failures or performance issues in real-time.

OpenObserve

1. Edge Device Health Mirror: Deploy Telegraf on thousands of industrial IoT devices to capture temperature, vibration, and power metrics, then use this output to push JSON batches to OpenObserve. Plant operators gain a real-time overview of machine health and can trigger maintenance based on anomalies without relying on heavyweight collectors.

2. Blue-Green Deployment Canary: Attach a lightweight Telegraf sidecar to each Kubernetes release-candidate pod that scrapes /metrics and forwards container stats to a dedicated “canary” stream in OpenObserve. Continuous comparison of error rates between blue and green versions empowers the CI pipeline to auto-roll back poor performers within seconds.

3. Multi-Tenant SaaS Billing Pipeline: Emit per-customer usage counters via Telegraf and tag them with tenant_id; the HTTP plugin posts them to OpenObserve where SQL reports aggregate usage into invoices, eliminating separate metering services and simplifying compliance audits.

4. Security Threat Scoring: Fuse Suricata events and host resource metrics in Telegraf, deliver them to OpenObserve’s analytics engine, and run stream-processing rules that correlate spikes in suspicious traffic with CPU saturation to produce an actionable threat score and automatically open tickets in a SOAR platform.

Feedback

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