OpenTelemetry and OpenSearch Integration

Input and output integration overview

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

<p>The OpenSearch Output Plugin allows users to send metrics directly to an OpenSearch instance using HTTP, thus facilitating effective data management and analytics within the OpenSearch ecosystem.</p>

Integration details

OpenTelemetry

<p>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.</p>

OpenSearch

<p>The OpenSearch Telegraf Plugin integrates with the OpenSearch database via HTTP, allowing for the streamlined collection and storage of metrics. As a powerful tool designed specifically for OpenSearch releases from 2.x, the plugin provides robust features while offering compatibility with 1.x through the original Elasticsearch plugin. This plugin facilitates the creation and management of indexes in OpenSearch, automatically managing templates and ensuring that data is structured efficiently for analysis. The plugin supports various configuration options such as index names, authentication, health checks, and value handling, allowing it to be tailored to diverse operational requirements. Its capabilities make it essential for organizations looking to harness the power of OpenSearch for metrics storage and querying.</p>

Configuration

OpenTelemetry

OpenSearch

Input and output integration examples

OpenTelemetry

<ol> <li> <p><strong>Unified Monitoring Across Services</strong>: 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.</p> </li> <li> <p><strong>Enhanced Debugging with Traces</strong>: 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.</p> </li> <li> <p><strong>Dynamic Load Testing and Performance Monitoring</strong>: 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.</p> </li> <li> <p><strong>Integrated Logging and Metrics for Real-Time Monitoring</strong>: 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.</p> </li> </ol>

OpenSearch

<ol> <li> <p><strong>Dynamic Indexing for Time-Series Data</strong>: Utilize the OpenSearch Telegraf plugin to dynamically create indexes for time-series metrics, ensuring that data is stored in an organized manner conducive to time-based queries. By defining index patterns using Go templates, users can leverage the plugin to create daily or monthly indexes, which can greatly simplify data management and retrieval over time, thus enhancing analytical performance.</p> </li> <li> <p><strong>Centralized Logging for Multi-Tenant Applications</strong>: Implement the OpenSearch plugin in a multi-tenant application where each tenant’s logs are sent to separate indexes. This enables targeted analysis and monitoring for each tenant while maintaining data isolation. By utilizing the index name templating feature, users can automatically create tenant-specific indexes, which not only streamlines the process but also enhances security and accessibility for tenant data.</p> </li> <li> <p><strong>Integration with Machine Learning for Anomaly Detection</strong>: Leverage the OpenSearch plugin alongside machine learning tools to automatically detect anomalies in metrics data. By configuring the plugin to send real-time metrics to OpenSearch, users can apply machine learning models on the incoming data streams to identify outliers or unusual patterns, facilitating proactive monitoring and swift remedial actions.</p> </li> <li> <p><strong>Enhanced Monitoring Dashboards with OpenSearch</strong>: Use the metrics collected from OpenSearch to create real-time dashboards that provide insights into system performance. By feeding metrics into OpenSearch, organizations can utilize OpenSearch Dashboards to visualize key performance indicators, allowing operations teams to quickly assess health and performance, and making data-driven decisions.</p> </li> </ol>

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.