Kinesis and M3DB Integration

Input and output integration overview

<p>The Kinesis plugin enables you to read from Kinesis data streams, supporting various data formats and configurations.</p>

<p>This plugin allows Telegraf to stream metrics to M3DB using the Prometheus Remote Write protocol, enabling scalable ingestion through the M3 Coordinator.</p>

Integration details

Kinesis

<p>The Kinesis Telegraf plugin is designed to read from Amazon Kinesis data streams, enabling users to gather metrics in real-time. As a service input plugin, it operates by listening for incoming data rather than polling at regular intervals. The configuration specifies various options including the AWS region, stream name, authentication credentials, and data formats. It supports tracking of undelivered messages to prevent data loss, and users can utilize DynamoDB for maintaining checkpoints of the last processed records. This plugin is particularly useful for applications requiring reliable and scalable stream processing alongside other monitoring needs.</p>

M3DB

<p>This configuration uses Telegraf’s HTTP output plugin with <code>prometheusremotewrite</code> format to send metrics directly to M3DB through the M3 Coordinator. M3DB is a distributed time series database designed for scalable, high-throughput metric storage. It supports ingestion of Prometheus remote write data via its Coordinator component, which manages translation and routing into the M3DB cluster. This approach enables organizations to collect metrics from systems that aren’t natively instrumented for Prometheus (e.g., Windows, SNMP, legacy systems) and ingest them efficiently into M3’s long-term, high-performance storage engine. The setup is ideal for high-scale observability stacks with Prometheus compatibility requirements.</p>

Configuration

Kinesis

M3DB

Input and output integration examples

Kinesis

<ol> <li> <p><strong>Real-Time Data Processing with Kinesis</strong>: This use case involves integrating the Kinesis plugin with a monitoring dashboard to analyze incoming data metrics in real-time. For instance, an application could consume logs from multiple services and present them visually, allowing operations teams to quickly identify trends and react to anomalies as they occur.</p> </li> <li> <p><strong>Serverless Log Aggregation</strong>: Utilize this plugin in a serverless architecture where Kinesis streams aggregate logs from various microservices. The plugin can create metrics that help detect issues in the system, automating alerting processes through third-party integrations, enabling teams to minimize downtime and improve reliability.</p> </li> <li> <p><strong>Dynamic Scaling Based on Stream Metrics</strong>: Implement a solution where stream metrics consumed by the Kinesis plugin could be used to adjust resources dynamically. For example, if the number of records processed spikes, corresponding scale-up actions could be triggered to handle the increased load, ensuring optimal resource allocation and performance.</p> </li> <li> <p><strong>Data Pipeline to S3 with Checkpointing</strong>: Create a robust data pipeline where Kinesis stream data is processed through the Telegraf Kinesis plugin, with checkpoints stored in DynamoDB. This approach can ensure data consistency and reliability, as it manages the state of processed data, enabling seamless integration with downstream data lakes or storage solutions.</p> </li> </ol>

M3DB

<ol> <li> <p><strong>Large-Scale Cloud Infrastructure Monitoring</strong>: Deploy Telegraf agents across thousands of virtual machines and containers to collect metrics and stream them into M3DB through the M3 Coordinator. This provides reliable, long-term visibility with minimal storage overhead and high availability.</p> </li> <li> <p><strong>Legacy System Metrics Ingestion</strong>: Use Telegraf to gather metrics from older systems that lack native Prometheus exporters (e.g., Windows servers, SNMP devices) and forward them to M3DB via remote write. This bridges modern observability workflows with legacy infrastructure.</p> </li> <li> <p><strong>Centralized App Telemetry Aggregation</strong>: Collect application-specific telemetry using Telegraf’s plugin ecosystem (e.g., <code>exec</code>, <code>http</code>, <code>jolokia</code>) and push it into M3DB for centralized storage and query via PromQL. This enables unified analytics across diverse data sources.</p> </li> <li> <p><strong>Hybrid Cloud Observability</strong>: Install Telegraf agents on-prem and in the cloud to collect and remote-write metrics into a centralized M3DB cluster. This ensures consistent visibility across environments while avoiding the complexity of running Prometheus federation layers.</p> </li> </ol>

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