Hashicorp Vault and M3DB Integration

Input and output integration overview

The Hashicorp Vault plugin for Telegraf allows for the collection of metrics from Hashicorp Vault services, facilitating monitoring and operational insights.

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

Integration details

Hashicorp Vault

The Hashicorp Vault plugin is designed to collect metrics from Vault agents running within a cluster. It enables Telegraf, an agent for collecting and reporting metrics, to interface with the Vault services, typically listening on a local address such as http://127.0.0.1:8200. This plugin requires a valid token for authorization, ensuring secure access to the Vault API. Users must configure either a token directly or provide a path to a token file, enhancing flexibility in authentication methods. Proper configuration of the timeout and optional TLS settings further relates to the security and responsiveness of the metrics collection process. As Vault is a critical tool in managing secrets and protecting sensitive data, monitoring its performance and health through this plugin is essential for maintaining operational security and efficiency.

M3DB

This configuration uses Telegraf’s HTTP output plugin with prometheusremotewrite 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.

Configuration

Hashicorp Vault

[[inputs.vault]]
  ## URL for the Vault agent
  # url = "http://127.0.0.1:8200"

  ## Use Vault token for authorization.
  ## Vault token configuration is mandatory.
  ## If both are empty or both are set, an error is thrown.
  # token_file = "/path/to/auth/token"
  ## OR
  token = "s.CDDrgg5zPv5ssI0Z2P4qxJj2"

  ## Set response_timeout (default 5 seconds)
  # response_timeout = "5s"

  ## Optional TLS Config
  # tls_ca = /path/to/cafile
  # tls_cert = /path/to/certfile
  # tls_key = /path/to/keyfile

M3DB

# Configuration for sending metrics to M3
[outputs.http]
  ## URL is the address to send metrics to
  url = "https://M3_HOST:M3_PORT/api/v1/prom/remote/write"

  ## HTTP Basic Auth credentials
  username = "admin"
  password = "password"

  ## Data format to output.
  data_format = "prometheusremotewrite"

  ## Outgoing HTTP headers
  [outputs.http.headers]
    Content-Type = "application/x-protobuf"
    Content-Encoding = "snappy"
    X-Prometheus-Remote-Write-Version = "0.1.0"

Input and output integration examples

Hashicorp Vault

1. Centralized Secret Management Monitoring: Utilize the Vault plugin to monitor multiple Vault instances across a distributed system, allowing for a unified view of secret access patterns and system health. This setup can help DevOps teams quickly identify any anomalies in secret access, providing essential insights into security postures across different environments.

2. Audit Logging Integration: Configure this plugin to feed monitoring metrics into an audit logging system, enabling organizations to have a comprehensive view of their Vault interactions. By correlating audit logs with metrics, teams can investigate issues, optimize performance, and ensure compliance with security policies more effectively.

3. Performance Benchmarking During Deployments: During application deployments that interact with Vault, use the plugin to monitor the effects of those deployments on Vault performance. This allows engineering teams to understand how changes impact secret management workflows and to proactively address performance bottlenecks, ensuring smooth deployment processes.

4. Alerting for Threshold Exceedance: Integrate this plugin with alerting mechanisms to notify administrators when metrics exceed predefined thresholds. This proactive monitoring can help teams respond swiftly to potential issues, maintaining system reliability and uptime by allowing them to take action before any serious incidents arise.

M3DB

1. Large-Scale Cloud Infrastructure Monitoring: 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.

2. Legacy System Metrics Ingestion: 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.

3. Centralized App Telemetry Aggregation: Collect application-specific telemetry using Telegraf’s plugin ecosystem (e.g., exec, http, jolokia) and push it into M3DB for centralized storage and query via PromQL. This enables unified analytics across diverse data sources.

4. Hybrid Cloud Observability: 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.

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