Google Cloud Storage and M3DB Integration

Input and output integration overview

The Google Cloud Storage plugin collects metrics from specified Google Cloud Storage buckets, providing insight into storage usage and performance.

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

Integration details

Google Cloud Storage

The Google Cloud Storage Telegraf plugin enables the collection of metrics from specified Google Cloud Storage buckets. As organizations increasingly rely on cloud storage solutions for their data management, the ability to monitor the performance and utilization of these resources becomes essential. This plugin is particularly useful for tracking how storage is used, understanding data patterns, and ensuring operational efficiency. By integrating with Google Cloud Storage APIs, it allows users to gather insights from their cloud environments, feeding metrics directly into monitoring systems for further analysis. The plugin supports various configuration options, enabling users to customize the data collection process based on their specific needs.

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

Google Cloud Storage

[[inputs.google_cloud_storage]]
  bucket = "my-bucket"
  # key_prefix = "my-bucket"
  offset_key = "offset_key"
  objects_per_iteration = 10
  data_format = "influx"
  # credentials_file = "path/to/my/creds.json"

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

Google Cloud Storage

1. Automated Backup Monitoring: Utilize the Google Cloud Storage plugin to regularly monitor the status of backup files stored in a Cloud Storage bucket. By configuring the plugin to track file metrics, organizations can automate alerts if backup sizes deviate from expected patterns, ensuring that data protection processes are functioning properly and any anomalies are promptly addressed.

2. Cost Optimization Insights: Integrate this plugin into a cost management tool to analyze the usage patterns of Cloud Storage. By collecting metrics on file sizes and access frequencies, teams can optimize their storage solutions and make informed decisions about data retention policies, potentially reducing unnecessary storage costs and improving resource allocation.

3. Compliance and Auditing: Use the plugin to generate metrics that aid in compliance verification for data stored in Google Cloud Storage. By providing detailed insights into data access and storage usage, organizations can ensure adherence to regulatory requirements, helping in audits and aligning with best practices for data governance.

4. Performance Benchmarking: Deploy the plugin to benchmark the performance of data retrieval and storage operations in Google Cloud Storage. By analyzing metrics over time, teams can identify performance bottlenecks or inefficiencies, allowing them to optimize their applications and infrastructure that depend on cloud storage services.

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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