Salesforce and M3DB Integration

Input and output integration overview

The Salesforce Telegraf plugin collects crucial metrics regarding the API usage and limits in Salesforce organizations, enabling effective monitoring and management of API consumption.

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

Integration details

Salesforce

The Salesforce plugin allows users to gather metrics about API usage limits and the remaining usage within their Salesforce organization. By leveraging Salesforce’s REST API, specifically the limits endpoint, this plugin provides critical insights into how much of the API usage has been consumed and what remains available. This is particularly important for organizations that rely on Salesforce for their operations, as exceeding API limits can interrupt service and hinder business processes. The plugin processes data into a structured format containing maximum and remaining values for various API operations, making it easier for teams to monitor their usage and plan accordingly. The provided configuration allows users to customize their credentials, environment type (sandbox or production), and API version, ensuring flexibility in different deployment scenarios.

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

Salesforce

[[inputs.salesforce]]
  ## specify your credentials
  ##
  username = "your_username"
  password = "your_password"
  ##
  ## (optional) security token
  # security_token = "your_security_token"
  ##
  ## (optional) environment type (sandbox or production)
  ## default is: production
  ##
  # environment = "production"
  ##
  ## (optional) API version (default: "39.0")
  ##
  # version = "39.0"

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

Salesforce

1. Monitoring API Limit Usage for Scaling Decisions: Use the Salesforce plugin to track API limit usage over time and make informed decisions about when to scale Salesforce resources. By visualizing API consumption patterns, organizations can predict peak usage times, allowing them to proactively adjust their infrastructure or request higher limits as needed. This optimization leads to better performance and less downtime during critical business operations.

2. Automated Alert System for API Limit Exceedance: Integrate this plugin with a notification system to alert teams when API usage approaches critical limits. This setup not only ensures teams are proactively notified to prevent disruptions, but also helps in maintaining operational continuity and customer satisfaction. The alerts can be configured to trigger automated scripts that either adjust load or inform stakeholders accordingly.

3. Comparative Analysis of Multiple Salesforces: Leverage the Salesforce Input Plugin to gather metrics from multiple Salesforce instances across different departments or business units. By centralizing this data, organizations can perform comparative analyses to identify departments that may be exceeding their API limits more frequently than others. This allows for targeted discussions and strategies to balance API usage across the organization, leading to better resource allocation and efficiency.

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.

Feedback

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