Mesos and AWS Redshift Integration

Input and output integration overview

This input plugin gathers metrics from Mesos.

This plugin enables Telegraf to send metrics to Amazon Redshift using the PostgreSQL plugin, allowing metrics to be stored in a scalable, SQL-compatible data warehouse.

Integration details

Mesos

The Mesos plugin for Telegraf is designed to collect and report metrics from Apache Mesos clusters, which is essential for monitoring and observability in container orchestration and resource management. Mesos, known for its scalability and ability to manage diverse workloads, generates various metrics about resource usage, tasks, frameworks, and overall system performance. By utilizing this plugin, users can track the health and efficiency of their Mesos clusters, gather insights into resource distribution, and ensure that applications receive the necessary resources in a timely manner. The configuration allows users to specify the relevant Mesos master’s details, along with the desired metric groups to collect, making it adaptable to different deployments and monitoring needs. Overall, this plugin integrates seamlessly within the Telegraf collection pipeline, supporting detailed observability for cloud-native environments.

AWS Redshift

This configuration uses the Telegraf PostgreSQL plugin to send metrics to Amazon Redshift, AWS’s fully managed cloud data warehouse that supports SQL-based analytics at scale. Although Redshift is based on PostgreSQL 8.0.2, it does not support all standard PostgreSQL features such as full JSONB, stored procedures, or upserts. Therefore, care must be taken to predefine compatible tables and schema when using Telegraf for Redshift integration. This setup is ideal for use cases that benefit from long-term, high-volume metric storage and integration with AWS analytics tools like QuickSight or Redshift Spectrum. Metrics stored in Redshift can be joined with business datasets for rich observability and BI analysis.

Configuration

Mesos

[[inputs.mesos]]
  ## Timeout, in ms.
  timeout = 100

  ## A list of Mesos masters.
  masters = ["http://localhost:5050"]

  ## Master metrics groups to be collected, by default, all enabled.
  master_collections = [
    "resources",
    "master",
    "system",
    "agents",
    "frameworks",
    "framework_offers",
    "tasks",
    "messages",
    "evqueue",
    "registrar",
    "allocator",
  ]

  ## A list of Mesos slaves, default is []
  # slaves = []

  ## Slave metrics groups to be collected, by default, all enabled.
  # slave_collections = [
  #   "resources",
  #   "agent",
  #   "system",
  #   "executors",
  #   "tasks",
  #   "messages",
  # ]

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

AWS Redshift

[[outputs.postgresql]]
  ## Redshift connection settings
  host = "redshift-cluster.example.us-west-2.redshift.amazonaws.com"
  port = 5439
  user = "telegraf"
  password = "YourRedshiftPassword"
  database = "metrics"
  sslmode = "require"

  ## Optional: specify a dynamic table template for inserting metrics
  table_template = "telegraf_metrics"

  ## Note: Redshift does not support all PostgreSQL features; ensure your table exists and is compatible

Input and output integration examples

Mesos

1. Resource Utilization Monitoring: Use the Mesos plugin to continually monitor CPU, memory, and disk usage across your Mesos cluster. For a rapidly scaling application, tracking these metrics helps ensure that resources are dynamically allocated according to workloads, preventing bottlenecks and optimizing performance.

2. Framework Performance Analysis: Integrate this plugin to measure the performance of different frameworks running on Mesos. By comparing active frameworks and their task success rates, you can identify which frameworks provide the best resource efficiency or may require optimization.

3. Alerts for System Health: Set up alerts based on metrics collected by the Mesos plugin to notify engineering teams when resource utilization exceeds key thresholds or when specific tasks fail. This allows for proactive intervention and maintenance before critical failures occur.

4. Capacity Planning: Utilize gathered metrics to analyze historical resource usage patterns to assist in capacity planning. By understanding peak loads and resource utilization trends, teams can make informed decisions on scaling infrastructure and deploying additional resources as needed.

AWS Redshift

1. Business-Aware Infrastructure Monitoring: Store infrastructure metrics from Telegraf in Redshift alongside sales, marketing, or customer engagement data. Analysts can correlate system performance with business KPIs using SQL joins and window functions.

2. Historical Trend Analysis for Cloud Resources: Use Telegraf to continuously log CPU, memory, and I/O metrics to Redshift. Combine with time-series SQL queries and visualization tools like Amazon QuickSight to spot trends and forecast resource demand.

3. Security Auditing of System Behavior: Send metrics related to system logins, file changes, or resource spikes into Redshift. Analysts can build dashboards or reports for compliance auditing using SQL queries across multi-year data sets.

4. Cross-Environment SLA Reporting: Aggregate SLA metrics from multiple cloud accounts and regions using Telegraf, and push them to a central Redshift warehouse. Enable unified SLA compliance dashboards and executive reporting via a single SQL interface.

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