Mesos and MongoDB Integration

Input and output integration overview

This input plugin gathers metrics from Mesos.

The MongoDB Telegraf Plugin enables users to send metrics to a MongoDB database, automatically managing time series collections.

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.

MongoDB

This plugin sends metrics to MongoDB and seamlessly integrates with its time series functionality, allowing for automatic creation of collections as time series when they don’t already exist. It requires MongoDB version 5.0 or higher to utilize the time series collections feature, which is vital for efficiently storing and querying time-based data. This plugin enhances the monitoring capabilities by ensuring that all relevant metrics are stored and organized correctly within MongoDB, providing users the ability to leverage MongoDB’s powerful querying and aggregation features for time series 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

MongoDB

[[outputs.mongodb]]
              # connection string examples for mongodb
              dsn = "mongodb://localhost:27017"
              # dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"

              # overrides serverSelectionTimeoutMS in dsn if set
              # timeout = "30s"

              # default authentication, optional
              # authentication = "NONE"

              # for SCRAM-SHA-256 authentication
              # authentication = "SCRAM"
              # username = "root"
              # password = "***"

              # for x509 certificate authentication
              # authentication = "X509"
              # tls_ca = "ca.pem"
              # tls_key = "client.pem"
              # # tls_key_pwd = "changeme" # required for encrypted tls_key
              # insecure_skip_verify = false

              # database to store measurements and time series collections
              # database = "telegraf"

              # granularity can be seconds, minutes, or hours.
              # configuring this value will be based on your input collection frequency.
              # see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
              # granularity = "seconds"

              # optionally set a TTL to automatically expire documents from the measurement collections.
              # ttl = "360h"

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.

MongoDB

1. Dynamic Logging to MongoDB for IoT Devices: Utilize this plugin to collect and store metrics from a fleet of IoT devices in real-time. By sending device logs directly to MongoDB, you can create a centralized database that allows for easy access and querying of health metrics and performance data, enabling proactive maintenance and troubleshooting based on historical trends.

2. Time Series Analysis of Web Traffic: Use the MongoDB Telegraf Plugin to gather and analyze web traffic metrics over time. This application can help you understand peak usage times, user interactions, and behavior patterns, which can guide marketing strategies and infrastructure scaling decisions for improved user experience.

3. Automated Monitoring and Alerting System: Integrate the MongoDB plugin into an automated monitoring system that tracks application performance metrics. With time series collections, you can set up alerts based on specific thresholds, allowing your team to respond to potential issues before they affect users. This proactive management can enhance service reliability and overall performance.

4. Data Retention and TTL Management in Metrics Storage: Leverage the TTL feature for documents within MongoDB collections to auto-expire outdated metrics. This is particularly useful for environments where only recent performance data is relevant, preventing your MongoDB database from becoming cluttered with old metrics and ensuring efficient data management.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.