Mesos and Graphite Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider Mesos and InfluxDB.

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Time series database
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Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

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Input and output integration overview

This input plugin gathers metrics from Mesos.

The Graphite plugin enables users to send metrics collected by Telegraf into Graphite via TCP. This integration allows for efficient storage and visualization of time-series data using Graphite’s powerful capabilities.

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.

Graphite

This plugin writes metrics to Graphite via raw TCP, allowing for seamless integration of Telegraf collected metrics into the Graphite ecosystem. With this plugin, users can configure multiple TCP endpoints for load balancing, ensuring high availability and reliability in metric transmission. The ability to customize metric naming with prefixes and utilize various templating options enhances flexibility in how data is represented within Graphite. Additionally, support for Graphite tags and options for strict sanitization of metric names allow for robust data management, catering to the varying needs of users. This capability is essential for organizations looking to leverage Graphite’s powerful metrics storage and visualization while maintaining control over data representation.

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

Graphite

# Configuration for Graphite server to send metrics to
[[outputs.graphite]]
  ## TCP endpoint for your graphite instance.
  ## If multiple endpoints are configured, the output will be load balanced.
  ## Only one of the endpoints will be written to with each iteration.
  servers = ["localhost:2003"]

  ## Local address to bind when connecting to the server
  ## If empty or not set, the local address is automatically chosen.
  # local_address = ""

  ## Prefix metrics name
  prefix = ""

  ## Graphite output template
  ## see https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_OUTPUT.md
  template = "host.tags.measurement.field"

  ## Strict sanitization regex
  ## This is the default sanitization regex that is used on data passed to the
  ## graphite serializer. Users can add additional characters here if required.
  ## Be aware that the characters, '/' '@' '*' are always replaced with '_',
  ## '..' is replaced with '.', and '\' is removed even if added to the
  ## following regex.
  # graphite_strict_sanitize_regex = '[^a-zA-Z0-9-:._=\p{L}]'

  ## Enable Graphite tags support
  # graphite_tag_support = false

  ## Applied sanitization mode when graphite tag support is enabled.
  ## * strict - uses the regex specified above
  ## * compatible - allows for greater number of characters
  # graphite_tag_sanitize_mode = "strict"

  ## Character for separating metric name and field for Graphite tags
  # graphite_separator = "."

  ## Graphite templates patterns
  ## 1. Template for cpu
  ## 2. Template for disk*
  ## 3. Default template
  # templates = [
  #  "cpu tags.measurement.host.field",
  #  "disk* measurement.field",
  #  "host.measurement.tags.field"
  #]

  ## timeout in seconds for the write connection to graphite
  # timeout = "2s"

  ## 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

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.

Graphite

  1. Dynamic Metric Visualization: The Graphite plugin can be utilized to feed real-time metrics from various sources, such as application performance data or server health metrics, into Graphite. This dynamic integration allows teams to create interactive dashboards that visualize key performance indicators, track trends over time, and make data-driven decisions to enhance system performance.

  2. Load Balanced Metrics Collection: By configuring multiple TCP endpoints within the plugin, organizations can implement load balancing for metric transmission. This use case ensures that metric delivery is both resilient and efficient, reducing the risk of data loss during high-traffic periods and maintaining a reliable flow of information to Graphite.

  3. Customized Metrics Tagging: With support for Graphite tags, users can employ the Graphite plugin to enhance the granularity of their metrics. Tagging metrics with relevant information, such as application environment or service type, allows for more refined queries and analytics, enabling teams to drill down into specific areas of interest for better operational insights.

  4. Enhanced Data Sanitization: Leveraging the plugin’s strict sanitization options, users can ensure that their metric names comply with Graphite’s requirements. This proactive measure eliminates potential issues arising from invalid characters in metric names, allowing for cleaner data management and more accurate visualizations.

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.

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

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