StatsD and Sensu 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 StatsD and InfluxDB.

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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.

See Ways to Get Started

Input and output integration overview

The StatsD input plugin captures metrics from a StatsD server by running a listener service in the background, allowing for comprehensive performance monitoring and metric aggregation.

This plugin writes metrics events to Sensu via its HTTP events API, enabling seamless integration with the Sensu monitoring platform.

Integration details

StatsD

The StatsD input plugin is designed to gather metrics from a StatsD server by running a backgrounded StatsD listener service while Telegraf is active. This plugin leverages the format of the StatsD messages as established by the original Etsy implementation, which allows for various types of metrics including gauges, counters, sets, timings, histograms, and distributions. The capabilities of the StatsD plugin extend to parsing tags and extending the standard protocol with features that accommodate InfluxDB’s tagging system. It can handle messages sent via different protocols (UDP or TCP), manage multiple metric metrics effectively, and offers advanced configurations for optimal metric handling such as percentiles calculation and data transformation templates. This flexibility empowers users to track application performance comprehensively, making it an essential tool for robust monitoring setups.

Sensu

This plugin writes metrics events to Sensu via its HTTP events API. Sensu is a monitoring system that enables users to collect, analyze, and manage metrics from various components in their infrastructure. The plugin facilitates the integration of Telegraf, a server agent for collecting and reporting metrics, with the Sensu monitoring platform. Users can configure settings such as backend and agent API URLs, API keys for authentication, and optional TLS settings. The plugin’s core functionality is centered around sending metric events, including check and entity specifications, to Sensu, allowing for comprehensive monitoring and alerting. The API reference provides extensive details about the events and metrics structure, ensuring users can efficiently leverage Sensu’s capabilities for observability and incident response.

Configuration

StatsD

[[inputs.statsd]]
  ## Protocol, must be "tcp", "udp4", "udp6" or "udp" (default=udp)
  protocol = "udp"

  ## MaxTCPConnection - applicable when protocol is set to tcp (default=250)
  max_tcp_connections = 250

  ## Enable TCP keep alive probes (default=false)
  tcp_keep_alive = false

  ## Specifies the keep-alive period for an active network connection.
  ## Only applies to TCP sockets and will be ignored if tcp_keep_alive is false.
  ## Defaults to the OS configuration.
  # tcp_keep_alive_period = "2h"

  ## Address and port to host UDP listener on
  service_address = ":8125"

  ## The following configuration options control when telegraf clears it's cache
  ## of previous values. If set to false, then telegraf will only clear it's
  ## cache when the daemon is restarted.
  ## Reset gauges every interval (default=true)
  delete_gauges = true
  ## Reset counters every interval (default=true)
  delete_counters = true
  ## Reset sets every interval (default=true)
  delete_sets = true
  ## Reset timings & histograms every interval (default=true)
  delete_timings = true

  ## Enable aggregation temporality adds temporality=delta or temporality=commulative tag, and
  ## start_time field, which adds the start time of the metric accumulation.
  ## You should use this when using OpenTelemetry output.
  # enable_aggregation_temporality = false

  ## Percentiles to calculate for timing & histogram stats.
  percentiles = [50.0, 90.0, 99.0, 99.9, 99.95, 100.0]

  ## separator to use between elements of a statsd metric
  metric_separator = "_"

  ## Parses tags in the datadog statsd format
  ## http://docs.datadoghq.com/guides/dogstatsd/
  ## deprecated in 1.10; use datadog_extensions option instead
  parse_data_dog_tags = false

  ## Parses extensions to statsd in the datadog statsd format
  ## currently supports metrics and datadog tags.
  ## http://docs.datadoghq.com/guides/dogstatsd/
  datadog_extensions = false

  ## Parses distributions metric as specified in the datadog statsd format
  ## https://docs.datadoghq.com/developers/metrics/types/?tab=distribution#definition
  datadog_distributions = false

  ## Keep or drop the container id as tag. Included as optional field
  ## in DogStatsD protocol v1.2 if source is running in Kubernetes
  ## https://docs.datadoghq.com/developers/dogstatsd/datagram_shell/?tab=metrics#dogstatsd-protocol-v12
  datadog_keep_container_tag = false

  ## Statsd data translation templates, more info can be read here:
  ## https://github.com/influxdata/telegraf/blob/master/docs/TEMPLATE_PATTERN.md
  # templates = [
  #     "cpu.* measurement*"
  # ]

  ## Number of UDP messages allowed to queue up, once filled,
  ## the statsd server will start dropping packets
  allowed_pending_messages = 10000

  ## Number of worker threads used to parse the incoming messages.
  # number_workers_threads = 5

  ## Number of timing/histogram values to track per-measurement in the
  ## calculation of percentiles. Raising this limit increases the accuracy
  ## of percentiles but also increases the memory usage and cpu time.
  percentile_limit = 1000

  ## Maximum socket buffer size in bytes, once the buffer fills up, metrics
  ## will start dropping.  Defaults to the OS default.
  # read_buffer_size = 65535

  ## Max duration (TTL) for each metric to stay cached/reported without being updated.
  # max_ttl = "10h"

  ## Sanitize name method
  ## By default, telegraf will pass names directly as they are received.
  ## However, upstream statsd now does sanitization of names which can be
  ## enabled by using the "upstream" method option. This option will a) replace
  ## white space with '_', replace '/' with '-', and remove characters not
  ## matching 'a-zA-Z_\-0-9\.;='.
  #sanitize_name_method = ""

  ## Replace dots (.) with underscore (_) and dashes (-) with
  ## double underscore (__) in metric names.
  # convert_names = false

  ## Convert all numeric counters to float
  ## Enabling this would ensure that both counters and guages are both emitted
  ## as floats.
  # float_counters = false

Sensu

[[outputs.sensu]]
  ## BACKEND API URL is the Sensu Backend API root URL to send metrics to
  ## (protocol, host, and port only). The output plugin will automatically
  ## append the corresponding backend API path
  ## /api/core/v2/namespaces/:entity_namespace/events/:entity_name/:check_name).
  ##
  ## Backend Events API reference:
  ## https://docs.sensu.io/sensu-go/latest/api/events/
  ##
  ## AGENT API URL is the Sensu Agent API root URL to send metrics to
  ## (protocol, host, and port only). The output plugin will automatically
  ## append the correspeonding agent API path (/events).
  ##
  ## Agent API Events API reference:
  ## https://docs.sensu.io/sensu-go/latest/api/events/
  ##
  ## NOTE: if backend_api_url and agent_api_url and api_key are set, the output
  ## plugin will use backend_api_url. If backend_api_url and agent_api_url are
  ## not provided, the output plugin will default to use an agent_api_url of
  ## http://127.0.0.1:3031
  ##
  # backend_api_url = "http://127.0.0.1:8080"
  # agent_api_url = "http://127.0.0.1:3031"

  ## API KEY is the Sensu Backend API token
  ## Generate a new API token via:
  ##
  ## $ sensuctl cluster-role create telegraf --verb create --resource events,entities
  ## $ sensuctl cluster-role-binding create telegraf --cluster-role telegraf --group telegraf
  ## $ sensuctl user create telegraf --group telegraf --password REDACTED
  ## $ sensuctl api-key grant telegraf
  ##
  ## For more information on Sensu RBAC profiles & API tokens, please visit:
  ## - https://docs.sensu.io/sensu-go/latest/reference/rbac/
  ## - https://docs.sensu.io/sensu-go/latest/reference/apikeys/
  ##
  # api_key = "${SENSU_API_KEY}"

  ## 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

  ## Timeout for HTTP message
  # timeout = "5s"

  ## HTTP Content-Encoding for write request body, can be set to "gzip" to
  ## compress body or "identity" to apply no encoding.
  # content_encoding = "identity"

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of
  ## the table

  ## Sensu Event details
  ##
  ## Below are the event details to be sent to Sensu.  The main portions of the
  ## event are the check, entity, and metrics specifications. For more information
  ## on Sensu events and its components, please visit:
  ## - Events - https://docs.sensu.io/sensu-go/latest/reference/events
  ## - Checks -  https://docs.sensu.io/sensu-go/latest/reference/checks
  ## - Entities - https://docs.sensu.io/sensu-go/latest/reference/entities
  ## - Metrics - https://docs.sensu.io/sensu-go/latest/reference/events#metrics
  ##
  ## Check specification
  ## The check name is the name to give the Sensu check associated with the event
  ## created. This maps to check.metadata.name in the event.
  [outputs.sensu.check]
    name = "telegraf"

  ## Entity specification
  ## Configure the entity name and namespace, if necessary. This will be part of
  ## the entity.metadata in the event.
  ##
  ## NOTE: if the output plugin is configured to send events to a
  ## backend_api_url and entity_name is not set, the value returned by
  ## os.Hostname() will be used; if the output plugin is configured to send
  ## events to an agent_api_url, entity_name and entity_namespace are not used.
  # [outputs.sensu.entity]
  #   name = "server-01"
  #   namespace = "default"

  ## Metrics specification
  ## Configure the tags for the metrics that are sent as part of the Sensu event
  # [outputs.sensu.tags]
  #   source = "telegraf"

  ## Configure the handler(s) for processing the provided metrics
  # [outputs.sensu.metrics]
  #   handlers = ["influxdb","elasticsearch"]

Input and output integration examples

StatsD

  1. Real-time Application Performance Monitoring: Utilize the StatsD input plugin to monitor application performance metrics in real-time. By configuring your application to send various metrics to a StatsD server, teams can leverage this plugin to analyze performance bottlenecks, track user activity, and ensure resource optimization dynamically. The combination of historical and real-time metrics allows for proactive troubleshooting and enhances the responsiveness of issue resolution processes.

  2. Tracking User Engagement Metrics in Web Applications: Use the StatsD plugin to gather user engagement statistics, such as page views, click events, and interaction times. By sending these metrics to the StatsD server, businesses can derive valuable insights into user behavior, enabling them to make data-driven decisions to improve user experience and interface design based on quantitative feedback. This can significantly enhance the effectiveness of marketing strategies and product development efforts.

  3. Infrastructure Health Monitoring: Deploy the StatsD plugin to monitor the health of your server infrastructure by tracking metrics such as resource utilization, server response times, and network performance. With this setup, DevOps teams can gain detailed visibility into system performance, effectively anticipating issues before they escalate. This enables a proactive approach to infrastructure management, minimizing downtimes and ensuring optimal service delivery.

  4. Creating Comprehensive Service Dashboards: Integrate StatsD with visualization tools to create comprehensive dashboards that reflect the status and health of services across an architecture. For instance, combining data from multiple services logged through StatsD can transform raw metrics into actionable insights, showcasing system performance trends over time. This capability empowers stakeholders to maintain oversight and drive decisions based on visualized data sets, enhancing overall operational transparency.

Sensu

  1. Real-Time Infrastructure Monitoring: Utilize the Sensu plugin to send performance metrics from various servers and services directly to Sensu. This real-time data flow enables teams to visualize infrastructure health, track resource usage, and receive immediate alerts for any anomalies detected. By centralizing monitoring through Sensu, organizations can create a holistic view of their systems and respond swiftly to issues.

  2. Automated Incident Response Workflows: Leverage the plugin to automatically trigger incident response workflows based on the metrics events sent to Sensu. For example, if CPU usage exceeds a defined threshold, the Sensu system can be configured to alert the operations team, which can then initiate automated remediation processes, reducing downtime and maintaining system reliability. This integration allows for proactive management of system resources.

  3. Dynamic Scaling of Resources: Use the Sensu plugin to feed metrics into an auto-scaling system that adjusts resources based on demand. By tracking metrics like request load and resource utilization, organizations can automatically scale their infrastructure up or down, ensuring optimal performance and cost efficiency without manual intervention.

  4. Centralized Logging and Monitoring: Combine the Sensu with logging tools to send logs and performance metrics to a centralized monitoring system. This comprehensive approach allows teams to correlate logs with metric events, providing deeper insights into system behavior and performance, which aids in troubleshooting and performance optimization over time.

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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