StatsD and Dynatrace Integration

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.

The Dynatrace plugin allows users to send metrics collected by Telegraf directly to Dynatrace for monitoring and analysis. This integration enhances the observability of systems and applications, providing valuable insights into performance and operational health.

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.

Dynatrace

The Dynatrace plugin for Telegraf facilitates the transmission of metrics to the Dynatrace platform via the Dynatrace Metrics API V2. This plugin can function in two modes: it can run alongside the Dynatrace OneAgent, which automates authentication, or it can operate in a standalone configuration that requires manual specification of the URL and API token for environments without a OneAgent. The plugin primarily reports metrics as gauges unless explicitly configured to treat certain metrics as delta counters using the available config options. This feature empowers users to customize the behavior of metrics sent to Dynatrace, harnessing the robust capabilities of the platform for comprehensive performance monitoring and observability. It’s crucial for users to ensure compliance with version requirements for both Dynatrace and Telegraf, thereby optimizing compatibility and performance when integrating with the Dynatrace ecosystem.

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

Dynatrace

[[outputs.dynatrace]]
  ## For usage with the Dynatrace OneAgent you can omit any configuration,
  ## the only requirement is that the OneAgent is running on the same host.
  ## Only setup environment url and token if you want to monitor a Host without the OneAgent present.
  ##
  ## Your Dynatrace environment URL.
  ## For Dynatrace OneAgent you can leave this empty or set it to "http://127.0.0.1:14499/metrics/ingest" (default)
  ## For Dynatrace SaaS environments the URL scheme is "https://{your-environment-id}.live.dynatrace.com/api/v2/metrics/ingest"
  ## For Dynatrace Managed environments the URL scheme is "https://{your-domain}/e/{your-environment-id}/api/v2/metrics/ingest"
  url = ""

  ## Your Dynatrace API token.
  ## Create an API token within your Dynatrace environment, by navigating to Settings > Integration > Dynatrace API
  ## The API token needs data ingest scope permission. When using OneAgent, no API token is required.
  api_token = ""

  ## Optional prefix for metric names (e.g.: "telegraf")
  prefix = "telegraf"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Optional flag for ignoring tls certificate check
  # insecure_skip_verify = false

  ## Connection timeout, defaults to "5s" if not set.
  timeout = "5s"

  ## If you want metrics to be treated and reported as delta counters, add the metric names here
  additional_counters = [ ]

  ## In addition or as an alternative to additional_counters, if you want metrics to be treated and
  ## reported as delta counters using regular expression pattern matching
  additional_counters_patterns = [ ]

  ## 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

  ## Optional dimensions to be added to every metric
  # [outputs.dynatrace.default_dimensions]
  # default_key = "default value"

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.

Dynatrace

1. Cloud Infrastructure Monitoring: Utilize the Dynatrace plugin to monitor a cloud infrastructure setup, feeding real-time metrics from Telegraf into Dynatrace. This integration provides a holistic view of resource utilization, application performance, and system health, enabling proactive responses to performance issues across various cloud environments.

2. Custom Application Performance Metrics: Implement custom application-specific metrics by configuring the Dynatrace output plugin to send tailored metrics from Telegraf. By leveraging additional counters and dimension options, development teams can gain insights that are precisely aligned with their application’s operational requirements, allowing for targeted optimization efforts.

3. Multi-Environment Metrics Management: For organizations running multiple Dynatrace environments (e.g., production, staging, and development), use this plugin to manage metrics for all environments from a single Telegraf instance. With proper configuration of endpoints and API tokens, teams can maintain consistent monitoring practices throughout the SDLC, ensuring that performance anomalies are detected early in the development process.

4. Automated Alerting Based on Metrics Changes: Integrate the Dynatrace output plugin with an alerting mechanism that triggers notifications when specific metrics exceed defined thresholds. This scenario involves configuring additional counters to monitor crucial application performance indicators, enabling swift remediation actions to maintain service availability and user satisfaction.

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.