StatsD and Nebius Cloud Monitoring 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.

This plugin allows users to effortlessly send aggregated metrics to Nebius Cloud Monitoring, leveraging the cloud’s monitoring solutions.

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.

Nebius Cloud Monitoring

The Nebius Cloud Monitoring plugin serves as an intermediary to send custom metrics to the Nebius Cloud Monitoring service. It is designed specifically to facilitate the monitoring of applications and services running within the Nebius ecosystem. This plugin is especially useful for users of the Nebius Cloud Platform who need to leverage cloud-based monitoring capabilities without significant configuration overhead. The plugin’s integration relies on Google Cloud metadata, allowing it to automatically fetch the necessary authentication credentials from the Compute instance it operates within. Key technical considerations include the management of reserved labels to ensure metrics are recorded correctly without conflicts.

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

Nebius Cloud Monitoring

[[outputs.nebius_cloud_monitoring]]
  ## Timeout for HTTP writes.
  # timeout = "20s"

  ## Nebius.Cloud monitoring API endpoint. Normally should not be changed
  # endpoint = "https://monitoring.api.il.nebius.cloud/monitoring/v2/data/write"

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.

Nebius Cloud Monitoring

1. Dynamic Application Monitoring: Integrate this plugin with your application to continuously send metrics related to resource usage, such as CPU and memory utilization, to Nebius Cloud Monitoring. By doing so, you can gain insights into the performance of your application, allowing for adjustments in real-time based on the metrics received.

2. Incident Response Automation: Use the Nebius Cloud Monitoring plugin to automatically send alerts and metrics when certain thresholds are reached. For instance, if a particular service’s uptime drops below a certain percentage, the plugin can be configured to report this directly to the monitoring service, enabling quicker incident response and resolution.

3. Comparative Service Analysis: Set up the plugin to send metrics from multiple cloud instances running different versions of the same application to Nebius Cloud Monitoring. This approach allows for a comparative analysis of resource usage and performance, helping teams determine which version performs best under similar workloads.

4. Aggregated Metrics Dashboard: Use this plugin to create a centralized dashboard displaying metrics from various services across your cloud instances. By aggregating different application metrics into one interface, stakeholders can assess the overall health and performance of their cloud environment easily.

Feedback

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