Netflow and Datadog Integration

Input and output integration overview

The Netflow plugin is designed to collect traffic flow data from devices using the Netflow v5, v9 and IPFIX protocols. By capturing detailed flow information, this plugin supports network observability and analysis, enabling administrators to monitor traffic patterns and performance metrics effectively.

The Datadog Telegraf Plugin enables the submission of metrics to the Datadog Metrics API, facilitating efficient monitoring and data analysis through a reliable metric ingestion process.

Integration details

Netflow

The Netflow plugin serves as a collector for flow data using protocols such as Netflow v5, Netflow v9, and IPFIX. This plugin allows users to gather important flow metrics from devices that support these protocols, including a variety of operational insights about traffic patterns, source/destination information, and protocol usage. The plugin leverages templates sent by flow devices to decode incoming data correctly, and it supports private enterprise number mappings for vendor-specific information. With features like adjustable service addresses and buffer sizes, the plugin provides flexibility in how it can be deployed within various network architectures, making it an essential tool for network monitoring and analysis.

Datadog

This plugin writes to the Datadog Metrics API, enabling users to send metrics for monitoring and performance analysis. By utilizing the Datadog API key, users can configure the plugin to establish a connection with Datadog’s v1 API. The plugin supports various configuration options including connection timeouts, HTTP proxy settings, and data compression methods, ensuring adaptability to different deployment environments. The ability to transform count metrics into rates enhances the integration of Telegraf with Datadog agents, particularly beneficial for applications that rely on real-time performance metrics.

Configuration

Netflow

[[inputs.netflow]]
  ## Address to listen for netflow,ipfix or sflow packets.
  ##   example: service_address = "udp://:2055"
  ##            service_address = "udp4://:2055"
  ##            service_address = "udp6://:2055"
  service_address = "udp://:2055"

  ## Set the size of the operating system's receive buffer.
  ##   example: read_buffer_size = "64KiB"
  ## Uses the system's default if not set.
  # read_buffer_size = ""

  ## Protocol version to use for decoding.
  ## Available options are
  ##   "ipfix"      -- IPFIX / Netflow v10 protocol (also works for Netflow v9)
  ##   "netflow v5" -- Netflow v5 protocol
  ##   "netflow v9" -- Netflow v9 protocol (also works for IPFIX)
  ##   "sflow v5"   -- sFlow v5 protocol
  # protocol = "ipfix"

  ## Private Enterprise Numbers (PEN) mappings for decoding
  ## This option allows to specify vendor-specific mapping files to use during
  ## decoding.
  # private_enterprise_number_files = []

  ## Log incoming packets for tracing issues
  # log_level = "trace"

Datadog

[[outputs.datadog]]
  ## Datadog API key
  apikey = "my-secret-key"

  ## Connection timeout.
  # timeout = "5s"

  ## Write URL override; useful for debugging.
  ## This plugin only supports the v1 API currently due to the authentication
  ## method used.
  # url = "https://app.datadoghq.com/api/v1/series"

  ## Set http_proxy
  # use_system_proxy = false
  # http_proxy_url = "http://localhost:8888"

  ## Override the default (none) compression used to send data.
  ## Supports: "zlib", "none"
  # compression = "none"

  ## When non-zero, converts count metrics submitted by inputs.statsd
  ## into rate, while dividing the metric value by this number.
  ## Note that in order for metrics to be submitted simultaenously alongside
  ## a Datadog agent, rate_interval has to match the interval used by the
  ## agent - which defaults to 10s
  # rate_interval = 0s

Input and output integration examples

Netflow

1. Traffic Analysis and Visualization: Use the Netflow plugin to collect traffic flow data and visualize it in real-time using an analytics platform. Administrators can create dashboards that display traffic patterns and anomalies, helping them understand bandwidth usage and user behavior.

2. Network Performance Optimization: Integrate the Netflow plugin with performance monitoring tools to identify bottlenecks and optimize the network. Analyze collected metrics to pinpoint areas where network resources can be improved, enhancing overall system performance.

3. Anomaly Detection for Security: Leverage the Netflow data for security analysis by feeding it into an anomaly detection system. This can help identify unusual traffic patterns that may indicate potential security threats, enabling quicker responses to prevent breaches.

4. Customized Alerts for Network Events: Configure threshold-based alerts using the Netflow plugin metrics to notify network administrators of unusual spikes or drops in traffic. This proactive monitoring can help in quickly addressing potential issues before they escalate.

Datadog

1. Real-Time Infrastructure Monitoring: Use the Datadog plugin to monitor server metrics in real-time by sending CPU usage and memory statistics directly to Datadog. This integration allows IT teams to visualize and analyze system performance metrics in a centralized dashboard, enabling proactive response to any emerging issues, such as resource bottlenecks or server overloads.

2. Application Performance Tracking: Leverage this plugin to submit application-specific metrics, such as request counts and error rates, to Datadog. By integrating with application monitoring tools, teams can correlate infrastructure metrics with application performance, providing insights that enable them to optimize code performance and improve user experience.

3. Anomaly Detection in Metrics: Configure the Datadog plugin to send metrics that can trigger alerts and notifications based on unusual patterns detected by Datadog’s machine learning features. This proactive monitoring helps teams swiftly react to potential outages or performance degradation before customers are impacted.

4. Integrating with Cloud Services: By utilizing the Datadog plugin to send metrics from cloud resources, IT teams can gain visibility into cloud application performance. Monitoring metrics like latency and error rates helps with ensuring service-level agreements (SLAs) are met and also assists in optimizing resource allocation across cloud environments.

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.