OpenStack and Datadog Integration

Input and output integration overview

This plugin collects metrics from essential OpenStack services, facilitating the monitoring and management of cloud infrastructures.

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

OpenStack

The OpenStack plugin allows users to collect performance metrics from various OpenStack services such as CINDER, GLANCE, HEAT, KEYSTONE, NEUTRON, and NOVA. It supports multiple OpenStack APIs to fetch critical metrics related to these services, enabling comprehensive monitoring and management of cloud resources. As organizations increasingly adopt OpenStack for their cloud infrastructure, this plugin plays a vital role in providing insights into resource usage, availability, and performance across the cloud environment. Configuration options allow for customized polling intervals and filtering unwanted tags to optimize performance and cardinals.

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

OpenStack

[[inputs.openstack]]
  ## The recommended interval to poll is '30m'

  ## The identity endpoint to authenticate against and get the service catalog from.
  authentication_endpoint = "https://my.openstack.cloud:5000"

  ## The domain to authenticate against when using a V3 identity endpoint.
  # domain = "default"

  ## The project to authenticate as.
  # project = "admin"

  ## User authentication credentials. Must have admin rights.
  username = "admin"
  password = "password"

  ## Available services are:
  ## "agents", "aggregates", "cinder_services", "flavors", "hypervisors",
  ## "networks", "nova_services", "ports", "projects", "servers",
  ## "serverdiagnostics", "services", "stacks", "storage_pools", "subnets",
  ## "volumes"
  # enabled_services = ["services", "projects", "hypervisors", "flavors", "networks", "volumes"]

  ## Query all instances of all tenants for the volumes and server services
  ## NOTE: Usually this is only permitted for administrators!
  # query_all_tenants = true

  ## output secrets (such as adminPass(for server) and UserID(for volume)).
  # output_secrets = false

  ## Amount of time allowed to complete the HTTP(s) request.
  # timeout = "5s"

  ## HTTP Proxy support
  # http_proxy_url = ""

  ## Optional TLS Config
  # tls_ca = /path/to/cafile
  # tls_cert = /path/to/certfile
  # tls_key = /path/to/keyfile
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## Options for tags received from Openstack
  # tag_prefix = "openstack_tag_"
  # tag_value = "true"

  ## Timestamp format for timestamp data received from Openstack.
  ## If false format is unix nanoseconds.
  # human_readable_timestamps = false

  ## Measure Openstack call duration
  # measure_openstack_requests = false

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

OpenStack

1. Cross-Cloud Management: Leverage the OpenStack plugin to monitor and manage multiple OpenStack clouds from a single Telegraf instance. By aggregating metrics across different clouds, organizations can gain insights into resource utilization and optimize their cloud architecture for cost and performance.

2. Automated Scaling Based on Metrics: Integrate the metrics gathered from OpenStack into an automated scaling solution. For example, if the plugin detects that a specific service’s performance is degraded, it can trigger auto-scaling rules to launch additional instances, ensuring that system performance remains optimal under varying workloads.

3. Performance Monitoring Dashboard: Use data collected by the OpenStack Telegraf plugin to power real-time monitoring dashboards. This setup provides visualizations of key metrics from OpenStack services, enabling stakeholders to quickly identify trends, pinpoint issues, and make data-driven decisions in managing their cloud infrastructure.

4. Reporting and Analysis of Service Availability: By utilizing the metrics collected from various OpenStack services, teams can generate detailed reports on service availability and performance over time. This information can help identify recurring issues, improve service delivery, and make informed decisions regarding changes in infrastructure or service configuration.

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.

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