Amazon CloudWatch and Zabbix Integration

Input and output integration overview

This plugin will pull Metric Statistics from Amazon CloudWatch, streamlining the process of monitoring and analyzing AWS resources.

This plugin sends metrics to Zabbix via traps, allowing for efficient monitoring of systems and applications. It supports automated configuration and data sending based on dynamic metrics collected by Telegraf.

Integration details

Amazon CloudWatch

The Amazon CloudWatch Plugin allows users to pull detailed metric statistics from Amazon’s CloudWatch service. As a monitoring solution, CloudWatch enables users to track various metrics related to AWS resources and applications, facilitating improved operational and performance insights. The plugin uses a structured authentication method that prioritizes security and flexibility through a combination of STS (Security Token Service), shared credentials, environment variables, and EC2 instance profiles, ensuring robust access control to AWS resources. Key features include the ability to define specific metric namespaces, aggregated periods for metrics, and optional inclusion of linked accounts for cross-account monitoring. A significant aspect of this plugin is its capacity to handle both sparse and dense metric formats, allowing for varied output structures depending on user preference. Thus, it supports versatile use cases in cloud monitoring and analytics by providing comprehensive, timely data directly from CloudWatch.

Zabbix

The Telegraf Zabbix plugin is designed to send metrics to Zabbix, an open-source monitoring solution, using the trap protocol. It supports various versions from 3.0 to 6.0, ensuring compatibility with recent updates. The plugin facilitates easy integration with the Zabbix ecosystem, allowing users to send collected metrics and monitor system performance seamlessly. Key functionalities include the ability to define the address and port of the Zabbix server, options for prefixing keys, determining the type of data sent (active vs. trapper), and features for low-level discovery (LLD) enabling dynamic item creation based on the metrics observed. Configuration options also allow for autoregistration and resending intervals for LLD data, ensuring that the metrics are up-to-date and relevant. Additionally, the trap format used for sending metrics is structured to facilitate efficient data transfer and processing in Zabbix.

Configuration

Amazon CloudWatch

[[inputs.cloudwatch]]
  region = "us-east-1"
  # access_key = ""
  # secret_key = ""
  # token = ""
  # role_arn = ""
  # web_identity_token_file = ""
  # role_session_name = ""
  # profile = ""
  # shared_credential_file = ""
  # include_linked_accounts = false
  # endpoint_url = ""
  # use_system_proxy = false
  # http_proxy_url = "http://localhost:8888"
  period = "5m"
  delay = "5m"
  interval = "5m"
  #recently_active = "PT3H"
  # cache_ttl = "1h"
  namespaces = ["AWS/ELB"]
  # metric_format = "sparse"
  # ratelimit = 25
  # timeout = "5s"
  # batch_size = 500
  # statistic_include = ["average", "sum", "minimum", "maximum", sample_count]
  # statistic_exclude = []
  # [[inputs.cloudwatch.metrics]]
  #  names = ["Latency", "RequestCount"]
  #  [[inputs.cloudwatch.metrics.dimensions]]
  #    name = "LoadBalancerName"
  #    value = "p-example"

Zabbix

[[outputs.zabbix]]
  ## Address and (optional) port of the Zabbix server
  address = "zabbix.example.com:10051"

  ## Send metrics as type "Zabbix agent (active)"
  # agent_active = false

  ## Add prefix to all keys sent to Zabbix.
  # key_prefix = "telegraf."

  ## Name of the tag that contains the host name. Used to set the host in Zabbix.
  ## If the tag is not found, use the hostname of the system running Telegraf.
  # host_tag = "host"

  ## Skip measurement prefix to all keys sent to Zabbix.
  # skip_measurement_prefix = false

  ## This field will be sent as HostMetadata to Zabbix Server to autoregister the host.
  ## To enable this feature, this option must be set to a value other than "".
  # autoregister = ""

  ## Interval to resend auto-registration data to Zabbix.
  ## Only applies if autoregister feature is enabled.
  ## This value is a lower limit, the actual resend should be triggered by the next flush interval.
  # autoregister_resend_interval = "30m"

  ## Interval to send LLD data to Zabbix.
  ## This value is a lower limit, the actual resend should be triggered by the next flush interval.
  # lld_send_interval = "10m"

  ## Interval to delete stored LLD known data and start capturing it again.
  ## This value is a lower limit, the actual resend should be triggered by the next flush interval.
  # lld_clear_interval = "1h"

Input and output integration examples

Amazon CloudWatch

1. Cross-Account Monitoring: Utilize this plugin to monitor resources across multiple AWS accounts by enabling the include_linked_accounts option. This scenario allows companies managing multiple AWS accounts to aggregate metrics into a central monitoring dashboard, providing a unified view of all metrics while ensuring secure data access and compliance through proper role management.

2. Dynamic Alerting System: Integrate this plugin with alerting tools to create an automated system that triggers alerts based on defined thresholds for CloudWatch metrics. For instance, if latency metrics exceed specified limits, alerts can be sent to relevant teams, enabling proactive responses to performance issues and reducing downtime.

3. Cost Management Dashboard: Use the metrics gathered from the plugin to build a cost management dashboard that visualizes AWS service usage metrics over time. By correlating these metrics with billing data, organizations can identify high-cost services and take informed actions to optimize their resource usage and spending.

4. Performance Benchmarking for Applications: Leverage the metrics collected from applications running on AWS to perform performance benchmarks. For example, by tracking latency and request count metrics for an ELB, developers can assess the impact of application changes on its performance, making data-driven decisions for optimization.

Zabbix

1. Dynamic Monitoring of Containerized Applications: Integration of the Zabbix plugin can be leveraged to monitor Docker containers dynamically. As containers are created and removed, the plugin can automatically update Zabbix with the appropriate metrics, ensuring that monitoring stays current without manual configuration. This enhances visibility into resource usage and performance metrics for microservices orchestrated with Kubernetes or Docker Swarm.

2. Real-Time Performance Monitoring with Auto-registration: By enabling the autoregister feature, the plugin can automatically register hosts in Zabbix based on the metrics received. This scenario provides a streamlined approach to add new hosts to monitoring without manual setup, which is particularly useful in environments where hosts may frequently spin up and down, such as serverless architectures or cloud-based deployments.

3. Leveraging Low-level Discovery for Flexible Metric Capture: Using low-level discovery, this plugin allows Zabbix to adaptively create items for metrics that are not predefined. In a scenario involving multiple network devices reporting different performance metrics, the plugin can dynamically inform Zabbix about new metrics as they appear, thus ensuring comprehensive monitoring capabilities that evolve with the monitored systems.

4. Centralized Monitoring of Distributed Systems: The Zabbix plugin can be utilized in a centralized monitoring setup for distributed systems where multiple Telegraf instances are running across different geographical locations. By sending all metrics to a central Zabbix server, organizations can achieve a holistic view of their infrastructure’s performance and make informed operational decisions.

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