NATS and Google Cloud Monitoring Integration

Input and output integration overview

The NATS Consumer Input Plugin enables real-time data consumption from NATS messaging subjects, integrating seamlessly into the Telegraf data pipeline for monitoring and metrics gathering.

The Stackdriver plugin allows users to send metrics directly to a specified project in Google Cloud Monitoring, facilitating robust monitoring capabilities across their cloud resources.

Integration details

NATS

The NATS Consumer Plugin allows Telegraf to read metrics from specified NATS subjects and create metrics based on supported input data formats. Utilizing a Queue Group allows multiple instances of Telegraf to read from a NATS cluster in parallel, enhancing throughput and reliability. This plugin also supports various authentication methods, including username/password, NATS credentials files, and nkey seed files, ensuring secure communication with the NATS servers. It is particularly useful in environments where data persistence and message reliability are critical, thanks to features such as JetStream that facilitate the consumption of historical messages. Additionally, the ability to configure various operational parameters makes this plugin suitable for high-throughput scenarios while maintaining performance integrity.

Google Cloud Monitoring

This plugin writes metrics to a project in Google Cloud Monitoring, which used to be known as Stackdriver. Authentication is a prerequisite and can be achieved via service accounts or user credentials. The plugin is designed to group metrics by a namespace variable and metric key, facilitating organized data management. However, users are encouraged to use the official naming format for enhanced query efficiency. The plugin supports additional configurations for managing metric representation and allows tags to be treated as resource labels. Notably, it imposes certain restrictions on the data it can accept, such as not allowing string values or points that are out of chronological order.

Configuration

NATS

[[inputs.nats_consumer]]
  ## urls of NATS servers
  servers = ["nats://localhost:4222"]

  ## subject(s) to consume
  ## If you use jetstream you need to set the subjects
  ## in jetstream_subjects
  subjects = ["telegraf"]

  ## jetstream subjects
  ## jetstream is a streaming technology inside of nats.
  ## With jetstream the nats-server persists messages and
  ## a consumer can consume historical messages. This is
  ## useful when telegraf needs to restart it don't miss a
  ## message. You need to configure the nats-server.
  ## https://docs.nats.io/nats-concepts/jetstream.
  jetstream_subjects = ["js_telegraf"]

  ## name a queue group
  queue_group = "telegraf_consumers"

  ## Optional authentication with username and password credentials
  # username = ""
  # password = ""

  ## Optional authentication with NATS credentials file (NATS 2.0)
  # credentials = "/etc/telegraf/nats.creds"

  ## Optional authentication with nkey seed file (NATS 2.0)
  # nkey_seed = "/etc/telegraf/seed.txt"

  ## Use Transport Layer Security
  # secure = false

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

  ## Sets the limits for pending msgs and bytes for each subscription
  ## These shouldn't need to be adjusted except in very high throughput scenarios
  # pending_message_limit = 65536
  # pending_bytes_limit = 67108864

  ## Max undelivered messages
  ## This plugin uses tracking metrics, which ensure messages are read to
  ## outputs before acknowledging them to the original broker to ensure data
  ## is not lost. This option sets the maximum messages to read from the
  ## broker that have not been written by an output.
  ##
  ## This value needs to be picked with awareness of the agent's
  ## metric_batch_size value as well. Setting max undelivered messages too high
  ## can result in a constant stream of data batches to the output. While
  ## setting it too low may never flush the broker's messages.
  # max_undelivered_messages = 1000

  ## Data format to consume.
  ## Each data format has its own unique set of configuration options, read
  ## more about them here:
  ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
  data_format = "influx"

Google Cloud Monitoring

[[outputs.stackdriver]]
  ## GCP Project
  project = "project-id"

  ## Quota Project
  ## Specifies the Google Cloud project that should be billed for metric ingestion.
  ## If omitted, the quota is charged to the service account’s default project.
  ## This is useful when sending metrics to multiple projects using a single service account.
  ## The caller must have the `serviceusage.services.use` permission on the specified project.
  # quota_project = ""

  ## The namespace for the metric descriptor
  ## This is optional and users are encouraged to set the namespace as a
  ## resource label instead. If omitted it is not included in the metric name.
  namespace = "telegraf"

  ## Metric Type Prefix
  ## The DNS name used with the metric type as a prefix.
  # metric_type_prefix = "custom.googleapis.com"

  ## Metric Name Format
  ## Specifies the layout of the metric name, choose from:
  ##  * path: 'metric_type_prefix_namespace_name_key'
  ##  * official: 'metric_type_prefix/namespace_name_key/kind'
  # metric_name_format = "path"

  ## Metric Data Type
  ## By default, telegraf will use whatever type the metric comes in as.
  ## However, for some use cases, forcing int64, may be preferred for values:
  ##   * source: use whatever was passed in
  ##   * double: preferred datatype to allow queries by PromQL.
  # metric_data_type = "source"

  ## Tags as resource labels
  ## Tags defined in this option, when they exist, are added as a resource
  ## label and not included as a metric label. The values from tags override
  ## the values defined under the resource_labels config options.
  # tags_as_resource_label = []

  ## Custom resource type
  # resource_type = "generic_node"

  ## Override metric type by metric name
  ## Metric names matching the values here, globbing supported, will have the
  ## metric type set to the corresponding type.
  # metric_counter = []
  # metric_gauge = []
  # metric_histogram = []

  ## 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

  ## Additional resource labels
  # [outputs.stackdriver.resource_labels]
  #   node_id = "$HOSTNAME"
  #   namespace = "myapp"
  #   location = "eu-north0"

Input and output integration examples

NATS

1. Real-Time Analytics Dashboard: Utilize the NATS plugin to gather metrics from various NATS subjects in real time and feed them into a centralized analytics dashboard. This setup allows for immediate visibility into live application performance, enabling teams to react swiftly to operational issues or performance degradation.

2. Distributed System Monitoring: Deploy multiple instances of Telegraf configured with the NATS plugin across a distributed architecture. This approach allows teams to aggregate metrics from various microservices efficiently, providing a holistic view of system health and performance while ensuring no messages are dropped during transmission.

3. Historical Message Recovery: Leverage the capabilities of NATS JetStream along with this plugin to recover and process historical messages after Telegraf has been restarted. This feature is particularly beneficial for applications that require high reliability, ensuring that no critical metrics are lost even in case of service disruptions.

4. Dynamic Load Balancing: Implement a dynamic load balancing scenario where Telegraf instances consume messages from a NATS cluster based on load. Adjust the queue group settings to control the number of active consumers, allowing for better resource utilization and performance scaling as demand fluctuations occur.

Google Cloud Monitoring

1. Multi-Project Metric Aggregation: Use this plugin to send aggregated metrics from various applications across different projects into a single Google Cloud Monitoring project. This use case helps centralize metrics for teams managing multiple applications, providing a unified view for performance monitoring and enhancing decision-making. By configuring different quota projects for billing, organizations can ensure proper cost management while benefiting from a consolidated monitoring strategy.

2. Anomaly Detection Setup: Integrate the plugin with a machine learning-based analytics tool that identifies anomalies in the collected metrics. Using the historical data provided by the plugin, the tool can learn normal baseline behavior and promptly alert the operations team when unusual patterns arise, enabling proactive troubleshooting and minimizing service disruptions.

3. Dynamic Resource Labeling: Implement dynamic tagging by utilizing the tags_as_resource_label option to adaptively attach resource labels based on runtime conditions. This setup allows metrics to provide context-sensitive information, such as varying environmental parameters or operational states, enhancing the granularity of monitoring and reporting without changing the fundamental metric structure.

4. Custom Metric Visualization Dashboards: Leverage the data collected by the Google Cloud Monitoring output plugin to feed a custom metrics visualization dashboard using a third-party framework. By visualizing metrics in real-time, teams can achieve better situational awareness, notably by correlating different metrics, improving operational decision-making, and streamlining performance management workflows.

Feedback

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