Docker and Google BigQuery Integration

Input and output integration overview

The Docker input plugin allows you to collect metrics from your Docker containers using the Docker Engine API, facilitating enhanced visibility and monitoring of containerized applications.

The Google BigQuery plugin allows you to send metrics from Telegraf to Google Cloud BigQuery, a powerful data analysis tool.

Integration details

Docker

The Docker input plugin for Telegraf gathers valuable metrics from the Docker Engine API, providing insights into running containers. This plugin utilizes the Official Docker Client to interface with the Engine API, allowing users to monitor various container states, resource allocations, and performance metrics. With options for filtering containers by names and states, along with customizable tags and labels, this plugin supports flexibility in monitoring containerized applications in diverse environments, whether on local systems or within orchestration platforms like Kubernetes. Additionally, it addresses security considerations by requiring permissions for accessing Docker’s daemon and emphasizes proper configuration when deploying within containerized environments.

Google BigQuery

This plugin writes to Google Cloud BigQuery and requires authentication with Google Cloud using either a service account or user credentials. It accesses APIs that are chargeable and might incur costs. The plugin requires the dataset to specify under which BigQuery dataset the corresponding metrics tables reside. Each metric should have a corresponding table in BigQuery, with specific schema requirements for timestamps, tags, and fields.

Configuration

Docker

[[inputs.docker]]
  ## Docker Endpoint
  ##   To use TCP, set endpoint = "tcp://[ip]:[port]"
  ##   To use environment variables (ie, docker-machine), set endpoint = "ENV"
  endpoint = "unix:///var/run/docker.sock"

  ## Set to true to collect Swarm metrics(desired_replicas, running_replicas)
  ## Note: configure this in one of the manager nodes in a Swarm cluster.
  ## configuring in multiple Swarm managers results in duplication of metrics.
  gather_services = false

  ## Only collect metrics for these containers. Values will be appended to
  ## container_name_include.
  ## Deprecated (1.4.0), use container_name_include
  container_names = []

  ## Set the source tag for the metrics to the container ID hostname, eg first 12 chars
  source_tag = false

  ## Containers to include and exclude. Collect all if empty. Globs accepted.
  container_name_include = []
  container_name_exclude = []

  ## Container states to include and exclude. Globs accepted.
  ## When empty only containers in the "running" state will be captured.
  # container_state_include = []
  # container_state_exclude = []

  ## Objects to include for disk usage query
  ## Allowed values are "container", "image", "volume" 
  ## When empty disk usage is excluded
  storage_objects = []

  ## Timeout for docker list, info, and stats commands
  timeout = "5s"

  ## Whether to report for each container per-device blkio (8:0, 8:1...),
  ## network (eth0, eth1, ...) and cpu (cpu0, cpu1, ...) stats or not.
  ## Usage of this setting is discouraged since it will be deprecated in favor of 'perdevice_include'.
  ## Default value is 'true' for backwards compatibility, please set it to 'false' so that 'perdevice_include' setting
  ## is honored.
  perdevice = true

  ## Specifies for which classes a per-device metric should be issued
  ## Possible values are 'cpu' (cpu0, cpu1, ...), 'blkio' (8:0, 8:1, ...) and 'network' (eth0, eth1, ...)
  ## Please note that this setting has no effect if 'perdevice' is set to 'true'
  # perdevice_include = ["cpu"]

  ## Whether to report for each container total blkio and network stats or not.
  ## Usage of this setting is discouraged since it will be deprecated in favor of 'total_include'.
  ## Default value is 'false' for backwards compatibility, please set it to 'true' so that 'total_include' setting
  ## is honored.
  total = false

  ## Specifies for which classes a total metric should be issued. Total is an aggregated of the 'perdevice' values.
  ## Possible values are 'cpu', 'blkio' and 'network'
  ## Total 'cpu' is reported directly by Docker daemon, and 'network' and 'blkio' totals are aggregated by this plugin.
  ## Please note that this setting has no effect if 'total' is set to 'false'
  # total_include = ["cpu", "blkio", "network"]

  ## docker labels to include and exclude as tags.  Globs accepted.
  ## Note that an empty array for both will include all labels as tags
  docker_label_include = []
  docker_label_exclude = []

  ## Which environment variables should we use as a tag
  tag_env = ["JAVA_HOME", "HEAP_SIZE"]

  ## 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

Google BigQuery

# Configuration for Google Cloud BigQuery to send entries
[[outputs.bigquery]]
  ## Credentials File
  credentials_file = "/path/to/service/account/key.json"

  ## Google Cloud Platform Project
  # project = ""

  ## The namespace for the metric descriptor
  dataset = "telegraf"

  ## Timeout for BigQuery operations.
  # timeout = "5s"

  ## Character to replace hyphens on Metric name
  # replace_hyphen_to = "_"

  ## Write all metrics in a single compact table
  # compact_table = ""
  

Input and output integration examples

Docker

1. Monitoring the Performance of Containerized Applications: Use the Docker input plugin in order to track the CPU, memory, disk I/O, and network activity of applications running in Docker containers. By collecting these metrics, DevOps teams can proactively manage resource allocation, troubleshoot performance bottlenecks, and ensure optimal application performance across different environments.

2. Integrating with Kubernetes: Leverage this plugin to gather metrics from Docker containers orchestrated by Kubernetes. By filtering out unnecessary Kubernetes labels and focusing on key metrics, teams can streamline their monitoring solutions and create dashboards that provide insights into the overall health of microservices running within the Kubernetes cluster.

3. Capacity Planning and Resource Optimization: Use the metrics collected by the Docker input plugin to perform capacity planning for Docker deployments. Analyzing usage patterns helps identify underutilized resources and over-provisioned containers, guiding decisions on scaling up or down based on actual usage trends.

4. Automated Alerting for Container Anomalies: Set up alerting rules based on the metrics collected through the Docker plugin to notify teams of unusual spikes in resource usage or service disruptions. This proactive monitoring approach helps maintain service reliability and optimize the performance of containerized applications.

Google BigQuery

1. Centralized Metric Storage: Use the Google BigQuery Output Plugin to store all your metrics in one centralized location, making it easier to analyze patterns and trends over time.
2. Cost Monitoring: If you’re running multiple services across Google Cloud, this plugin can help you monitor and analyze costs associated with different metrics by sending them to BigQuery for deeper insights and reporting.
3. Real-Time Analytics: Combine this plugin with other Google Cloud services to enable real-time analytics on metric data, helping you make informed decisions quickly.

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.