Kubernetes and PostgreSQL Integration

Input and output integration overview

This plugin captures metrics for Kubernetes pods and containers by communicating with the Kubelet API.

The Telegraf PostgreSQL plugin allows you to efficiently write metrics to a PostgreSQL database while automatically managing the database schema.

Integration details

Kubernetes

The Kubernetes input plugin interfaces with the Kubelet API to gather metrics for running pods and containers on a single host, ideally as part of a daemonset in a Kubernetes installation. By operating on each node within the cluster, it collects metrics from the locally running kubelet, ensuring that the data reflects the real-time state of the environment. Being a rapidly evolving project, Kubernetes sees frequent updates, and this plugin adheres to the major cloud providers’ supported versions, maintaining compatibility across multiple releases within a limited time span. Significant consideration is given to the potential high series cardinality, which can burden the database; thus, users are advised to implement filtering techniques and retention policies to manage this load effectively. Configuration options provide flexible customization of the plugin’s behavior to integrate seamlessly into different setups, enhancing its utility in monitoring Kubernetes environments.

PostgreSQL

The PostgreSQL plugin enables users to write metrics to a PostgreSQL database or a compatible database, providing robust support for schema management by automatically updating missing columns. The plugin is designed to facilitate integration with monitoring solutions, allowing users to efficiently store and manage time series data. It offers configurable options for connection settings, concurrency, and error handling, and supports advanced features such as JSONB storage for tags and fields, foreign key tagging, templated schema modifications, and support for unsigned integer data types through the pguint extension.

Configuration

Kubernetes

[[inputs.kubernetes]]
  ## URL for the kubelet, if empty read metrics from all nodes in the cluster
  url = "http://127.0.0.1:10255"

  ## Use bearer token for authorization. ('bearer_token' takes priority)
  ## If both of these are empty, we'll use the default serviceaccount:
  ## at: /var/run/secrets/kubernetes.io/serviceaccount/token
  ##
  ## To re-read the token at each interval, please use a file with the
  ## bearer_token option. If given a string, Telegraf will always use that
  ## token.
  # bearer_token = "/var/run/secrets/kubernetes.io/serviceaccount/token"
  ## OR
  # bearer_token_string = "abc_123"

  ## Kubernetes Node Metric Name
  ## The default Kubernetes node metric name (i.e. kubernetes_node) is the same
  ## for the kubernetes and kube_inventory plugins. To avoid conflicts, set this
  ## option to a different value.
  # node_metric_name = "kubernetes_node"

  ## Pod labels to be added as tags.  An empty array for both include and
  ## exclude will include all labels.
  # label_include = []
  # label_exclude = ["*"]

  ## Set response_timeout (default 5 seconds)
  # response_timeout = "5s"

  ## 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

PostgreSQL

# Publishes metrics to a postgresql database
[[outputs.postgresql]]
  ## Specify connection address via the standard libpq connection string:
  ##   host=... user=... password=... sslmode=... dbname=...
  ## Or a URL:
  ##   postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
  ## See https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
  ##
  ## All connection parameters are optional. Environment vars are also supported.
  ## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
  ## All supported vars can be found here:
  ##  https://www.postgresql.org/docs/current/libpq-envars.html
  ##
  ## Non-standard parameters:
  ##   pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
  ##   pool_min_conns (default: 0) - Minimum size of connection pool.
  ##   pool_max_conn_lifetime (default: 0s) - Maximum age of a connection before closing.
  ##   pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
  ##   pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
  # connection = ""

  ## Postgres schema to use.
  # schema = "public"

  ## Store tags as foreign keys in the metrics table. Default is false.
  # tags_as_foreign_keys = false

  ## Suffix to append to table name (measurement name) for the foreign tag table.
  # tag_table_suffix = "_tag"

  ## Deny inserting metrics if the foreign tag can't be inserted.
  # foreign_tag_constraint = false

  ## Store all tags as a JSONB object in a single 'tags' column.
  # tags_as_jsonb = false

  ## Store all fields as a JSONB object in a single 'fields' column.
  # fields_as_jsonb = false

  ## Name of the timestamp column
  ## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
  # timestamp_column_name = "time"

  ## Type of the timestamp column
  ## Currently, "timestamp without time zone" and "timestamp with time zone"
  ## are supported
  # timestamp_column_type = "timestamp without time zone"

  ## Templated statements to execute when creating a new table.
  # create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }})''',
  # ]

  ## Templated statements to execute when adding columns to a table.
  ## Set to an empty list to disable. Points containing tags for which there is no column will be skipped. Points
  ## containing fields for which there is no column will have the field omitted.
  # add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## Templated statements to execute when creating a new tag table.
  # tag_table_create_templates = [
  #   '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
  # ]

  ## Templated statements to execute when adding columns to a tag table.
  ## Set to an empty list to disable. Points containing tags for which there is no column will be skipped.
  # tag_table_add_column_templates = [
  #   '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
  # ]

  ## The postgres data type to use for storing unsigned 64-bit integer values (Postgres does not have a native
  ## unsigned 64-bit integer type).
  ## The value can be one of:
  ##   numeric - Uses the PostgreSQL "numeric" data type.
  ##   uint8 - Requires pguint extension (https://github.com/petere/pguint)
  # uint64_type = "numeric"

  ## When using pool_max_conns>1, and a temporary error occurs, the query is retried with an incremental backoff. This
  ## controls the maximum backoff duration.
  # retry_max_backoff = "15s"

  ## Approximate number of tag IDs to store in in-memory cache (when using tags_as_foreign_keys).
  ## This is an optimization to skip inserting known tag IDs.
  ## Each entry consumes approximately 34 bytes of memory.
  # tag_cache_size = 100000

  ## Enable & set the log level for the Postgres driver.
  # log_level = "warn" # trace, debug, info, warn, error, none

Input and output integration examples

Kubernetes

1. Dynamic Resource Allocation Monitoring: By utilizing the Kubernetes plugin, teams can set up alerts for resource usage patterns across various pods and containers. This proactive monitoring approach enables automatic scaling of resources in response to specific thresholds—helping to optimize performance while minimizing costs during peak usage.

2. Multi-tenancy Resource Isolation Analysis: Organizations using Kubernetes can leverage this plugin to track resource consumption per namespace. In a multi-tenant scenario, understanding the resource allocations and usages across different teams becomes critical for ensuring fair access and performance guarantees, leading to better resource management strategies.

3. Real-time Health Dashboards: Integrate the data captured by the Kubernetes plugin into visualization tools like Grafana to create real-time dashboards. These dashboards provide insights into the overall health and performance of the Kubernetes environment, allowing teams to quickly identify and rectify issues across clusters, pods, and containers.

4. Automated Incident Response Workflows: By combining the Kubernetes plugin with alert management systems, teams can automate incident response procedures based on real-time metrics. If a pod’s resource usage exceeds predefined limits, an automated workflow can trigger remediation actions, such as restarting the pod or reallocating resources—all of which can help improve system resilience.

PostgreSQL

1. Real-Time Analytics with Complex Queries: Leverage the PostgreSQL plugin to store metrics from various sources in a PostgreSQL database, enabling real-time analytics using complex queries. This setup can help data scientists and analysts uncover patterns and trends, as they manipulate relational data across multiple tables while utilizing PostgreSQL’s robust query optimization features. Specifically, users can create sophisticated reports with JOIN operations across different metric tables, revealing insights that would typically remain hidden in embedded systems.

2. Integrating with TimescaleDB for Time-Series Data: Utilize the PostgreSQL plugin within a TimescaleDB instance to efficiently handle and analyze time-series data. By implementing hypertables, users can achieve greater performance and partitioning of topics over the time dimension. This integration allows users to run analytical queries over large amounts of time-series data while retaining the full power of PostgreSQL’s SQL queries, ensuring reliability and efficiency in metrics analysis.

3. Data Versioning and Historical Analysis: Implement a strategy using the PostgreSQL plugin to maintain different versions of metrics over time. Users can set up an immutable data table structure where older versions of tables are retained, enabling easy historical analysis. This approach not only provides insights into data evolution but also aids compliance with data retention policies, ensuring that the historical integrity of the datasets remains intact.

4. Dynamic Schema Management for Evolving Metrics: Use the plugin’s templating capabilities to create a dynamically changing schema that responds to metric variations. This use case allows organizations to adapt their data structure as metrics evolve, adding necessary fields and ensuring adherence to data integrity policies. By leveraging templated SQL commands, users can extend their database without manual intervention, facilitating agile data management practices.

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