RabbitMQ and PostgreSQL Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider RabbitMQ and InfluxDB.

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Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

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Input and output integration overview

This plugin reads metrics from RabbitMQ servers, providing essential insights into the performance and state of the messaging system.

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

Integration details

RabbitMQ

The RabbitMQ plugin for Telegraf allows users to gather metrics from RabbitMQ servers via the RabbitMQ Management Plugin. This capability is crucial for monitoring the performance and health of RabbitMQ instances, which are widely utilized for message queuing and processing in various applications. The plugin provides comprehensive insights into key RabbitMQ metrics, including message rates, queue depths, and node health statistics, thereby enabling operators to maintain optimal performance and robustness of their messaging infrastructure. Additionally, it supports secret-stores for managing sensitive credentials securely, making integration with existing systems smoother. Configuration options allow for flexibility in specifying the nodes, queues, and exchanges to monitor, providing valuable adaptability for diverse deployment scenarios.

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

RabbitMQ

[[inputs.rabbitmq]]
  ## Management Plugin url. (default: http://localhost:15672)
  # url = "http://localhost:15672"
  ## Tag added to rabbitmq_overview series; deprecated: use tags
  # name = "rmq-server-1"
  ## Credentials
  # username = "guest"
  # password = "guest"

  ## 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

  ## Optional request timeouts
  ## ResponseHeaderTimeout, if non-zero, specifies the amount of time to wait
  ## for a server's response headers after fully writing the request.
  # header_timeout = "3s"
  ##
  ## client_timeout specifies a time limit for requests made by this client.
  ## Includes connection time, any redirects, and reading the response body.
  # client_timeout = "4s"

  ## A list of nodes to gather as the rabbitmq_node measurement. If not
  ## specified, metrics for all nodes are gathered.
  # nodes = ["rabbit@node1", "rabbit@node2"]

  ## A list of queues to gather as the rabbitmq_queue measurement. If not
  ## specified, metrics for all queues are gathered.
  ## Deprecated in 1.6: Use queue_name_include instead.
  # queues = ["telegraf"]

  ## A list of exchanges to gather as the rabbitmq_exchange measurement. If not
  ## specified, metrics for all exchanges are gathered.
  # exchanges = ["telegraf"]

  ## Metrics to include and exclude. Globs accepted.
  ## Note that an empty array for both will include all metrics
  ## Currently the following metrics are supported: "exchange", "federation", "node", "overview", "queue"
  # metric_include = []
  # metric_exclude = []

  ## Queues to include and exclude. Globs accepted.
  ## Note that an empty array for both will include all queues
  # queue_name_include = []
  # queue_name_exclude = []

  ## Federation upstreams to include and exclude specified as an array of glob
  ## pattern strings.  Federation links can also be limited by the queue and
  ## exchange filters.
  # federation_upstream_include = []
  # federation_upstream_exclude = []

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

RabbitMQ

  1. Monitoring Queue Performance Metrics: Use the RabbitMQ plugin to keep track of queue performance over time. This involves setting up monitoring dashboards that visualize crucial queue metrics such as message rates, the number of consumers, and message delivery rates. With this information, teams can proactively address any bottlenecks or performance issues by analyzing trends and making data-informed decisions about scaling or optimizing their RabbitMQ configuration.

  2. Alerting on System Health: Integrate the RabbitMQ plugin with an alerting system to notify operational teams of potential issues within RabbitMQ instances. For example, if the number of unacknowledged messages reaches a critical threshold or if queues become overwhelmed, alerts can trigger, allowing for immediate investigation and swift remedial action to maintain the health of message flows.

  3. Analyzing Message Processing Metrics: Employ the plugin to gather detailed metrics on message processing performance, such as the rates of messages published, acknowledged, and redelivered. By analyzing these metrics, teams can evaluate the efficiency of their message consumer applications and make adjustments to configuration or code where necessary, thereby enhancing overall system throughput and resilience.

  4. Cross-System Data Integration: Leverage the metrics collected by the RabbitMQ plugin to integrate data flows between RabbitMQ and other systems or services. For example, use the gathered metrics to drive automated workflows or analytics pipelines that utilize messages processed in RabbitMQ, enabling organizations to optimize workflows and enhance data agility across their ecosystems.

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.

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.

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

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