RabbitMQ and SQLite Integration

Input and output integration overview

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

Telegraf’s SQL output plugin stores metrics in an SQL database by creating tables dynamically for each metric type. When configured for SQLite, it utilizes a file-based DSN and a minimal SQL schema tailored for lightweight, embedded database usage.

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.

SQLite

The SQL output plugin writes Telegraf metrics to an SQL database using a dynamic schema where each metric type corresponds to a table. For SQLite, the plugin uses the modernc.org/sqlite driver and requires a DSN in the format of a file URI (e.g., ‘file:/path/to/telegraf.db?cache=shared’). This configuration leverages standard ANSI SQL for table creation and data insertion, ensuring compatibility with SQLite’s capabilities.

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 = []

SQLite

[[outputs.sql]]
  ## Database driver
  ## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
  ## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
  driver = "sqlite"

  ## Data source name
  ## For SQLite, the DSN is a filename or URL with the scheme "file:".
  ## Example: "file:/path/to/telegraf.db?cache=shared"
  data_source_name = "file:/path/to/telegraf.db?cache=shared"

  ## Timestamp column name
  timestamp_column = "timestamp"

  ## Table creation template
  ## Available template variables:
  ##  {TABLE}        - table name as a quoted identifier
  ##  {TABLELITERAL} - table name as a quoted string literal
  ##  {COLUMNS}      - column definitions (list of quoted identifiers and types)
  table_template = "CREATE TABLE {TABLE} ({COLUMNS})"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - table name as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL (optional)
  init_sql = ""

  ## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
  connection_max_idle_time = "0s"

  ## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
  connection_max_lifetime = "0s"

  ## Maximum number of connections in the idle connection pool. 0 means unlimited.
  connection_max_idle = 2

  ## Maximum number of open connections to the database. 0 means unlimited.
  connection_max_open = 0

  ## Metric type to SQL type conversion
  ## The values on the left are the data types Telegraf has and the values on the right are the SQL types used when writing to SQLite.
  #[outputs.sql.convert]
  #  integer       = "INT"
  #  real          = "DOUBLE"
  #  text          = "TEXT"
  #  timestamp     = "TIMESTAMP"
  #  defaultvalue  = "TEXT"
  #  unsigned      = "UNSIGNED"
  #  bool          = "BOOL"

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.

SQLite

1. Local Monitoring Storage: Configure the plugin to write metrics to a local SQLite database file. This is ideal for lightweight deployments where setting up a full-scale database server is not required.
2. Embedded Applications: Use SQLite as the backend for applications embedded in edge devices, benefiting from its file-based architecture and minimal resource requirements.
3. Quick Setup for Testing: Leverage SQLite’s ease of use to quickly set up a testing environment for Telegraf metrics collection without the need for external database services.
4. Custom Schema Management: Adjust the table creation templates to predefine your schema if you require specific column types or indexes, ensuring compatibility with your application’s needs.

Feedback

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