Azure Event Hubs and Librato Integration

Input and output integration overview

The Azure Event Hubs Input Plugin allows Telegraf to consume data from Azure Event Hubs and Azure IoT Hub, enabling efficient data processing and monitoring of event streams from these cloud services.

The Librato plugin for Telegraf is designed to facilitate seamless integration with the Librato Metrics API, allowing for efficient metric reporting and monitoring.

Integration details

Azure Event Hubs

This plugin serves as a consumer for Azure Event Hubs and Azure IoT Hub, allowing users to ingest data streams from these platforms efficiently. Azure Event Hubs is a highly scalable data streaming platform and event ingestion service capable of receiving and processing millions of events per second, while Azure IoT Hub enables secure device-to-cloud and cloud-to-device communication in IoT applications. The Event Hub Input Plugin interacts seamlessly with these services, providing reliable message consumption and stream processing capabilities. Key features include dynamic management of consumer groups, message tracking to prevent data loss, and customizable settings for prefetch counts, user agents, and metadata handling. This plugin is designed to support a range of use cases, including real-time telemetry data collection, IoT data processing, and integration with various data analysis and monitoring tools within the broader Azure ecosystem.

Librato

The Librato plugin enables Telegraf to send metrics to the Librato Metrics API. To authenticate, users must provide an api_user and api_token, which can be acquired from the Librato account settings. This integration allows for efficient monitoring and reporting of custom metrics within the Librato platform. The plugin also utilizes a source_tag option that can enrich the metrics with contextual information from Point Tags; however, it does not currently support sending associated Point Tags. It is essential to note that any point value sent that cannot be converted to a float64 type will be skipped, ensuring that only valid metrics are processed and sent to Librato. The plugin also supports secret-store options for managing sensitive authentication credentials securely, facilitating best practices in credential management.

Configuration

Azure Event Hubs

[[inputs.eventhub_consumer]]
  ## The default behavior is to create a new Event Hub client from environment variables.
  ## This requires one of the following sets of environment variables to be set:
  ##
  ## 1) Expected Environment Variables:
  ##    - "EVENTHUB_CONNECTION_STRING"
  ##
  ## 2) Expected Environment Variables:
  ##    - "EVENTHUB_NAMESPACE"
  ##    - "EVENTHUB_NAME"
  ##    - "EVENTHUB_KEY_NAME"
  ##    - "EVENTHUB_KEY_VALUE"

  ## 3) Expected Environment Variables:
  ##    - "EVENTHUB_NAMESPACE"
  ##    - "EVENTHUB_NAME"
  ##    - "AZURE_TENANT_ID"
  ##    - "AZURE_CLIENT_ID"
  ##    - "AZURE_CLIENT_SECRET"

  ## Uncommenting the option below will create an Event Hub client based solely on the connection string.
  ## This can either be the associated environment variable or hard coded directly.
  ## If this option is uncommented, environment variables will be ignored.
  ## Connection string should contain EventHubName (EntityPath)
  # connection_string = ""

  ## Set persistence directory to a valid folder to use a file persister instead of an in-memory persister
  # persistence_dir = ""

  ## Change the default consumer group
  # consumer_group = ""

  ## By default the event hub receives all messages present on the broker, alternative modes can be set below.
  ## The timestamp should be in https://github.com/toml-lang/toml#offset-date-time format (RFC 3339).
  ## The 3 options below only apply if no valid persister is read from memory or file (e.g. first run).
  # from_timestamp =
  # latest = true

  ## Set a custom prefetch count for the receiver(s)
  # prefetch_count = 1000

  ## Add an epoch to the receiver(s)
  # epoch = 0

  ## Change to set a custom user agent, "telegraf" is used by default
  # user_agent = "telegraf"

  ## To consume from a specific partition, set the partition_ids option.
  ## An empty array will result in receiving from all partitions.
  # partition_ids = ["0","1"]

  ## 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

  ## Set either option below to true to use a system property as timestamp.
  ## You have the choice between EnqueuedTime and IoTHubEnqueuedTime.
  ## It is recommended to use this setting when the data itself has no timestamp.
  # enqueued_time_as_ts = true
  # iot_hub_enqueued_time_as_ts = true

  ## Tags or fields to create from keys present in the application property bag.
  ## These could for example be set by message enrichments in Azure IoT Hub.
  # application_property_tags = []
  # application_property_fields = []

  ## Tag or field name to use for metadata
  ## By default all metadata is disabled
  # sequence_number_field = "SequenceNumber"
  # enqueued_time_field = "EnqueuedTime"
  # offset_field = "Offset"
  # partition_id_tag = "PartitionID"
  # partition_key_tag = "PartitionKey"
  # iot_hub_device_connection_id_tag = "IoTHubDeviceConnectionID"
  # iot_hub_auth_generation_id_tag = "IoTHubAuthGenerationID"
  # iot_hub_connection_auth_method_tag = "IoTHubConnectionAuthMethod"
  # iot_hub_connection_module_id_tag = "IoTHubConnectionModuleID"
  # iot_hub_enqueued_time_field = "IoTHubEnqueuedTime"

  ## 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"

Librato

[[outputs.librato]]
  ## Librato API Docs
  ## http://dev.librato.com/v1/metrics-authentication
  ## Librato API user
  api_user = "[email protected]" # required.
  ## Librato API token
  api_token = "my-secret-token" # required.
  ## Debug
  # debug = false
  ## Connection timeout.
  # timeout = "5s"
  ## Output source Template (same as graphite buckets)
  ## see https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_OUTPUT.md#graphite
  ## This template is used in librato's source (not metric's name)
  template = "host"

Input and output integration examples

Azure Event Hubs

1. Real-Time IoT Device Monitoring: Use the Azure Event Hubs Plugin to monitor telemetry data from IoT devices like sensors and actuators. By streaming device data into monitoring dashboards, organizations can gain insights into system performances, track usage patterns, and quickly respond to irregularities. This setup allows for proactive management of devices, improving operational efficiency and reducing downtime.

2. Event-Driven Data Processing Workflows: Leverage this plugin to trigger data processing workflows in response to events received from Azure Event Hubs. For instance, when a new event arrives, it can initiate data transformation, aggregation, or storage processes, allowing businesses to automate their workflows more effectively. This integration enhances responsiveness and streamlines operations across systems.

3. Integration with Analytics Platforms: Implement the plugin to funnel event data into analytics platforms like Azure Synapse or Power BI. By integrating real-time streaming data into analytics tools, organizations can perform comprehensive data analysis, drive business intelligence efforts, and create interactive visualizations that inform decision-making.

4. Cross-Platform Data Sync: Utilize the Azure Event Hubs Plugin to synchronize data streams across diverse systems or platforms. By consuming data from Azure Event Hubs and forwarding it to other systems like databases or cloud storage, organizations can maintain consistent and up-to-date information across their entire architecture, enabling cohesive data strategies.

Librato

1. Real-time Application Monitoring: Utilize Librato to collect performance metrics from a web application in real-time. This setup involves sending response times, error rates, and user interactions to Librato, allowing developers to monitor the application’s health and performance metrics closely. By analyzing these metrics, teams can quickly identify and address performance bottlenecks or application failures before they impact end users.

2. Infrastructure Metrics Aggregation: Leverage this plugin to gather and send metrics from various infrastructure components, such as servers or containers, to Librato for centralized monitoring. Configuring the plugin to send CPU, memory usage, and disk I/O metrics enables system administrators to have a comprehensive view of infrastructure performance, assisting in capacity planning and resource optimization strategies.

3. Custom Metrics for Business Operations: Feed business-specific metrics, such as sales transactions or user sign-ups, to the Librato service using this plugin. By tracking these custom metrics, businesses can gain insights into their operational performance and make data-driven decisions to enhance their strategies, marketing efforts, or product development initiatives.

4. Anomaly Detection in Metrics: Implement monitoring tools that utilize machine learning for anomaly detection. By continuously sending real-time metrics to Librato, teams can analyze trends and automatically flag unusual behavior, such as sudden spikes in latency or unusual traffic patterns, enabling timely intervention and troubleshooting.

Feedback

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