Kinesis and Sensu 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 Kinesis 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.

See Ways to Get Started

Input and output integration overview

The Kinesis plugin enables you to read from Kinesis data streams, supporting various data formats and configurations.

This plugin writes metrics events to Sensu via its HTTP events API, enabling seamless integration with the Sensu monitoring platform.

Integration details

Kinesis

The Kinesis Telegraf plugin is designed to read from Amazon Kinesis data streams, enabling users to gather metrics in real-time. As a service input plugin, it operates by listening for incoming data rather than polling at regular intervals. The configuration specifies various options including the AWS region, stream name, authentication credentials, and data formats. It supports tracking of undelivered messages to prevent data loss, and users can utilize DynamoDB for maintaining checkpoints of the last processed records. This plugin is particularly useful for applications requiring reliable and scalable stream processing alongside other monitoring needs.

Sensu

This plugin writes metrics events to Sensu via its HTTP events API. Sensu is a monitoring system that enables users to collect, analyze, and manage metrics from various components in their infrastructure. The plugin facilitates the integration of Telegraf, a server agent for collecting and reporting metrics, with the Sensu monitoring platform. Users can configure settings such as backend and agent API URLs, API keys for authentication, and optional TLS settings. The plugin’s core functionality is centered around sending metric events, including check and entity specifications, to Sensu, allowing for comprehensive monitoring and alerting. The API reference provides extensive details about the events and metrics structure, ensuring users can efficiently leverage Sensu’s capabilities for observability and incident response.

Configuration

Kinesis


# Configuration for the AWS Kinesis input.
[[inputs.kinesis_consumer]]
  ## Amazon REGION of kinesis endpoint.
  region = "ap-southeast-2"

  ## Amazon Credentials
  ## Credentials are loaded in the following order
  ## 1) Web identity provider credentials via STS if role_arn and web_identity_token_file are specified
  ## 2) Assumed credentials via STS if role_arn is specified
  ## 3) explicit credentials from 'access_key' and 'secret_key'
  ## 4) shared profile from 'profile'
  ## 5) environment variables
  ## 6) shared credentials file
  ## 7) EC2 Instance Profile
  # access_key = ""
  # secret_key = ""
  # token = ""
  # role_arn = ""
  # web_identity_token_file = ""
  # role_session_name = ""
  # profile = ""
  # shared_credential_file = ""

  ## Endpoint to make request against, the correct endpoint is automatically
  ## determined and this option should only be set if you wish to override the
  ## default.
  ##   ex: endpoint_url = "http://localhost:8000"
  # endpoint_url = ""

  ## Kinesis StreamName must exist prior to starting telegraf.
  streamname = "StreamName"

  ## Shard iterator type (only 'TRIM_HORIZON' and 'LATEST' currently supported)
  # shard_iterator_type = "TRIM_HORIZON"

  ## 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

  ## 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"

  ##
  ## The content encoding of the data from kinesis
  ## If you are processing a cloudwatch logs kinesis stream then set this to "gzip"
  ## as AWS compresses cloudwatch log data before it is sent to kinesis (aws
  ## also base64 encodes the zip byte data before pushing to the stream.  The base64 decoding
  ## is done automatically by the golang sdk, as data is read from kinesis)
  ##
  # content_encoding = "identity"

  ## Optional
  ## Configuration for a dynamodb checkpoint
  [inputs.kinesis_consumer.checkpoint_dynamodb]
    ## unique name for this consumer
    app_name = "default"
    table_name = "default"

Sensu

[[outputs.sensu]]
  ## BACKEND API URL is the Sensu Backend API root URL to send metrics to
  ## (protocol, host, and port only). The output plugin will automatically
  ## append the corresponding backend API path
  ## /api/core/v2/namespaces/:entity_namespace/events/:entity_name/:check_name).
  ##
  ## Backend Events API reference:
  ## https://docs.sensu.io/sensu-go/latest/api/events/
  ##
  ## AGENT API URL is the Sensu Agent API root URL to send metrics to
  ## (protocol, host, and port only). The output plugin will automatically
  ## append the correspeonding agent API path (/events).
  ##
  ## Agent API Events API reference:
  ## https://docs.sensu.io/sensu-go/latest/api/events/
  ##
  ## NOTE: if backend_api_url and agent_api_url and api_key are set, the output
  ## plugin will use backend_api_url. If backend_api_url and agent_api_url are
  ## not provided, the output plugin will default to use an agent_api_url of
  ## http://127.0.0.1:3031
  ##
  # backend_api_url = "http://127.0.0.1:8080"
  # agent_api_url = "http://127.0.0.1:3031"

  ## API KEY is the Sensu Backend API token
  ## Generate a new API token via:
  ##
  ## $ sensuctl cluster-role create telegraf --verb create --resource events,entities
  ## $ sensuctl cluster-role-binding create telegraf --cluster-role telegraf --group telegraf
  ## $ sensuctl user create telegraf --group telegraf --password REDACTED
  ## $ sensuctl api-key grant telegraf
  ##
  ## For more information on Sensu RBAC profiles & API tokens, please visit:
  ## - https://docs.sensu.io/sensu-go/latest/reference/rbac/
  ## - https://docs.sensu.io/sensu-go/latest/reference/apikeys/
  ##
  # api_key = "${SENSU_API_KEY}"

  ## 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

  ## Timeout for HTTP message
  # timeout = "5s"

  ## HTTP Content-Encoding for write request body, can be set to "gzip" to
  ## compress body or "identity" to apply no encoding.
  # content_encoding = "identity"

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of
  ## the table

  ## Sensu Event details
  ##
  ## Below are the event details to be sent to Sensu.  The main portions of the
  ## event are the check, entity, and metrics specifications. For more information
  ## on Sensu events and its components, please visit:
  ## - Events - https://docs.sensu.io/sensu-go/latest/reference/events
  ## - Checks -  https://docs.sensu.io/sensu-go/latest/reference/checks
  ## - Entities - https://docs.sensu.io/sensu-go/latest/reference/entities
  ## - Metrics - https://docs.sensu.io/sensu-go/latest/reference/events#metrics
  ##
  ## Check specification
  ## The check name is the name to give the Sensu check associated with the event
  ## created. This maps to check.metadata.name in the event.
  [outputs.sensu.check]
    name = "telegraf"

  ## Entity specification
  ## Configure the entity name and namespace, if necessary. This will be part of
  ## the entity.metadata in the event.
  ##
  ## NOTE: if the output plugin is configured to send events to a
  ## backend_api_url and entity_name is not set, the value returned by
  ## os.Hostname() will be used; if the output plugin is configured to send
  ## events to an agent_api_url, entity_name and entity_namespace are not used.
  # [outputs.sensu.entity]
  #   name = "server-01"
  #   namespace = "default"

  ## Metrics specification
  ## Configure the tags for the metrics that are sent as part of the Sensu event
  # [outputs.sensu.tags]
  #   source = "telegraf"

  ## Configure the handler(s) for processing the provided metrics
  # [outputs.sensu.metrics]
  #   handlers = ["influxdb","elasticsearch"]

Input and output integration examples

Kinesis

  1. Real-Time Data Processing with Kinesis: This use case involves integrating the Kinesis plugin with a monitoring dashboard to analyze incoming data metrics in real-time. For instance, an application could consume logs from multiple services and present them visually, allowing operations teams to quickly identify trends and react to anomalies as they occur.

  2. Serverless Log Aggregation: Utilize this plugin in a serverless architecture where Kinesis streams aggregate logs from various microservices. The plugin can create metrics that help detect issues in the system, automating alerting processes through third-party integrations, enabling teams to minimize downtime and improve reliability.

  3. Dynamic Scaling Based on Stream Metrics: Implement a solution where stream metrics consumed by the Kinesis plugin could be used to adjust resources dynamically. For example, if the number of records processed spikes, corresponding scale-up actions could be triggered to handle the increased load, ensuring optimal resource allocation and performance.

  4. Data Pipeline to S3 with Checkpointing: Create a robust data pipeline where Kinesis stream data is processed through the Telegraf Kinesis plugin, with checkpoints stored in DynamoDB. This approach can ensure data consistency and reliability, as it manages the state of processed data, enabling seamless integration with downstream data lakes or storage solutions.

Sensu

  1. Real-Time Infrastructure Monitoring: Utilize the Sensu plugin to send performance metrics from various servers and services directly to Sensu. This real-time data flow enables teams to visualize infrastructure health, track resource usage, and receive immediate alerts for any anomalies detected. By centralizing monitoring through Sensu, organizations can create a holistic view of their systems and respond swiftly to issues.

  2. Automated Incident Response Workflows: Leverage the plugin to automatically trigger incident response workflows based on the metrics events sent to Sensu. For example, if CPU usage exceeds a defined threshold, the Sensu system can be configured to alert the operations team, which can then initiate automated remediation processes, reducing downtime and maintaining system reliability. This integration allows for proactive management of system resources.

  3. Dynamic Scaling of Resources: Use the Sensu plugin to feed metrics into an auto-scaling system that adjusts resources based on demand. By tracking metrics like request load and resource utilization, organizations can automatically scale their infrastructure up or down, ensuring optimal performance and cost efficiency without manual intervention.

  4. Centralized Logging and Monitoring: Combine the Sensu with logging tools to send logs and performance metrics to a centralized monitoring system. This comprehensive approach allows teams to correlate logs with metric events, providing deeper insights into system behavior and performance, which aids in troubleshooting and performance optimization over time.

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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Kinesis and InfluxDB Integration

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