Kinesis and Datadog Integration

Input and output integration overview

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

The Datadog Telegraf Plugin enables the submission of metrics to the Datadog Metrics API, facilitating efficient monitoring and data analysis through a reliable metric ingestion process.

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.

Datadog

This plugin writes to the Datadog Metrics API, enabling users to send metrics for monitoring and performance analysis. By utilizing the Datadog API key, users can configure the plugin to establish a connection with Datadog’s v1 API. The plugin supports various configuration options including connection timeouts, HTTP proxy settings, and data compression methods, ensuring adaptability to different deployment environments. The ability to transform count metrics into rates enhances the integration of Telegraf with Datadog agents, particularly beneficial for applications that rely on real-time performance metrics.

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"

Datadog

[[outputs.datadog]]
  ## Datadog API key
  apikey = "my-secret-key"

  ## Connection timeout.
  # timeout = "5s"

  ## Write URL override; useful for debugging.
  ## This plugin only supports the v1 API currently due to the authentication
  ## method used.
  # url = "https://app.datadoghq.com/api/v1/series"

  ## Set http_proxy
  # use_system_proxy = false
  # http_proxy_url = "http://localhost:8888"

  ## Override the default (none) compression used to send data.
  ## Supports: "zlib", "none"
  # compression = "none"

  ## When non-zero, converts count metrics submitted by inputs.statsd
  ## into rate, while dividing the metric value by this number.
  ## Note that in order for metrics to be submitted simultaenously alongside
  ## a Datadog agent, rate_interval has to match the interval used by the
  ## agent - which defaults to 10s
  # rate_interval = 0s

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.

Datadog

1. Real-Time Infrastructure Monitoring: Use the Datadog plugin to monitor server metrics in real-time by sending CPU usage and memory statistics directly to Datadog. This integration allows IT teams to visualize and analyze system performance metrics in a centralized dashboard, enabling proactive response to any emerging issues, such as resource bottlenecks or server overloads.

2. Application Performance Tracking: Leverage this plugin to submit application-specific metrics, such as request counts and error rates, to Datadog. By integrating with application monitoring tools, teams can correlate infrastructure metrics with application performance, providing insights that enable them to optimize code performance and improve user experience.

3. Anomaly Detection in Metrics: Configure the Datadog plugin to send metrics that can trigger alerts and notifications based on unusual patterns detected by Datadog’s machine learning features. This proactive monitoring helps teams swiftly react to potential outages or performance degradation before customers are impacted.

4. Integrating with Cloud Services: By utilizing the Datadog plugin to send metrics from cloud resources, IT teams can gain visibility into cloud application performance. Monitoring metrics like latency and error rates helps with ensuring service-level agreements (SLAs) are met and also assists in optimizing resource allocation across cloud environments.

Feedback

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