Kinesis and Graphite Integration

Input and output integration overview

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

The Graphite plugin enables users to send metrics collected by Telegraf into Graphite via TCP. This integration allows for efficient storage and visualization of time-series data using Graphite’s powerful capabilities.

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.

Graphite

This plugin writes metrics to Graphite via raw TCP, allowing for seamless integration of Telegraf collected metrics into the Graphite ecosystem. With this plugin, users can configure multiple TCP endpoints for load balancing, ensuring high availability and reliability in metric transmission. The ability to customize metric naming with prefixes and utilize various templating options enhances flexibility in how data is represented within Graphite. Additionally, support for Graphite tags and options for strict sanitization of metric names allow for robust data management, catering to the varying needs of users. This capability is essential for organizations looking to leverage Graphite’s powerful metrics storage and visualization while maintaining control over data representation.

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"

Graphite

# Configuration for Graphite server to send metrics to
[[outputs.graphite]]
  ## TCP endpoint for your graphite instance.
  ## If multiple endpoints are configured, the output will be load balanced.
  ## Only one of the endpoints will be written to with each iteration.
  servers = ["localhost:2003"]

  ## Local address to bind when connecting to the server
  ## If empty or not set, the local address is automatically chosen.
  # local_address = ""

  ## Prefix metrics name
  prefix = ""

  ## Graphite output template
  ## see https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_OUTPUT.md
  template = "host.tags.measurement.field"

  ## Strict sanitization regex
  ## This is the default sanitization regex that is used on data passed to the
  ## graphite serializer. Users can add additional characters here if required.
  ## Be aware that the characters, '/' '@' '*' are always replaced with '_',
  ## '..' is replaced with '.', and '\' is removed even if added to the
  ## following regex.
  # graphite_strict_sanitize_regex = '[^a-zA-Z0-9-:._=\p{L}]'

  ## Enable Graphite tags support
  # graphite_tag_support = false

  ## Applied sanitization mode when graphite tag support is enabled.
  ## * strict - uses the regex specified above
  ## * compatible - allows for greater number of characters
  # graphite_tag_sanitize_mode = "strict"

  ## Character for separating metric name and field for Graphite tags
  # graphite_separator = "."

  ## Graphite templates patterns
  ## 1. Template for cpu
  ## 2. Template for disk*
  ## 3. Default template
  # templates = [
  #  "cpu tags.measurement.host.field",
  #  "disk* measurement.field",
  #  "host.measurement.tags.field"
  #]

  ## timeout in seconds for the write connection to graphite
  # timeout = "2s"

  ## 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

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.

Graphite

1. Dynamic Metric Visualization: The Graphite plugin can be utilized to feed real-time metrics from various sources, such as application performance data or server health metrics, into Graphite. This dynamic integration allows teams to create interactive dashboards that visualize key performance indicators, track trends over time, and make data-driven decisions to enhance system performance.

2. Load Balanced Metrics Collection: By configuring multiple TCP endpoints within the plugin, organizations can implement load balancing for metric transmission. This use case ensures that metric delivery is both resilient and efficient, reducing the risk of data loss during high-traffic periods and maintaining a reliable flow of information to Graphite.

3. Customized Metrics Tagging: With support for Graphite tags, users can employ the Graphite plugin to enhance the granularity of their metrics. Tagging metrics with relevant information, such as application environment or service type, allows for more refined queries and analytics, enabling teams to drill down into specific areas of interest for better operational insights.

4. Enhanced Data Sanitization: Leveraging the plugin’s strict sanitization options, users can ensure that their metric names comply with Graphite’s requirements. This proactive measure eliminates potential issues arising from invalid characters in metric names, allowing for cleaner data management and more accurate visualizations.

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.