Kinesis and Datadog Integration
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Table of Contents
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.
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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 to the Datadog Metrics API and requires an apikey which can be obtained for the account. This plugin supports the v1 API.
Integration details
Kinesis
This plugin reads from a Kinesis data stream and creates metrics using supported input data formats. It supports various configuration options for AWS Kinesis and DynamoDB checkpointing.
Datadog
Datadog metric names are formed by joining the Telegraf metric name and the field key with a .
character.
Field values are converted to floating point numbers. Strings and floats that cannot be sent over JSON, namely NaN and Inf, are ignored.
Setting rate_interval
to non-zero will convert count
metrics to rate
and divide its value by this interval before submitting to Datadog.
This allows Telegraf to submit metrics alongside Datadog agents when their rate
intervals are the same (Datadog defaults to 10s
).
Note that this only supports metrics ingested via inputs.statsd
given the dependency on the metric_type
tag it creates. There is only support for
counter
metrics, and count
values from timing
and histogram
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
- Basic Configuration: Set up the Kinesis Consumer to read from a specific stream in a specified AWS region.
- Checkpointing: Use DynamoDB to checkpoint processed records to ensure data is not lost during stream consumption.
- Data Format Management: Configure the plugin to handle different data formats, allowing for flexibility in how data is ingested.
Datadog
- Basic Metric Submission: Utilize the Datadog Output Plugin to transmit metrics from your Telegraf instance to Datadog. By configuring the
apikey
and enabling necessary metrics, you can easily monitor application performance over time. - Debugging Write URL: In cases where you need to troubleshoot your metric submissions, you can override the default write URL with a custom endpoint to debug the metrics being sent, ensuring that they are reaching the correct destination.
- Proxy Configuration: If your network setup requires routing through a proxy for outgoing requests, use the
http_proxy_url
option to set the appropriate proxy. This allows for seamless integration in restrictive network environments.
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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