AWS Data Firehose and VictoriaMetrics Integration

Input and output integration overview

This plugin listens for metrics sent via HTTP from AWS Data Firehose in supported data formats, providing real-time data ingestion capabilities.

This plugin enables Telegraf to efficiently write metrics directly into VictoriaMetrics using the InfluxDB line protocol, leveraging the performance and scalability features of VictoriaMetrics for large-scale time-series data.

Integration details

AWS Data Firehose

The AWS Data Firehose Telegraf plugin is designed to receive metrics from AWS Data Firehose via HTTP. This plugin listens for incoming data in various formats and processes it according to the request-response schema outlined in the official AWS documentation. Unlike standard input plugins that operate on a fixed interval, this service plugin initializes a listener that remains active, waiting for incoming metrics. This allows for real-time data ingestion from AWS Data Firehose, making it suitable for scenarios where immediate data processing is required. Key features include the ability to specify service addresses, paths, and support for TLS connections for secure data transmission. Additionally, the plugin accommodates optional authentication keys and custom tags, enhancing its flexibility in various use cases involving data streaming and processing.

VictoriaMetrics

VictoriaMetrics supports direct ingestion of metrics in the InfluxDB line protocol, making this plugin ideal for efficient real-time metric storage and retrieval. The integration combines Telegraf’s extensive metric collection capabilities with VictoriaMetrics’ optimized storage and querying features, including compression, fast ingestion rates, and efficient disk utilization. Ideal for cloud-native and large-scale monitoring scenarios, this plugin offers simplicity, robust performance, and high reliability, enabling advanced operational insights and long-term storage solutions for large volumes of metrics.

Configuration

AWS Data Firehose

[[inputs.firehose]]
  ## Address and port to host HTTP listener on
  service_address = ":8080"

  ## Paths to listen to.
  # paths = ["/telegraf"]

  ## maximum duration before timing out read of the request
  # read_timeout = "5s"
  ## maximum duration before timing out write of the response
  # write_timeout = "5s"

  ## Set one or more allowed client CA certificate file names to
  ## enable mutually authenticated TLS connections
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

  ## Add service certificate and key
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"

  ## Minimal TLS version accepted by the server
  # tls_min_version = "TLS12"

  ## Optional access key to accept for authentication.
  ## AWS Data Firehose uses "x-amz-firehose-access-key" header to set the access key.
  ## If no access_key is provided (default), authentication is completely disabled and
  ## this plugin will accept all request ignoring the provided access-key in the request!
  # access_key = "foobar"

  ## Optional setting to add parameters as tags
  ## If the http header "x-amz-firehose-common-attributes" is not present on the
  ## request, no corresponding tag will be added. The header value should be a
  ## json and should follow the schema as describe in the official documentation:
  ## https://docs.aws.amazon.com/firehose/latest/dev/httpdeliveryrequestresponse.html#requestformat
  # parameter_tags = ["env"]

  ## 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"

VictoriaMetrics

[[outputs.influxdb]]
  ## URL of the VictoriaMetrics write endpoint
  urls = ["http://localhost:8428"]

  ## VictoriaMetrics accepts InfluxDB line protocol directly
  database = "db_name"

  ## Optional authentication
  # username = "username"
  # password = "password"
  # skip_database_creation = true
  # exclude_retention_policy_tag = true
  # content_encoding = "gzip"

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

  ## Optional TLS configuration
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

Input and output integration examples

AWS Data Firehose

1. Real-Time Data Analytics: Using the AWS Data Firehose plugin, organizations can stream data in real-time from various sources, such as application logs or IoT devices, directly into analytics platforms. This allows data teams to analyze incoming data as it is generated, enabling rapid insights and operational adjustments based on fresh metrics.

2. Profile Access Patterns for Optimization: By collecting data about how clients interact with applications through AWS Data Firehose, businesses can gain valuable insights into user behavior. This can drive content personalization strategies or optimize server architecture for better performance based on traffic patterns.

3. Automated Alerting Mechanism: Integrating AWS Data Firehose with alerting systems via this plugin allows teams to set up automated alerts based on specific metrics collected. For example, if a particular threshold is reached in the input data, alerts can trigger operations teams to investigate potential issues before they escalate.

VictoriaMetrics

1. Cloud-Native Application Monitoring: Stream metrics from microservices deployed on Kubernetes directly into VictoriaMetrics. By centralizing metrics, organizations can perform real-time monitoring, rapid anomaly detection, and seamless scalability across dynamically evolving cloud environments.

2. Scalable IoT Data Management: Use the plugin to ingest sensor data from IoT deployments into VictoriaMetrics. This approach facilitates real-time analytics, predictive maintenance, and efficient management of massive volumes of sensor data with minimal storage overhead.

3. Financial Systems Performance Tracking: Leverage VictoriaMetrics via this plugin to store and analyze metrics from financial systems, capturing latency, transaction volume, and error rates. Organizations can rapidly identify and resolve performance bottlenecks, ensuring high availability and regulatory compliance.

4. Cross-Environment Performance Dashboards: Integrate metrics from diverse infrastructure components—such as cloud instances, containers, and physical servers into VictoriaMetrics. Using visualization tools, teams can build comprehensive dashboards for end-to-end performance visibility, proactive troubleshooting, and infrastructure optimization.

Feedback

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