NATS and M3DB Integration

Input and output integration overview

The NATS Consumer Input Plugin enables real-time data consumption from NATS messaging subjects, integrating seamlessly into the Telegraf data pipeline for monitoring and metrics gathering.

This plugin allows Telegraf to stream metrics to M3DB using the Prometheus Remote Write protocol, enabling scalable ingestion through the M3 Coordinator.

Integration details

NATS

The NATS Consumer Plugin allows Telegraf to read metrics from specified NATS subjects and create metrics based on supported input data formats. Utilizing a Queue Group allows multiple instances of Telegraf to read from a NATS cluster in parallel, enhancing throughput and reliability. This plugin also supports various authentication methods, including username/password, NATS credentials files, and nkey seed files, ensuring secure communication with the NATS servers. It is particularly useful in environments where data persistence and message reliability are critical, thanks to features such as JetStream that facilitate the consumption of historical messages. Additionally, the ability to configure various operational parameters makes this plugin suitable for high-throughput scenarios while maintaining performance integrity.

M3DB

This configuration uses Telegraf’s HTTP output plugin with prometheusremotewrite format to send metrics directly to M3DB through the M3 Coordinator. M3DB is a distributed time series database designed for scalable, high-throughput metric storage. It supports ingestion of Prometheus remote write data via its Coordinator component, which manages translation and routing into the M3DB cluster. This approach enables organizations to collect metrics from systems that aren’t natively instrumented for Prometheus (e.g., Windows, SNMP, legacy systems) and ingest them efficiently into M3’s long-term, high-performance storage engine. The setup is ideal for high-scale observability stacks with Prometheus compatibility requirements.

Configuration

NATS

[[inputs.nats_consumer]]
  ## urls of NATS servers
  servers = ["nats://localhost:4222"]

  ## subject(s) to consume
  ## If you use jetstream you need to set the subjects
  ## in jetstream_subjects
  subjects = ["telegraf"]

  ## jetstream subjects
  ## jetstream is a streaming technology inside of nats.
  ## With jetstream the nats-server persists messages and
  ## a consumer can consume historical messages. This is
  ## useful when telegraf needs to restart it don't miss a
  ## message. You need to configure the nats-server.
  ## https://docs.nats.io/nats-concepts/jetstream.
  jetstream_subjects = ["js_telegraf"]

  ## name a queue group
  queue_group = "telegraf_consumers"

  ## Optional authentication with username and password credentials
  # username = ""
  # password = ""

  ## Optional authentication with NATS credentials file (NATS 2.0)
  # credentials = "/etc/telegraf/nats.creds"

  ## Optional authentication with nkey seed file (NATS 2.0)
  # nkey_seed = "/etc/telegraf/seed.txt"

  ## Use Transport Layer Security
  # secure = false

  ## 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

  ## Sets the limits for pending msgs and bytes for each subscription
  ## These shouldn't need to be adjusted except in very high throughput scenarios
  # pending_message_limit = 65536
  # pending_bytes_limit = 67108864

  ## 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"

M3DB

# Configuration for sending metrics to M3
[outputs.http]
  ## URL is the address to send metrics to
  url = "https://M3_HOST:M3_PORT/api/v1/prom/remote/write"

  ## HTTP Basic Auth credentials
  username = "admin"
  password = "password"

  ## Data format to output.
  data_format = "prometheusremotewrite"

  ## Outgoing HTTP headers
  [outputs.http.headers]
    Content-Type = "application/x-protobuf"
    Content-Encoding = "snappy"
    X-Prometheus-Remote-Write-Version = "0.1.0"

Input and output integration examples

NATS

1. Real-Time Analytics Dashboard: Utilize the NATS plugin to gather metrics from various NATS subjects in real time and feed them into a centralized analytics dashboard. This setup allows for immediate visibility into live application performance, enabling teams to react swiftly to operational issues or performance degradation.

2. Distributed System Monitoring: Deploy multiple instances of Telegraf configured with the NATS plugin across a distributed architecture. This approach allows teams to aggregate metrics from various microservices efficiently, providing a holistic view of system health and performance while ensuring no messages are dropped during transmission.

3. Historical Message Recovery: Leverage the capabilities of NATS JetStream along with this plugin to recover and process historical messages after Telegraf has been restarted. This feature is particularly beneficial for applications that require high reliability, ensuring that no critical metrics are lost even in case of service disruptions.

4. Dynamic Load Balancing: Implement a dynamic load balancing scenario where Telegraf instances consume messages from a NATS cluster based on load. Adjust the queue group settings to control the number of active consumers, allowing for better resource utilization and performance scaling as demand fluctuations occur.

M3DB

1. Large-Scale Cloud Infrastructure Monitoring: Deploy Telegraf agents across thousands of virtual machines and containers to collect metrics and stream them into M3DB through the M3 Coordinator. This provides reliable, long-term visibility with minimal storage overhead and high availability.

2. Legacy System Metrics Ingestion: Use Telegraf to gather metrics from older systems that lack native Prometheus exporters (e.g., Windows servers, SNMP devices) and forward them to M3DB via remote write. This bridges modern observability workflows with legacy infrastructure.

3. Centralized App Telemetry Aggregation: Collect application-specific telemetry using Telegraf’s plugin ecosystem (e.g., exec, http, jolokia) and push it into M3DB for centralized storage and query via PromQL. This enables unified analytics across diverse data sources.

4. Hybrid Cloud Observability: Install Telegraf agents on-prem and in the cloud to collect and remote-write metrics into a centralized M3DB cluster. This ensures consistent visibility across environments while avoiding the complexity of running Prometheus federation layers.

Feedback

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