MQTT and Cortex Integration
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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
Input and output integration overview
The MQTT Telegraf plugin is designed to read from specified MQTT topics and create metrics, enabling users to leverage MQTT for real-time data collection and monitoring.
This plugin enables Telegraf to send metrics to Cortex using the Prometheus remote write protocol, allowing seamless ingestion into Cortex’s scalable, multi-tenant time series storage.
Integration details
MQTT
The MQTT plugin allows for reading metrics from specified MQTT topics, creating metrics using supported input data formats. This plugin operates as a service input, which listens for incoming metrics or events rather than gathering them at set intervals like normal plugins. The flexibility of the plugin is enhanced with support for various broker URLs, topics, and connection features, including Quality of Service (QoS) levels and persistent sessions. Its configuration options incorporate global settings to modify metrics and handle startup errors effectively. It also supports secret-store configurations for securing username and password options, ensuring secure connections to MQTT servers.
Cortex
With Telegraf’s HTTP output plugin and the prometheusremotewrite
data format you can send metrics directly to Cortex, a horizontally scalable, long-term storage backend for Prometheus. Cortex supports multi-tenancy and accepts remote write requests using the Prometheus protobuf format. By using Telegraf as the collection agent and Remote Write as the transport mechanism, organizations can extend observability into sources not natively supported by Prometheus—such as Windows hosts, SNMP-enabled devices, or custom application metrics—while leveraging Cortex’s high-availability and long-retention capabilities.
Configuration
MQTT
[[inputs.mqtt_consumer]]
servers = ["tcp://127.0.0.1:1883"]
topics = [
"telegraf/host01/cpu",
"telegraf/+/mem",
"sensors/#",
]
# topic_tag = "topic"
# qos = 0
# connection_timeout = "30s"
# keepalive = "60s"
# ping_timeout = "10s"
# max_undelivered_messages = 1000
# persistent_session = false
# client_id = ""
# username = "telegraf"
# password = "metricsmetricsmetricsmetrics"
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
# insecure_skip_verify = false
# client_trace = false
data_format = "influx"
# [[inputs.mqtt_consumer.topic_parsing]]
# topic = ""
# measurement = ""
# tags = ""
# fields = ""
# [inputs.mqtt_consumer.topic_parsing.types]
# key = type
Cortex
[[outputs.http]]
## Cortex Remote Write endpoint
url = "http://cortex.example.com/api/v1/push"
## Use POST to send data
method = "POST"
## Send metrics using Prometheus remote write format
data_format = "prometheusremotewrite"
## Optional HTTP headers for authentication
# [outputs.http.headers]
# X-Scope-OrgID = "your-tenant-id"
# Authorization = "Bearer YOUR_API_TOKEN"
## 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
## Request timeout
timeout = "10s"
Input and output integration examples
MQTT
-
Smart Home Monitoring: Use the MQTT Consumer plugin to monitor various sensors in a smart home setup. In this scenario, the plugin can be configured to subscribe to topics for different devices, such as temperature, humidity, and energy consumption. By aggregating this data, homeowners can visualize trends and receive alerts for unusual patterns, enhancing the overall quality and efficiency of home automation systems.
-
IoT Environmental Sensing: Deploy the MQTT Consumer to gather environmental data from sensors distributed across different locations. For instance, this can include readings from air quality sensors, temperature sensors, and noise level meters. The plugin can be configured to extract relevant tags and fields from the MQTT topics which allows for detailed analyses and reporting on environmental conditions at scale, supporting better decision making for urban planning or environmental initiatives.
-
Real-Time Vehicle Tracking and Telemetry: Integrate the MQTT Consumer plugin within a vehicle telemetry system that collects data from various sensors in real-time. With the plugin, metrics related to vehicle performance, location, and fuel consumption can be sent to a centralized monitoring dashboard. This real-time telemetry data enables fleet managers to optimize routes, reduce fuel costs, and improve vehicle maintenance schedules through proactive data analysis.
-
Agricultural Monitoring System: Leverage this plugin to collect data from agricultural sensors that monitor soil moisture, crop health, and weather conditions. The MQTT Consumer can subscribe to multiple topics associated with farming equipment and environmental sensors, allowing farmers to make data-driven decisions to improve crop yields while also conserving resources, enhancing sustainability in agriculture.
Cortex
-
Unified Multi-Tenant Monitoring: Use Telegraf to collect metrics from different teams or environments and push them to Cortex with separate
X-Scope-OrgID
headers. This enables isolated data ingestion and querying per tenant, ideal for managed services and platform teams. -
Extending Prometheus Coverage to Edge Devices: Deploy Telegraf on edge or IoT devices to collect system metrics and send them to a centralized Cortex cluster. This approach ensures consistent observability even for environments without local Prometheus scrapers.
-
Global Service Observability with Federated Tenants: Aggregate metrics from global infrastructure by configuring Telegraf agents to push data into regional Cortex clusters, each tagged with tenant identifiers. Cortex handles deduplication and centralized access across regions.
-
Custom App Telemetry Pipeline: Collect app-specific telemetry via Telegraf’s
exec
orhttp
input plugins and forward it to Cortex. This allows DevOps teams to monitor app-specific KPIs in a scalable, query-efficient format while keeping metrics logically grouped by tenant or service.
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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