MQTT and MongoDB Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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Time series database
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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.
See Ways to Get Started
Input and output integration overview
The MQTT plugin reads from the specified topics and creates metrics using the supported input data formats.
The MongoDB Plugin allows you to send metrics to a MongoDB instance.
Integration details
MQTT
This plugin allows Telegraf to consume metrics from specified MQTT topics. It supports a variety of configuration options to connect to MQTT brokers and manage message subscriptions, including features for handling startup errors and using TLS for secure connections.
MongoDB
This plugin sends metrics to MongoDB, automatically creating time series collections where they don’t already exist. Time series collections require MongoDB 5.0+.
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
MongoDB
[[outputs.mongodb]]
# connection string examples for mongodb
dsn = "mongodb://localhost:27017"
# dsn = "mongodb://mongod1:27017,mongod2:27017,mongod3:27017/admin&replicaSet=myReplSet&w=1"
# overrides serverSelectionTimeoutMS in dsn if set
# timeout = "30s"
# default authentication, optional
# authentication = "NONE"
# for SCRAM-SHA-256 authentication
# authentication = "SCRAM"
# username = "root"
# password = "***"
# for x509 certificate authentication
# authentication = "X509"
# tls_ca = "ca.pem"
# tls_key = "client.pem"
# # tls_key_pwd = "changeme" # required for encrypted tls_key
# insecure_skip_verify = false
# database to store measurements and time series collections
# database = "telegraf"
# granularity can be seconds, minutes, or hours.
# configuring this value will be based on your input collection frequency.
# see https://docs.mongodb.com/manual/core/timeseries-collections/#create-a-time-series-collection
# granularity = "seconds"
# optionally set a TTL to automatically expire documents from the measurement collections.
# ttl = "360h"
Input and output integration examples
MQTT
-
Basic Configuration: This example connects to a local MQTT broker, subscribes to specific topics for CPU and memory metrics, and outputs using the Influx data format.
-
Topic Parsing: Extracts tag values from MQTT topics for better data organization and analysis, allowing metrics to be categorized based on their topics.
-
Field Pivoting: Demonstrates how to pivot single-valued metrics into a multi-field metric. This is useful for consolidating data from multiple sensors into a single metric.
MongoDB
-
Log Management: Integrate this plugin to send application logs directly to MongoDB for structured storage and flexible querying. You can analyze logs as time series data, aggregating logs by hour, day, or month.
-
Metric Capture: Use the plugin to capture system metrics (CPU, memory usage) in real-time and store them in MongoDB. The time-series collections will allow for efficient queries over time ranges.
-
Monitoring Solutions: Combine this output plugin with inputs from various sources, such as disk usage metrics, network statistics, or application performance data. It allows for consolidated monitoring dashboards with historical trends saved in MongoDB.
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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