MQTT and SQLite Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
5B+
Telegraf downloads
#1
Time series database
Source: DB Engines
1B+
Downloads of InfluxDB
2,800+
Contributors
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 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.
Telegraf’s SQL output plugin stores metrics in an SQL database by creating tables dynamically for each metric type. When configured for SQLite, it utilizes a file-based DSN and a minimal SQL schema tailored for lightweight, embedded database usage.
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.
SQLite
The SQL output plugin writes Telegraf metrics to an SQL database using a dynamic schema where each metric type corresponds to a table. For SQLite, the plugin uses the modernc.org/sqlite driver and requires a DSN in the format of a file URI (e.g., ‘file:/path/to/telegraf.db?cache=shared’). This configuration leverages standard ANSI SQL for table creation and data insertion, ensuring compatibility with SQLite’s 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
SQLite
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
driver = "sqlite"
## Data source name
## For SQLite, the DSN is a filename or URL with the scheme "file:".
## Example: "file:/path/to/telegraf.db?cache=shared"
data_source_name = "file:/path/to/telegraf.db?cache=shared"
## Timestamp column name
timestamp_column = "timestamp"
## Table creation template
## Available template variables:
## {TABLE} - table name as a quoted identifier
## {TABLELITERAL} - table name as a quoted string literal
## {COLUMNS} - column definitions (list of quoted identifiers and types)
table_template = "CREATE TABLE {TABLE} ({COLUMNS})"
## Table existence check template
## Available template variables:
## {TABLE} - table name as a quoted identifier
table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"
## Initialization SQL (optional)
init_sql = ""
## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
connection_max_idle_time = "0s"
## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
connection_max_lifetime = "0s"
## Maximum number of connections in the idle connection pool. 0 means unlimited.
connection_max_idle = 2
## Maximum number of open connections to the database. 0 means unlimited.
connection_max_open = 0
## Metric type to SQL type conversion
## The values on the left are the data types Telegraf has and the values on the right are the SQL types used when writing to SQLite.
#[outputs.sql.convert]
# integer = "INT"
# real = "DOUBLE"
# text = "TEXT"
# timestamp = "TIMESTAMP"
# defaultvalue = "TEXT"
# unsigned = "UNSIGNED"
# bool = "BOOL"
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.
SQLite
- Local Monitoring Storage: Configure the plugin to write metrics to a local SQLite database file. This is ideal for lightweight deployments where setting up a full-scale database server is not required.
- Embedded Applications: Use SQLite as the backend for applications embedded in edge devices, benefiting from its file-based architecture and minimal resource requirements.
- Quick Setup for Testing: Leverage SQLite’s ease of use to quickly set up a testing environment for Telegraf metrics collection without the need for external database services.
- Custom Schema Management: Adjust the table creation templates to predefine your schema if you require specific column types or indexes, ensuring compatibility with your application’s needs.
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
Related Integrations
Related Integrations
HTTP and InfluxDB Integration
The HTTP plugin collects metrics from one or more HTTP(S) endpoints. It supports various authentication methods and configuration options for data formats.
View IntegrationKafka and InfluxDB Integration
This plugin reads messages from Kafka and allows the creation of metrics based on those messages. It supports various configurations including different Kafka settings and message processing options.
View IntegrationKinesis and InfluxDB Integration
The Kinesis plugin allows for reading metrics from AWS Kinesis streams. It supports multiple input data formats and offers checkpointing features with DynamoDB for reliable message processing.
View Integration