MQTT and Snowflake Integration
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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.
Telegraf’s SQL output plugin allows seamless metric storage in SQL databases. When configured for Snowflake, it employs a specialized DSN format and dynamic table creation to map metrics to the appropriate schema.
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.
Snowflake
The SQL output plugin enables Telegraf to send metrics to an SQL database using a dynamic schema. For Snowflake, the plugin utilizes the Go snowflake driver and a DSN that includes connection details such as username, password, and account identifiers. Note that this integration is experimental due to limited unit testing for the Go snowflake driver.
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
Snowflake
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
driver = "snowflake"
## Data source name
## For Snowflake, the DSN format typically includes the username, password, account identifier, and optional warehouse, database, and schema.
## Example DSN: "username:password@account/warehouse/db/schema"
data_source_name = "username:password@account/warehouse/db/schema"
## 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
## Defaults to ANSI/ISO SQL types unless overridden. Adjust if needed for Snowflake compatibility.
#[outputs.sql.convert]
# integer = "INT"
# real = "DOUBLE"
# text = "TEXT"
# timestamp = "TIMESTAMP"
# defaultvalue = "TEXT"
# unsigned = "UNSIGNED"
# bool = "BOOL"
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.
Snowflake
- Basic Snowflake Integration: Set the driver to ‘snowflake’ and configure the DSN with your Snowflake account details to start ingesting metrics.
- Custom Schema Management: Modify the table creation template to predefine specific column types or indexes that align with your data model in Snowflake.
- Initialization Commands: Utilize the init_sql setting to run any necessary Snowflake-specific SQL commands upon connection initialization.
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
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