AMQP and Snowflake Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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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 AMQP Consumer Input Plugin allows you to ingest data from an AMQP 0-9-1 compliant message broker, such as RabbitMQ, enabling seamless data collection for monitoring and analytics purposes.
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
AMQP
This plugin provides a consumer for use with AMQP 0-9-1, a prominent implementation of which is RabbitMQ. AMQP, or Advanced Message Queuing Protocol, was originally developed to enable reliable, interoperable messaging between diverse systems in a network. The plugin reads metrics from a topic exchange using a configured queue and binding key, delivering a flexible and efficient means of collecting data from AMQP-compliant messaging systems. This enables users to leverage existing RabbitMQ implementations to monitor their applications effectively by capturing detailed metrics for analysis and alerting.
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
AMQP
[[inputs.amqp_consumer]]
## Brokers to consume from. If multiple brokers are specified a random broker
## will be selected anytime a connection is established. This can be
## helpful for load balancing when not using a dedicated load balancer.
brokers = ["amqp://localhost:5672/influxdb"]
## Authentication credentials for the PLAIN auth_method.
# username = ""
# password = ""
## Name of the exchange to declare. If unset, no exchange will be declared.
exchange = "telegraf"
## Exchange type; common types are "direct", "fanout", "topic", "header", "x-consistent-hash".
# exchange_type = "topic"
## If true, exchange will be passively declared.
# exchange_passive = false
## Exchange durability can be either "transient" or "durable".
# exchange_durability = "durable"
## Additional exchange arguments.
# exchange_arguments = { }
# exchange_arguments = {"hash_property" = "timestamp"}
## AMQP queue name.
queue = "telegraf"
## AMQP queue durability can be "transient" or "durable".
queue_durability = "durable"
## If true, queue will be passively declared.
# queue_passive = false
## Additional arguments when consuming from Queue
# queue_consume_arguments = { }
# queue_consume_arguments = {"x-stream-offset" = "first"}
## A binding between the exchange and queue using this binding key is
## created. If unset, no binding is created.
binding_key = "#"
## Maximum number of messages server should give to the worker.
# prefetch_count = 50
## 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
## Timeout for establishing the connection to a broker
# timeout = "30s"
## Auth method. PLAIN and EXTERNAL are supported
## Using EXTERNAL requires enabling the rabbitmq_auth_mechanism_ssl plugin as
## described here: https://www.rabbitmq.com/plugins.html
# auth_method = "PLAIN"
## 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
## Content encoding for message payloads, can be set to
## "gzip", "identity" or "auto"
## - Use "gzip" to decode gzip
## - Use "identity" to apply no encoding
## - Use "auto" determine the encoding using the ContentEncoding header
# content_encoding = "identity"
## Maximum size of decoded message.
## Acceptable units are B, KiB, KB, MiB, MB...
## Without quotes and units, interpreted as size in bytes.
# max_decompression_size = "500MB"
## 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"
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
AMQP
-
Integrating Application Metrics with AMQP: Use the AMQP Consumer plugin to gather application metrics that are published to a RabbitMQ exchange. By configuring the plugin to listen to specific queues, teams can gain insights into application performance, track request rates, error counts, and latency metrics, all in real-time. This setup not only aids in anomaly detection but also provides valuable data for capacity planning and system optimization.
-
Event-Driven Monitoring: Configure the AMQP Consumer to trigger specific monitoring events whenever certain conditions are met within an application. For instance, if a message indicating a high error rate is received, the plugin can feed this data into monitoring tools, generating alerts or scaling events. This integration can improve responsiveness to issues and automate parts of the operations workflow.
-
Cross-Platform Data Aggregation: Leverage the AMQP Consumer plugin to consolidate metrics from various applications distributed across different platforms. By utilizing RabbitMQ as a centralized message broker, organizations can unify their monitoring data, allowing for comprehensive analysis and dashboarding through Telegraf, thus maintaining visibility across heterogeneous environments.
-
Real-Time Log Processing: Extend the use of the AMQP Consumer to capture log data sent to a RabbitMQ exchange, processing logs in real time for monitoring and alerting purposes. This application ensures that operational issues are detected and addressed swiftly by analyzing log patterns, trends, and anomalies as they occur.
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
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