OpenTelemetry and Snowflake Integration
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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 OpenTelemetry Input Plugin enables the collection of observed data for analysis and monitoring.
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
OpenTelemetry
This plugin receives traces, metrics, and logs from OpenTelemetry clients and agents via gRPC. It supports configuration options for service address, connection timeout, message size, and attributes to be included as tags.
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
OpenTelemetry
[[inputs.opentelemetry]]
## Override the default (0.0.0.0:4317) destination OpenTelemetry gRPC service
## address:port
# service_address = "0.0.0.0:4317"
## Override the default (5s) new connection timeout
# timeout = "5s"
## gRPC Maximum Message Size
# max_msg_size = "4MB"
## Override the default span attributes to be used as line protocol tags.
## These are always included as tags:
## - trace ID
## - span ID
## Common attributes can be found here:
## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
# span_dimensions = ["service.name", "span.name"]
## Override the default log record attributes to be used as line protocol tags.
## These are always included as tags, if available:
## - trace ID
## - span ID
## Common attributes can be found here:
## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
## When using InfluxDB for both logs and traces, be certain that log_record_dimensions
## matches the span_dimensions value.
# log_record_dimensions = ["service.name"]
## Override the default profile attributes to be used as line protocol tags.
## These are always included as tags, if available:
## - profile_id
## - address
## - sample
## - sample_name
## - sample_unit
## - sample_type
## - sample_type_unit
## Common attributes can be found here:
## - https://github.com/open-telemetry/opentelemetry-collector/tree/main/semconv
# profile_dimensions = []
## Override the default (prometheus-v1) metrics schema.
## Supports: "prometheus-v1", "prometheus-v2"
## For more information about the alternatives, read the Prometheus input
## plugin notes.
# metrics_schema = "prometheus-v1"
## Optional TLS Config.
## For advanced options: https://github.com/influxdata/telegraf/blob/v1.18.3/docs/TLS.md
##
## Set one or more allowed client CA certificate file names to
## enable mutually authenticated TLS connections.
# tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
## Add service certificate and key.
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
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
OpenTelemetry
- Basic Setup: Use this plugin to gather metrics from your OpenTelemetry-enabled applications running in a microservices architecture.
- Comprehensive Monitoring: Combine logs and traces to provide full visibility of your application performance and detect issues quickly.
- Data Enrichment: Enhance your metrics by including additional span dimensions and attributes, which can provide valuable context for analysis.
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
Related Integrations
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View Integration