OpenTelemetry and MySQL 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
This plugin receives traces, metrics, and logs from OpenTelemetry clients and agents via gRPC, enabling comprehensive observability of applications.
The Telegraf SQL plugin allows you to store metrics from Telegraf directly into a MySQL database, making it easier to analyze and visualize the collected metrics.
Integration details
OpenTelemetry
The OpenTelemetry plugin is designed to receive telemetry data such as traces, metrics, and logs from clients and agents implementing OpenTelemetry via gRPC. This plugin initiates a gRPC service that listens for incoming telemetry data, making it distinct from standard plugins that collect metrics at defined intervals. The OpenTelemetry ecosystem aids developers in observing and understanding their applications’ performance by providing a vendor-neutral way to instrument, generate, collect, and export telemetry data. Key features of this plugin include customizable connection timeouts, adjustable maximum message sizes for incoming data, and options for specifying span, log, and profile dimensions to tag the incoming metrics. With this flexibility, organizations can tailor their telemetry collection to meet precise observability requirements and ensure seamless data integration into systems like InfluxDB.
MySQL
Telegraf’s SQL output plugin is designed to seamlessly write metric data to a SQL database by dynamically creating tables and columns based on the incoming metrics. When configured for MySQL, the plugin leverages the go-sql-driver/mysql, which requires enabling the ANSI_QUOTES SQL mode to ensure proper handling of quoted identifiers. This dynamic schema creation approach ensures that each metric is stored in its own table with a structure derived from its fields and tags, providing a detailed, timestamped record of system performance. The flexibility of the plugin allows it to handle high-throughput environments, making it ideal for scenarios that demand robust, granular metric logging and historical data analysis.
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"
MySQL
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com) clickhouse (ClickHouse)
driver = "mysql"
## Data source name
## The format of the data source name is different for each database driver.
## See the plugin readme for details.
data_source_name = "username:password@tcp(host:port)/dbname"
## 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} - tablename as a quoted identifier
table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"
## Initialization SQL
init_sql = "SET sql_mode='ANSI_QUOTES';"
## 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
## NOTE: Due to the way TOML is parsed, tables must be at the END of the
## plugin definition, otherwise additional config options are read as part of the
## table
## 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 data types Telegraf will use when sending to a database.
##
## The database values used must be data types the destination database
## understands. It is up to the user to ensure that the selected data type is
## available in the database they are using. Refer to your database
## documentation for what data types are available and supported.
#[outputs.sql.convert]
# integer = "INT"
# real = "DOUBLE"
# text = "TEXT"
# timestamp = "TIMESTAMP"
# defaultvalue = "TEXT"
# unsigned = "UNSIGNED"
# bool = "BOOL"
# ## This setting controls the behavior of the unsigned value. By default the
# ## setting will take the integer value and append the unsigned value to it. The other
# ## option is "literal", which will use the actual value the user provides to
# ## the unsigned option. This is useful for a database like ClickHouse where
# ## the unsigned value should use a value like "uint64".
# # conversion_style = "unsigned_suffix"
Input and output integration examples
OpenTelemetry
-
Unified Monitoring Across Services: Use the OpenTelemetry plugin to collect and consolidate telemetry data from various microservices within a Kubernetes environment. By instrumenting each service with OpenTelemetry, you can utilize this plugin to gather a holistic view of application performance and dependencies in real-time, enabling faster troubleshooting and improved reliability of complex systems.
-
Enhanced Debugging with Traces: Implement this plugin to capture end-to-end traces of requests flowing through multiple services. For instance, when a user initiates a transaction that triggers several backend services, the OpenTelemetry plugin can record detailed traces that highlight performance bottlenecks, giving developers the necessary insights to debug issues and optimize their code.
-
Dynamic Load Testing and Performance Monitoring: Leverage the capabilities of this plugin during load testing phases by collecting live metrics and traces under simulated higher loads. This approach helps to evaluate the resilience of the application components and identify potential performance degradations preemptively, ensuring a smooth user experience in production.
-
Integrated Logging and Metrics for Real-Time Monitoring: Combine the OpenTelemetry plugin with logging frameworks to gather real-time logs alongside metric data, creating a powerful observability platform. For example, integrate it within a CI/CD pipeline to monitor builds and deployments, while collecting logs that help diagnose failures or performance issues in real-time.
MySQL
-
Real-Time Web Analytics Storage: Leverage the plugin to capture website performance metrics and store them in MySQL. This setup enables teams to monitor user interactions, analyze traffic patterns, and dynamically adjust site features based on real-time data insights.
-
IoT Device Monitoring: Utilize the plugin to collect metrics from a network of IoT sensors and log them into a MySQL database. This use case supports continuous monitoring of device health and performance, allowing for predictive maintenance and immediate response to anomalies.
-
Financial Transaction Logging: Record high-frequency financial transaction data with precise timestamps. This approach supports robust audit trails, real-time fraud detection, and comprehensive historical analysis for compliance and reporting purposes.
-
Application Performance Benchmarking: Integrate the plugin with application performance monitoring systems to log metrics into MySQL. This facilitates detailed benchmarking and trend analysis over time, enabling organizations to identify performance bottlenecks and optimize resource allocation effectively.
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