Supervisor and Microsoft SQL Server 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 gathers information about processes running under Supervisor using the XML-RPC API.
Telegraf’s SQL plugin facilitates the storage of metrics in SQL databases. When configured for Microsoft SQL Server, it supports the specific DSN format and schema requirements, allowing for seamless integration with SQL Server.
Integration details
Supervisor
The Supervisor plugin for Telegraf is designed to collect metrics about processes managed by the Supervisor process control system using its XML-RPC API. The plugin is able to track various metrics, including process states and uptime, and provides options for configuring which metrics to collect through include or exclude lists. This integration is particularly useful for monitoring applications running under Supervisor, providing insights into their operational status and performance metrics. A minimum tested Supervisor version is 3.3.2, and it is recommended to secure the HTTP server with basic authentication for better security.
Microsoft SQL Server
Telegraf’s SQL output plugin for Microsoft SQL Server is designed to capture and store metric data by dynamically creating tables and columns that match the structure of incoming data. This integration leverages the go-mssqldb driver, which follows the SQL Server connection protocol through a DSN that includes server, port, and database details. Although the driver is considered experimental due to limited unit tests, it provides robust support for dynamic schema generation and data insertion, enabling detailed time-stamped records of system performance. This flexibility makes it a valuable tool for environments that demand reliable and granular metric logging, despite its experimental status.
Configuration
Supervisor
[[inputs.supervisor]]
## Url of supervisor's XML-RPC endpoint if basic auth enabled in supervisor http server,
## than you have to add credentials to url (ex. http://login:pass@localhost:9001/RPC2)
# url="http://localhost:9001/RPC2"
## With settings below you can manage gathering additional information about processes
## If both of them empty, then all additional information will be collected.
## Currently supported supported additional metrics are: pid, rc
# metrics_include = []
# metrics_exclude = ["pid", "rc"]
Microsoft SQL Server
[[outputs.sql]]
## Database driver
## Valid options: mssql (Microsoft SQL Server), mysql (MySQL), pgx (Postgres),
## sqlite (SQLite3), snowflake (snowflake.com), clickhouse (ClickHouse)
driver = "mssql"
## Data source name
## For Microsoft SQL Server, the DSN typically includes the server, port, username, password, and database name.
## Example DSN: "sqlserver://username:password@localhost:1433?database=telegraf"
data_source_name = "sqlserver://username:password@localhost:1433?database=telegraf"
## 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
## You can customize the mapping if needed.
#[outputs.sql.convert]
# integer = "INT"
# real = "DOUBLE"
# text = "TEXT"
# timestamp = "TIMESTAMP"
# defaultvalue = "TEXT"
# unsigned = "UNSIGNED"
# bool = "BOOL"
Input and output integration examples
Supervisor
-
Centralized Monitoring Dashboard: Implement this plugin to feed Supervisor metrics directly into a centralized monitoring dashboard, allowing teams to visualize the health and performance of their applications in real-time. This integration enables quick identification of issues, helps track service performance over time, and aids in capacity planning based on observed trends.
-
Alerting for Process Failures: Utilize the metrics gathered by the Supervisor plugin to create an alerting mechanism that notifies engineers when critical processes go down or enter a fatal state. By setting thresholds in your monitoring system, teams can respond proactively to potential problems, minimizing downtime and ensuring system reliability.
-
Historical Analysis of Process States: Store the metrics collected over time to analyze process state changes and patterns. By examining historical data, teams can identify recurring issues, track the impact of deployment changes, and optimize resource allocation based on process trends, leading to improved overall system performance.
-
Integration with Incident Management Systems: Configure the Supervisor plugin to automatically send alerts to incident management systems like PagerDuty or OpsGenie when a process reaches a critical state. This integration streamlines the incident response process, ensuring that the right team members are notified promptly and can take action without delay.
Microsoft SQL Server
-
Enterprise Application Monitoring: Leverage the plugin to capture detailed performance metrics from enterprise applications running on SQL Server. This setup allows IT teams to analyze system performance, track transaction times, and identify bottlenecks across complex, multi-tier environments.
-
Dynamic Infrastructure Auditing: Deploy the plugin to create a dynamic audit log of infrastructure changes and performance metrics in SQL Server. This use case is ideal for organizations that require real-time monitoring and historical analysis of system performance for compliance and optimization.
-
Automated Performance Benchmarking: Use the plugin to continuously record and analyze performance metrics of SQL Server databases. This enables automated benchmarking, where historical data is compared against current performance, helping to quickly identify anomalies or degradation in service.
-
Integrated DevOps Dashboards: Integrate the plugin with DevOps monitoring tools to feed real-time metrics from SQL Server into centralized dashboards. This provides a holistic view of application health, allowing teams to correlate SQL Server performance with application-level events for faster troubleshooting and proactive maintenance.
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