Jenkins 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
The Jenkins plugin collects vital information regarding jobs and nodes from a Jenkins instance through its API, facilitating comprehensive monitoring and analysis.
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
Jenkins
The Jenkins Telegraf plugin allows users to gather metrics from a Jenkins instance without needing to install any additional plugins on Jenkins itself. By utilizing the Jenkins API, the plugin retrieves information about nodes and jobs running in the Jenkins environment. This integration provides a comprehensive overview of the Jenkins infrastructure, including real-time metrics that can be used for monitoring and analysis. Key features include configurable filters for job and node selection, optional TLS security settings, and the ability to manage request timeouts and connection limits effectively. This makes it an essential tool for teams that rely on Jenkins for continuous integration and delivery, ensuring they have the insights they need to maintain optimal performance and reliability.
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
Jenkins
[[inputs.jenkins]]
## The Jenkins URL in the format "schema://host:port"
url = "http://my-jenkins-instance:8080"
# username = "admin"
# password = "admin"
## Set response_timeout
response_timeout = "5s"
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use SSL but skip chain & host verification
# insecure_skip_verify = false
## Optional Max Job Build Age filter
## Default 1 hour, ignore builds older than max_build_age
# max_build_age = "1h"
## Optional Sub Job Depth filter
## Jenkins can have unlimited layer of sub jobs
## This config will limit the layers of pulling, default value 0 means
## unlimited pulling until no more sub jobs
# max_subjob_depth = 0
## Optional Sub Job Per Layer
## In workflow-multibranch-plugin, each branch will be created as a sub job.
## This config will limit to call only the lasted branches in each layer,
## empty will use default value 10
# max_subjob_per_layer = 10
## Jobs to include or exclude from gathering
## When using both lists, job_exclude has priority.
## Wildcards are supported: [ "jobA/*", "jobB/subjob1/*"]
# job_include = [ "*" ]
# job_exclude = [ ]
## Nodes to include or exclude from gathering
## When using both lists, node_exclude has priority.
# node_include = [ "*" ]
# node_exclude = [ ]
## Worker pool for jenkins plugin only
## Empty this field will use default value 5
# max_connections = 5
## When set to true will add node labels as a comma-separated tag. If none,
## are found, then a tag with the value of 'none' is used. Finally, if a
## label contains a comma it is replaced with an underscore.
# node_labels_as_tag = false
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
Jenkins
-
Continuous Integration Monitoring: Use the Jenkins plugin to monitor the performance of continuous integration pipelines by collecting metrics on job durations and failure rates. This can help teams identify bottlenecks in the pipeline and improve overall build efficiency.
-
Resource Allocation Analysis: Leverage Jenkins node metrics to assess resource usage across different agents. By understanding how resources are allocated, teams can optimize their Jenkins architecture, potentially reallocating agents or adjusting job configurations for better performance.
-
Job Execution Trends: Analyze historical job performance metrics to identify trends in job execution over time. With this data, teams can proactively address potential issues before they grow, making adjustments to the jobs or their configurations as needed.
-
Alerting for Job Failures: Implement alerts that leverage the Jenkins job metrics to notify team members in case of job failures. This proactive approach can enhance operational awareness and speed up response times to failures, ensuring that critical jobs are monitored effectively.
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