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Jenkins and TimescaleDB Integration

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider <a href="/integrations/jenkins-influxdb/">Jenkins and InfluxDB</a>.

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Infrastructure Monitoring with Telegraf

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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.

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Input and output integration overview

<p>The Jenkins plugin collects vital information regarding jobs and nodes from a Jenkins instance through its API, facilitating comprehensive monitoring and analysis.</p>

<p>This output plugin delivers a reliable and efficient mechanism for routing Telegraf collected metrics directly into TimescaleDB. By leveraging PostgreSQL’s robust ecosystem combined with TimescaleDB’s time series optimizations, it supports high-performance data ingestion and advanced querying capabilities.</p>

Integration details

Jenkins

<p>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.</p>

TimescaleDB

<p>TimescaleDB is an open source time series database built as an extension to PostgreSQL, designed to handle large scale, time-oriented data efficiently. Launched in 2017, TimescaleDB emerged in response to the growing need for a robust, scalable solution that could manage vast volumes of data with high insert rates and complex queries. By leveraging PostgreSQL’s familiar SQL interface and enhancing it with specialized time series capabilities, TimescaleDB quickly gained popularity among developers looking to integrate time series functionality into existing relational databases. Its hybrid approach allows users to benefit from PostgreSQL’s flexibility, reliability, and ecosystem while providing optimized performance for time series data.</p> <p>The database is particularly effective in environments that demand fast ingestion of data points combined with sophisticated analytical queries over historical periods. TimescaleDB has a number of innovative features like hypertables which transparently partition data into manageable chunks and built-in continuous aggregation. These allow for significantly improved query speed and resource efficiency.</p>

Configuration

Jenkins

<pre><code class="language-toml">[[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 &amp; 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 </code></pre>

TimescaleDB

<pre><code class="language-toml"># Publishes metrics to a TimescaleDB database [[outputs.postgresql]] ## Specify connection address via the standard libpq connection string: ## host=... user=... password=... sslmode=... dbname=... ## Or a URL: ## postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full] ## See https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING ## ## All connection parameters are optional. Environment vars are also supported. ## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE ## All supported vars can be found here: ## https://www.postgresql.org/docs/current/libpq-envars.html ## ## Non-standard parameters: ## pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts. ## pool_min_conns (default: 0) - Minimum size of connection pool. ## pool_max_conn_lifetime (default: 0s) - Maximum connection age before closing. ## pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing. ## pool_health_check_period (default: 0s) - Duration between health checks on idle connections. # connection = "" ## Postgres schema to use. # schema = "public" ## Store tags as foreign keys in the metrics table. Default is false. # tags_as_foreign_keys = false ## Suffix to append to table name (measurement name) for the foreign tag table. # tag_table_suffix = "_tag" ## Deny inserting metrics if the foreign tag can't be inserted. # foreign_tag_constraint = false ## Store all tags as a JSONB object in a single 'tags' column. # tags_as_jsonb = false ## Store all fields as a JSONB object in a single 'fields' column. # fields_as_jsonb = false ## Name of the timestamp column ## NOTE: Some tools (e.g. Grafana) require the default name so be careful! # timestamp_column_name = "time" ## Type of the timestamp column ## Currently, "timestamp without time zone" and "timestamp with time zone" ## are supported # timestamp_column_type = "timestamp without time zone" ## Templated statements to execute when creating a new table. # create_templates = [ # '''CREATE TABLE {{ .table }} ({{ .columns }})''', # ] ## Templated statements to execute when adding columns to a table. ## Set to an empty list to disable. Points containing tags for which there is ## no column will be skipped. Points containing fields for which there is no ## column will have the field omitted. # add_column_templates = [ # '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''', # ] ## Templated statements to execute when creating a new tag table. # tag_table_create_templates = [ # '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''', # ] ## Templated statements to execute when adding columns to a tag table. ## Set to an empty list to disable. Points containing tags for which there is ## no column will be skipped. # tag_table_add_column_templates = [ # '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''', # ] ## The postgres data type to use for storing unsigned 64-bit integer values ## (Postgres does not have a native unsigned 64-bit integer type). ## The value can be one of: ## numeric - Uses the PostgreSQL "numeric" data type. ## uint8 - Requires pguint extension (https://github.com/petere/pguint) # uint64_type = "numeric" ## When using pool_max_conns &gt; 1, and a temporary error occurs, the query is ## retried with an incremental backoff. This controls the maximum duration. # retry_max_backoff = "15s" ## Approximate number of tag IDs to store in in-memory cache (when using ## tags_as_foreign_keys). This is an optimization to skip inserting known ## tag IDs. Each entry consumes approximately 34 bytes of memory. # tag_cache_size = 100000 ## Cut column names at the given length to not exceed PostgreSQL's ## 'identifier length' limit (default: no limit) ## (see https://www.postgresql.org/docs/current/limits.html) ## Be careful to not create duplicate column names! # column_name_length_limit = 0 ## Enable &amp; set the log level for the Postgres driver. # log_level = "warn" # trace, debug, info, warn, error, none </code></pre>

Input and output integration examples

Jenkins

<ol> <li> <p><strong>Continuous Integration Monitoring</strong>: 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.</p> </li> <li> <p><strong>Resource Allocation Analysis</strong>: 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.</p> </li> <li> <p><strong>Job Execution Trends</strong>: 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.</p> </li> <li> <p><strong>Alerting for Job Failures</strong>: 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.</p> </li> </ol>

TimescaleDB

<ol> <li> <p><strong>Real-Time IoT Data Ingestion</strong>: Use the plugin to collect and store sensor data from thousands of IoT devices in real time. This setup facilitates immediate analysis, helping organizations monitor operational efficiency and respond quickly to changing conditions.</p> </li> <li> <p><strong>Cloud Application Performance Monitoring</strong>: Leverage the plugin to feed detailed performance metrics from distributed cloud applications into TimescaleDB. This integration supports real-time dashboards and alerts, enabling teams to swiftly identify and mitigate performance bottlenecks.</p> </li> <li> <p><strong>Historical Data Analysis and Reporting</strong>: Implement a system where long-term metrics are stored in TimescaleDB for comprehensive historical analysis. This approach allows businesses to perform trend analysis, generate detailed reports, and make data-driven decisions based on archived time-series data.</p> </li> <li> <p><strong>Adaptive Alerting and Anomaly Detection</strong>: Integrate the plugin with automated anomaly detection workflows. By continuously streaming metrics to TimescaleDB, machine learning models can analyze data patterns and trigger alerts when anomalies occur, enhancing system reliability and proactive maintenance.</p> </li> </ol>

Feedback

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Useful Links

Documentation

Telegraf Quickstart Training

Infrastructure Monitoring with Telegraf

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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