Jenkins and AWS Timestream Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

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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 Jenkins and InfluxDB.

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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.

The AWS Timestream Telegraf plugin enables users to send metrics directly to Amazon’s Timestream service, which is designed for time series data management. This plugin offers a variety of configuration options for authentication, data organization, and retention settings.

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.

AWS Timestream

This plugin is designed to efficiently write metrics to Amazon’s Timestream service, a time series database optimized for IoT and operational applications. With this plugin Telegraf can send data collected from various sources and supports a flexible configuration for authentication, data organization, and retention management. It utilizes a credential chain for authentication, allowing various methods such as web identity, assumed roles, and shared profiles. Users can define how metrics are organized in Timestream—whether to use a single table or multiple tables, alongside control over aspect such as retention periods for both magnetic and memory stores. A key feature is its ability to handle multi-measure records, enabling efficient data ingestion and helping to reduce the overhead of multiple writes. In terms of error handling, the plugin includes mechanisms for addressing common issues related to AWS errors during data writes, such as retry logic for throttling and the ability to create tables as needed.

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

AWS Timestream

[[outputs.timestream]]
  ## Amazon Region
  region = "us-east-1"

  ## Amazon Credentials
  ## Credentials are loaded in the following order:
  ## 1) Web identity provider credentials via STS if role_arn and web_identity_token_file are specified
  ## 2) Assumed credentials via STS if role_arn is specified
  ## 3) explicit credentials from 'access_key' and 'secret_key'
  ## 4) shared profile from 'profile'
  ## 5) environment variables
  ## 6) shared credentials file
  ## 7) EC2 Instance Profile
  #access_key = ""
  #secret_key = ""
  #token = ""
  #role_arn = ""
  #web_identity_token_file = ""
  #role_session_name = ""
  #profile = ""
  #shared_credential_file = ""

  ## Endpoint to make request against, the correct endpoint is automatically
  ## determined and this option should only be set if you wish to override the
  ## default.
  ##   ex: endpoint_url = "http://localhost:8000"
  # endpoint_url = ""

  ## Timestream database where the metrics will be inserted.
  ## The database must exist prior to starting Telegraf.
  database_name = "yourDatabaseNameHere"

  ## Specifies if the plugin should describe the Timestream database upon starting
  ## to validate if it has access necessary permissions, connection, etc., as a safety check.
  ## If the describe operation fails, the plugin will not start
  ## and therefore the Telegraf agent will not start.
  describe_database_on_start = false

  ## Specifies how the data is organized in Timestream.
  ## Valid values are: single-table, multi-table.
  ## When mapping_mode is set to single-table, all of the data is stored in a single table.
  ## When mapping_mode is set to multi-table, the data is organized and stored in multiple tables.
  ## The default is multi-table.
  mapping_mode = "multi-table"

  ## Specifies if the plugin should create the table, if the table does not exist.
  create_table_if_not_exists = true

  ## Specifies the Timestream table magnetic store retention period in days.
  ## Check Timestream documentation for more details.
  ## NOTE: This property is valid when create_table_if_not_exists = true.
  create_table_magnetic_store_retention_period_in_days = 365

  ## Specifies the Timestream table memory store retention period in hours.
  ## Check Timestream documentation for more details.
  ## NOTE: This property is valid when create_table_if_not_exists = true.
  create_table_memory_store_retention_period_in_hours = 24

  ## Specifies how the data is written into Timestream.
  ## Valid values are: true, false
  ## When use_multi_measure_records is set to true, all of the tags and fields are stored
  ## as a single row in a Timestream table.
  ## When use_multi_measure_record is set to false, Timestream stores each field in a
  ## separate table row, thereby storing the tags multiple times (once for each field).
  ## The recommended setting is true.
  ## The default is false.
  use_multi_measure_records = "false"

  ## Specifies the measure_name to use when sending multi-measure records.
  ## NOTE: This property is valid when use_multi_measure_records=true and mapping_mode=multi-table
  measure_name_for_multi_measure_records = "telegraf_measure"

  ## Specifies the name of the table to write data into
  ## NOTE: This property is valid when mapping_mode=single-table.
  # single_table_name = ""

  ## Specifies the name of dimension when all of the data is being stored in a single table
  ## and the measurement name is transformed into the dimension value
  ## (see Mapping data from Influx to Timestream for details)
  ## NOTE: This property is valid when mapping_mode=single-table.
  # single_table_dimension_name_for_telegraf_measurement_name = "namespace"

  ## Only valid and optional if create_table_if_not_exists = true
  ## Specifies the Timestream table tags.
  ## Check Timestream documentation for more details
  # create_table_tags = { "foo" = "bar", "environment" = "dev"}

  ## Specify the maximum number of parallel go routines to ingest/write data
  ## If not specified, defaulted to 1 go routines
  max_write_go_routines = 25

  ## Please see README.md to know how line protocol data is mapped to Timestream
  ##

Input and output integration examples

Jenkins

  1. 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.

  2. 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.

  3. 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.

  4. 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.

AWS Timestream

  1. IoT Data Metrics: Use the Timestream plugin to send real-time metrics from IoT devices to Timestream, allowing for quick analysis and visualization of sensor data. By organizing device readings into a time series format, users can track trends, identify anomalies, and streamline operational decisions based on device performance.

  2. Application Performance Monitoring: Leverage Timestream alongside application monitoring tools to send metrics about service performance over time. This integration enables engineers to perform historical analysis of application performance, correlate it with business metrics, and optimize resource allocation based on usage patterns viewed over time.

  3. Automated Data Archiving: Configure the Timestream plugin to write data to Timestream while simultaneously managing retention periods. This setup can automate archiving strategies, ensuring that older data is preserved according to predefined criteria. This is especially useful for compliance and historical analysis, allowing businesses to maintain their data lifecycle with minimal manual intervention.

  4. Multi-Application Metrics Aggregation: Utilize the Timestream plugin to aggregate metrics from multiple applications into Timestream. By creating a unified database of performance metrics, organizations can gain holistic insights across various services, improving visibility into system-wide performance and facilitating cross-application troubleshooting.

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

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