Apache Aurora and AWS Timestream Integration

Input and output integration overview

This plugin gathers metrics from Apache Aurora schedulers, providing insights necessary for effective monitoring of Aurora clusters.

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

Apache Aurora

The Aurora plugin is designed to gather metrics from Apache Aurora schedulers. This plugin connects to specified schedulers using their respective URLs and retrieves operational metrics that help in monitoring the health and performance of Aurora clusters. It primarily captures numeric data from the /vars endpoint, ensuring key metrics related to task execution and resource utilization are monitored. The plugin enhances operational insights by utilizing HTTP Basic Authentication for secure access. With optional TLS configuration, it further bolsters security when transmitting data. The plugin provides a robust way to interface with Apache Aurora, reflecting a focus on operational reliability and ongoing performance assessment across distributed systems.

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

Apache Aurora

[[inputs.aurora]]
  ## Schedulers are the base addresses of your Aurora Schedulers
  schedulers = ["http://127.0.0.1:8081"]

  ## Set of role types to collect metrics from.
  ##
  ## The scheduler roles are checked each interval by contacting the
  ## scheduler nodes; zookeeper is not contacted.
  # roles = ["leader", "follower"]

  ## Timeout is the max time for total network operations.
  # timeout = "5s"

  ## Username and password are sent using HTTP Basic Auth.
  # username = "username"
  # password = "pa$$word"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = 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

Apache Aurora

1. Dynamic Resource Allocation Monitoring: Utilize the Aurora plugin to build a real-time dashboard displaying metrics related to resource allocation in your Aurora clusters. By aggregating data from multiple schedulers, you can visualize how resources are distributed among various roles (leader and follower), enabling proactive management of resource utilization and helping prevent bottlenecks in production workloads.

2. Alerting on Scheduler Health: Implement alerting mechanisms where the Aurora plugin checks the health of schedulers periodically. If a scheduler role responds with a status that indicates a failure to communicate (non-200 status), alerts can be automatically generated and sent to the operations team via email or messaging apps, ensuring immediate attention to critical issues and maintaining availability in service management.

3. Performance Benchmarking Over Time: By continuously collecting metrics such as job update events and execution times, this plugin can assist teams in benchmarking the performance of their Apache Aurora deployment over time. Relevant metrics can be logged into a time-series database, enabling historical analysis, trend identification, and understanding how changes in the system, such as configuration adjustments or workload changes, impact performance.

4. Integration with CI/CD Pipelines: Integrate the metrics collected via the Aurora plugin with CI/CD pipeline tools to monitor how deployments affect runtime metrics in Aurora. Teams can thereby ensure that new releases do not adversely impact scheduler performance and can roll back changes seamlessly if any metric exceeds predefined thresholds after deployment.

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.