Apache Aurora and AWS Redshift Integration

Input and output integration overview

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

This plugin enables Telegraf to send metrics to Amazon Redshift using the PostgreSQL plugin, allowing metrics to be stored in a scalable, SQL-compatible data warehouse.

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 Redshift

This configuration uses the Telegraf PostgreSQL plugin to send metrics to Amazon Redshift, AWS’s fully managed cloud data warehouse that supports SQL-based analytics at scale. Although Redshift is based on PostgreSQL 8.0.2, it does not support all standard PostgreSQL features such as full JSONB, stored procedures, or upserts. Therefore, care must be taken to predefine compatible tables and schema when using Telegraf for Redshift integration. This setup is ideal for use cases that benefit from long-term, high-volume metric storage and integration with AWS analytics tools like QuickSight or Redshift Spectrum. Metrics stored in Redshift can be joined with business datasets for rich observability and BI analysis.

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 Redshift

[[outputs.postgresql]]
  ## Redshift connection settings
  host = "redshift-cluster.example.us-west-2.redshift.amazonaws.com"
  port = 5439
  user = "telegraf"
  password = "YourRedshiftPassword"
  database = "metrics"
  sslmode = "require"

  ## Optional: specify a dynamic table template for inserting metrics
  table_template = "telegraf_metrics"

  ## Note: Redshift does not support all PostgreSQL features; ensure your table exists and is compatible

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 Redshift

1. Business-Aware Infrastructure Monitoring: Store infrastructure metrics from Telegraf in Redshift alongside sales, marketing, or customer engagement data. Analysts can correlate system performance with business KPIs using SQL joins and window functions.

2. Historical Trend Analysis for Cloud Resources: Use Telegraf to continuously log CPU, memory, and I/O metrics to Redshift. Combine with time-series SQL queries and visualization tools like Amazon QuickSight to spot trends and forecast resource demand.

3. Security Auditing of System Behavior: Send metrics related to system logins, file changes, or resource spikes into Redshift. Analysts can build dashboards or reports for compliance auditing using SQL queries across multi-year data sets.

4. Cross-Environment SLA Reporting: Aggregate SLA metrics from multiple cloud accounts and regions using Telegraf, and push them to a central Redshift warehouse. Enable unified SLA compliance dashboards and executive reporting via a single SQL interface.

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