Apache Aurora and Redis Integration

Input and output integration overview

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

The Redis plugin enables users to send metrics collected by Telegraf directly to Redis. This integration is ideal for applications that require robust time series data storage and analysis.

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.

Redis

The Redis Telegraf plugin is designed for writing metrics to RedisTimeSeries, a specialized Redis database module for time series data. This plugin facilitates the integration of Telegraf with RedisTimeSeries, allowing for the efficient storage and retrieval of timestamped data. With RedisTimeSeries, users can take advantage of enhanced capabilities for managing time series data, including aggregated views and range queries. The plugin offers various configuration options to enable the flexibility needed to connect securely to your Redis database, including support for Authentication, Timeouts, data type conversions, and TLS configurations. The underlying technology leverages Redis’ efficiency and scalability, making it an excellent choice for high-volume metric environments, where real-time processing is essential.

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

Redis

[[outputs.redistimeseries]]
  ## The address of the RedisTimeSeries server.
  address = "127.0.0.1:6379"

  ## Redis ACL credentials
  # username = ""
  # password = ""
  # database = 0

  ## Timeout for operations such as ping or sending metrics
  # timeout = "10s"

  ## Enable attempt to convert string fields to numeric values
  ## If "false" or in case the string value cannot be converted the string
  ## field will be dropped.
  # convert_string_fields = true

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  # insecure_skip_verify = false

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.

Redis

1. Monitoring IoT Sensor Data: Utilize the Redis Telegraf plugin to collect and store data from IoT sensors in real-time. By connecting the plugin to a RedisTimeSeries database, users can analyze trends in temperature, humidity, or other environmental factors. The ability to query historical sensor data efficiently will aid in predictive maintenance and help in resource management.

2. Financial Market Data Aggregation: Employ this plugin to track and store time-sensitive financial data from various sources. By sending metrics to Redis, financial institutions can aggregate and analyze market trends or price changes over time, providing them with actionable insights derived from reliable time series analytics.

3. Application Performance Monitoring (APM): Implement the Redis plugin for gathering application performance metrics such as response times and CPU usage. Users can visualize their application’s performance over time with RedisTimeSeries, allowing them to identify bottlenecks and optimize resource allocation swiftly.

4. Energy Consumption Tracking: Leverage this plugin to monitor energy usage in buildings over time. By integrating with smart meters and sending data to RedisTimeSeries, municipalities or enterprises can analyze energy consumption patterns, helping to implement energy-saving measures and sustainability practices.

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