Apache Aurora and CrateDB Integration

Input and output integration overview

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

The CrateDB plugin facilitates the writing of metrics to a CrateDB database, leveraging its PostgreSQL-compatible protocol to ensure a seamless experience for users.

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.

CrateDB

This plugin writes to CrateDB via its PostgreSQL protocol, allowing for metrics to be efficiently stored in a scalable database. CrateDB is designed for high-speed analytics, supporting time-series data and complicated queries, making it ideal for applications that require fast ingestion and analysis of large datasets. By utilizing the PostgreSQL protocol, the CrateDB output plugin ensures compatibility with existing PostgreSQL client libraries and tools, enabling a smooth integration for users who are already familiar with PostgreSQL’s ecosystem. The plugin provides options such as automatic table creation, connection parameters, and query timeouts, offering flexibility in how metrics are handled and stored within the database.

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

CrateDB

[[outputs.cratedb]]
  ## Connection parameters for accessing the database see
  ##   https://pkg.go.dev/github.com/jackc/pgx/v4#ParseConfig
  ## for available options
  url = "postgres://user:password@localhost/schema?sslmode=disable"

  ## Timeout for all CrateDB queries.
  # timeout = "5s"

  ## Name of the table to store metrics in.
  # table = "metrics"

  ## If true, and the metrics table does not exist, create it automatically.
  # table_create = false

  ## The character(s) to replace any '.' in an object key with
  # key_separator = "_"

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.

CrateDB

1. Real-Time Analytics for IoT Devices: Collect and store metrics from thousands of IoT devices. By setting up a dynamic metrics table for each device, users can perform real-time analytics on the collected data, enabling quick insights into device performance, patterns, and potential failures. This setup benefits from CrateDB’s ability to handle high-throughput data ingestion while providing the necessary analytics capabilities to derive actionable insights.

2. Website Performance Monitoring: Track key performance metrics from web applications, such as request latency and user activity. By storing metrics in CrateDB, teams can leverage the power of SQL-like queries to analyze traffic patterns, user engagement, and server performance over time, leading to optimized application performance and enhanced user experiences.

3. Financial Transaction Analysis: Manage large volumes of financial transaction data for real-time fraud detection and analysis. With CrateDB’s scalable infrastructure, users can store, query, and analyze transaction metrics efficiently, allowing for the detection of anomalies and illicit activities based on transaction patterns and trends.

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