[FEATURE] Implement Asynchronous Alert Fetcher Service

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Overview

The current synchronous alert fetching mechanism in our system is causing delays in the system. To improve performance and responsiveness of alert processing and counter updates, we propose the implementation of an asynchronous service to handle NWS API communication and initial alert processing. This service will fetch alerts asynchronously, update counters immediately, and distribute alerts to a queue for further processing.

Motivation

The current synchronous alert fetching mechanism is a bottleneck in our system, causing delays in alert processing and counter updates. By implementing an asynchronous service, we can improve the performance and responsiveness of our system, providing a better user experience.

Proposed Implementation

To implement the asynchronous alert fetcher service, we will follow these steps:

Async NWS API Client

  • Create AsyncNWSClient class: We will create a new class, AsyncNWSClient, to handle asynchronous NWS API communication.
  • Implement rate limiting: We will implement rate limiting to prevent API abuse and ensure that our service does not exceed the allowed request limit.
  • Add error handling: We will add robust error handling to handle any exceptions that may occur during API communication.
  • Setup request retries: We will setup request retries to handle any temporary failures that may occur during API communication.

Alert Processing Pipeline

  • Create AlertProcessor class: We will create a new class, AlertProcessor, to handle alert processing.
  • Implement concurrent processing: We will implement concurrent processing to process alerts in parallel, improving the overall performance of our system.
  • Add validation and filtering: We will add validation and filtering to ensure that only valid and relevant alerts are processed.
  • Setup error handling: We will add robust error handling to handle any exceptions that may occur during alert processing.

Counter Management

  • Create atomic counter updates: We will create atomic counter updates to ensure that counter updates are thread-safe and consistent.
  • Implement real-time file writing: We will implement real-time file writing to ensure that counter updates are written to disk in real-time.
  • Add counter verification: We will add counter verification to ensure that counter updates are accurate and consistent.
  • Setup monitoring: We will setup monitoring to track counter updates and ensure that they are functioning correctly.

Queue Integration

  • Implement queue producer: We will implement a queue producer to distribute alerts to a queue for further processing.
  • Add message formatting: We will add message formatting to ensure that alerts are formatted correctly for further processing.
  • Setup batch processing: We will setup batch processing to process alerts in batches, improving the overall performance of our system.
  • Add failure handling: We will add robust failure handling to handle any exceptions that may occur during queue processing.

User Experience

Users will see immediate counter updates and faster alert processing times, providing a better user experience.

Technical Requirements

  • Python 3.10+: We will use Python 3.10 or later as the programming language for our service.
  • aiohttp: We will use aiohttp as the asynchronous HTTP client library.
  • Redis client**: We will use a Redis client to interact with the Redis queue.
  • AsyncIO: We will use AsyncIO to handle asynchronous operations.
  • Prometheus metrics (optional): We will use Prometheus metrics to track performance metrics, if required.

Acceptance Criteria

  • Service fetches alerts asynchronously: The service must fetch alerts asynchronously to improve performance.
  • Counter updates immediately after fetch: Counter updates must be immediate after fetch to provide a better user experience.
  • Alerts are processed concurrently: Alerts must be processed concurrently to improve the overall performance of our system.
  • Rate limiting prevents API abuse: Rate limiting must prevent API abuse to ensure that our service does not exceed the allowed request limit.
  • Error handling is robust: Error handling must be robust to handle any exceptions that may occur during service operation.
  • Performance metrics are available: Performance metrics must be available to track the performance of our service.

Priority

High

Estimated Effort

Large

Related PRs/Issues

  • Depends on Redis Queue Infrastructure
  • Will be required by Alert Display Service
    Q&A: Implementing Asynchronous Alert Fetcher Service =====================================================

Q: What is the main goal of implementing an asynchronous alert fetcher service?

A: The main goal of implementing an asynchronous alert fetcher service is to improve the performance and responsiveness of alert processing and counter updates in our system.

Q: How will the asynchronous alert fetcher service improve performance?

A: The asynchronous alert fetcher service will improve performance by fetching alerts asynchronously, which means that it will not block the main thread of our system. This will allow our system to process alerts in parallel, improving the overall performance of our system.

Q: What are the benefits of using an asynchronous service?

A: The benefits of using an asynchronous service include:

  • Improved performance: Asynchronous services can handle multiple tasks simultaneously, improving the overall performance of our system.
  • Better responsiveness: Asynchronous services can provide a better user experience by responding quickly to user requests.
  • Improved scalability: Asynchronous services can handle a large number of requests without affecting the performance of our system.

Q: How will the asynchronous alert fetcher service handle errors?

A: The asynchronous alert fetcher service will handle errors using robust error handling mechanisms, including:

  • Try-except blocks: To catch and handle exceptions that may occur during service operation.
  • Error logging: To log errors for debugging and troubleshooting purposes.
  • Error notification: To notify administrators of errors that may occur during service operation.

Q: How will the asynchronous alert fetcher service handle rate limiting?

A: The asynchronous alert fetcher service will handle rate limiting using rate limiting mechanisms, including:

  • Token bucket algorithm: To limit the number of requests that can be made to the NWS API within a certain time period.
  • Leaky bucket algorithm: To limit the number of requests that can be made to the NWS API within a certain time period.

Q: How will the asynchronous alert fetcher service integrate with the Redis queue?

A: The asynchronous alert fetcher service will integrate with the Redis queue using the following mechanisms:

  • Queue producer: To distribute alerts to the Redis queue for further processing.
  • Message formatting: To format alerts correctly for further processing.
  • Batch processing: To process alerts in batches, improving the overall performance of our system.

Q: What are the technical requirements for implementing the asynchronous alert fetcher service?

A: The technical requirements for implementing the asynchronous alert fetcher service include:

  • Python 3.10+: As the programming language for our service.
  • aiohttp: As the asynchronous HTTP client library.
  • Redis client: To interact with the Redis queue.
  • AsyncIO: To handle asynchronous operations.
  • Prometheus metrics (optional): To track performance metrics.

Q: What are the acceptance criteria for implementing the asynchronous alert fetcher service?

A: The acceptance criteria for implementing the asynchronous alert fetcher service include:

  • Service fetches alerts asynchronously.
  • Counter updates immediately after fetch.
  • Alerts are processed concurrently.
  • Rate limiting prevents API abuse.
  • Error handling is robust.
  • Performance metrics are available.

Q: What is the priority of implementing the asynchronous alert fetcher service?

A: The priority of implementing the asynchronous alert fetcher service is high, as it will improve the performance and responsiveness of alert processing and counter updates in our system.

Q: What is the estimated effort required to implement the asynchronous alert fetcher service?

A: The estimated effort required to implement the asynchronous alert fetcher service is large, as it will require significant development and testing efforts.