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issue This PR Solves In microservices, logs are often collected using busy waiting loops, where the system constantly checks for new logs. This leads to several issues: Wasting Resources: The system keeps checking for new logs, using up CPU resources even when there are no logs to process. Increased Latency: Logs are delayed because the system is busy checking for new data rather than processing logs as they arrive. This PR solves these problems by switching to a more efficient event-driven log aggregation system. Instead of constantly checking for new logs, the system reacts only when new logs are available, reducing unnecessary CPU usage and improving scalability and performance.
The Scheduler design pattern provides a clean and structured solution to manage and execute tasks automatically. It involves: Scheduler: A component responsible for managing the execution of tasks, ensuring they run at the correct time or under the right conditions. Task: A unit of work that encapsulates the logic to be executed. Trigger/Condition: Defines when and how often a task should execute, whether it's based on time intervals (e.g., every minute) or events Execution Context: Ensures that tasks are executed in the right environment, managing resources, states, and concurrency. By using this pattern, tasks are managed centrally, execution timing is automated, and the system can handle various triggers and execution conditions efficiently, allowing developers to focus on defining what needs to be done instead of when and how to execute it.
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Jan 5, 2025
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Unfortunately the changes seem to affect only README.md files, so the software behavior is still unfixed
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closed this by deleting the head repository
Jan 5, 2025
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in microservices, logs are often collected using busy waiting loops, where the system constantly checks for new logs. This leads to several issues:
Wasting Resources: The system keeps checking for new logs, using up CPU resources even when there are no logs to process.
Scalability Problems: As the number of services grows, busy waiting loops become inefficient and slow, causing delays and performance issues.
Increased Latency: Logs are delayed because the system is busy checking for new data rather than processing logs as they arrive.
This PR solves these problems by switching to a more efficient event-driven log aggregation system. Instead of constantly checking for new logs, the system reacts only when new logs are available, reducing unnecessary CPU usage and improving scalability and performance