Day 23: Implement Log Partitioning by Source or Time for Scalability & Cost Savings | SDC-Java

Описание: This video on Day 23 of our series teaches you how to implement robust log partitioning strategies, specifically by source or time, to drastically improve log management scalability, query performance, and reduce storage costs for large-scale distributed systems. Log partitioning, a critical data architecture technique, organizes high-volume log data from various applications (like Kubernetes services or microservices) into smaller, manageable segments. We explore *source-based partitioning*, using metadata such as `application ID`, `service name`, or `source IP` to separate logs, which is ideal for troubleshooting and enforcing granular access controls. Additionally, we delve into *time-based partitioning*, leveraging `timestamp` fields (e.g., daily, weekly partitions) to optimize `data retention policies`, facilitate `cold storage archival` in systems like `AWS S3` or `Google Cloud Storage`, and enhance `log analytics` speed for platforms like `Elasticsearch` or `Splunk`. Understand how these strategies are vital for `observability`, `DevOps` practices, and ensuring `compliance` with regulations like `GDPR` while achieving significant `cost optimization` and superior `performance optimization` in your `distributed logging` pipeline, often powered by `Apache Kafka` or `message queues`.

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