Designing a distributed log analytics system involves several key components and considerations to ensure it can handle large volumes of log data efficiently and reliably. Here’s a high-level overview of the design: 1. Requirements Gathering Functional Requirements : Log Collection : Collect logs from various sources. Log Storage : Store logs in a distributed and scalable manner. Log Processing : Process logs for real-time analytics. Querying and Visualization : Provide tools for querying and visualizing log data. Non-Functional Requirements : Scalability : Handle increasing volumes of log data. Reliability : Ensure data is not lost and system is fault-tolerant. Performance : Low latency for log ingestion and querying. Security : Secure log data and access. 2. Architecture Components Log Producers : Applications, services, and systems generating logs. Log Collectors : Agents or services that collect logs from producers (e.g., Fluentd, Logstash). Message Queue : A distributed queue
Designing a distributed cache system to handle 1 billion queries per minute for both read and write operations is a complex task. Here’s a high-level overview of how you might approach this: 1. Requirements Gathering Functional Requirements : Read Data : Quickly retrieve data from the cache. Write Data : Store data in the cache. Eviction Policy : Automatically evict least recently/frequently used items. Replication : Replicate data across multiple nodes for fault tolerance. Consistency : Ensure data consistency across nodes. Node Management : Add and remove cache nodes dynamically. Non-Functional Requirements : Performance : Low latency for read and write operations. Scalability : System should scale horizontally by adding more nodes. Reliability : Ensure high availability and fault tolerance. Durability : Persist data if required. Security : Secure access to the cache system. 2. Capacity Estimation Traffic Estimate : Read Traffic : Estimate the number of read requests per second