I'd seen a version of this floating around before so I wasn't totally blind going in.
Start by clarifying requirements and defining a Logger interface with a core log method and a way to register handlers. Then design a chain of responsibility or pipeline where each handler processes the log message in sequence, allowing easy addition of new handlers. Implement a basic Logger class and demonstrate with handlers like capitalize and add.
Pro tip: Emphasize extensibility and separation of concerns: handlers should be independent and composable, and the logger should not need modification to add new handlers. This shows you understand design principles like Open/Closed and Single Responsibility.
Ask questions to understand the scope: What types of handlers? Should handlers be applied in a specific order? Is thread safety required? What is the expected input/output format?
Design a Handler interface with a method like handle(message) that returns a modified message. Define a Logger interface with methods to add handlers and log messages.
Decide on the composition pattern: handlers can be chained in a list, and each handler processes the message and passes it to the next. Alternatively, use a pipeline where the logger iterates through handlers.
Implement the Logger class that maintains a list of handlers and applies them sequentially to each log message. Implement concrete handlers like CapitalizeHandler and AddHandler.
Show example usage and explain how new handlers can be added without modifying existing code. Discuss potential improvements like async handling or error handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My first instinct was just to iterate through all the logs and scan for the keyword, which I said out loud.
Start by clarifying the scale and constraints of the log system, then propose a multi-tiered search strategy that balances speed and resource usage. Discuss indexing techniques like inverted indexes or full-text search engines, and explain how you would handle real-time ingestion and large volumes.
Pro tip: Mention that you would first check if the logs are already indexed by a system like Elasticsearch or if you need to build a custom solution, showing awareness of existing infrastructure. Also, highlight the importance of measuring search latency and throughput to validate your approach.
Ask about log volume, query frequency, latency requirements, and whether logs are structured or unstructured. This determines the appropriate search technology.
Consider using off-the-shelf tools like Elasticsearch, Loki, or Splunk if not already in use. If building custom, discuss inverted index or trie-based approaches.
Propose building an inverted index mapping keywords to log entries, with considerations for tokenization, stemming, and stop words. For real-time, use a streaming indexer.
Discuss sharding, partitioning by time or source, and caching frequent queries. Mention trade-offs between index size, update speed, and query latency.
Address handling of special characters, case sensitivity, and partial matches. Also consider fallback to grep-like scanning for rare queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.