Timesliced reservoir sampling: a new(?) algorithm for profilers
Read OriginalThis article discusses reservoir sampling algorithms for extracting random samples from event streams of unknown length, with a focus on performance profilers. It covers basic reservoir sampling for one or multiple items, highlights problems with standard approaches when a timeline is needed, and introduces a novel 'timesliced' variant that ensures samples are spread evenly across time. The article includes Python code examples and explains why random sampling works for identifying slow code.
0 comments
Comments
No comments yet
Be the first to share your thoughts!
Browser Extension
Get instant access to AllDevBlogs from your browser
Top of the Week
1
Limit token usage in Microsoft Agent Framework
Jesse Liberty
•
1 votes
2
How to Roll Back AI Agents: Incident Response, Circuit Breakers, and Recovery Patterns
Paul Bryant
•
1 votes
3
Avoiding Reasoning Model Failures with Microsoft Foundry
Luke Murray
•
1 votes
4
When Your AI Agent Lies: Silent LLM Fallbacks
Luke Murray
•
1 votes
5
Adding a custom MCP server to Claude and ChatGPT
Simon Willison
•
1 votes
6
Testing AI prompts and comparing models with promptfoo
Tim Deschryver
•
1 votes
7
Superlogical
Mitchell Hashimoto
•
1 votes