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Rate limit

As explained in Starting Data Analytics with Generative AI and Python

A technique that restricts the frequency or volume of requests accepted by a service.

A rate limit restricts how frequently or how many requests an API or SDK integration sends or accepts. It helps control usage, costs, and plan limits, especially when processing large datasets. section 1.3.6

Use rate limiting as part of a reliable integration workflow:

  • Monitor API usage and costs so request volume remains within the selected plan’s limits. section 1.3.6
  • Combine rate limiting with caching or other optimizations to reduce unnecessary API calls. section 1.3.6
  • Handle rate-limit errors with appropriate error handling and retry mechanisms to preserve integration stability. section 1.3.6

Large-dataset processing

Before sending requests for a large dataset, monitor expected usage, apply rate limiting, and add retries for rate-limit errors. Check the resulting workflow for reliability and unexpected costs. section 1.3.6

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Connects implementation guidance with monitoring, caching, error handling, and retries, but does not specify a limiting algorithm.
Examples
Applies rate limiting when processing large datasets and recovering from rate-limit errors.
What it assumes
Assumes familiarity with the chosen API or SDK documentation and compatible request and response data formats.
What you can do after
Operate API or SDK integrations more reliably while controlling request usage and unexpected costs.
How it connects
Links rate limiting with APIs, SDKs, caching, usage monitoring, error handling, and retry mechanisms.

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This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.