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Concurrency

As explained in Starting Data Analytics with Generative AI and Python

Concurrency is the capacity of a system to handle multiple queries, processes, or tasks at the same time.

Concurrency measures how many data-analysis queries or processes a system can handle simultaneously without performance degradation. It helps answer whether users can keep viewing data while processing continues and whether capacity matches expected or future load. section 7.1

Assess concurrency alongside related performance measures:

  • Latency measures how quickly initial results appear after input is complete. section 7.1
  • Throughput measures how much data the system processes within a given time. section 7.1
  • Response time measures total task completion time, including computation and result retrieval. section 7.1
  • Availability, scalability, reliability, and bandwidth reveal whether concurrent use remains practical as demand and data volumes change. section 7.1

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Defines concurrency and frames load-baseline questions, but stops short of implementation techniques or capacity-planning procedures.
Examples
Compares simultaneous queries or processes with latency, throughput, and response-time measurements.
What you can do after
Evaluate whether a data-analysis system can support current or anticipated simultaneous demand without degraded performance.
How it connects
Links concurrency with latency, throughput, bandwidth, response time, availability, scalability, and reliability.

Related concepts

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.