Weight

As explained in Kubernetes in Action, Second Edition

A weight is a routing value that determines a backend's proportional share of requests relative to the weights of other backends.

spec:
  rules:
  - backendRefs:           #1
    - name: kiada-stable     #2
      port: 80               #2
      weight: 9              #2
    - name: kiada-new     #3
      port: 80            #3
      weight: 1           #3

How Kubernetes in Action, Second Edition covers it

How deep it goes

Explains proportional shares with a 9:1 worked example and a request-count check, without detailing routing internals.

Examples

Uses stable and new Services with weights of 9 and 1, and suggests checking request counts with curl.

What it assumes

Builds on HTTPRoute backend references to Services, beginning from a route that forwards traffic to one Service.

What you can do after

Configure an HTTPRoute to send a chosen proportional share of requests to multiple backend Services.

How it connects

Connects HTTPRoute backendRefs and Kubernetes Services with traffic splitting and canary deployments.

liveBookmanning.com

Read Kubernetes in Action, Second Edition on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Data Without LabelsA weight is a learned numerical parameter that scales the contribution of an input or intermediate activation in a neural network.
• 

  liveBookmanning.com
  Fabulous Adventures in Data Structures and AlgorithmsAn integer weight is a whole-number quantity representing the relative contribution of an outcome to a weighted distribution.
• 

  liveBookmanning.com
  Introduction to Generative AI, Second EditionA model weight is a learned numerical parameter that determines the strength of a computational connection in a machine-learning model.

Compare how 4 books explain weight →

Related concepts

• fine-tuningused by · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• deep learningpart of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• neural networkpart of · A neural network is a computational model of interconnected, parameterized units that learns transformations from inputs to outputs.
• parameterequivalent to · A model parameter is a numerical value, such as a weight or bias, that is learned or set to determine how a model computes outputs.
• linear regressionused by · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

This explainer was generated by Manning's knowledge graph from the text of Kubernetes in Action, Second Edition. The section links point to the parts of the book it draws on.