Completeness

As explained in Think Distributed Systems

The property of a mental model in which every relevant fact of the system is represented in the model.

Completeness means representing every fact relevant to the question at hand in a mental model. Because no model captures everything, relevance depends on the purpose: a circuit model may predict current yet omit the luminous efficiency needed to predict brightness. section 1.1.1 section 1.1.3

Figure 1.1 Mental model and system

Use the system-and-model illustration to compare ground truth with the facts represented in a mental model. section 1.1.1

Transactions hide distributed-system facts

A money-transfer handler visibly subtracts from one account and adds to another, but it need not display concurrency or failure handling when transactional guarantees provide those protections. Undo records operations before execution, enabling either a complete commit or an abort that restores the prior state. section 5.2 section 5.3.3

How Think Distributed Systems covers it

How deep it goes

Defines relevance precisely, illustrates omission, and connects completeness to transactional Undo recovery.

Examples

Uses an electric circuit, light-bulb brightness, and a transactional money-transfer endpoint.

What it assumes

Assumes only the distinction between a system's facts and a mental model's perceived facts.

What you can do after

Evaluate whether a model omits facts needed for a specific prediction or system guarantee.

How it connects

Connects completeness with correctness, mental models, transactions, failure recovery, Undo, and Redo.

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Also explained in

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  The Well-Grounded Data AnalystCompleteness is the extent to which the expected records or attributes in a dataset are present and available.
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  Statistics Slam DunkIncomplete data contains missing, blank, or otherwise unavailable values for one or more recorded fields.
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  Building LLM Applications with DSPyCompleteness is the degree to which a summary includes the important information from its source document.
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  Effective Data AnalysisCompleteness is the degree to which all required data is present for the intended analysis or operation.
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  Data Without LabelsCompleteness is a data-quality attribute indicating that required values, records, variables, and time periods are present.

Compare how 6 books explain completeness →

Related concepts

• data qualitypart of · Data quality is the degree to which data is accurate, complete, consistent, relevant, and suitable for its intended use.
• database transactionproduced by · A database transaction is a logical unit of database operations whose changes are committed together or rolled back as a whole.
• DSPy programmeasured by · A DSPy program is an executable composition of one or more module instances that can be called with inputs declared by a signature.
• signatureused by · A signature is a declarative specification of the expected input and output fields of a language-model task or operation.
• metrickind of · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.

This explainer was generated by Manning's knowledge graph from the text of Think Distributed Systems. The section links point to the parts of the book it draws on.