66 lines
1.9 KiB
Markdown
66 lines
1.9 KiB
Markdown
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# Can a Query Engine Be Built for Non-SQL Databases?
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## Short answer
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Yes.
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A query engine is not inherently tied to SQL. SQL is just one front-end language for expressing declarative queries. The deeper idea is:
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- some data model exists
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- users need to ask questions over it
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- the system needs a planner/executor to answer those questions
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## What changes
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What changes from system to system is the data model, query language, and operator set.
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For example:
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- a relational engine centers on tables, joins, filters, and aggregates
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- a graph engine centers on nodes, edges, traversals, pattern matching, and path expansion
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- a document engine centers on nested objects, array traversal, field extraction, and structural predicates
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- a Datalog engine centers on facts, rules, unification, recursion, and fixpoint evaluation
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## Typical examples
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You can build query engines for:
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- document databases
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- key-value stores
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- graph databases
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- RDF or triple stores
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- Datalog engines
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- log or event stores
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- custom domain-specific databases
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## What stays the same
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Even outside SQL, the high-level architecture is often similar:
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1. parse a query language or API request
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2. build an internal logical representation
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3. optimize or rewrite it
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4. execute it against the underlying store
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The important point is that SQL is only one possible query language. A query engine is the broader idea.
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## Better mental model
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The right way to think about it is:
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- SQL engines optimize relational algebra
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- non-SQL engines optimize some other algebra or execution model
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Examples include:
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- graph pattern matching
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- document/path navigation
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- logical rule evaluation
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- custom search or traversal operators
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So the answer is not just "yes, but differently." It is that query engines are a general architectural pattern, and SQL is only one instance of it.
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## Changelog
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* **Mar 31, 2026** -- First version created.
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