From 40ccf7ae69394d1b240b3cf6a4d1794fee0a1835 Mon Sep 17 00:00:00 2001
From: Hassan Abedi <cogitator.tech@gmail.com>
Date: Wed, 1 Apr 2026 13:52:34 +0200
Subject: [PATCH] Add note files about fast search and rule engines

---
 hqew/010-search-and-vector-query-engines.md   | 104 ++++++++++++++++++
 ...11-rule-engines-and-fixpoint-evaluation.md |  98 +++++++++++++++++
 2 files changed, 202 insertions(+)
 create mode 100644 hqew/010-search-and-vector-query-engines.md
 create mode 100644 hqew/011-rule-engines-and-fixpoint-evaluation.md

diff --git a/hqew/010-search-and-vector-query-engines.md b/hqew/010-search-and-vector-query-engines.md
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+# Search and Vector Query Engines
+
+A reference for how search-oriented engines differ from classical relational ones.
+
+---
+
+## Short answer
+
+Search and vector engines are still query engines, but their core operators, indexes, and ranking models differ from standard relational SQL systems.
+
+They are often built around retrieval rather than exact relational transformation.
+
+---
+
+## Search engines
+
+Traditional search engines center on:
+
+- inverted indexes
+- document retrieval
+- ranking and scoring
+- boolean and text queries
+
+The core question is often not just "which rows satisfy a predicate?" but "which documents are most relevant?"
+
+That makes ranking a first-class part of execution.
+
+---
+
+## Vector engines
+
+Vector engines center on:
+
+- embeddings
+- nearest-neighbor search
+- approximate similarity retrieval
+- metadata filtering
+
+The core operation is often "find the closest vectors to this query vector," which is different from equality joins or exact predicate evaluation.
+
+Approximation is often acceptable because speed matters and exact nearest-neighbor search can be too expensive at scale.
+
+---
+
+## Hybrid systems
+
+Many modern systems combine:
+
+- lexical search
+- vector search
+- metadata filters
+- reranking
+
+This means the engine may need to merge several candidate-generation and scoring paths into one final result.
+
+---
+
+## How these engines differ from relational ones
+
+Search and vector engines often emphasize:
+
+- ranking
+- retrieval quality
+- approximate indexing
+- candidate generation
+- top-k execution
+
+Relational engines more often emphasize:
+
+- exact semantics
+- joins
+- aggregations
+- transactional or analytical correctness
+
+The difference is not that one has a query engine and the other does not. The difference is what the engine is optimizing for.
+
+---
+
+## Example systems
+
+Examples of search and vector query engines include:
+
+- Lucene
+- Vespa
+- Weaviate
+- Qdrant
+
+They differ in packaging and scope, but all have real planning and execution concerns around retrieval.
+
+---
+
+## Practical mental model
+
+Relational engines are often about exact transformation of structured data.
+
+Search and vector engines are often about efficient retrieval and ranking over high-dimensional or text-heavy data.
+
+That is the cleanest conceptual distinction.
+
+---
+
+## Changelog
+
+* **April 1, 2026** -- First version created.
diff --git a/hqew/011-rule-engines-and-fixpoint-evaluation.md b/hqew/011-rule-engines-and-fixpoint-evaluation.md
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+# Rule Engines and Fixpoint Evaluation
+
+A reference for query engines whose core work is inference, recursion, or repeated rule application.
+
+---
+
+## Short answer
+
+Not all query engines are centered on SQL-style scans, joins, and aggregates.
+
+Some are organized around:
+
+- facts
+- rules
+- recursion
+- derivation until no new information appears
+
+That is the world of rule engines, Datalog engines, and chase-style systems.
+
+---
+
+## The core idea
+
+Instead of asking only for direct data retrieval, a rule engine often asks:
+
+- what facts follow from these rules?
+- what closure or least fixpoint do these inputs imply?
+- what new values must exist to satisfy constraints?
+
+This makes the engine iterative in a deeper way than many classical relational plans.
+
+---
+
+## Fixpoint evaluation
+
+Fixpoint evaluation means:
+
+1. start with known facts
+2. apply rules
+3. add newly derived facts
+4. repeat until nothing new is produced
+
+That final stable state is the fixpoint.
+
+This is central to:
+
+- recursive Datalog
+- transitive closure
+- many inference systems
+
+---
+
+## How this differs from standard relational execution
+
+Relational engines often execute one finite plan over a fixed input.
+
+Rule engines often need:
+
+- iterative scheduling
+- duplicate elimination
+- dependency tracking
+- recursion support
+- possibly witness generation or equality merging
+
+So the runtime model is often different even when some individual operators overlap.
+
+---
+
+## Why this matters
+
+Rule/fixpoint execution is especially relevant when the system needs:
+
+- recursive reasoning
+- closure computation
+- derivation of implicit facts
+- chase-like enforcement of dependencies
+
+This makes it a natural topic whenever query engines overlap with logic or theorem-like data processing.
+
+---
+
+## Practical mental model
+
+Standard query engines answer:
+
+- what result follows from this query over this data?
+
+Rule engines also ask:
+
+- what additional facts become true once the rules have run to completion?
+
+That is the essential distinction.
+
+---
+
+## Changelog
+
+* **April 1, 2026** -- First version created.