exonware-xwquery 0.9.0.7

Universal query language for data structures - 50+ operations, 35+ format converters

xwquery

Company: eXonware.com
Author: eXonware Backend Team
Email: [e-mail]

Query and transform native Python data structures with one API: SQL-style scripts, graph patterns, aggregations, and 35+ alternate surface syntaxes (Cypher, GraphQL, JMESPath, and others). It works directly with dictionaries, lists, and mixed in-memory structures; xwnode, xwdata, and xwentity integrations are optional add-ons when you want deeper stack features.

Install

pip install exonware-xwquery

Basic usage

from exonware.xwquery import XWQuery

data = {'users': [
    {'name': 'Alice', 'age': 30, 'city': 'NYC'},
    {'name': 'Bob', 'age': 25, 'city': 'LA'},
    {'name': 'Charlie', 'age': 35, 'city': 'NYC'}
]}

result = XWQuery.execute("""
    SELECT name, age
    FROM users
    WHERE age > 25 AND city = 'NYC'
""", data)

print(result)
# [{'name': 'Alice', 'age': 30}, {'name': 'Charlie', 'age': 35}]

What you get

• Broad operation set - Core CRUD (SELECT, INSERT, UPDATE, DELETE, CREATE, ALTER, DROP), filtering (WHERE, FILTER, LIKE, IN, RANGE, …), aggregation (GROUP BY, HAVING, SUM, COUNT, …), graph helpers (MATCH, PATH, …), and advanced pieces (JOIN, UNION, WITH, WINDOW, PIPE, …).
• Many input languages - Strategies for SQL-family dialects, Cypher/Gremlin/SPARQL/GraphQL, document and log query languages (MQL, Elasticsearch DSL, PromQL, Flux, …), and more. Parse or convert between them where supported.
• Structure-aware execution - The engine can adapt work to linear, tree, graph, or hybrid shapes when the backend exposes that metadata.

linear_data = [1, 2, 3, 4, 5]
tree_data = {'a': 1, 'b': 2, 'c': 3}
graph_data = {'nodes': [...], 'edges': [...]}

XWQuery.execute("SELECT * WHERE value > 2", linear_data)
XWQuery.execute("SELECT * WHERE key BETWEEN 'a' AND 'c'", tree_data)
XWQuery.execute("MATCH (n)-[r]->(m)", graph_data)

Script examples

SELECT name, email, age FROM users WHERE age >= 18;

SELECT department, COUNT(*) AS employee_count, AVG(salary) AS avg_salary
FROM employees
GROUP BY department
HAVING avg_salary > 50000;

MATCH (u:User)-[:FRIENDS_WITH]->(f:User)
WHERE u.age > 25
RETURN u.name, f.name;

Format conversion

sql_query = "SELECT id, name FROM users WHERE age > 25"
graphql = XWQuery.convert(sql_query, from_format='sql', to_format='graphql')

cypher_query = "MATCH (u:User)-[:WORKS_AT]->(c:Company) RETURN u.name, c.name"
sql = XWQuery.convert(cypher_query, from_format='cypher', to_format='sql')

any_query = XWQuery.parse(query_string)
target_format = any_query.to_format('mongodb')

Stack integration

xwnode

from exonware.xwnode import XWNode
from exonware.xwquery import XWQuery

node = XWNode.from_native({'users': [...]})
result = XWQuery.execute("SELECT * FROM users WHERE active = true", node)

xwdata

from exonware.xwdata import XWData
from exonware.xwquery import XWQuery

data = XWData.load('users.json')
filtered = XWQuery.execute("SELECT * WHERE age > 18", data)
filtered.save('adults.xml')

xwentity

from exonware.xwentity import XWEntity
from exonware.xwquery import XWQuery

class User(XWEntity):
    name: str
    age: int
    email: str

users = XWQuery.execute("SELECT * FROM User WHERE age > 18")

Docs

• docs/INDEX.md - full map
• docs/GUIDE_01_USAGE.md - usage
• docs/REF_15_API.md - API
• docs/REF_13_ARCH.md - architecture
• docs/PROJECT_PHASES.md - phases and roadmap
• docs/REF_51_TEST.md - tests

Development

pip install -e .
python tests/runner.py
python tests/runner.py --core
python tests/runner.py --unit
python tests/runner.py --integration

License

MIT - see LICENSE.

Ecosystem

• xwsystem
• xwnode
• xwquery (this repo)
• xwdata
• xwschema
• xwaction
• xwentity
• xwstorage
• xwbase
  Version: 0.9.0.7 | Updated: 30-Mar-2026

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