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devicehub-teal/ereuse_devicehub/resources/search.py

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2018-09-29 10:24:22 +00:00
"""Full text search module.
Implements full text search by using Postgre's capabilities and
creating temporary tables containing keywords as ts_vectors.
"""
from enum import Enum
from typing import Tuple
from ereuse_devicehub.db import db
class Weight(Enum):
"""TS Rank weight as an Enum."""
A = 'A'
B = 'B'
C = 'C'
D = 'D'
class Search:
"""Methods for building queries with Postgre's Full text search.
Based on `Rachid Belaid's post <http://rachbelaid.com/
postgres-full-text-search-is-good-enough/>`_ and
`Code for America's post <https://www.codeforamerica.org/blog/2015/07/02/
multi-table-full-text-search-with-postgres-flask-and-sqlalchemy/>`.
"""
LANG = 'english'
@staticmethod
def match(column: db.Column, search: str, lang=LANG):
"""Query that matches a TSVECTOR column with search words."""
return column.op('@@')(db.func.websearch_to_tsquery(lang, search))
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@staticmethod
def rank(column: db.Column, search: str, lang=LANG):
"""Query that ranks a TSVECTOR column with search words."""
return db.func.ts_rank(column, db.func.websearch_to_tsquery(lang, search))
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@staticmethod
def _vectorize(col: db.Column, weight: Weight = Weight.D, lang=LANG):
return db.func.setweight(db.func.to_tsvector(lang, db.func.coalesce(col, '')), weight.name)
@classmethod
def vectorize(cls, *cols_with_weights: Tuple[db.Column, Weight], lang=LANG):
"""Produces a query that takes one ore more columns and their
respective weights, and generates one big TSVECTOR.
This method takes care of `null` column values.
"""
first, rest = cols_with_weights[0], cols_with_weights[1:]
tokens = cls._vectorize(*first, lang=lang)
for unit in rest:
tokens = tokens.concat(cls._vectorize(*unit, lang=lang))
return tokens