Filtering - Paradedb

Adding filters to a search query is as simple as using PostgreSQL's built-in WHERE clauses and operators. For instance, the following query filters out results that do not meet rating > 2.

<CodeGroup> ```sql SQL SELECT description, rating, category FROM mock_items WHERE description ||| 'running shoes' AND rating > 2; ```

import { and, gt } from "drizzle-orm";
import { search } from "@paradedb/drizzle-paradedb";

await db
  .select({
    description: mockItems.description,
    rating: mockItems.rating,
    category: mockItems.category,
  })
  .from(mockItems)
  .where(
    and(
      search.matchAny(mockItems.description, "running shoes"),
      gt(mockItems.rating, 2),
    ),
  );

python

from paradedb import MatchAny, ParadeDB

MockItem.objects.filter(
    description=ParadeDB(MatchAny('running shoes')),
    rating__gt=2
).values('description', 'rating', 'category')

python

from sqlalchemy import select
from sqlalchemy.orm import Session
from paradedb.sqlalchemy import search

stmt = (
    select(MockItem.description, MockItem.rating, MockItem.category)
    .where(search.match_any(MockItem.description, "running shoes"), MockItem.rating > 2)
)

with Session(engine) as session:
    session.execute(stmt).all()

ruby

MockItem.search(:description)
        .match_any("running shoes")
        .where(rating: 3..)
        .select(:description, :rating, :category)

await dbContext
    .MockItems.Where(item =>
        EF.Functions.MatchAny(item.Description, "running shoes") && item.Rating > 2
    )
    .Select(item => new { item.Description, item.Rating, item.Category })
    .ToListAsync();

</CodeGroup> <Note> In order for the ParadeDB index to be used, at least one ParadeDB operator must be present in the query. Any of the following search operators qualify:

Operator	Meaning
`@@@`	General search predicate
`\|\|\|`	Match disjunction — contains any of the terms
`&&&`	Match conjunction — contains all of the terms
`===`	Term — exact token match
`###`	Phrase — tokens in order and position

For queries that do not require text search, add pdb.all to force the ParadeDB index without changing the query output.

</Note>

Filter Pushdown

Non-Text Fields

While not required, filtering performance over non-text columns can be improved by including them in the ParadeDB index. When these columns are part of the index, WHERE clauses that reference them can be pushed down into the index scan itself. This can result in faster query execution over large datasets.

For example, if rating and created_at are frequently used in filters, they can be added to the ParadeDB index during index creation:

sql

CREATE INDEX search_idx ON mock_items
USING paradedb(id, description, rating, created_at)
WITH (key_field = 'id');

Filter pushdown is currently supported for the following combinations of types and operators:

Operator	Left Operand Type	Right Operand Type	Example
`=`, `<`, `>`, `<=`, `>=`, `<>`, `BETWEEN`	`int2`	`int2`	`WHERE rating = 2`
	`int4`	`int4`
	`int8`	`int8`
	`int2`	`int4`
	`int2`	`int8`
	`int4`	`int8`
	`float4`	`float4`
	`float8`	`float8`
	`float4`	`float8`
	`numeric`	`numeric`	`WHERE price = 99.99`
	`date`	`date`
	`time`	`time`
	`timetz`	`timetz`
	`timestamp`	`timestamp`
	`timestamptz`	`timestamptz`
	`uuid`	`uuid`
`=`	`bool`	`bool`	`WHERE in_stock = true`
`IN`, `ANY`, `ALL`	`bool`	`bool[]`	`WHERE rating IN (1,2,3)`
	`int2`	`int2[]`
	`int4`	`int4[]`
	`int8`	`int8[]`
	`int2`	`int4[]`
	`int2`	`int8[]`
	`int4`	`int8[]`
	`float4`	`float4[]`
	`float8`	`float8[]`
	`float4`	`float8[]`
	`date`	`date[]`
	`timetz`	`timetz[]`
	`timestamp`	`timestamp[]`
	`timestamptz`	`timestamptz[]`
	`uuid`	`uuid[]`
`IS`, `IS NOT`	`bool`	`bool`	`WHERE in_stock IS true`
`IS NULL`, `IS NOT NULL`	`bool`		`WHERE rating IS NOT NULL`
	`int2`
	`int4`
	`int8`
	`int2`
	`int2`
	`int4`
	`float4`
	`float8`
	`float4`
	`date`
	`time`
	`timetz`
	`timestamp`
	`timestamptz`
	`uuid`

Text Fields

Suppose we have a text filter that looks for an exact string match like category = 'Footwear':

<CodeGroup> ```sql SQL SELECT description, rating, category FROM mock_items WHERE description === 'shoes' AND category = 'Footwear'; ```

import { and, eq } from "drizzle-orm";
import { search } from "@paradedb/drizzle-paradedb";

await db
  .select({
    description: mockItems.description,
    rating: mockItems.rating,
    category: mockItems.category,
  })
  .from(mockItems)
  .where(
    and(
      search.term(mockItems.description, "shoes"),
      eq(mockItems.category, "Footwear"),
    ),
  );

python

from paradedb import ParadeDB, Term

MockItem.objects.filter(
    description=ParadeDB(Term('shoes')),
    category='Footwear'
).values('description', 'rating', 'category')

python

from sqlalchemy import select
from sqlalchemy.orm import Session
from paradedb.sqlalchemy import search

stmt = (
    select(MockItem.description, MockItem.rating, MockItem.category)
    .where(search.term(MockItem.description, "shoes"), MockItem.category == "Footwear")
)

with Session(engine) as session:
    session.execute(stmt).all()

ruby

MockItem.search(:description)
        .term("shoes")
        .where(category: "Footwear")
        .select(:description, :rating, :category)

await dbContext
    .MockItems.Where(item =>
        EF.Functions.Term(item.Description, "shoes") && item.Category == "Footwear"
    )
    .Select(item => new { item.Description, item.Rating, item.Category })
    .ToListAsync();

</CodeGroup>

To push down the category = 'Footwear' filter, category must be indexed using the literal tokenizer:

sql

CREATE INDEX search_idx ON mock_items
USING paradedb(id, description, (category::pdb.literal))
WITH (key_field = 'id');

Pushdown of set filters over text fields also requires the literal tokenizer:

<CodeGroup> ```sql SQL SELECT description, rating, category FROM mock_items WHERE description === 'shoes' AND category IN ('Footwear', 'Apparel'); ```

import { and, inArray } from "drizzle-orm";
import { search } from "@paradedb/drizzle-paradedb";

await db
  .select({
    description: mockItems.description,
    rating: mockItems.rating,
    category: mockItems.category,
  })
  .from(mockItems)
  .where(
    and(
      search.term(mockItems.description, "shoes"),
      inArray(mockItems.category, ["Footwear", "Apparel"]),
    ),
  );

python

from paradedb import ParadeDB, Term

MockItem.objects.filter(
    description=ParadeDB(Term('shoes')),
    category__in=['Footwear', 'Apparel']
).values('description', 'rating', 'category')

python

from sqlalchemy import select
from sqlalchemy.orm import Session
from paradedb.sqlalchemy import search

stmt = (
    select(MockItem.description, MockItem.rating, MockItem.category)
    .where(search.term(MockItem.description, "shoes"), MockItem.category.in_(["Footwear", "Apparel"]))
)

with Session(engine) as session:
    session.execute(stmt).all()

ruby

MockItem.search(:description)
        .term("shoes")
        .where(category: ["Footwear", "Apparel"])
        .select(:description, :rating, :category)

await dbContext
    .MockItems.Where(item =>
        EF.Functions.Term(item.Description, "shoes")
        && new[] { "Footwear", "Apparel" }.Contains(item.Category)
    )
    .Select(item => new { item.Description, item.Rating, item.Category })
    .ToListAsync();

</CodeGroup>