Example - Index and query JSON documents

Learn how to use the Redis query engine with JSON

This example shows how to create a search index for JSON data and run queries against the index.

Make sure that you have Redis Stack and redis-py installed.

Import dependencies:

import redis
from redis.commands.json.path import Path
import redis.commands.search.aggregation as aggregations
import redis.commands.search.reducers as reducers
from redis.commands.search.field import TextField, NumericField, TagField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import NumericFilter, Query

Connect to your Redis database.

r = redis.Redis(host='localhost', port=6379)

Let's create some test data to add to your database.

user1 = {
    "name": "Paul John",
    "email": "paul.john@example.com",
    "age": 42,
    "city": "London"
}
user2 = {
    "name": "Eden Zamir",
    "email": "eden.zamir@example.com",
    "age": 29,
    "city": "Tel Aviv"
}
user3 = {
    "name": "Paul Zamir",
    "email": "paul.zamir@example.com",
    "age": 35,
    "city": "Tel Aviv"
}

Define indexed fields and their data types using schema. Use JSON path expressions to map specific JSON elements to the schema fields.

schema = (
    TextField("$.name", as_name="name"), 
    TagField("$.city", as_name="city"), 
    NumericField("$.age", as_name="age")
)

Create an index. In this example, all JSON documents with the key prefix user: will be indexed. For more information, see Query syntax.

rs = r.ft("idx:users")
rs.create_index(
    schema,
    definition=IndexDefinition(
        prefix=["user:"], index_type=IndexType.JSON
    )
)
# b'OK'

Use JSON.SET to set each user value at the specified path.

r.json().set("user:1", Path.root_path(), user1)
r.json().set("user:2", Path.root_path(), user2)
r.json().set("user:3", Path.root_path(), user3)

Let's find user Paul and filter the results by age.

res = rs.search(
    Query("Paul @age:[30 40]")
)
# Result{1 total, docs: [Document {'id': 'user:3', 'payload': None, 'json': '{"name":"Paul Zamir","email":"paul.zamir@example.com","age":35,"city":"Tel Aviv"}'}]}

Query using JSON Path expressions.

rs.search(
    Query("Paul").return_field("$.city", as_field="city")
).docs
# [Document {'id': 'user:1', 'payload': None, 'city': 'London'}, Document {'id': 'user:3', 'payload': None, 'city': 'Tel Aviv'}]

Aggregate your results using FT.AGGREGATE.

req = aggregations.AggregateRequest("*").group_by('@city', reducers.count().alias('count'))
print(rs.aggregate(req).rows)
# [[b'city', b'Tel Aviv', b'count', b'2'], [b'city', b'London', b'count', b'1']]
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