Object mapping, and more, for Redis and Python
Redis OM Python makes it easy to model Redis data in your Python applications.
Redis OM Python | Redis OM Node.js | Redis OM Spring | Redis OM .NET
Table of contents
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💡
Why Redis OM?
Redis OM provides high-level abstractions that make it easy to model and query data in Redis with modern Python applications.
This preview release contains the following features:
- Declarative object mapping for Redis objects
- Declarative secondary-index generation
- Fluent APIs for querying Redis
📇
Modeling Your Data
Redis OM contains powerful declarative models that give you data validation, serialization, and persistence to Redis.
Check out this example of modeling customer data with Redis OM. First, we create a Customer model:
import datetime
from typing import Optional
from pydantic import EmailStr
from redis_om import HashModel
class Customer(HashModel):
first_name: str
last_name: str
email: EmailStr
join_date: datetime.date
age: int
bio: Optional[str]
Now that we have a Customer model, let's use it to save customer data to Redis.
import datetime
from typing import Optional
from pydantic import EmailStr
from redis_om import HashModel
class Customer(HashModel):
first_name: str
last_name: str
email: EmailStr
join_date: datetime.date
age: int
bio: Optional[str]
# First, we create a new `Customer` object:
andrew = Customer(
first_name="Andrew",
last_name="Brookins",
email="[email protected]",
join_date=datetime.date.today(),
age=38,
bio="Python developer, works at Redis, Inc."
)
# The model generates a globally unique primary key automatically
# without needing to talk to Redis.
print(andrew.pk)
# > "01FJM6PH661HCNNRC884H6K30C"
# We can save the model to Redis by calling `save()`:
andrew.save()
# To retrieve this customer with its primary key, we use `Customer.get()`:
assert Customer.get(andrew.pk) == andrew
Ready to learn more? Check out the getting started guide.
Or, continue reading to see how Redis OM makes data validation a snap.
✓ Validating Data With Your Model
Redis OM uses Pydantic to validate data based on the type annotations you assign to fields in a model class.
This validation ensures that fields like first_name, which the Customer model marked as a str, are always strings. But every Redis OM model is also a Pydantic model, so you can use Pydantic validators like EmailStr, Pattern, and many more for complex validations!
For example, because we used the EmailStr type for the email field, we'll get a validation error if we try to create a Customer with an invalid email address:
import datetime
from typing import Optional
from pydantic import EmailStr, ValidationError
from redis_om import HashModel
class Customer(HashModel):
first_name: str
last_name: str
email: EmailStr
join_date: datetime.date
age: int
bio: Optional[str]
try:
Customer(
first_name="Andrew",
last_name="Brookins",
email="Not an email address!",
join_date=datetime.date.today(),
age=38,
bio="Python developer, works at Redis, Inc."
)
except ValidationError as e:
print(e)
"""
pydantic.error_wrappers.ValidationError: 1 validation error for Customer
email
value is not a valid email address (type=value_error.email)
"""
Any existing Pydantic validator should work as a drop-in type annotation with a Redis OM model. You can also write arbitrarily complex custom validations!
To learn more, see the documentation on data validation.
🔎
Rich Queries and Embedded Models
Data modeling, validation, and saving models to Redis all work regardless of how you run Redis.
Next, we'll show you the rich query expressions and embedded models Redis OM provides when the RediSearch and RedisJSON modules are installed in your Redis deployment, or you're using Redis Enterprise.
TIP: Wait, what's a Redis module? If you aren't familiar with Redis modules, review the So, How Do You Get RediSearch and RedisJSON? section of this README.
Querying
Redis OM comes with a rich query language that allows you to query Redis with Python expressions.
To show how this works, we'll make a small change to the Customer model we defined earlier. We'll add Field(index=True) to tell Redis OM that we want to index the last_name and age fields:
import datetime
from typing import Optional
from pydantic import EmailStr
from redis_om import (
Field,
HashModel,
Migrator
)
class Customer(HashModel):
first_name: str
last_name: str = Field(index=True)
email: EmailStr
join_date: datetime.date
age: int = Field(index=True)
bio: Optional[str]
# Now, if we use this model with a Redis deployment that has the
# RediSearch module installed, we can run queries like the following.
# Before running queries, we need to run migrations to set up the
# indexes that Redis OM will use. You can also use the `migrate`
# CLI tool for this!
Migrator().run()
# Find all customers with the last name "Brookins"
Customer.find(Customer.last_name == "Brookins").all()
# Find all customers that do NOT have the last name "Brookins"
Customer.find(Customer.last_name != "Brookins").all()
# Find all customers whose last name is "Brookins" OR whose age is
# 100 AND whose last name is "Smith"
Customer.find((Customer.last_name == "Brookins") | (
Customer.age == 100
) & (Customer.last_name == "Smith")).all()
These queries -- and more! -- are possible because Redis OM manages indexes for you automatically.
Querying with this index features a rich expression syntax inspired by the Django ORM, SQLAlchemy, and Peewee. We think you'll enjoy it!
Embedded Models
Redis OM can store and query nested models like any document database, with the speed and power you get from Redis. Let's see how this works.
In the next example, we'll define a new Address model and embed it within the Customer model.
import datetime
from typing import Optional
from redis_om import (
EmbeddedJsonModel,
JsonModel,
Field,
Migrator,
)
class Address(EmbeddedJsonModel):
address_line_1: str
address_line_2: Optional[str]
city: str = Field(index=True)
state: str = Field(index=True)
country: str
postal_code: str = Field(index=True)
class Customer(JsonModel):
first_name: str = Field(index=True)
last_name: str = Field(index=True)
email: str = Field(index=True)
join_date: datetime.date
age: int = Field(index=True)
bio: Optional[str] = Field(index=True, full_text_search=True,
default="")
# Creates an embedded model.
address: Address
# With these two models and a Redis deployment with the RedisJSON
# module installed, we can run queries like the following.
# Before running queries, we need to run migrations to set up the
# indexes that Redis OM will use. You can also use the `migrate`
# CLI tool for this!
Migrator().run()
# Find all customers who live in San Antonio, TX
Customer.find(Customer.address.city == "San Antonio",
Customer.address.state == "TX")
💻
Installation
Installation is simple with pip, Poetry, or Pipenv.
# With pip
$ pip install redis-om
# Or, using Poetry
$ poetry add redis-om
📚
Documentation
The Redis OM documentation is available here.
⛏️
Troubleshooting
If you run into trouble or have any questions, we're here to help!
Hit us up on the Redis Discord Server or open an issue on GitHub.
✨
So How Do You Get RediSearch and RedisJSON?
Some advanced features of Redis OM rely on core features from two source available Redis modules: RediSearch and RedisJSON.
You can run these modules in your self-hosted Redis deployment, or you can use Redis Enterprise, which includes both modules.
To learn more, read our documentation.
❤️
Contributing
We'd love your contributions!
Bug reports are especially helpful at this stage of the project. You can open a bug report on GitHub.
You can also contribute documentation -- or just let us know if something needs more detail. Open an issue on GitHub to get started.
📝
License
Redis OM uses the BSD 3-Clause license.
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