Cache Data in Redis with Python

A beginner-friendly Redis cache helper that stores JSON strings with a TTL and retrieves them with the redis-py client.

Easy Python 3.9+ Aug 9, 2026 Caching & Redis 14 views 0 copies

redis cache ttl key-value redis-py

Requires third-party packages — install first

pip install redis

Python code

20 lines

Python 3.9+

import redis


class DataCache:
    def __init__(self, host="localhost", port=6379, db=0):
        self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)

    def cache_data(self, key, value, ttl=60):
        self.client.setex(key, ttl, value)

    def get_cached_data(self, key):
        return self.client.get(key)


if __name__ == "__main__":
    cache = DataCache()
    cache.cache_data("user:1", '{"name": "Alice", "age": 30}', ttl=30)
    data = cache.get_cached_data("user:1")
    print(f"Cached data: {data}")
    print(f"Cache TTL remaining: {cache.client.ttl('user:1')} seconds")

Output

stdout

Cached data: {"name": "Alice", "age": 30}
Cache TTL remaining: 29 seconds

How it works

The DataCache class wraps a redis.Redis client, enabling setex to store a key with an expiry time and get to fetch the value. With decode_responses=True, keys and values are returned as strings instead of bytes, making the data easier to work with in Python. The ttl method reports the remaining lifetime of the key in seconds, confirming that the expiry mechanism is working as expected. This pattern gives you a clean, reusable interface for caching simple data structures like JSON strings without repeating client setup code.

Common mistakes

Forgetting to start the Redis server before running the script, which causes a connection error.
Storing non-string values (like ints or dicts) without serializing them to JSON or another string format first.
Assuming data persists beyond the TTL — Redis deletes the key automatically after expiration.

Variations

Use `json.dumps` and `json.loads` to automatically serialize and deserialize dictionaries as cached values instead of pre-built JSON strings.
Add a `delete` method and connection pooling for production-scale applications with high request volumes.

Real-world use cases

Caching user profile responses in a web API to reduce database load on frequent requests.
Storing session tokens with short TTLs to enforce automatic logout after inactivity periods.
Buffering rate-limited data like survey responses to batch-process them before a scheduled job.

Cache Data in Redis with Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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How it works

Common mistakes

Variations

Real-world use cases

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