Redis Cache Helper Class in Python with TTL

Build a DataHelper class that caches function results in Redis with a default TTL, using get_or_set and clear methods.

Easy Python 3.9+ Aug 9, 2026 Caching & Redis 12 views 0 copies

redis caching cache-aside ttl class

Requires third-party packages — install first

pip install redis

Python code

40 lines

Python 3.9+

import redis
import json
import time


class DataHelper:
    def __init__(self, host="localhost", port=6379, db=0, default_ttl=60):
        self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
        self.default_ttl = default_ttl

    def get_or_set(self, key, data_func, ttl=None):
        cached = self.client.get(key)
        if cached is not None:
            return json.loads(cached)

        fresh_data = data_func()
        ttl = ttl or self.default_ttl
        self.client.setex(key, ttl, json.dumps(fresh_data))
        return fresh_data

    def clear(self, key):
        return self.client.delete(key)


if __name__ == "__main__":
    helper = DataHelper(default_ttl=5)

    def expensive_data():
        return {"timestamp": time.time(), "items": [1, 2, 3]}

    first = helper.get_or_set("demo:data", expensive_data)
    print(f"First call: {first}")

    second = helper.get_or_set("demo:data", expensive_data)
    print(f"Second call (cached): {second}")
    print(f"Values equal: {first == second}")

    helper.clear("demo:data")
    third = helper.get_or_set("demo:data", expensive_data)
    print(f"After clear — new value: {third}")

Output

stdout

First call: {'timestamp': 1713123456.78, 'items': [1, 2, 3]}
Second call (cached): {'timestamp': 1713123456.78, 'items': [1, 2, 3]}
Values equal: True
After clear — new value: {'timestamp': 1713123456.99, 'items': [1, 2, 3]}

How it works

The DataHelper class wraps a Redis client with decode_responses=True so cached values are returned as strings, not bytes. get_or_set first checks if the key exists; if it does, it returns the decoded JSON. Otherwise it calls data_func, serializes the result with json.dumps, and stores it with setex using the TTL (default 60 seconds, overridden here to 5). This pattern is a classic cache-aside implementation: check cache, on miss fetch the source and populate the cache. The clear method deletes the key so the next call triggers a fresh computation. Using JSON as the serialization format keeps the cache human-readable and works well for dict/list structures.

Common mistakes

Forgetting to set `decode_responses=True` leads to bytes objects and errors when comparing with strings.
Not using `setex` and manually setting expiration with `set` + `expire` — `setex` is atomic and simpler.
Assuming cached data is always fresh; TTL might be too long for frequently changing data.

Variations

Use `pickle` instead of `json` for complex Python objects, although it's less portable.
Add a `get_or_set` with a callable that accepts extra arguments by using `functools.partial`.

Real-world use cases

Caching database query results behind an API endpoint to reduce latency and load.
Storing expensive computed or fetched data (e.g., third-party API calls) with a short TTL.
Implementing a simple session or config store where keys map to JSON objects.

Redis Cache Helper Class in Python with TTL

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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How it works

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Real-world use cases

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