Redis -- Portfolio Projects
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Project 1: Caching Layer for a REST API
Level: Beginner | Time: 1-2 days | GitHub: redis-api-cache
Implement cache-aside pattern to reduce database load for a read-heavy API -- measure and document your own actual hit-rate reduction rather than assuming a fixed percentage, since the real reduction depends entirely on your workload's read/write ratio and key access distribution.
python
import redis, json, time
from functools import wraps
r = redis.Redis(host="localhost", decode_responses=True)
def cache(ttl: int = 300):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
key = f"cache:{func.__name__}:{args}:{kwargs}"
cached = r.get(key)
if cached:
print(f"Cache HIT: {key}")
return json.loads(cached)
print(f"Cache MISS: {key}")
result = func(*args, **kwargs)
r.setex(key, ttl, json.dumps(result))
return result
return wrapper
return decorator
@cache(ttl=60)
def get_product(product_id: int):
time.sleep(0.1) # Simulates DB query
return {"id": product_id, "name": "Product", "price": 999}
# Benchmark
import timeit
print("First call:", timeit.timeit(lambda: get_product(1), number=1), "s") # ~0.1s DB
print("Cached call:", timeit.timeit(lambda: get_product(1), number=1), "s") # <0.001s Redis
Steps: Redis via Docker, cache-aside pattern, measure hit rate, cache invalidation on updates
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Project 2: Rate Limiter with Lua Scripts
Level: Intermediate | Time: 1 day | GitHub: redis-rate-limiter
Atomic rate limiting using Lua -- thread-safe, no race conditions.
lua
-- rate_limit.lua
local key = KEYS[1]
local limit = tonumber(ARGV[1])
local window = tonumber(ARGV[2])
local current = tonumber(redis.call("GET", key) or "0")
if current + 1 > limit then
return 0 -- Rate limited
end
redis.call("INCR", key)
if current == 0 then
redis.call("EXPIRE", key, window)
end
return 1 -- Allowed
Steps: Lua script, fixed-window algorithm (extend to a sorted-set-based sliding window as a stretch goal), test with concurrent requests, integrate with FastAPI
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Project 3: Real-Time Leaderboard with Sorted Sets
Level: Advanced | Time: 2 days | GitHub: redis-leaderboard
Gaming leaderboard updating in real-time -- millions of users, sub-millisecond queries.
python
import redis
r = redis.Redis(decode_responses=True)
# Update player score
r.zadd("leaderboard", {"player:alice": 1500})
# Get top 10 with scores
top10 = r.zrange("leaderboard", 0, 9, withscores=True, rev=True)
# Player rank (1-indexed)
rank = r.zrevrank("leaderboard", "player:alice") + 1
Steps: Populate with 1M players, benchmark ZADD/ZRANGE operations, add expiring scores for weekly leaderboard
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Portfolio Checklist
•[ ] Benchmark documented: before cache vs with cache (response time)
•[ ] Cache invalidation strategy documented and implemented
•[ ] Rate limiter tested under concurrent load
•[ ] Redis persistence configured (RDB or AOF)
