Practice: Caching
Generated by Claude through GigAI (karthik446/gigai-learning-center 4c0cb514) on 2026-10-13.
Main tool: Redis · Redis exposes every caching decision in this lesson as something you can set and observe on a laptop: per-key TTLs, a memory limit, selectable eviction policies and hit and miss counters. · Alternatives: Memcached is a simpler multi-threaded key-value cache with no rich data types or persistence; Valkey is the open source fork that keeps the same commands and protocol; Caffeine is an in-process local cache for the JVM with no network hop; ElastiCache is a managed cloud service that runs Redis-compatible engines for you; Varnish caches whole HTTP responses in front of a service; a CDN caches responses at the edge, close to users.
Every step links into the tool's own documentation; links were checked on 2026-10-13 · version 1
Setup (one time)
1. Install Docker and start a single Redis Open Source container on the default port.
A container gives you a disposable cache server that you can stop, wipe and restart, which the later cold start and outage steps rely on.
Explained in: Install Redis Open Source | Docs (redis.io), checked 2026-10-13
Done when: The container shows as running and the server answers PONG to a PING.
2. Install the redis-py client library into a fresh Python virtual environment.
The read and write patterns live in application code, so you need a client to write them rather than only typing commands by hand.
Explained in: redis-py guide (Python) | Docs (redis.io), checked 2026-10-13
Done when: Importing the redis module in a Python session raises no error.
3. Install Redis Insight and connect it to the local server.
A visual key browser lets you watch keys appear, expire and get evicted while your scripts run.
Explained in: Client tools | Docs (redis.io), checked 2026-10-13
Done when: Redis Insight lists your local database and shows an empty key list.
4. Check the Redis Data Integration quickstart prerequisites against your laptop so the last step is ready to run.
The final production-like step keeps a cache in sync with a source database and needs a local VM or local cluster plus PostgreSQL, which is worth confirming early.
Explained in: Quickstart | Docs (redis.io), checked 2026-10-13
Done when: You have a written note of which prerequisites you already meet and which you must install before the last step.
Steps
1. Run your first SET and GET against the local server with the Redis command line interface.
A cache is first of all a fast key lookup, and typing the commands by hand shows the request and reply model before any library hides it.
Explained in: Client tools | Docs (redis.io), checked 2026-10-13
Done when: A value you stored under a key comes back unchanged from a GET.
2. Create a key naming scheme for cached objects, with an entity name, an identifier and a version segment, and store a few entries under it.
Key design decides what you can invalidate together and how you roll out a changed value format without reading stale shapes.
Explained in: Keys and values | Docs (redis.io), checked 2026-10-13
Done when: Redis Insight shows your sample keys grouped under the prefixes you designed.
3. Read the LangCache architecture section to see a cache modelled as lookup, miss, fill and store; it is a hosted cloud service, so the local substitute is the exact-match string cache you build in the next step.
The same hit, miss and fill flow underlies every cache, and seeing it for semantic matching makes clear which parts are general and which are specific to one use.
Explained in: Redis LangCache | Docs (redis.io), checked 2026-10-13
Done when: You can sketch the hit path and the miss path from memory and name where the entry is written.
4. Write a cache-aside read function with redis-py that checks Redis first, falls back to a slow stand-in data source on a miss and then stores the result.
Cache-aside is the most common read pattern, and writing it yourself exposes the miss path that write-through and write-behind move elsewhere.
Explained in: redis-py guide (Python) | Docs (redis.io), checked 2026-10-13
Done when: The first call for an identifier takes the slow path and the second call returns quickly from Redis.
5. Configure a TTL on every entry your function writes, and delete the key whenever the stand-in source is updated.
Expiry bounds how stale a value can get without any coordination, while explicit deletion on write is the invalidation path for data that must be fresh sooner.
Explained in: Keys and values | Docs (redis.io), checked 2026-10-13
Done when: The remaining time to live on a key counts down, the key disappears at zero, and an update to the source removes it at once.
6. Write a short-lived lock key with an expiry so that only one caller rebuilds a missed entry, then run twenty concurrent readers against an expired hot key.
When a popular key expires, every reader misses at once and stampedes the source; a lock that expires on its own lets one caller refill without risking a stuck lock.
Explained in: Keys and values | Docs (redis.io), checked 2026-10-13
Done when: Your stand-in source logs a single rebuild for the twenty concurrent readers instead of twenty.
7. Configure a small maxmemory limit on the server and load more cached entries than fit.
Sizing a cache means deciding how much of the working set to hold, and a deliberately small limit lets you watch what happens at the boundary.
Explained in: Key eviction | Docs (redis.io), checked 2026-10-13
Done when: The reported memory use stays at or under the limit while you keep writing.
8. Compare the noeviction, allkeys-lru, allkeys-lfu and volatile-ttl policies by replaying the same skewed read and write workload under each.
The policy decides which data survives memory pressure and whether writes fail instead, so the right choice depends on your access pattern and on whether every key has a TTL.
Explained in: Key eviction | Docs (redis.io), checked 2026-10-13
Done when: You have a small table of hit rate and write errors per policy for the same workload.
9. Run a cold start by emptying the cache and replaying the workload while reading the hit, miss and evicted key counters from INFO.
An empty cache sends the full read load to the source, and the counters show how long the warm-up lasts and how much load the source must absorb during it.
Explained in: Key eviction | Docs (redis.io), checked 2026-10-13
Done when: You can plot the miss count rising steeply after the wipe and flattening as the cache warms.
10. Reproduce the whole experiment from a pinned Redis image tag, then stop the container mid-run and add a timeout and fallback so reads still succeed from the source.
A pinned image makes your results repeatable on any machine or in CI, and stopping the server proves whether the cache is an optimisation or a hidden dependency.
Explained in: Install Redis Open Source | Docs (redis.io), checked 2026-10-13
Done when: A fresh machine reproduces your numbers from the pinned image, and reads keep returning correct values while the container is stopped.
11. Compare an in-process dictionary cache placed in front of Redis with Redis-only reads, then read how eviction behaves across replicated databases to reason about distributed and edge copies.
A local cache removes the network hop but each process holds its own possibly stale copy, while a shared or replicated cache trades latency for consistency across instances.
Explained in: Data eviction | Docs (redis.io), checked 2026-10-13
Done when: You have latency figures for both layouts and a one-paragraph note on which copy can be stale and for how long.
12. Deploy the Redis Data Integration quickstart pipeline on a local VM or local cluster so changes in PostgreSQL flow into Redis, then change a source row.
Feeding the cache from the database change stream is a production-grade alternative to cache-aside: the cache is kept warm and current without application invalidation code.
Explained in: Quickstart | Docs (redis.io), checked 2026-10-13
Done when: A row you update in PostgreSQL shows its new value in the matching Redis key without your application writing it.