← Back to all projects

LEARN ADVANCED CACHING ARCHITECTURES

In modern distributed systems, the difference between a responsive application and a failing one often comes down to its caching strategy. While sticking it in Redis is a common first step, advanced caching is about managing the **edge cases of success**: what happens when 10,000 users request the same expired key at the same microsecond? What happens when your database is slow and your cache is empty?

Learn Advanced Caching Architectures: From Zero to Caching Master

Goal: Deeply understand the engineering behind high-performance caching systems. You will progress from simple in-memory storage to building resilient middleware that prevents cache stampedes using probabilistic algorithms, implements soft TTLs for high availability, and manages complex multi-layer consistency across distributed environments. By the end, you’ll be able to design caching strategies that protect backend systems from “thundering herds” and “cache penetration” while maintaining ultra-low latency.

Why Advanced Caching Matters

In modern distributed systems, the difference between a responsive application and a failing one often comes down to its caching strategy. While “sticking it in Redis” is a common first step, advanced caching is about managing the edge cases of success: what happens when 10,000 users request the same expired key at the same microsecond? What happens when your database is slow and your cache is empty?

Historical Context

Caching stems from the “Memory Hierarchy” concept in CPU design, where small, fast caches (L1/L2) sit in front of larger, slower RAM. As web systems scaled, we moved this concept to the network. Memcached (2003) and Redis (2009) revolutionized the industry by providing shared memory for distributed applications.

The Impact of Modern Caching

  • Resilience: A well-designed cache acts as a shield, preventing backend databases from melting down during traffic spikes.
  • Latency: Reducing 100ms DB queries to 1ms cache hits is the primary driver of perceived performance.
  • Cost: Serving data from memory is often significantly cheaper than running complex relational queries at scale.

[Include ASCII diagrams to visualize core concepts]

Core Concept Analysis

1. The Caching Hierarchy

                    [ USER BROWSER / MOBILE APP ]
                                |
                    [ CDN / EDGE CACHE (Cloudflare) ]
                                |
                    [ API GATEWAY / LOAD BALANCER ]
                                |
                    +-----------+-----------+
                    |                       |
            [ APP INSTANCE 1 ]      [ APP INSTANCE 2 ]
            | L1: Local RAM  |      | L1: Local RAM  |
            +-------+--------+      +-------+--------+
                    |                       |
                    +-----------+-----------+
                                |
                    [ L2: DISTRIBUTED CACHE ]
                    [    (Redis Cluster)    ]
                                |
                    [ PERSISTENT DATABASE   ]

2. The Cache Stampede Problem (Dog-piling)

A cache stampede occurs when a “hot” key expires, and a surge of concurrent requests all see a cache miss and attempt to recompute the value simultaneously.

Time: T (Key Expires)
       |
       | Request 1 -> MISS -> [ Regenerating... ]
       | Request 2 -> MISS -> [ Regenerating... ] --+
       | Request 3 -> MISS -> [ Regenerating... ]   |--> THUNDERING HERD
       | Request 4 -> MISS -> [ Regenerating... ] --+
       | ...
       V
[ BACKEND DATABASE ] <--- OVERWHELMED by N concurrent recomputations

3. Consistency vs. Availability (Soft TTLs)

In advanced architectures, we often use Soft TTLs (stale-while-revalidate).

  • Hard TTL: Data is deleted at expiry. Requests block until recomputed.
  • Soft TTL: Data remains in cache but is marked “stale.” The first request triggers a background refresh while serving the stale data to others immediately.

Project 1: The Raw Foundation (In-Memory LRU)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Go, Rust, C++
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 1: Beginner
  • Knowledge Area: Data Structures / Memory Management
  • Software or Tool: Python (standard library)
  • Main Book: “Algorithms, Fourth Edition” by Robert Sedgewick

What you’ll build: A thread-safe, fixed-capacity in-memory cache using an LRU (Least Recently Used) eviction policy.

Why it teaches Advanced Caching: You cannot understand Redis if you don’t understand how to manage a finite “memory budget.” This project forces you to implement the O(1) time complexity requirement for both get and put by combining a Hash Map with a Doubly Linked List.

Core challenges you’ll face:

  • O(1) updates → How to move a node to the front of the list without iterating?
  • Thread Safety → Protecting the internal pointers during concurrent access.
  • Eviction Logic → Ensuring the “oldest” item is pruned exactly when capacity is exceeded.

Real World Outcome

You will have a Python class that manages its own memory footprint. When integrated into a script, it will behave like a mini-database in RAM.

Example Output:

cache = LRUCache(capacity=2)
cache.put("user_1", {"name": "Alice"})
cache.put("user_2", {"name": "Bob"})
print(cache.get("user_1")) # Returns Alice, moves Alice to FRONT
cache.put("user_3", {"name": "Charlie"}) # EVICTS user_2 because it's Least Recently Used
print(cache.get("user_2")) # Returns None

The Core Question You’re Answering

“How do I maintain a constant-time memory of ‘most recent’ items when I have more data than space?”

Before you write any code, sit with this question. If you only used a dictionary, you’d know what’s there, but you wouldn’t know what to delete first. If you only used a list, you’d know the order, but lookups would be slow (O(N)). How do you glue them together?

Concepts You Must Understand First

Stop and research these before coding:

  1. Doubly Linked Lists
    • Why do we need prev and next pointers to achieve O(1) removal?
    • Book Reference: “Algorithms” Ch 1.3 - Sedgewick
  2. Hash Map/Dictionary Internals
    • How does a hash map find a value in O(1)?
    • Book Reference: “Algorithms” Ch 3.4 - Sedgewick
  3. Mutex/Locks
    • What happens if two threads try to update the head pointer at the exact same time?

Questions to Guide Your Design

  1. Structure
    • Does your Node class store the key as well as the value? (Hint: You need it during eviction).
  2. Edge Cases
    • What happens if you put a key that already exists? Does it count as an “access” for LRU?
    • How do you handle capacity 1?

Thinking Exercise

The Pointer Dance

Draw a diagram of 3 nodes: A, B, and C.

  1. B is the MRU (Most Recently Used).
  2. A is the LRU (Least Recently Used).
  3. Someone calls get(A). Trace the exactly pointers that must change to make A the new MRU. How many next pointers change? How many prev pointers?

The Interview Questions They’ll Ask

  1. “Why use a Doubly Linked List instead of a Singly Linked List for LRU?”
  2. “How would you implement an LFU (Least Frequently Used) cache differently?”
  3. “What is the space complexity of this structure?”
  4. “Is this implementation thread-safe?”
  5. “How would you handle very large values that exceed the total RAM available?”

Hints in Layers

Hint 1: The Glue Store a Node object inside your Dictionary. The dictionary points directly to the node in the list.

Hint 2: Dummy Nodes Use a dummy_head and dummy_tail to avoid checking for null pointers constantly during insertion and deletion.

Hint 3: Atomic Operations Use a single Mutex lock around the entire get and put method. While not the most performant, it’s the safest first step.

Project 2: The Distributed Bridge (Redis Client)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Go, Node.js
  • Coolness Level: Level 2: Practical but Forgettable
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Networking / Serialization
  • Software or Tool: Redis, redis-py
  • Main Book: “Designing Data-Intensive Applications” Ch 3

What you’ll build: A wrapper client for Redis that handles connection pooling, JSON serialization, and automatic retries with exponential backoff.

Why it teaches Advanced Caching: In production, your cache is over the network. Network calls fail. Connections leak. This project teaches you to handle the “unreliable network” between your app and your data.

Core challenges you’ll face:

  • Serialization → Redis only stores bytes. You must handle the conversion of complex objects.
  • Resilience → What happens if Redis is down for 500ms?
  • Connection Management → Ensuring you don’t exhaust the OS file descriptors.

Real World Outcome

A robust client that your team can use in any project without worrying about raw Redis commands.

Example Output:

$ python client_test.py
[SUCCESS] Set user:123
[ERROR] Connection lost. Retrying in 100ms...
[ERROR] Connection lost. Retrying in 200ms...
[SUCCESS] Retrieved user:123 -> {'name': 'Alice'}

The Interview Questions They’ll Ask

  1. “Why use a connection pool instead of opening a new connection per request?”
  2. “What’s the difference between JSON and MessagePack for Redis serialization?”
  3. “How does Redis handle expiration internally?”
  4. “What happens to your application if Redis is slow (high latency)?”

Project 3: The Workhorse (Cache-Aside Web API)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python (FastAPI/Flask)
  • Alternative Programming Languages: Go (Gin), Node.js (Express)
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 5. The “Industry Disruptor”
  • Difficulty: Level 2: Intermediate
  • Knowledge Area: Web Backend / Database Performance
  • Software or Tool: Redis, Postgres (or SQLite)
  • Main Book: “Designing Data-Intensive Applications” Ch 4

What you’ll build: A REST API that serves data from a database but uses the “Cache-Aside” pattern to drastically reduce DB load.

Why it teaches Advanced Caching: This is the “Hello World” of production caching. You will implement the logic: Check Cache -> Hit? Return. -> Miss? Fetch DB -> Save Cache -> Return.

Core challenges you’ll face:

  • Consistency → When a user updates their profile, how do you ensure the cache isn’t serving the old one?
  • Cache Warming → How to handle the first 1,000 requests on a “cold” cache.
  • Key Namespacing → Organizing keys so they don’t collide (e.g., user:v1:123).

Real World Outcome

A measurable performance improvement. You will use a tool like ab or wrk to prove that your cached endpoint is 10x-50x faster than the DB-only endpoint.

Example Benchmarks:

# DB Only
Requests per second: 45.2
Mean latency: 220ms

# With Cache-Aside
Requests per second: 1240.8
Mean latency: 8ms

The Interview Questions They’ll Ask

  1. “What is the ‘Cache-Aside’ pattern and when is it preferred over ‘Write-Through’?”
  2. “How do you handle the ‘stale data’ problem if the database is updated by a different service?”
  3. “What is a ‘Cold Start’ in caching and how do you mitigate it?”

Project 5: The Chaos Monkey (Cache Stampede Simulator)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python (Asyncio)
  • Alternative Programming Languages: Go (Goroutines)
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Performance Testing / Concurrency
  • Software or Tool: Redis, locust (or custom script)
  • Main Book: “Systems Performance” Ch 11

What you’ll build: A stress-testing script that simulates a “Hot Key” expiry. It will launch 1,000 concurrent requests for the same key the moment its TTL hits zero, measuring the latency spike and DB connection count.

Why it teaches Advanced Caching: You cannot fix what you cannot see. This project makes the “Thundering Herd” visible. You’ll observe your database connections spike from 1 to 100+ as every request tries to “helpfully” regenerate the cache.

Core challenges you’ll face:

  • Simulating Real Latency → Making the recomputation take 500ms so the herd has time to form.
  • Precise Timing → Synchronizing 1,000 workers to hit the “Miss” window at the exact same millisecond.

Real World Outcome

A terrifying graph or log file showing your database CPU hitting 100% and latency jumping from 5ms to 5,000ms just because a single cache entry expired.

The Core Question You’re Answering

“Why is my database dying when my cache hit rate is 99%?”

Before coding, sit with this. A 99% hit rate sounds great, but that 1% miss can be fatal if it happens to 10,000 users at once for a high-computation key.

Project 6: The Shield (Locking / Request Coalescing)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Go (using singleflight), Rust
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Concurrency Control
  • Software or Tool: Redis (Distributed Locks)
  • Main Book: “Distributed Systems” by van Steen

What you’ll build: A middleware that intercept cache misses. If 10 requests miss, the 1st acquires a lock and fetches the data; the other 9 wait for the 1st to finish, then return the 1st one’s result.

Why it teaches Advanced Caching: This is the first level of “Stampede Prevention.” You learn about Request Coalescing. It’s the difference between a panicked mob and a structured queue.

Core challenges you’ll face:

  • The “Wait” Strategy → Should the 9 requests poll Redis? Or should they block on an internal event?
  • Lock Timeouts → What if the 1st request crashes while holding the lock? You don’t want the other 9 to wait forever (Deadlock).

Real World Outcome

Run your Project 5 simulator against this new code. You will see the DB load drop from 1,000 concurrent queries to exactly one.

The Interview Questions They’ll Ask

  1. “How do you avoid deadlocks if the lock holder fails?”
  2. “What is the downside of waiting? (Latency for the 9 followers).”
  3. “Can you implement this at the L1 (process) level without a distributed lock?”

Project 7: The Math Wizard (Probabilistic Early Expiration)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python / C
  • Alternative Programming Languages: Rust, Go
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 1. The “Resume Gold”
  • Difficulty: Level 4: Expert
  • Knowledge Area: Probability / Performance Engineering
  • Software or Tool: Redis, Math logic
  • Main Book: “Optimal Probabilistic Cache Stampede Prevention” (Vattani Paper)

What you’ll build: A cache client that implements the XFetch algorithm. Instead of waiting for expiry, it uses a random probability (based on remaining TTL and computation time) to decide to refresh the cache before it expires.

Why it teaches Advanced Caching: This is the “Industry Standard” for extreme scale. It prevents stampedes without the complexity of locks. It’s beautiful, mathematical, and highly effective.

Core challenges you’ll face:

  • The Formula → t - (gap * beta * log(rand())) > TTL. You’ll need to understand what gap (computation time) and beta (aggressiveness) actually do.
  • Measurement → You must measure how long the recomputation takes and store that metadata in the cache alongside the value.

The Core Question You’re Answering

“Can we prevent a stampede before it even starts, without anyone ever seeing a cache miss?”

Thinking Exercise

The Gap Problem

If your recomputation takes 100ms, and your TTL is 60s. At 59.950s, you have 50ms left. If a request comes in, should it refresh? If you have 1,000 requests per second, how many “chances” do you have in that last 100ms to trigger a refresh?

Project 9: The Harmonizer (Multi-Layer L1+L2)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Java, Go
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 5. The “Industry Disruptor”
  • Difficulty: Level 4: Expert
  • Knowledge Area: Architectural Patterns / Distributed Systems
  • Software or Tool: LRU Cache (Project 1), Redis Client (Project 2)
  • Main Book: “Designing Data-Intensive Applications” Ch 3

What you’ll build: A “Unified Cache” object that transparently manages both an L1 (Local RAM) and L2 (Distributed Redis) cache.

Why it teaches Advanced Caching: This is the peak of retrieval optimization. You’ll implement:

  1. Get -> Check L1. Found? Return.
  2. Miss? Check L2. Found? Save to L1 and Return.
  3. Miss? Fetch DB. Save to L2 AND L1. Return.

Core challenges you’ll face:

  • Consistency → If L2 is updated, L1 is now stale. (Integration with Project 8).
  • TTL Variance → Usually, L1 TTL should be much shorter than L2 TTL. Why?

The Core Question You’re Answering

“How do I minimize network hops (L2) while still having a shared state across servers?”

Project 10: The Safety Net (Circuit Breaker Cache)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Go, Java (Hystrix style)
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Reliability Engineering
  • Software or Tool: Redis, pybreaker
  • Main Book: “Release It!” Ch 5

What you’ll build: A cache-miss handler that “opens” a circuit if the database starts timing out.

Why it teaches Advanced Caching: Advanced caching isn’t just about speed; it’s about not making a bad situation worse. If your DB is slow, 1,000 cache misses will finish it off. The Circuit Breaker says “Stop! The DB is dying. Return a default/stale value instead of making the call.”

Core challenges you’ll face:

  • Defining “Failure” → Is a 500ms response a failure? Or only an error?
  • Soft Failures → Serving “Stale Data” instead of an error message.

Real World Outcome

Kill your database process. Observe your API continue to serve “Stale” or “Default” content at 2ms latency instead of timing out at 30,000ms.

Project 11: The Invisible Proxy (Read-Through Middleware)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Java (Ehcache), Go
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Encapsulation / API Design
  • Software or Tool: Redis
  • Main Book: “Clean Code” Ch 6

What you’ll build: A “Data Access Object” (DAO) where the caching logic is entirely hidden from the application. The app just calls db.get_user(id), and the DAO handles the caching internally.

Why it teaches Advanced Caching: It teaches the Read-Through pattern. This keeps your business logic clean and ensures that caching isn’t “bolted on” as an afterthought.

The Core Question You’re Answering

“How do I make my application code unaware that a cache even exists?”

Project 13: The Scaler (Consistent Hashing Cluster)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python / Go
  • Alternative Programming Languages: Rust, C++
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 4. The “Open Core” Infrastructure
  • Difficulty: Level 4: Expert
  • Knowledge Area: Distributed Systems / Load Balancing
  • Software or Tool: Multiple Redis instances
  • Main Book: “Designing Data-Intensive Applications” Ch 6

What you’ll build: A client that distributes data across 3 separate Redis servers using Consistent Hashing.

Why it teaches Advanced Caching: In high-traffic systems, one Redis server isn’t enough. You need a cluster. You’ll learn how to map keys to servers so that if you add a 4th server, you don’t lose all your cached data (only ~25%).

Core challenges you’ll face:

  • The Hash Ring → Implementing the ring and the “Binary Search” to find the next server.
  • Virtual Nodes → How to ensure the data is distributed evenly if one server has a “unlucky” hash.

The Core Question You’re Answering

“How do I grow my cache cluster from 1 server to 100 without causing a massive ‘Cold Start’ failure?”

Project 14: The Smart Expire (Adaptive TTLs)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Go, Java
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 2. The “Micro-SaaS / Pro Tool”
  • Difficulty: Level 4: Expert
  • Knowledge Area: Heuristics / Optimization
  • Software or Tool: Redis
  • Main Book: “Systems Performance” Ch 11

What you’ll build: A cache that tracks “popularity.” Popular items get their TTL extended automatically. Unpopular items get shorter TTLs to save memory.

Why it teaches Advanced Caching: You move from “Static TTLs” (60 seconds for everyone) to “Smart TTLs.” This optimizes your Hit-per-Byte ratio.

Thinking Exercise

The Long Tail

If 10% of your items get 90% of your traffic, why keep the other 90% in the cache for 1 hour? How much memory could you save if you pruned unpopular items after 1 minute?

Project 15: The Traffic Cop (Sliding Window Rate Limiter)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python (Redis Lua)
  • Alternative Programming Languages: Go, Node.js
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Security / Scripting
  • Software or Tool: Redis (Lua scripts)
  • Main Book: “Designing Data-Intensive Applications” Ch 11

What you’ll build: A rate-limiter that allows 100 requests per minute per IP, using a Redis Sorted Set for a precise sliding window.

Why it teaches Advanced Caching: Redis isn’t just for caching data; it’s a high-performance Counter. This project teaches you Redis Lua Scripts (atomic server-side execution), which is a key skill for advanced caching.

The Interview Questions They’ll Ask

  1. “Why use a Sorted Set for rate limiting instead of just a counter with an expiry?”
  2. “What is the complexity of ZREMRANGEBYSCORE and how does it affect Redis performance?”
  3. “Why use Lua scripts for this instead of multiple Python commands?”

Project 17: The World Traveler (Geo-Distributed Simulation)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python
  • Alternative Programming Languages: Go, Java
  • Coolness Level: Level 4: Hardcore Tech Flex
  • Business Potential: 4. The “Open Core” Infrastructure
  • Difficulty: Level 4: Expert
  • Knowledge Area: Global Latency / Replication
  • Software or Tool: 3 Redis instances (simulating regions)
  • Main Book: “Designing Data-Intensive Applications” Ch 5

What you’ll build: A system that simulates 3 global regions (US-East, EU-West, AP-East). You’ll implement “Latency-Aware Routing” where the app always tries to hit the “local” cache first.

Why it teaches Advanced Caching: You’ll learn about the “Global Consistency” nightmare. If data is updated in US-East, how long does it take for a user in Japan to see it? How do you invalidate caches across oceans?

Project 18: The Watchtower (Monitoring Dashboard)

  • File: LEARN_ADVANCED_CACHING_ARCHITECTURES.md
  • Main Programming Language: Python + Javascript
  • Alternative Programming Languages: Go + Grafana
  • Coolness Level: Level 3: Genuinely Clever
  • Business Potential: 3. The “Service & Support” Model
  • Difficulty: Level 3: Advanced
  • Knowledge Area: Observability / Metrics
  • Software or Tool: Redis, Prometheus, Grafana
  • Main Book: “Systems Performance” Ch 11

What you’ll build: A live dashboard showing:

  1. Cache Hit Rate (The heartbeat of your app).
  2. Eviction Rate (Is your cache too small?).
  3. Stampede Count (How many locks are being acquired?).
  4. Latency Percentiles (P50, P99).

Why it teaches Advanced Caching: You learn to diagnose the “Invisible Failures.” A cache that’s constantly evicting (Churn) can be slower than no cache at all. Monitoring tells you why.

Project Comparison Table

Project Difficulty Time Depth of Understanding Fun Factor
1. In-Memory LRU Level 1 Weekend High (Foundational) 3
5. Stampede Sim Level 3 1-2 weeks High (Observability) 4
7. XFetch Wizard Level 4 1 month+ Extreme (Expert) 5
9. Multi-Layer Level 4 1 month+ High (Architecture) 4
12. Gatekeeper Level 4 1-2 weeks Medium (Probabilistic) 4
19. Master Mid Level 5 2 months+ Maximum (Architect) 5

Recommendation

If you are a backend engineer: Start with Project 3 (Cache-Aside) and Project 5 (Stampede Simulator). These will give you the most “bang for your buck” in your current job.

If you want to be a Systems Architect: You must complete Project 1 (LRU), Project 7 (XFetch), and Project 13 (Consistent Hashing). These are the “Dark Arts” that define high-level engineering.

Summary

This learning path covers Advanced Caching Architectures through 19 hands-on projects.

# Project Name Main Language Difficulty Time Estimate
1 In-Memory LRU Python Level 1 Weekend
2 Redis Client Python Level 2 Weekend
3 Cache-Aside API Python Level 2 1-2 weeks
4 Write-Behind Python Level 3 1-2 weeks
5 Stampede Simulator Python Level 3 1-2 weeks
6 Thundering Herd Shield Python Level 3 1-2 weeks
7 XFetch (Probabilistic) Python Level 4 1 month+
8 Pub/Sub Invalidation Python Level 3 1-2 weeks
9 Multi-Layer (L1+L2) Python Level 4 1 month+
10 Circuit Breaker Python Level 3 1-2 weeks
11 Read-Through Python Level 3 1 week
12 Bloom Filter Python Level 4 1-2 weeks
13 Consistent Hashing Python Level 4 1 month+
14 Adaptive TTLs Python Level 4 2 weeks
15 Rate Limiter Python Level 3 1 week
16 Binary Serialization Python Level 2 Weekend
17 Geo-Distribution Python Level 4 1 month+
18 Monitoring Dashboard Python Level 3 2 weeks
19 Master Middleware Python Level 5 2 months+

Expected Outcomes

After completing these projects, you will:

  • Understand exactly how to prevent system meltdowns during hot-key expiry (Cache Stampede).
  • Know when to trade off consistency for availability using Soft TTLs.
  • Be able to implement multi-layered caching that maintains coherence across hundreds of nodes.
  • Master probabilistic data structures (Bloom Filters, XFetch) for extreme performance.
  • Have a professional-grade portfolio of systems engineering projects.