Distributed Cache (Redis / Memcached)

System Design Concepts Used

Consistent Hashing · LRU / LFU Eviction · Cache-Aside Pattern · Write-Through / Write-Behind · Replication · Sharding · Thundering Herd Protection · Hot Key Mitigation · TTL Management · Gossip Protocol · Hash Slots (Redis Cluster) · Probabilistic Early Expiration

1. Functional Requirements

1. GET — retrieve a value by key with sub-millisecond latency
2. SET — store a key-value pair with optional TTL
3. DELETE — explicitly invalidate a cached entry
4. Bulk operations — MGET/MSET for batch access (reduce round trips)
5. Atomic operations — increment/decrement, compare-and-swap (CAS)
6. Eviction — automatically remove entries when memory is full (LRU, LFU, or TTL-based)
7. Pub/Sub notifications — notify clients of key invalidation events

2. Non-Functional Requirements

3. Capacity Estimation

/* --- NAPKIN MATH: Cluster Sizing --- */
Cluster-wide read QPS: 10,000,000 ops/sec
Cluster-wide write QPS: 1,000,000 ops/sec
Total QPS: 11M ops/sec

/* --- PER-NODE CAPACITY (Redis single-threaded) --- */
Single Redis node: ~100K ops/sec (with pipeline: ~500K)
Nodes needed for reads: 10M / 100K = 100 nodes (no pipelining)
With pipelining + io-threads: 10M / 300K = ~34 nodes
Safety margin (2x): 68 read-serving nodes

/* --- MEMORY --- */
Total data: 100 TB
Average value size: 1 KB
Average key size: 50 bytes
Entries: 100 TB / 1 KB = 100 billion entries
Per-node memory (100 nodes): 100 TB / 100 = 1 TB per node
Redis overhead (~2x raw data): need 2 TB RAM per node? Too expensive.
Better: 200 nodes × 512 GB RAM = 100 TB addressable

/* --- NETWORK BANDWIDTH --- */
Read bandwidth: 10M × 1 KB = 10 GB/sec cluster-wide
Per node (200 nodes): 50 MB/sec (well within 10 Gbps NIC)

/* --- REPLICATION OVERHEAD --- */
Replication factor: 2 (1 primary + 1 replica)
Total nodes: 200 primaries + 200 replicas = 400 nodes
Total RAM: 400 × 512 GB = 200 TB raw capacity (100 TB usable)

/* --- HOT KEY CONSIDERATION --- */
Power law: 1% of keys serve 30% of traffic
Hottest 1M keys: 30% × 10M = 3M ops/sec
Single node cannot handle 3M ops/sec → need hot key replication

4. "Why X, Not Y?" — Tradeoff Analysis

Why Redis and not Memcached?

Redis wins for most use cases because it offers data structures (hashes, sorted sets, lists), persistence (RDB/AOF), replication, and Lua scripting. A session store needs hash fields. A leaderboard needs sorted sets. A rate limiter needs atomic Lua scripts. Redis provides all of these natively, eliminating application-layer complexity.

Memcached advantage: Multi-threaded architecture delivers higher throughput per node for pure key-value workloads. Facebook serves 5 billion requests/sec on Memcached because they only need GET key → blob. If your access pattern is strictly GET/SET with large values (>100 KB), Memcached's slab allocator avoids memory fragmentation better than Redis's jemalloc.

Why consistent hashing and not hash slots (Redis Cluster)?

Hash slots (Redis Cluster's 16,384 fixed slots) simplify resharding — you move discrete slot ranges between nodes rather than rebalancing a hash ring. Slots are deterministic: any client can compute CRC16(key) mod 16384 and know exactly which node owns it. No need for a centralized ring metadata service.

Consistent hashing advantage: More flexible weight distribution (virtual nodes), easier heterogeneous hardware support, and better for custom implementations where you control the routing layer (like Memcached with mcrouter). Consistent hashing is the interview-friendly answer; hash slots are what Redis actually uses.

Why write-behind and not write-through for the database sync?

Write-behind (write-back) decouples cache writes from database writes, giving sub-millisecond write latency. Writes go to cache immediately; a background process asynchronously flushes to the database in batches. This is critical at 1M writes/sec — the database cannot sustain synchronous writes at that rate.

Write-through advantage: Guarantees cache and database are always consistent. Every write goes to both synchronously. Use when data loss is unacceptable (financial transactions). The trade-off: write latency increases to database latency (5-20ms), and the database must handle 1M writes/sec — requiring significant sharding.

Why LRU eviction and not LFU?

LRU (Least Recently Used) is simpler, has lower overhead (just a timestamp per entry), and works well for most access patterns. Redis's approximated LRU samples 5 random keys and evicts the oldest — O(1) per eviction with no frequency counter overhead.

LFU advantage: Better for scan-resistant caching. A single full-table scan with LRU pollutes the entire cache with one-time-access keys, evicting genuinely popular entries. LFU tracks access frequency, so a key accessed 10,000 times survives even if not accessed in the last minute. Use LFU for CDN-like patterns where popularity is stable over hours.

5. High-Level Architecture

%%{init: {'theme': 'base', 'themeVariables': {'fontSize': '13px', 'fontFamily': 'Inter, -apple-system, sans-serif'}, 'flowchart': {'nodeSpacing': 30, 'rankSpacing': 50, 'padding': 12, 'curve': 'basis'}, 'sequence': {'actorMargin': 60, 'messageMargin': 40}, 'class': {'padding': 12}}}%%
graph TB
    subgraph Clients
        A[Service A<br/>User Profile]
        B[Service B<br/>Product Catalog]
        C[Service C<br/>Session Store]
    end

    subgraph "Cache Proxy Layer"
        P1[Cache Proxy 1<br/>Routing + Connection Pool]
        P2[Cache Proxy 2<br/>Routing + Connection Pool]
    end

    subgraph "Cache Cluster (Consistent Hashing Ring)"
        direction LR
        N1[Node 1<br/>Slots 0-4095<br/>512 GB RAM]
        N2[Node 2<br/>Slots 4096-8191<br/>512 GB RAM]
        N3[Node 3<br/>Slots 8192-12287<br/>512 GB RAM]
        N4[Node 4<br/>Slots 12288-16383<br/>512 GB RAM]
    end

    subgraph "Replicas (HA)"
        R1[Replica 1<br/>async repl from Node 1]
        R2[Replica 2<br/>async repl from Node 2]
        R3[Replica 3<br/>async repl from Node 3]
        R4[Replica 4<br/>async repl from Node 4]
    end

    subgraph "Backend Data Stores"
        DB1[(PostgreSQL<br/>Shard 1)]
        DB2[(PostgreSQL<br/>Shard 2)]
        DB3[(MySQL<br/>Shard 3)]
    end

    subgraph "Monitoring & Config"
        MON[Prometheus + Grafana<br/>Hit rate, latency, evictions]
        CFG[Config Service<br/>Cluster topology, failover]
    end

    A --> P1
    B --> P1
    C --> P2
    P1 --> N1
    P1 --> N2
    P2 --> N3
    P2 --> N4
    N1 -.->|async| R1
    N2 -.->|async| R2
    N3 -.->|async| R3
    N4 -.->|async| R4
    N1 -->|miss| DB1
    N2 -->|miss| DB1
    N3 -->|miss| DB2
    N4 -->|miss| DB3
    MON --> N1
    MON --> N2
    MON --> N3
    MON --> N4
    CFG --> P1
    CFG --> P2

Component Descriptions

Cache Proxy Layer (mcrouter / Envoy / Twemproxy): Sits between application services and cache nodes. Maintains persistent connection pools to all cache nodes, performs consistent hashing to route keys to the correct shard, handles automatic failover when a node becomes unreachable, and supports request coalescing (deduplication of identical in-flight requests).

Cache Nodes (Primary Shards): Each node owns a range of the hash space and stores key-value pairs entirely in RAM. Executes GET/SET/DEL operations in O(1). Runs eviction when memory pressure exceeds the configured maxmemory threshold. Single-threaded command execution ensures atomicity without locks.

Replica Nodes: Receive asynchronous replication streams from their primary. Serve read traffic to offload primaries (read replicas). Automatically promote to primary if the original primary fails (sentinel/cluster manager handles election).

Config Service (ZooKeeper / etcd / Redis Sentinel): Maintains the authoritative cluster topology — which nodes own which slots, which replicas map to which primaries. Detects node failures via heartbeats and orchestrates failover (replica promotion, slot reassignment). Publishes topology changes to proxy layers.

Monitoring (Prometheus + Grafana): Tracks hit rate, miss rate, eviction rate, memory utilization, replication lag, and p99 latency per node. Alerts on anomalies: hit rate drop > 5% (indicates cache pollution), eviction spike (memory pressure), replication lag > 1s (risk of data loss on failover).

6. Consistent Hashing Ring

%%{init: {'theme': 'base', 'themeVariables': {'fontSize': '13px', 'fontFamily': 'Inter, -apple-system, sans-serif'}, 'flowchart': {'nodeSpacing': 30, 'rankSpacing': 50, 'padding': 12, 'curve': 'basis'}, 'sequence': {'actorMargin': 60, 'messageMargin': 40}, 'class': {'padding': 12}}}%%
graph LR
    subgraph "Hash Ring (0 to 2^32)"
        direction TB
        H0["0"]
        H1["Node A (pos: 1000)<br/>+ VNode A' (pos: 4500)"]
        H2["Node B (pos: 2000)<br/>+ VNode B' (pos: 6000)"]
        H3["Node C (pos: 3500)<br/>+ VNode C' (pos: 7500)"]
    end

    K1["key: user:12345<br/>hash = 1200<br/>→ routes to Node B"]
    K2["key: session:abc<br/>hash = 5100<br/>→ routes to Node B'"]
    K3["key: product:99<br/>hash = 3600<br/>→ routes to Node A'"]

    K1 --> H2
    K2 --> H2
    K3 --> H1

How it works:

1. Each physical node is placed at multiple positions on a circular hash ring (virtual nodes). A 200-node cluster with 150 virtual nodes each = 30,000 points on the ring.
2. To find which node owns a key: hash the key, walk clockwise on the ring until you hit the first node position. That node owns the key.
3. When a node is added: it takes ownership of keys between its position and the previous node. Only ~1/N of keys are redistributed (where N = total nodes).
4. When a node is removed: its keys are redistributed to the next clockwise node. Again, only ~1/N of keys move.
5. Virtual nodes ensure even distribution — without them, 3 nodes could end up owning 10%, 60%, and 30% of the ring due to hash randomness.

7. Architecture Flow — A Cache Read for user:12345

Service A needs the profile for user 12345 to render a dashboard.

Flow 1: Cache Hit (Happy Path)

T+0.0ms: Service A calls cache.get("user:12345"). The client library computes CRC16("user:12345") mod 16384 = slot 7291. Slot 7291 maps to Node 3 (slots 8192-12287? No — slot 7291 maps to Node 2: slots 4096-8191).

T+0.1ms: Client sends GET user:12345 to Node 2 over a pre-established TCP connection (connection pool, no handshake).

T+0.2ms: Node 2 looks up the key in its hash table — O(1) lookup. The entry exists with 14 minutes remaining TTL. Node 2 updates the LRU access timestamp.

T+0.3ms: Node 2 returns the serialized JSON blob (800 bytes) to Service A.

Service A → hash("user:12345") → Node 2 → HIT (0.3ms total)

Flow 2: Cache Miss (Cold Key)

T+0.0ms: Same routing. GET user:12345 reaches Node 2.

T+0.2ms: Node 2 returns nil — key not found (expired 2 hours ago, evicted by LRU).

T+0.3ms: Service A detects the miss. It acquires a distributed lock (SET user:12345:lock NX EX 5) to prevent thundering herd — only one caller regenerates the cache.

T+0.5ms: Lock acquired. Service A queries PostgreSQL: SELECT * FROM users WHERE id = 12345. Database responds in 4ms.

T+4.5ms: Service A writes the result back to cache: SET user:12345 <json> EX 3600 (1-hour TTL).

T+4.7ms: Service A returns the profile to the caller.

T+4.7ms (parallel): Other Service A instances that also missed now retry the cache — this time it is a HIT. The lock prevented 50 simultaneous database queries.

Service A → Node 2 (MISS) → acquire lock → PostgreSQL (4ms) → SET to cache → return (4.7ms)
Other instances → Node 2 (HIT after 4.7ms) → return (0.3ms)

Flow 3: Cache Write (Profile Update)

T+0.0ms: User updates their bio. The Profile Service writes to PostgreSQL first (source of truth).

T+5ms: PostgreSQL confirms the write.

T+5.1ms: Profile Service invalidates the cache: DEL user:12345. (Cache-aside pattern: delete on write, let the next read repopulate.)

T+5.2ms: Alternatively, with write-through: SET user:12345 <new_json> EX 3600 — update cache immediately so the next read is guaranteed fresh.

Profile Service → PostgreSQL (write, 5ms) → DEL cache key → done
                                          or → SET cache key (write-through)

Flow 4: Multi-Region Cache Sync

A user in Tokyo updates their profile. Users in New York must eventually see the update.

T+0ms (Tokyo): Write hits Tokyo PostgreSQL primary. Confirmed at T+5ms.

T+5.1ms (Tokyo): Tokyo cache cluster: DEL user:12345. Tokyo readers will repopulate from local DB.

T+5.2ms (Tokyo): Invalidation event published to cross-region message bus (Kafka MirrorMaker / AWS EventBridge).

T+80ms (New York): Invalidation event arrives at New York consumer. Consumer issues DEL user:12345 to the New York cache cluster.

T+80.5ms (New York): Next read in New York misses cache, queries local read replica of PostgreSQL (which received the write via async replication at ~T+50ms). Fresh data is now served.

Tokyo write → Tokyo cache DEL → Kafka cross-region (80ms) → NY cache DEL → NY read repopulates

Window of staleness: Between T+5ms (Tokyo invalidated) and T+80ms (New York invalidated), New York serves stale data. This 75ms window is acceptable for user profiles. For financial data, use synchronous cross-region invalidation (at the cost of write latency: 80ms added to every write).

8. Failure & Recovery Scenarios

Node Crash (Primary Dies)

Impact: All keys owned by that node are temporarily unavailable. At 200 nodes, this affects 0.5% of the key space.

Detection: Sentinel/cluster manager detects no heartbeat for 3 consecutive checks (default: 3 x 1s = 3s).

Recovery: Replica promotes to primary in <5 seconds. Proxy layer updates its routing table. During the 3-5s gap, reads to this shard return misses — application falls through to the database. Because only 0.5% of keys are affected, the database sees a modest spike (50K extra QPS for 5 seconds), not a meltdown.

Data loss: Asynchronous replication means 0-2 seconds of writes may be lost on failover. For a cache, this is acceptable — the data exists in the source-of-truth database.

Network Partition (Split Brain)

Impact: Clients connected to the minority partition continue writing to a node that is no longer authoritative. After the partition heals, those writes are lost.

Mitigation: Configure min-replicas-to-write 1 — a primary refuses writes if it cannot reach at least one replica. This prevents the minority-side primary from accepting writes during a split. Clients receive errors and must fall through to the database.

Thundering Herd (Hot Key Expires)

Impact: A cache key serving 100K reads/sec expires. All 100K requests simultaneously miss and hit the database.

Mitigation stack (layered):

1. Lock-based protection: Only one caller regenerates; others wait or serve stale.
2. Probabilistic early expiration: Each read recomputes whether to refresh early: if (TTL_remaining < random() * BETA * compute_time) then refresh proactively before actual expiry.
3. Stale-while-revalidate: Return the expired value immediately while one background thread refreshes it asynchronously.

Hot Key Problem (Single Key Overloads One Node)

Impact: A celebrity's profile cached as user:celebrity_123 receives 500K reads/sec — far beyond what a single node can handle (~100K ops/sec).

Mitigation:

1. Local caching (L1): Application servers cache the hottest 1,000 keys in process memory (10ms TTL). 90% of reads never leave the application.
2. Key replication across shards: Replicate hot keys to all N nodes with a suffix: user:celebrity_123:shard1, user:celebrity_123:shard2, etc. Client randomly picks a shard per request, distributing load.
3. Read from replicas: Route reads for detected hot keys to replica nodes, spreading load across primary + replicas.

Cascading Failure (Cache Layer Completely Down)

Impact: Database receives 10M reads/sec — 100x its capacity. Total system failure within seconds.

Mitigation:

1. Circuit breaker: When cache error rate > 50%, immediately start returning cached stale data or degraded responses instead of hammering the database.
2. Request shedding: Load balancer drops excess traffic with 503 Service Unavailable to protect the database.
3. Warm-up strategy: When bringing cache nodes back online, gradually shift traffic using weighted routing (10% → 25% → 50% → 100%) to avoid cold-cache stampede.

9. Data Model

Key Structure Conventions

/* --- KEY NAMING PATTERN: {entity}:{id}:{field} --- */

user:12345                    → full serialized user profile (JSON, 1-2 KB)
user:12345:settings           → user preferences subset (200 bytes)
session:abc-def-ghi           → session data (auth token, cart, 500 bytes)
product:99:inventory          → stock count (integer, 8 bytes)
rate:api_key_xyz:minute       → request count in current minute window
leaderboard:game42            → sorted set of player scores

/* --- TTL STRATEGY --- */
User profiles:    TTL = 1 hour    (changes infrequently)
Session data:     TTL = 30 min    (must expire for security)
Product inventory: TTL = 30 sec   (changes frequently, staleness = oversell)
Rate limit counters: TTL = 60 sec (window-aligned, auto-cleanup)

Eviction Policies

Memory Layout (Redis)

/* --- PER-KEY OVERHEAD IN REDIS --- */
Key pointer:          8 bytes
Value pointer:        8 bytes
Expire pointer:       8 bytes (if TTL set)
LRU clock:            3 bytes
Ref count:            4 bytes
dictEntry overhead:   ~70 bytes total per key-value pair

/* --- EXAMPLE CALCULATION --- */
100 billion keys × 70 bytes overhead = 7 TB just for metadata
+ 100 billion × (50 byte key + 1 KB value) = ~100 TB for actual data
Total: ~107 TB across 400 nodes (267 GB/node)
Fits in 512 GB RAM nodes with room for fragmentation

10. Algorithms Under the Hood

Consistent Hashing with Virtual Nodes

/* --- HASH RING IMPLEMENTATION --- */
RING_SIZE = 2^32 (4 billion positions)
VNODES_PER_NODE = 150

function add_node(node):
    for i in 0..VNODES_PER_NODE:
        position = hash(node.id + ":" + i) % RING_SIZE
        ring.insert(position, node)   // sorted tree (TreeMap)

function remove_node(node):
    for i in 0..VNODES_PER_NODE:
        position = hash(node.id + ":" + i) % RING_SIZE
        ring.remove(position)

function get_node(key):
    position = hash(key) % RING_SIZE
    // Walk clockwise: find first node position >= key position
    entry = ring.ceiling(position)
    if entry == null:
        entry = ring.first()   // wrap around
    return entry.node

/* --- KEY REDISTRIBUTION ON NODE ADD --- */
// Adding Node D between Node B (pos 2000) and Node C (pos 3500):
// Node D takes position 2800.
// Keys with hash in [2001, 2800] move from Node C → Node D.
// Only ~1/N of total keys move (N = number of nodes).

Approximated LRU Eviction (Redis Implementation)

/* --- REDIS APPROXIMATED LRU --- */
// Redis does NOT maintain a true LRU linked list (too expensive).
// Instead: sample K random keys, evict the one with oldest access time.

function evict():
    candidates = random_sample(all_keys, K=5)  // default sample size
    oldest = candidates.min_by(key -> key.last_access_time)
    delete(oldest)

// Why approximated? True LRU requires O(N) linked list operations.
// With K=10 samples, approximated LRU achieves ~95% of true LRU hit rate
// at O(1) cost per eviction.

/* --- LRU CLOCK (24-bit) --- */
// Each key stores a 24-bit timestamp (seconds resolution, wraps every 194 days)
// On access: key.lru_clock = server.lru_clock (global clock updated each second)
// On eviction: idle_time = server.lru_clock - key.lru_clock
//              → evict key with largest idle_time among samples

Cache-Aside Pattern (Application-Level)

/* --- READ PATH --- */
function get(key):
    value = cache.GET(key)
    if value != null:
        return value                     // HIT: 0.3ms

    // MISS: fetch from source of truth
    value = database.query(key)          // 5-20ms
    if value != null:
        cache.SET(key, value, EX=3600)   // populate cache
    return value

/* --- WRITE PATH (Invalidate on Write) --- */
function update(key, new_value):
    database.write(key, new_value)       // source of truth first
    cache.DEL(key)                       // invalidate cache
    // Next read will repopulate from fresh DB data

Write-Through Pattern

/* --- WRITE-THROUGH: cache is always fresh --- */
function update(key, new_value):
    cache.SET(key, new_value, EX=3600)   // update cache first (fast)
    database.write(key, new_value)       // then persist (slower)
    // If DB write fails: cache has stale data → need compensation logic
    // Mitigation: wrap in a transaction or use outbox pattern

Write-Behind (Write-Back) Pattern

/* --- WRITE-BEHIND: async DB persistence --- */
function update(key, new_value):
    cache.SET(key, new_value, EX=3600)   // fast: only cache write
    write_queue.enqueue(key, new_value)  // buffer for async flush
    return SUCCESS                        // immediate response to client

// Background flusher (runs every 100ms or when queue > 1000 entries)
function flush():
    batch = write_queue.drain(max=1000)
    database.batch_write(batch)          // single round-trip for many writes
    // Risk: data loss if cache node crashes before flush
    // Mitigation: replicate the write queue OR use AOF persistence

Probabilistic Early Expiration (Thundering Herd Prevention)

/* --- XFetch Algorithm (Vattani et al., 2015) --- */
function get_with_early_refresh(key):
    value, ttl_remaining, compute_time = cache.GET_WITH_META(key)

    if value == null:
        return fetch_and_cache(key)      // true miss

    // Probabilistically refresh BEFORE expiry
    BETA = 1.0  // tuning parameter (higher = earlier refresh)
    random_threshold = compute_time * BETA * log(random())
    if ttl_remaining + random_threshold <= 0:
        // This request "volunteers" to refresh
        async { fetch_and_cache(key) }

    return value  // serve current (soon-to-expire) value immediately

// Effect: as TTL approaches 0, probability of refresh increases.
// One random request refreshes the key ~10s before expiry.
// Other requests never see the miss. No thundering herd.

11. Scaling Considerations

12. Monitoring & Observability

A distributed cache without observability is a ticking time bomb — you will not know it is degrading until the database collapses.

Key Metrics to Track

Dashboard Layout

/* --- OPERATIONAL DASHBOARD (Grafana) --- */
Row 1: [Hit Rate %] [Miss Rate %] [Ops/sec by command type] [Error Rate]
Row 2: [Memory Used vs Max] [Evictions/sec] [Key Count] [Expired Keys/sec]
Row 3: [p50/p95/p99 Latency] [Network In/Out MB/s] [Connected Clients]
Row 4: [Replication Lag per Replica] [Slow Log entries] [CPU per node]
Row 5: [Per-key-prefix hit rates] [Hot key detection (top-K)] [Cross-region sync lag]

Alerting Rules

CRITICAL: hit_rate < 85% for 5 minutes → page on-call (database will be overwhelmed)
CRITICAL: memory_used > 95% for 2 minutes → page on-call (eviction storm imminent)
WARNING:  eviction_rate > 500/sec for 10 minutes → capacity planning needed
WARNING:  replication_lag > 2s → failover will lose significant data
INFO:     slow_log_entries > 10/minute → investigate large key operations

13. Cache Patterns — Decision Matrix

Choosing the right caching pattern depends on your read/write ratio, consistency requirements, and tolerance for stale data.

When to Combine Patterns

Most production systems use Cache-Aside for reads + Write-Through or Invalidation for writes:

/* --- HYBRID PATTERN (most common in production) --- */

READ PATH:  Cache-Aside
  app → cache.GET(key) → hit? return : fetch DB → cache.SET → return

WRITE PATH: Invalidate-on-Write
  app → DB.write(key, value) → cache.DEL(key)
  // next read will repopulate with fresh data

ALTERNATIVE WRITE PATH: Write-Through (for latency-sensitive reads)
  app → cache.SET(key, value) → DB.write(key, value)
  // immediate read consistency, higher write latency

Netflix's approach: Cache-Aside with TTL-based refresh. All cache entries have TTLs between 30s and 24h depending on data freshness requirements. No explicit invalidation on writes — they accept the staleness window. This simplifies the write path enormously at the cost of serving stale data for up to TTL seconds.

Facebook's approach: Cache-Aside with lease-based invalidation. On write, the service sends a delete to Memcached AND a version marker. Readers who find a deleted key must present the correct lease token to repopulate, preventing stale sets from concurrent readers who started their DB query before the write.

14. What If the Interviewer Pushes Back?

15. Quick Recall

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