Throughput Optimization - Learning Module

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Horizontal Scaling: Multiplying Capacity Through Distribution

Beyond Single Machine Limits

Every individual machine has finite capacity—there's only so much CPU, memory, network bandwidth, and disk I/O available. Vertical scaling (upgrading to more powerful hardware) eventually hits physical limits and rapidly escalating costs. A server with twice the CPU might cost four times as much.

Horizontal scaling takes a fundamentally different approach: instead of making one machine more powerful, distribute work across many machines. If one server handles 1,000 requests per second, ten servers can handle 10,000. A hundred servers can handle 100,000.

The transformation:

Vertical Scaling                  Horizontal Scaling

┌─────────────────┐              ┌─────────┐ ┌─────────┐ ┌─────────┐
│                 │              │ Server  │ │ Server  │ │ Server  │
│   BIG SERVER    │   versus     │    1    │ │    2    │ │   ...   │
│   (expensive)   │              │ (cheap) │ │ (cheap) │ │ (cheap) │
│                 │              └─────────┘ └─────────┘ └─────────┘
└─────────────────┘                        │            
                                 ┌─────────▼─────────┐
                                 │   Load Balancer   │
                                 └───────────────────┘

This page explores how to design systems that scale horizontally—the architectural patterns, challenges, and trade-offs that enable web-scale throughput.

What You Will Learn

This page covers horizontal scaling fundamentals: stateless service design, data partitioning strategies, distributed coordination, capacity planning, and the challenges of maintaining consistency across distributed nodes. You'll understand when horizontal scaling applies and how to architect systems that scale gracefully.

Horizontal vs Vertical Scaling

Both scaling strategies have their place. Understanding the trade-offs helps choose the right approach for each situation.

Scaling Strategy Comparison
Aspect	Vertical Scaling	Horizontal Scaling
Mechanism	Bigger/faster hardware	More machines
Limit	Physical hardware limits	Practically unlimited
Cost curve	Exponential (diminishing returns)	Linear (economies of scale)
Downtime	Often required for upgrades	Zero-downtime scaling possible
Complexity	Simple (no distribution)	Complex (distributed systems)
Failure domain	Single point of failure	Tolerates individual failures
Data consistency	Strong (single machine)	Requires coordination
Best for	Databases, stateful systems	Stateless services, reads

The Cost Curve Reality:

Cost ($)
    ▲
    │                           ╱ Vertical (exponential)
    │                          ╱
    │                        ╱
    │                      ╱
    │                   ╱
    │               ╱─────────── Horizontal (linear)
    │           ╱
    │       ╱
    │   ╱
    │ ╱
    └────────────────────────────────▶ Capacity

Example:
- 1x capacity: $100/month (small server)
- 2x capacity vertical: $400/month (larger server)
- 2x capacity horizontal: $200/month (two small servers)
- 10x capacity vertical: Often impossible
- 10x capacity horizontal: $1000/month (ten small servers)

Use Vertical Scaling When

•Current hardware isn't maxed out
•Workload requires strong consistency
•State is difficult to partition
•Team lacks distributed systems expertise
•Quick capacity boost needed
•Single-threaded bottlenecks (need faster CPU)

Use Horizontal Scaling When

•Nearing hardware limits
•Need to handle traffic spikes
•Failure tolerance required
•Workload is parallelizable
•Cost efficiency matters at scale
•Zero-downtime deployments needed

Stateless Service Design

The key to horizontal scaling is statelessness. A stateless service doesn't store any data locally that needs to persist between requests. Any server can handle any request, enabling perfect load distribution.

Stateful vs Stateless:

Stateful Service (Cannot scale horizontally):

┌─────────────────────────────────┐
│           Server 1              │
│  ┌───────────────────────────┐  │ User session stored locally
│  │  User A session: {...}    │  │ Request MUST go to Server 1
│  │  User B session: {...}    │  │ Server failure = data loss
│  └───────────────────────────┘  │
└─────────────────────────────────┘

Stateless Service (Easy horizontal scaling):

┌───────────┐  ┌───────────┐  ┌───────────┐
│  Server 1 │  │  Server 2 │  │  Server 3 │
│ (no state)│  │ (no state)│  │ (no state)│
└─────┬─────┘  └─────┬─────┘  └─────┬─────┘
      │              │              │
      └──────────────┼──────────────┘
                     │
              ┌──────▼──────┐
              │ Shared State│
              │ (Redis, DB) │
              └─────────────┘

Any server can handle any request.
Load balancer distributes freely.

Patterns for Statelessness

•Externalize Session State — Store sessions in Redis, Memcached, or database. Session ID in cookie; actual data in shared store.
•Client-Side State (JWT) — Encode user state in signed tokens. Server validates signature without lookup. Tradeoff: Can't revoke easily.
•Externalize Caching — Use Redis/Memcached cluster instead of local caches. All servers share same cache view.
•Shared Storage — File uploads go to S3/GCS, not local disk. All servers access same files.
•Event-Driven Updates — Instead of local state, subscribe to events. State changes broadcast to all nodes.

stateless-patterns.ts
TypeScript
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// Patterns for stateless service design
 
// Pattern 1: Externalized session with Redis
import session from 'express-session';
import RedisStore from 'connect-redis';
import { createClient } from 'redis';
 
const redisClient = createClient({ url: 'redis://session-cluster:6379' });
await redisClient.connect();
 
app.use(session({
  store: new RedisStore({ client: redisClient }),
  secret: process.env.SESSION_SECRET!,
  resave: false,
  saveUninitialized: false,
  cookie: { 
    secure: true, 
    maxAge: 24 * 60 * 60 * 1000  // 24 hours
  },
}));
 
// Session works across ALL servers in the cluster
app.get('/dashboard', (req, res) => {
  const userId = req.session.userId;  // Retrieved from Redis
  // ...
});
 
 
// Pattern 2: JWT for stateless authentication
import jwt from 'jsonwebtoken';
 
// Login: Create token with user state
app.post('/login', async (req, res) => {
  const user = await authenticateUser(req.body);
  
  const token = jwt.sign(
    { 
      userId: user.id, 
      roles: user.roles,
      // Include only necessary state - keep token small
    },
    process.env.JWT_SECRET!,
    { expiresIn: '24h' }
  );
  
  res.json({ token });
});
 
// Protected route: Validate token (no database lookup needed!)
app.get('/api/data', (req, res) => {
  const token = req.headers.authorization?.replace('Bearer ', '');
  
  try {
    const payload = jwt.verify(token!, process.env.JWT_SECRET!);
    // payload.userId, payload.roles available without DB query
    // Any server can validate - pure stateless
  } catch (err) {
    res.status(401).json({ error: 'Invalid token' });
  }
});
 
 
// Pattern 3: Externalized cache
import Redis from 'ioredis';
 
const cache = new Redis.Cluster([
  { host: 'cache-1', port: 6379 },
  { host: 'cache-2', port: 6379 },
  { host: 'cache-3', port: 6379 },
]);
 
async function getUserWithCache(userId: string): Promise<User> {
  const cacheKey = `user:${userId}`;
  
  // Check shared cache (accessible from any server)
  const cached = await cache.get(cacheKey);
  if (cached) {
    return JSON.parse(cached);
  }
  
  // Cache miss: fetch from database
  const user = await db.users.findById(userId);
  
  // Store in shared cache
  await cache.setex(cacheKey, 3600, JSON.stringify(user));
  
  return user;
}
 
 
// Pattern 4: File uploads to S3 (not local disk)
import { S3Client, PutObjectCommand } from '@aws-sdk/client-s3';
 
const s3 = new S3Client({ region: 'us-east-1' });
 
app.post('/upload', upload.single('file'), async (req, res) => {
  const file = req.file!;
  const key = `uploads/${Date.now()}-${file.originalname}`;
  
  // Store in S3, not local filesystem
  await s3.send(new PutObjectCommand({
    Bucket: 'my-app-uploads',
    Key: key,
    Body: file.buffer,
    ContentType: file.mimetype,
  }));
  
  // Return S3 URL - any server can serve this
  res.json({ url: `https://my-app-uploads.s3.amazonaws.com/${key}` });
});

Load Distribution Strategies

With multiple servers, you need a strategy to distribute incoming requests. The distribution mechanism significantly impacts throughput and reliability.

Load Balancer Architecture:

                    Internet
                        │
                        ▼
               ┌────────────────┐
               │  DNS / CDN     │
               │  (Geographic)  │
               └───────┬────────┘
                       │
               ┌───────▼────────┐
               │ Load Balancer  │  ← L7: HTTP routing
               │   (L4/L7)      │  ← L4: TCP distribution
               └───────┬────────┘
                       │
      ┌────────────────┼────────────────┐
      │                │                │
┌─────▼─────┐   ┌─────▼─────┐   ┌─────▼─────┐
│  Server 1 │   │  Server 2 │   │  Server 3 │
│  (AZ-a)   │   │  (AZ-b)   │   │  (AZ-c)   │
└───────────┘   └───────────┘   └───────────┘

Load Distribution Algorithms

•Round Robin — Requests distributed sequentially. Simple, works well for homogeneous servers with similar request costs.
•Weighted Round Robin — Servers receive traffic proportional to weight. Useful when servers have different capacities.
•Least Connections — Route to server with fewest active connections. Handles variable request durations well.
•Least Response Time — Route to server with lowest observed latency. Adaptive to actual performance.
•IP Hash — Hash client IP to determine server. Provides sticky routing without session state.
•Consistent Hashing — Distribution survives server additions/removals with minimal redistribution. Essential for caching layers.
•Random — Simple random selection. Statistically even distribution with many requests.

Load Balancing Algorithm Selection
Algorithm	Best For	Weakness
Round Robin	Homogeneous workloads	Ignores server health/capacity
Weighted Round Robin	Mixed server capacities	Weights may become stale
Least Connections	Variable request duration	Doesn't account for server capacity
Least Response Time	Latency-sensitive apps	Measurement overhead
IP Hash	Sticky sessions without state	Uneven distribution with few clients
Consistent Hashing	Distributed caches	More complex to implement

Health Checks Are Critical

Load balancers must detect unhealthy servers and stop routing to them. Configure active health checks (periodic probes) or use passive health checks (track response codes). A load balancer routing to dead servers is worse than no load balancer at all.

Data Partitioning (Sharding)

While stateless services scale easily, stateful systems (databases, caches) require data partitioning (sharding) to scale horizontally. Sharding divides data across multiple nodes, each responsible for a subset.

Sharding Strategies:

Range-Based Sharding:              Hash-Based Sharding:

Shard 1: user_id 1-1M              Shard = hash(user_id) % N
Shard 2: user_id 1M-2M
Shard 3: user_id 2M-3M             User 12345 → hash(12345) % 3 = 1 → Shard 1
                                   User 67890 → hash(67890) % 3 = 0 → Shard 0
Pros: Range scans efficient
Cons: Hotspots if ranges uneven    Pros: Even distribution
                                   Cons: Range queries require all shards


Directory-Based Sharding:          Geographic Sharding:

┌──────────────┐                   ┌─────────────┐
│  Lookup      │                   │   US Data   │── US Shard
│  Service     │                   │   EU Data   │── EU Shard  
│ user → shard │                   │   AP Data   │── AP Shard
└──────────────┘                   └─────────────┘

Flexible but lookup overhead       Data locality for latency

Sharding Challenges

•Shard Key Selection — The key determines data distribution. Poor keys create hotspots (one shard overloaded). Good keys distribute evenly.
•Cross-Shard Queries — Queries spanning multiple shards require scatter-gather, increasing latency and complexity.
•Cross-Shard Transactions — ACID transactions across shards require distributed transactions (2PC), which are slow and complex.
•Resharding — Adding/removing shards requires data migration. With hash-based sharding, this affects most data.
•Referential Integrity — Foreign keys across shards are impossible to enforce at database level.
•Operational Complexity — Backups, schema migrations, and monitoring multiply by shard count.

sharding-implementation.ts
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// Application-level sharding implementation
 
interface ShardConfig {
  id: number;
  host: string;
  port: number;
  database: string;
}
 
class ShardedDatabase {
  private shards: Map<number, Database> = new Map();
  private readonly shardCount: number;
  
  constructor(shardConfigs: ShardConfig[]) {
    this.shardCount = shardConfigs.length;
    
    for (const config of shardConfigs) {
      this.shards.set(config.id, new Database(config));
    }
  }
  
  // Determine shard for a key
  private getShardId(shardKey: string): number {
    // Consistent hashing for stable distribution
    const hash = this.hashFunction(shardKey);
    return hash % this.shardCount;
  }
  
  private hashFunction(key: string): number {
    // Simple hash - use murmur3 or xxhash in production
    let hash = 0;
    for (let i = 0; i < key.length; i++) {
      hash = ((hash << 5) - hash) + key.charCodeAt(i);
      hash = hash & hash;  // Convert to 32-bit integer
    }
    return Math.abs(hash);
  }
  
  // Get database connection for a shard key
  getShard(shardKey: string): Database {
    const shardId = this.getShardId(shardKey);
    const shard = this.shards.get(shardId);
    if (!shard) {
      throw new Error(`Shard ${shardId} not found`);
    }
    return shard;
  }
  
  // Insert to correct shard
  async insert(userId: string, data: Record<string, unknown>) {
    const shard = this.getShard(userId);
    return shard.insert('users', { userId, ...data });
  }
  
  // Query single shard
  async findById(userId: string) {
    const shard = this.getShard(userId);
    return shard.findOne('users', { userId });
  }
  
  // Scatter-gather for cross-shard queries
  async findAll(filter: Record<string, unknown>): Promise<any[]> {
    // Query all shards in parallel
    const results = await Promise.all(
      Array.from(this.shards.values()).map(shard => 
        shard.find('users', filter)
      )
    );
    
    // Merge results
    return results.flat();
  }
}
 
// Usage
const shardedDb = new ShardedDatabase([
  { id: 0, host: 'shard-0', port: 5432, database: 'users' },
  { id: 1, host: 'shard-1', port: 5432, database: 'users' },
  { id: 2, host: 'shard-2', port: 5432, database: 'users' },
]);
 
// Single-shard operation (fast)
const user = await shardedDb.findById('user-123');
 
// Cross-shard query (slower, hits all shards)
const premiumUsers = await shardedDb.findAll({ plan: 'premium' });
 
 
// Consistent hashing for cache sharding (handles node changes gracefully)
import { ConsistentHash } from 'consistent-hash';
 
const ring = new ConsistentHash();
ring.add('cache-1');
ring.add('cache-2');
ring.add('cache-3');
 
function getCacheNode(key: string): string {
  return ring.get(key);  // Returns node for this key
}
 
// Adding new node only reassigns ~1/N of keys
ring.add('cache-4');  // Minimal redistribution

Auto-Scaling: Dynamic Capacity

Static horizontal scaling requires predicting peak load and provisioning accordingly. Auto-scaling adds and removes capacity dynamically based on actual demand, optimizing both cost and performance.

Auto-Scaling Loop:

         ┌───────────────────────────────────┐
         │                                   │
         ▼                                   │
┌─────────────────┐                          │
│ Collect Metrics │                          │
│ (CPU, Memory,   │                          │
│  Queue Depth)   │                          │
└────────┬────────┘                          │
         │                                   │
         ▼                                   │
┌─────────────────┐                          │
│ Evaluate Rules  │                          │
│ Scale up if     │                          │
│ CPU > 70%       │                          │
│ Scale down if   │                          │
│ CPU < 30%       │                          │
└────────┬────────┘                          │
         │                                   │
         ▼                                   │
┌─────────────────┐                          │
│ Execute Scaling │                          │
│ Add/remove      │                          │
│ instances       │◄─── Cooldown ────────────┤
└────────┬────────┘     (prevent thrashing)  │
         │                                   │
         └───────────────────────────────────┘

Auto-Scaling Metrics

•CPU Utilization — Scale when average CPU exceeds threshold. Simple, widely applicable, but lags actual demand.
•Memory Utilization — Scale when memory pressure high. Important for memory-intensive workloads.
•Request Count — Scale based on requests per second. Direct measure of traffic, but ignores request complexity.
•Request Latency — Scale when latency increases. User-experience focused, but latency can spike before CPU is saturated.
•Queue Depth — Scale when message queue grows. Excellent for async workers and event-driven systems.
•Custom Metrics — Application-specific metrics (active users, game sessions). Most accurate but requires instrumentation.

auto-scaling-config.ts
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// AWS CDK Auto-Scaling configuration
import * as autoscaling from 'aws-cdk-lib/aws-autoscaling';
import * as cloudwatch from 'aws-cdk-lib/aws-cloudwatch';
 
// EC2 Auto-Scaling Group
const asg = new autoscaling.AutoScalingGroup(this, 'WebServers', {
  instanceType: ec2.InstanceType.of(ec2.InstanceClass.M5, ec2.InstanceSize.LARGE),
  machineImage: ec2.MachineImage.latestAmazonLinux(),
  vpc,
  
  // Capacity bounds
  minCapacity: 2,
  maxCapacity: 100,
  desiredCapacity: 4,
  
  // Health check
  healthCheck: autoscaling.HealthCheck.elb({
    grace: Duration.minutes(5),  // Time for new instances to warm up
  }),
});
 
// Scale based on CPU
asg.scaleOnCpuUtilization('CpuScaling', {
  targetUtilizationPercent: 70,
  cooldown: Duration.minutes(5),  // Wait before another scaling action
  scaleInCooldown: Duration.minutes(10),  // Longer cooldown for scale-in
});
 
// Scale based on request count
asg.scaleOnRequestCount('RequestScaling', {
  targetRequestsPerMinute: 10000,  // Per instance
  cooldown: Duration.minutes(3),
});
 
// Scale based on custom metric (active game sessions)
asg.scaleOnMetric('SessionScaling', {
  metric: new cloudwatch.Metric({
    namespace: 'GameServer',
    metricName: 'ActiveSessions',
    dimensionsMap: {
      AutoScalingGroup: asg.autoScalingGroupName,
    },
    statistic: 'Sum',
  }),
  scalingSteps: [
    { upper: 100, change: -2 },   // Below 100 sessions: remove 2
    { lower: 500, change: +1 },   // Above 500: add 1
    { lower: 1000, change: +3 },  // Above 1000: add 3
  ],
  adjustmentType: autoscaling.AdjustmentType.CHANGE_IN_CAPACITY,
});
 
 
// Kubernetes Horizontal Pod Autoscaler (HPA)
// kubectl apply -f hpa.yaml
 
/*
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-server-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-server
  
  minReplicas: 2
  maxReplicas: 50
  
  metrics:
  # CPU-based scaling
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
        
  # Memory-based scaling
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
        
  # Custom metric: requests per second
  - type: Pods
    pods:
      metric:
        name: http_requests_per_second
      target:
        type: AverageValue
        averageValue: "1000"  # 1000 rps per pod
  
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300  # Wait 5 min before scale down
      policies:
      - type: Percent
        value: 25             # Remove max 25% of pods
        periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 0    # Scale up immediately
      policies:
      - type: Percent
        value: 100            # Can double capacity
        periodSeconds: 60
      - type: Pods
        value: 4              # Or add 4 pods
        periodSeconds: 60
      selectPolicy: Max       # Use whichever adds more
*/

Distributed Coordination Challenges

Horizontal scaling introduces complexity that doesn't exist in single-machine systems. Understanding these challenges is essential for building reliable distributed systems.

Distributed System Challenges

•Consistency — When data is replicated across nodes, updates may not be immediately visible everywhere. CAP theorem forces trade-offs between consistency and availability.
•Partial Failures — Individual nodes can fail while others continue. System must detect failures and handle gracefully.
•Network Partitions — Network issues can split the cluster. Nodes must decide whether to continue operating or stop to prevent inconsistency.
•Clock Synchronization — Each machine has its own clock. Events on different machines can't be reliably ordered by timestamp alone.
•Leader Election — When a coordinator fails, remaining nodes must elect a new leader without creating split-brain scenarios.
•Distributed Locking — Coordinating exclusive access across machines requires distributed consensus, which is complex and has failure modes.

The CAP Theorem Trade-off:

                 Consistency (C)
                      ╱╲
                     ╱  ╲
                    ╱    ╲
                   ╱ CP   ╲
                  ╱ systems╲
                 ╱   e.g.  ╲
                ╱  Zookeeper╲
               ╱─────────────╲
              ╱       ╲       ╲
             ╱         ╲       ╲
            ╱    CA     ╲ AP    ╲
           ╱   systems   ╲systems╲
          ╱    e.g.       ╲ e.g. ╲
         ╱   PostgreSQL    ╲DynamoDB
        ▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔
      Availability (A)     Partition
                          Tolerance (P)

In a distributed system, you can only guarantee 2 of 3.
Since networks can always partition, you're really choosing
between CP (consistent but may be unavailable) and 
AP (available but may be inconsistent).

Embrace Eventual Consistency

Most web applications don't need strong consistency for every operation. User profile updates, feed posts, and product views can tolerate seconds of delay. Reserve strong consistency for critical operations: payments, inventory, and account balance. This hybrid approach enables horizontal scaling where it matters.

Capacity Planning for Scale

Effective horizontal scaling requires understanding current capacity and projecting future needs.

Capacity Planning Process:

1. Measure Current Performance
   │
   ├── What's the maximum RPS per instance?
   ├── At what point does latency degrade?
   ├── What's the limiting resource (CPU, memory, network)?
   │
   ▼
2. Project Future Load
   │
   ├── Historical growth rate
   ├── Planned feature launches
   ├── Marketing campaigns / events
   │
   ▼
3. Calculate Required Capacity
   │
   ├── Peak instances = Peak RPS / (RPS per instance × 0.7)
   │                                         ↑
   │                        Target 70% utilization for headroom
   ├── Add buffer for failures (N+1 or N+2)
   │
   ▼
4. Implement with Headroom
   │
   ├── Set auto-scaling min = projected average
   ├── Set auto-scaling max = projected peak × 1.5
   └── Review quarterly

Capacity Planning Example
Metric	Current	6-Month Projection	12-Month Projection
Average RPS	5,000	10,000	20,000
Peak RPS	15,000	30,000	60,000
RPS per instance	500	500	500
Instances at average	10	20	40
Instances at peak	30	60	120
Instance with buffer (+50%)	45	90	180
Monthly cost (est.)	$4,500	$9,000	$18,000

Capacity Planning Best Practices

•Load Test Regularly — Know your actual per-instance capacity, not theoretical. Real load tests reveal bottlenecks documentation doesn't.
•Plan for Peak, Not Average — Systems must handle peak load without degradation. Size for Black Friday, not a normal Tuesday.
•Include Failure Buffer — If you need 10 instances, have capacity for 12. Instance failures and zone outages happen.
•Reserve Capacity for Critical Events — Pre-scale before product launches or marketing pushes. Auto-scaling has reaction time.
•Monitor Headroom — Track how close you are to capacity. Alert at 70% sustained utilization to plan scaling.
•Review Quarterly — Growth isn't linear. Reassess projections as you learn more about actual patterns.

Summary: Horizontal Scaling for Throughput

We've explored horizontal scaling as the ultimate throughput multiplier. Here are the key insights:

Key Takeaways

•Horizontal scaling overcomes hardware limits — Instead of bigger machines, use more machines. Capacity becomes limited only by budget and architecture.
•Statelessness is the prerequisite — Design services without local state. Externalize sessions, caches, and files to shared storage.
•Load distribution is critical — Choose algorithms matching your workload. Implement health checks to avoid routing to failed nodes.
•Data partitioning enables database scaling — Shard by key, but understand the complexity: cross-shard queries, transactions, and resharding.
•Auto-scaling optimizes cost and capacity — Use metrics-based scaling, but configure cooldowns to prevent thrashing.
•Distributed systems have inherent complexity — CAP theorem forces trade-offs. Design for eventual consistency where strong consistency isn't essential.
•Capacity planning prevents surprises — Load test to know actual limits. Plan for peaks with buffer for failures.

Module Conclusion:

This module has covered the core techniques for throughput optimization:

Parallelization — Utilize multiple processors simultaneously
Batching — Amortize fixed costs across multiple operations
Connection Reuse — Eliminate establishment overhead
Queue-Based Processing — Decouple for load leveling and reliability
Horizontal Scaling — Multiply capacity through distribution

These techniques compound. A system that batches requests, reuses connections, processes asynchronously via queues, and scales horizontally can achieve throughput orders of magnitude beyond naive implementation. The art of system design is knowing which techniques apply to your specific constraints.

Module Complete

You've completed the Throughput Optimization module! You now have a comprehensive understanding of techniques to maximize the work your systems can accomplish—from parallelization and batching at the code level, through connection reuse and queue-based architectures, to horizontal scaling across distributed systems. Apply these techniques systematically to build systems that handle massive scale.