Apache Ignite Architecture Series: Part 1 - When Multi-System Complexity Compounds at Scale

Apache Ignite shows what really happens once your good enough multi-system setup starts cracking under high-volume load. This piece breaks down why the old stack stalls at scale and how a unified, memory-first architecture removes the latency tax entirely.

Your high-velocity application started with smart architectural choices. PostgreSQL for reliable transactions, Redis for fast cache access, custom processing for specific business logic. These decisions enabled initial success and growth.

But success changes the game. Your application now processes thousands of events per second and customers expect microsecond response times. Real-time insights drive competitive advantage. The same architectural choices that enabled growth now create performance bottlenecks that compound with every additional event.

At high event volumes, data movement between systems becomes the primary performance constraint.

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The Scale Reality for High-Velocity Applications

As event volume grows, architectural compromises that once seemed reasonable at lower scale become critical bottlenecks. Consider a financial trading platform, gaming backend, or IoT processor handling tens of thousands of operations per second.

Event Processing Under Pressure

High-frequency event characteristics

• Events arrive faster than traditional batch processing can handle
• Each event requires immediate consistency checks against live data
• Results must update multiple downstream systems simultaneously
• Network delays compound into user-visible lag
• Traffic spikes create systemic pressure — traditional stacks drop connections or crash when overwhelmed

The compounding effect

• At 100 events per second, network latency of 2ms adds minimal overhead.
• At 10,000 events per second, that same 2ms latency creates a 20-second processing backlog within system boundaries.
• During traffic spikes (50,000+ events per second), traditional systems collapse entirely, dropping connections and losing data when they're needed most.

The math scales against you.

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When Smart Choices Become Scaling Limits

Initial Architecture, works great at lower scale:

flowchart TB
    subgraph "Event Processing"
        Events[High-Volume Events
10,000/sec]
    end

    subgraph "Multi-System Architecture"
        Redis[(Cache
Session Data
2ms latency)]
        PostgreSQL[(Relational DB
Transaction Data
5ms latency)]
        Processing[Custom Processing
Business Logic
3ms latency]
    end

    Events --> Redis
    Events --> PostgreSQL
    Events --> Processing

    Redis <-->|Sync Overhead| PostgreSQL
    PostgreSQL <-->|Data Movement| Processing
    Processing <-->|Cache Updates| Redis

What happens at scale

• Network-latency tax: Every system hop adds milliseconds that compound
• Synchronization delays: Keeping systems consistent creates processing queues
• Memory overhead: Each system caches the same data in different formats
• Consistency windows: Brief periods where systems show different data states

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The Hidden Cost of Multi-System Success

Data Movement Overhead:

Your events don't just need processing, they need processing that maintains consistency across all systems.

Each event triggers:

• Cache lookup (Redis): ≈ 0.5 ms network + processing
• Transaction validation (PostgreSQL): ≈ 2 ms network + disk I/O
• Business-logic execution (Custom): ≈ 1ms processing + data marshalling
• Result synchronization (across systems): ≈ 3 ms coordination overhead

Minimum per-event cost ≈ 7 ms before business logic.

At 10,000 events/s, you’d need 70 seconds of processing capacity just for data movement per real-time second!

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The Performance Gap That Grows With Success

Why Traditional Options Fail

Option 1: Scale out each system

• Strategy: Add cache clusters, database replicas, processing nodes
• Result: More systems to coordinate, exponentially more complexity
• Reality: Network overhead grows faster than processing capacity

Option 2: Custom optimization

• Strategy: Build application-layer caching, custom consistency protocols
• Result: Engineering team maintains complex, system-specific optimizations
• Reality: Solutions don't generalize; each optimization creates technical debt

Option 3: Accept compromises

• Strategy: Use async processing, eventual consistency, and accept delayed insights
• Result: Business requirements compromised to fit architectural limitations
• Reality: Competitive disadvantage as customer expectations grow

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The Critical Performance Gap

Component	Optimized for	Typical Latency
Database	ACID transactions	Milliseconds
Cache	Access speed	Microseconds
Compute	Throughput	Minutes – hours

Applications needing microsecond insights on millisecond transactions have no good options at scale in traditional architectures.

During traffic spikes, traditional architectures either drop connections (data loss) or degrade performance (missed SLAs). High-velocity applications need intelligent flow control that guarantees stability under pressure while preserving data integrity.

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Event Processing at Scale

Here's what traditional multi-system event processing costs:

// Traditional multi-system event processing
long startTime = System.nanoTime();

// 1. Cache lookup for session data
String sessionData = redisClient.get("session:" + eventId);  // ~500μs network
if (sessionData == null) {
    sessionData = postgresDB.query("SELECT * FROM sessions WHERE id = ?", eventId);  // ~2ms fallback
    redisClient.setex("session:" + eventId, 300, sessionData);  // ~300μs cache update
}

// 2. Transaction processing
postgresDB.executeTransaction(tx -> {  // ~2-5ms transaction
    tx.execute("INSERT INTO events VALUES (?, ?, ?)", eventId, userId, eventData);
    tx.execute("UPDATE user_stats SET event_count = event_count + 1 WHERE user_id = ?", userId);
});

// 3. Custom processing with consistency coordination
ProcessingResult result = customProcessor.process(eventData, sessionData);  // ~1ms processing
redisClient.setex("result:" + eventId, 600, result);  // ~300μs result caching

// 4. Synchronization across systems
ensureConsistency(eventId, sessionData, result);  // ~2-3ms coordination

long totalTime = System.nanoTime() - startTime;
// Total: 6-12ms per event (not including queuing delays)

Compound Effect at Scale:

Rate	Required processing time / s
1,000 events / s	6–12 s
5,000 events / s	30–60 s
10,000 events / s	60–120 s

The math doesn’t work: parallelism helps, but coordination overhead grows exponentially with system count.

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Real-World Breaking Points

• Financial services: Trading platforms hitting 10,000+ trades/second discover that compliance reporting delays impact trading decisions.
• Gaming platforms: Multiplayer backends processing user actions find that leaderboard updates lag behind gameplay events.
• IoT analytics: Manufacturing systems processing sensor data realize that anomaly detection arrives too late for preventive action.

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The Apache Ignite Alternative

Eliminating Multi-System Overhead

flowchart TB
    subgraph "Event Processing"
        Events[High-Volume Events
10,000/sec]
    end

    subgraph "Apache Ignite Platform"
        subgraph "Collocated Processing"
            Memory[Memory-First Storage
Optimized Access Times]
            Transactions[MVCC Transactions
ACID Guarantees]
            Compute[Event Processing
Where Data Lives]
        end
    end

    Events --> Memory
    Memory --> Transactions
    Transactions --> Compute

    Memory <-->|Minimal Copying| Transactions
    Transactions <-->|Collocated| Compute
    Compute <-->|Direct Access| Memory

Key difference: events process where the data lives, eliminating inter-system latency.

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Apache Ignite Performance Reality Check

Here's the same event processing with integrated architecture:

// Apache Ignite integrated event processing
try (IgniteClient client = IgniteClient.builder().addresses("cluster:10800").build()) {
    // Single integrated transaction spanning cache, database, and compute
    client.transactions().runInTransaction(tx -> {
        // 1. Access session data (in memory, no network overhead)
        Session session = client.tables().table("sessions")
            .keyValueView().get(tx, Tuple.create().set("id", eventId));

        // 2. Process event with ACID guarantees (same memory space)
        client.sql().execute(tx, "INSERT INTO events VALUES (?, ?, ?)",
                           eventId, userId, eventData);

        // 3. Execute processing collocated with data
        ProcessingResult result = client.compute().execute(
            JobTarget.colocated("events", Tuple.create().set("id", eventId)),
            EventProcessor.class, eventData);

        // 4. Update derived data (same transaction, guaranteed consistency)
        client.sql().execute(tx, "UPDATE user_stats SET event_count = event_count + 1 WHERE user_id = ?", userId);

        return result;
    });
}
// Result: microsecond-range event processing through integrated architecture

Processing 10,000 events/s is achievable with integrated architecture eliminating network overhead.

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The Unified Data-Access Advantage

Here's what eliminates the need for separate systems:

// The SAME data, THREE access paradigms, ONE system
Table customerTable = client.tables().table("customers");

// 1. Key-value access for cache-like performance
Customer customer = customerTable.keyValueView()
    .get(tx, Tuple.create().set("customer_id", customerId));

// 2. SQL access for complex analytics
ResultSet<SqlRow> analytics = client.sql().execute(tx,
    "SELECT segment, AVG(order_value) FROM customers WHERE region = ?", region);

// 3. Record access for type-safe operations
CustomerRecord record = customerTable.recordView()
    .get(tx, new CustomerRecord(customerId));

// All three: same schema, same data, same transaction model

Eliminates:

• Cache API for cache operations
• Data movement during distributed joins for analytical queries
• Custom mapping logic for type-safe access
• Data synchronization between cache and database
• Schema drift risks across different systems

Unified advantage: One schema, one transaction model, multiple access paths.

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Apache Ignite Architecture Preview

The ability to handle high-velocity events without multi-system overhead requires specific technical innovations:

• Memory-first storage: Event data lives in memory with optimized access times typically under 10 microseconds for cache-resident data
• Collocated compute: Processing happens where data already exists, eliminating movement
• Integrated transactions: ACID guarantees span cache, database, and compute operations
• Minimal data copying: Events process against live data through collocated processing and direct memory access

These innovations address the compound effects that make multi-system architectures unsuitable for high-velocity applications.

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Business Impact of Architectural Evolution

Cost Efficiency

• Reduced infrastructure: one platform instead of several
• Lower network costs: eliminate inter-system bandwidth overhead
• Simplified operations: fewer platforms to monitor, backup, and scale

Performance Gains

• Microsecond latency: eliminates network overhead
• Higher throughput: more events on existing hardware
• Predictable scaling: consistent performance under load

Developer Experience

• Single API: one model for all data operations
• Consistent behavior: no synchronization anomalies
• Faster delivery: one integrated system to test and debug

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The Architectural Evolution Decision

Every successful application reaches this point: the architecture that once fueled growth now constrains it.

The question isn't whether you'll hit multi-system scaling limits. It's how you'll evolve past them.

Apache Ignite consolidates transactions, caching, and compute into a single, memory-first platform designed for high-velocity workloads. Instead of managing the compound complexity of coordinating multiple systems at scale, you consolidate core operations into a platform designed for high-velocity applications.

Your winning architecture doesn't have to become your scaling limit. It can evolve into the foundation for your next phase of growth.

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Return next Tuesday for Part 2, where we examine how Apache Ignite’s memory-first architecture enables optimized event processing while maintaining durability, forming the basis for true high-velocity performance.