Distributed systems traditionally force a choice between consistency and speed. Apache Ignite's RAFT implementation delivers both: strong guarantees that protect your business without the coordination penalties that limit throughput.
When network partitions split your cluster, duplicate payments happen. Apache Ignite prevents this through automatic coordination that maintains consistency without performance penalties.
Business scenario: Your payment processing system handles 10,000 transactions per second across 5 nodes. Network issues isolate 2 nodes from the remaining 3 nodes. Both groups continue processing payments. Customer payment gets charged twice.
The solution: Automatic consensus ensures only one group can process payments during network splits. Your application code stays simple while the platform handles distributed coordination automatically.
Performance guarantee: Consensus happens in the background without blocking transaction processing.
Business Problems from Inconsistent Data
Duplicate Payment Scenario
Network partitions create business-critical consistency problems:
What happens during network partition without proper coordination:
// Network partition splits payment cluster
// Both partitions continue processing payments independently
// Partition A (3 nodes) processes payment:
PaymentResult resultA = processPayment(payment); // SUCCESS: Balance updated to $500
// Partition B (2 nodes) processes same payment:
PaymentResult resultB = processPayment(payment); // SUCCESS: Balance updated to $500
// When network heals: customer charged twice, balance corrupted
// Expected: $500 final balance
// Actual: $0 final balance (double charge)
Business impact of consistency failures:
- Customer complaints: Duplicate charges from partition processing
- Financial exposure: Regulatory penalties for transaction sequence errors
- System downtime: Manual reconciliation required when partitions heal
- Business risk: Revenue loss during extended partition scenarios
Why Traditional Solutions Fail
Performance penalty: Traditional consensus adds 20-50ms to every transaction through multiple network round-trips. At 10,000 payments per second, this creates a 200-500 second processing backlog.
Failure problems:
- Network delays: Every transaction requires multiple round-trips to all nodes
- Processing blocks: All nodes wait for slowest participant
- Failure stops processing: Single node failure blocks all transactions
- Partition stops everything: Network partition stops all processing
Apache Ignite Automatic Coordination Solution
Zero-Code Consistency for Normal Operations
Your application code stays simple. Apache Ignite handles distributed coordination automatically.
// Your application code - simple and clean
public PaymentResult processPayment(PaymentRequest payment) {
return ignite.transactions().runInTransaction(tx -> {
Table accounts = ignite.tables().table("accounts");
// Standard database operations
Tuple account = accounts.recordView().get(tx,
Tuple.create().set("account_id", payment.accountId));
if (account.decimalValue("balance").compareTo(payment.amount) < 0) {
return PaymentResult.INSUFFICIENT_FUNDS;
}
// Update account balance
accounts.recordView().upsert(tx, Tuple.create()
.set("account_id", payment.accountId)
.set("balance", account.decimalValue("balance").subtract(payment.amount)));
return PaymentResult.SUCCESS;
});
// Apache Ignite handles all distributed coordination automatically:
// - Prevents duplicate processing during network partitions
// - Ensures balance consistency across all nodes
// - Maintains transaction ordering for compliance
// - Provides automatic failure recovery
}Automatic coordination benefits:
- No coordination code: Write business logic, Ignite handles distributed consistency
- No duplicate processing: Network partitions cannot create duplicate payments
- No configuration needed: Consistency works automatically without setup
- No performance penalty: Coordination happens in the background without blocking operations
Advanced Features: Manual Coordination Control
For specialized use cases, Apache Ignite provides manual control over distributed coordination. Most applications never need this.
When Manual Control Matters
- Custom distributed workflows with specific ordering requirements
- Multi-step operations requiring atomic coordination across steps
- Application-specific conflict resolution logic
- Performance-critical operations needing direct coordination control
// Advanced RAFT control for custom distributed algorithms
public class CustomPaymentWorkflowRaft {
private final RaftGroupService paymentRaftGroup;
private final ClusterService clusterService;
public CompletableFuture<PaymentResult> processCustomPaymentWorkflow(PaymentRequest payment) {
// Custom multi-step payment workflow requiring specific consensus behavior
CustomPaymentCommand paymentCommand = new CustomPaymentCommand(payment);
// Direct RAFT group control for specialized workflow
return paymentRaftGroup.run(paymentCommand)
.thenApply(result -> {
// Custom workflow processed with specialized consensus logic
return (PaymentResult) result;
});
}
// Custom command for specialized payment workflows
public static class CustomPaymentCommand implements Command {
private final PaymentRequest payment;
public CustomPaymentCommand(PaymentRequest payment) {
this.payment = payment;
}
public PaymentRequest getPayment() {
return payment;
}
}
// Custom RAFT group listener for specialized processing
public class CustomPaymentRaftGroupListener implements RaftGroupListener {
@Override
public void onWrite(Iterator<CommandClosure<WriteCommand>> iterator) {
while (iterator.hasNext()) {
CommandClosure<WriteCommand> closure = iterator.next();
if (closure.command() instanceof CustomPaymentCommand) {
CustomPaymentCommand cmd = (CustomPaymentCommand) closure.command();
PaymentResult result = processCustomPaymentWorkflow(cmd.getPayment());
closure.result(result);
}
}
}
private PaymentResult processCustomPaymentWorkflow(PaymentRequest payment) {
// Custom distributed workflow requiring specific consensus behavior
// This would implement specialized logic not available through standard transactions
return new PaymentResult();
}
}
}
Advanced Control Use Cases:
- Custom distributed state machines
- Specialized ordering requirements
- Performance-critical consensus operations
- Application-specific replication logic
RAFT Performance Advantages:
- Leader-based: Single node processes operations, eliminating coordination overhead
- Pipeline efficiency: Leaders process operations without blocking
- Majority consensus: Only majority nodes required (faster than unanimous)
- Log-based replication: Efficient state machine replication
RAFT Group Configuration
RAFT groups are created and managed through RaftManager for specialized processing requirements:
// RAFT group configuration for specialized processing
public class CustomRaftConfiguration {
private final RaftManager raftManager;
private final ClusterService clusterService;
private final String groupId = "custom-processing-group";
public RaftGroupService createCustomRaftGroup() {
ClusterNode localNode = clusterService.topologyService().localMember();
RaftNodeId nodeId = new RaftNodeId(groupId, localNode);
PeersAndLearners configuration = selectProcessingNodes();
CustomRaftGroupListener listener = new CustomRaftGroupListener();
RaftGroupEventsListener eventsListener = new CustomRaftGroupEventsListener();
return raftManager.startRaftGroupNode(
nodeId,
configuration,
listener,
eventsListener,
RaftGroupService::new,
createRaftOptionsConfigurer()
);
}
private PeersAndLearners selectProcessingNodes() {
// Select available cluster nodes for RAFT group
List<Peer> peers = clusterService.topologyService().allMembers().stream()
.limit(5) // Optimal RAFT group size for consensus
.map(node -> new Peer(node.name()))
.collect(Collectors.toList());
// Create learners list (can be empty for basic setup)
List<Peer> learners = List.of();
return PeersAndLearners.fromPeers(peers, learners);
}
private RaftGroupOptionsConfigurer createRaftOptionsConfigurer() {
return options -> {
// Configure RAFT group options for specialized processing
RaftGroupOptions groupOptions = (RaftGroupOptions) options;
groupOptions.serverDataPath(Paths.get("/path/to/raft/data"));
};
}
}Consensus Performance Under High-Velocity Load
RAFT Consensus Performance Impact
Standard Transaction Performance:
@Benchmark
public class StandardTransactionPerformance {
@Benchmark
public PaymentResult processStandardPayment() {
long startTime = System.nanoTime();
// Standard transaction - RAFT consensus handled automatically
PaymentResult result = ignite.transactions().runInTransaction(tx - {
// Business logic - Ignite handles distributed consistency
return processPaymentLogic(paymentRequest, tx);
});
long processingTime = System.nanoTime() - startTime;
// Performance includes automatic RAFT consensus overhead
return result;
}
}
Standard Operation Performance:
- Transaction processing: Milliseconds range depending on cluster configuration
- Automatic consensus: Ignite optimizes RAFT for table operations
- Developer simplicity: No consensus code required
- Throughput: Scales with cluster capacity
Advanced RAFT Performance (when using direct control):
- Custom processing: Microseconds for specialized logic
- Direct consensus: Application controls consensus behavior
- Specialized optimization: Custom performance tuning possible
- Complex workflows: Multi-step distributed operations
Handling Network Partitions During High Load
Partition Tolerance in Distributed Processing:
public class RaftPartitionHandling {
private final RaftGroupService raftGroupService;
private final ClusterService clusterService;
public CompletableFuture<String> handleOperationDuringPartition(String operationData) {
// RAFT automatically handles network partitions through leader-based consensus
Peer currentLeader = raftGroupService.leader();
if (currentLeader != null && isNodeInCluster()) {
// This node can communicate with cluster - process operation
CustomCommand command = new CustomCommand(operationData);
return raftGroupService.run(command)
.thenApply(result -> "Operation completed: " + result);
} else {
// This node is isolated or no leader available - reject operation
return CompletableFuture.completedFuture("Operation temporarily unavailable");
}
}
private boolean isNodeInCluster() {
// Check if this node is still part of the active cluster topology
ClusterNode localNode = clusterService.topologyService().localMember();
Collection<ClusterNode> allMembers = clusterService.topologyService().allMembers();
return allMembers.contains(localNode);
}
// Custom command implementation for RAFT processing
public static class CustomCommand implements Command {
private final String data;
public CustomCommand(String data) {
this.data = data;
}
public String getData() {
return data;
}
}
}
Partition Behavior:
- Majority partition: Continues processing operations with full consistency
- Minority partition: Stops processing to prevent split-brain scenarios
- Recovery: Partitions automatically reconcile when network heals
- Data safety: No data loss or duplication across partitions
Leader Election Performance
Fast Leader Election for Business Continuity:
public class RaftLeaderElection {
public void handleLeaderFailureDuringPeakLoad() {
// RAFT leader election for distributed processing
long electionStart = System.currentTimeMillis();
raftGroup.refreshLeader().thenAccept(newLeader -> {
long electionTime = System.currentTimeMillis() - electionStart;
// Election time depends on network conditions and RAFT configuration
log.info("Leader election completed in {}ms", electionTime);
// Resume processing immediately
resumeProcessing(newLeader);
});
}
private void resumeProcessing(Peer newLeader) {
// New leader immediately continues from RAFT log state
// No data loss or inconsistency during leadership change
log.info("Processing resumed under new leader: {}", newLeader.consistentId());
}
}
Leader Election Characteristics:
- Election time: Configurable based on network conditions and RAFT settings
- Service continuity: Processing resumes immediately after election
- Data consistency: All committed operations preserved across leader changes
- Automatic recovery: No manual intervention required
Real-World Consensus Scenarios
Bank Payment Processing Under Load
Daily Payment Volume: 1 million payments across 24 hours
Standard Bank Payment Processing:
// Bank payment processing - Ignite handles RAFT automatically
@Service
public class BankPaymentProcessor {
public PaymentResult processInterBankTransfer(TransferRequest transfer) {
// Standard transaction processing with automatic consistency
return ignite.transactions().runInTransaction(tx -> {
Table senderAccounts = ignite.tables().table("sender_accounts");
Table receiverAccounts = ignite.tables().table("receiver_accounts");
Table auditLog = ignite.tables().table("audit_log");
// Atomic transfer with automatic RAFT consensus
Tuple senderKey = Tuple.create().set("account_id", transfer.senderAccountId);
Tuple senderAccount = senderAccounts.recordView().get(tx, senderKey);
if (senderAccount.decimalValue("balance").compareTo(transfer.amount) < 0) {
return PaymentResult.INSUFFICIENT_FUNDS;
}
// Update both accounts atomically with automatic consensus
BigDecimal newSenderBalance = senderAccount.decimalValue("balance").subtract(transfer.amount);
senderAccounts.recordView().upsert(tx,
Tuple.create()
.set("account_id", transfer.senderAccountId)
.set("balance", newSenderBalance));
receiverAccounts.recordView().upsert(tx,
Tuple.create()
.set("account_id", transfer.receiverAccountId)
.set("balance", transfer.amount));
// Audit logging with automatic replication
auditLog.recordView().insert(tx, createAuditRecord(transfer));
return PaymentResult.SUCCESS;
});
}
}
For Complex Multi-Bank Workflows (advanced scenarios):
// Custom consensus for specialized inter-bank protocols
public class AdvancedInterBankProcessor {
public PaymentResult processComplexInterBankWorkflow(TransferRequest transfer) {
// Specialized workflow requiring custom consensus behavior
InterBankWorkflowCommand command = new InterBankWorkflowCommand(transfer);
return paymentRaftGroup.run(command)
.thenApply(result -> {
// Custom workflow with specialized consistency requirements
return (PaymentResult) result;
});
}
}
Performance Under Peak Load:
- Peak hour volume: 100,000 payments/hour (27/second average, 200/second peak)
- Consensus latency: Depends on network and storage configuration
- System availability: High availability through RAFT consensus and automatic failover
- Regulatory compliance: Guaranteed transaction ordering accuracy
E-commerce Order Processing
Flash Sale Event: 50,000 orders in 1 hour
Standard Order Processing:
// E-commerce order processing - automatic consistency
public class FlashSaleOrderProcessor {
public OrderResult processFlashSaleOrder(OrderRequest order) {
// Standard transaction handling flash sale inventory
return ignite.transactions().runInTransaction(tx -> {
Table inventory = ignite.tables().table("inventory");
Table orders = ignite.tables().table("orders");
Table payments = ignite.tables().table("payments");
// Inventory check with automatic consistency
Tuple productKey = Tuple.create().set("product_id", order.productId);
Tuple product = inventory.recordView().get(tx, productKey);
int availableQuantity = product.intValue("quantity");
if (availableQuantity < order.quantity) {
return OrderResult.OUT_OF_STOCK;
}
// Atomic inventory update, order creation, payment processing
inventory.recordView().upsert(tx,
Tuple.create()
.set("product_id", order.productId)
.set("quantity", availableQuantity - order.quantity));
orders.recordView().insert(tx, createOrderRecord(order));
payments.recordView().insert(tx, createPaymentRecord(order.payment));
return OrderResult.SUCCESS;
});
}
}
For Specialized Inventory Algorithms (advanced scenarios):
// Custom consensus for complex inventory management
public class AdvancedInventoryProcessor {
public OrderResult processWithCustomInventoryLogic(OrderRequest order) {
// Specialized inventory workflow requiring custom consensus
CustomInventoryCommand command = new CustomInventoryCommand(order);
return inventoryRaftGroup.run(command)
.thenApply(result -> (OrderResult) result);
}
}
Flash Sale Performance:
- Order processing rate: Scales with cluster capacity and configuration
- Inventory consistency: Maintained through RAFT consensus (prevents overselling)
- Payment consistency: Guaranteed through RAFT consensus (prevents duplicate charges)
- Customer experience: Response time depends on network and processing configuration
Business Impact of Distributed Consistency
Risk Mitigation
Financial Risk Reduction:
- Payment accuracy: Eliminates duplicate payments and lost transactions
- Regulatory compliance: Guaranteed transaction ordering for audit trails
- System reliability: Automatic failover maintains business continuity
- Data protection: Strong consistency prevents data corruption
Operational Risk Reduction:
- Manual intervention: Consensus automation reduces human error
- System recovery: Automatic partition healing reduces downtime
- Capacity planning: Predictable consensus behavior enables accurate sizing
- Incident response: Built-in fault tolerance reduces emergency responses
Revenue Protection
Payment Processing Firm Benefits:
- Transaction volume: Supports high-volume payment processing
- System availability: RAFT consensus provides automatic failover
- Consistency guarantees: Eliminates payment errors that damage customer trust
- Scalability: Horizontal scaling through RAFT group partitioning
E-commerce Platform Benefits:
- Peak load handling: Flash sales process high volumes without consistency issues
- Customer trust: Zero payment errors maintain customer confidence
- Competitive advantage: Reliable order processing during high-demand events
- Revenue capture: Consistent order processing vs eventual consistency risks
Operational Efficiency
Development Team Benefits:
- Simplified architecture: Single consistency model across all operations
- Reduced debugging: Strong consistency eliminates race condition bugs
- Faster deployment: Consensus handles failure scenarios automatically
- Predictable behavior: Deterministic failure modes simplify testing
Operations Team Benefits:
- Automated failover: RAFT consensus reduces manual intervention requirements
- Consistent monitoring: Single consistency model simplifies observability
- Predictable recovery: Automated partition healing reduces incident response time
- Scalable operations: Consensus qscales with cluster size
The Consistency-Performance Balance
Traditional distributed systems force trade-offs between consistency and performance. Strong consistency requires coordination overhead. Eventual consistency risks business-critical errors.
Apache Ignite's RAFT implementation optimizes for both consistency and performance. Strong consistency guarantees protect business operations while consensus performance supports high-velocity application requirements.
The principle: Consistency should enable performance, not limit it.
When your distributed system maintains strong consistency without significant performance penalties, you eliminate the architectural compromises that force trade-offs between business safety and operational speed.
High-velocity applications need both consistency guarantees and performance characteristics. RAFT consensus provides the distributed coordination foundation that enables both requirements simultaneously.
Return next Tuesday for Part 7, that examines how MVCC transactions build on the RAFT consensus foundation to provide ACID guarantees optimized for high-frequency operations. This ensures that distributed consistency enables rather than constrains transaction processing performance.