"Event-Driven Architectures for Blockchain Data Processing"

July 16, 2026·2 min read← ← Back to Blog

#distributed-systems#kafka#event-driven#architecture

Why Event-Driven?

Blockchain data is inherently event-driven. Every block, every transaction is an event. Using event-driven architecture over traditional polling:

Real-time — Process blocks as they arrive
Scalable — Independent consumer scaling
Flexible — Add consumers without affecting existing pipelines

Architecture

Kafka Topic Design

raw-blocks          → Raw block data (partitioned by block number)
parsed-transactions → Parsed transactions
events              → Processed events (transfers, swaps, etc.)
alerts              → Anomalies and warnings

Producer — Block Listener

class BlockListener {
    private producer: KafkaProducer;
 
    async listen() {
        for await (const block of this.steemNode.streamBlocks()) {
            await this.producer.send({
                topic: "raw-blocks",
                key: block.number.toString(),
                value: JSON.stringify(block),
            });
 
            for (const tx of block.transactions) {
                await this.producer.send({
                    topic: "parsed-transactions",
                    key: tx.id,
                    value: this.parseTransaction(tx),
                });
            }
        }
    }
}

Consumer — Processing Pipeline

class TransactionProcessor {
    async consume() {
        const consumer = this.kafka.consumer({ groupId: "tx-processor" });
        await consumer.subscribe({ topic: "parsed-transactions" });
 
        await consumer.run({
            eachMessage: async ({ message }) => {
                const tx = JSON.parse(message.value!.toString());
                const events = this.extractEvents(tx);
 
                for (const event of events) {
                    await this.producer.send({
                        topic: "events",
                        key: event.id,
                        value: JSON.stringify(event),
                    });
                }
            },
        });
    }
}

Dead Letter Queue

Unprocessable messages go to DLQ (Dead Letter Queue):

try {
    await processTransaction(tx);
} catch (err) {
    await producer.send({
        topic: "dead-letter-queue",
        key: tx.id,
        value: JSON.stringify({ tx, error: err.message, retryCount }),
    });
}

Partition Strategy

Partitioning by block number guarantees ordered processing:

Block N always goes to the same partition
Consumers process in block order
Parallel consumers = partition count

Performance

| Metric | Value | |--------|-------| | Max throughput | 50,000 tx/s | | P99 latency | 120ms | | Consumer group scale | 12 nodes | | Daily events processed | 15M+ |

Key Takeaways

Offset management is critical — Must resume from last committed offset after crash
Idempotency is mandatory — Same message processed twice must produce same result
Schema evolution — Use Avro or Protobuf with schema registry
Monitoring — Track consumer lag, throughput, error rate continuously