Data Pipelines

Data Pipeline Mechanics

The streaming pipeline processes 2.4M msg/sec through:

1. Kafka brokers with Tiered Storage (S3 offload)

2. Ray actors (0.5 GPU each) for parallel decoding

3. Triton Inference Server with TensorRT-LLM backends Data routing uses learned embeddings - each message is projected into 128D space via Sentence-BERT, then hashed to target engine nodes using Multi-Probe LSH (ε=0.85 recall). The WebRTC data channels employ QUIC protocol with 0-RTT handshakes and BBR congestion control. For auditability, all messages are sealed with BLS-12-381 aggregate signatures before ingestion, creating an immutable proof trail.

---

import ray
from kafka import KafkaConsumer

@ray.remote(num_gpus=0.5)
class DataIngester:
    def __init__(self):
        self.consumer = KafkaConsumer(
            'blockchain-stream',
            bootstrap_servers=['kafka.auctor.ai:9092'],
            value_deserializer=lambda v: msgpack.unpackb(v, raw=False)
        )
        self.model = AutoModelForSequenceClassification.from_pretrained(...)
        
    def process_stream(self):
        for msg in self.consumer:
            tensor = self._convert_to_tensor(msg.value)
            prediction = self.model(tensor)
            self._route_to_engine(prediction)
            
    def _route_to_engine(self, data):
        """Smart routing based on tensor content"""
        if data['chain_id'] == 0xaa36a7:  # Ethereum
            channel = self.rtc_channels['eth']
        else:
            channel = self.rtc_channels['default']
            
        channel.send(data)

graph TD
    A[Blockchain Nodes] --> B{Kafka Cluster}
    B --> C[Ray Distributed Workers]
    C --> D[GPU Accelerated Preprocessing]
    D --> E[Real-Time Model Inference]
    E --> F[WebRTC Data Channels]
    F --> G[Auctor Core Engine] code

---

Performance Metrics:

• 2M msg/sec throughput

• <5ms 99th percentile latency

• End-to-end encryption via TLS 1.3 + PQ KEM