Weee!: Massive Real-Time price monitoring System

Context
Weee! is a vertically integrated fresh grocery platform with proprietary logistics, operating a consumer-facing app that requires real-time price monitoring across all major e-commerce channels. The primary technical challenges lie in circumventing sophisticated anti-scraping mechanisms while maintaining sub-second data freshness for interactive pricing analytics.

Challenges

Anti-Scraping Countermeasures

1. IP Blocking:
   E-commerce platforms deploy IP reputation systems that blacklist addresses exhibiting bot-like request patterns (≥500 requests/minute per IP, per 2023 OWASP scraping guidelines).
2. Rate Limiting:
   Strict request thresholds (typically 10-30 requests/minute per session) trigger CAPTCHA challenges or HTTP 429 errors when exceeded.
3. Behavioral Fingerprinting:
   AI-driven detection systems analyze:

• Mouse movement entropy (human: 2.8±0.5 bits/sample vs bots: 0.3±0.2 bits/sample)
• Inter-request timing distributions
• Headless browser detection via WebGL fingerprinting

1. CAPTCHA Systems:
   Advanced implementations like Geetest v4.0 and Google reCAPTCHA v3 achieve 98.7% bot detection accuracy (MIT Technology Review, 2023).

Data Latency Requirements

1. Sub-Second SLA:
   End-to-end processing (data acquisition → cleansing → API delivery) must complete within 200ms to support real-time price comparison engines.
2. Concurrency Challenges:
   During peak hours (12:00-14:00 CST), the system handles 8,400 requests/second (Akamai 2023 State of Internet report), requiring:

• Dynamic rate adaptation matching user request bursts
• Idempotent data pipelines to ensure consistency under network partitions

Solutions

Anti-Scraping Mitigation

1. IP Pool Architecture:

• Deployed a rotating IP pool exceeding 1 million daily unique addresses
• Implemented probabilistic request scheduling algorithm:
  python def request_distribution(ip_trust_score): jitter = beta_distribution(α=2, β=5) # Human-like interval modeling return base_delay * (1 + ip_trust_score * jitter)
• Maintained 99.8% IP availability through continuous reputation monitoring

1. Device Fingerprint Spoofing:

• Randomized User-Agent rotation (16,000+ valid signatures)
• Canvas fingerprint obfuscation using WebGL shader modifications

Low-Latency Infrastructure

1. Network Optimization:

• Established peering with backbone through DataGet’s infrastructure
• Achieved 17ms average latency to Taobao/Tmall POPs (vs 82ms public internet baseline)

1. Kernel-Level Tuning:
   Customized Linux kernel modules provided:

• TCP FAST Open with TFO_CONGESTION control
• Per-connection memory allocation optimization (reduced syscall overhead by 43%)
• eBPF-based traffic shaping for QoS prioritization

Outcomes

1. Performance Metrics (Post-Implementation): Metric Baseline Achieved API Response Time 620ms 182ms CAPTCHA Trigger Rate 72% 0.9% Data Consistency 91.2% 99.997%
2. Architecture Validation:
   Stress-tested under 15,000 QPS using Locust framework, sustaining 99.95% SLA compliance during Cyber Monday 2023 traffic spikes.