From Grid to Cloud

TL;DR: In 2005 a grid of 10 Intel based PC with 512 Mb memory extracted semantic data from text at 50 docs per second. The database server was running Windows 2000 with Oracle 9 as the engine. The task was comparing words in documents to knowledge graph concepts with semantic tags. The GRID managed about 1,125,000 category compares a second. Today implementing this process in AWS with S3 as the database and Hadoop map-reduce on elastic beanstalk provides an estimate throughput with comparable 10 worker nodes exceeds 400,000 docs per second.

LLM: Claude-3.5-Sonnet
Analyze this scenario and provide a modern AWS implementation comparison:

2005 System Specs:

• 10 PCs with 512MB RAM each
• 50 docs/second processing rate
• ~1.125M category comparisons/second
• Oracle 9 for storage/retrieval
• Windows 2000 environment

Modern AWS Implementation:

Storage:

• S3 replaces Oracle 9
• Documents stored in S3 buckets with metadata tags
• Categories/semantic tags stored as JSON in DynamoDB for fast lookup

Processing Architecture:

• EMR (Elastic MapReduce) cluster running Hadoop
• m5.xlarge instances (4 vCPU, 16GB RAM)
• 10 worker nodes (matching original setup size)
• Auto-scaling enabled between 5-15 nodes based on queue depth

Processing Flow:

• Documents land in S3 input bucket
• EMR cluster picks up documents in batches
• Map phase: Documents split into words/phrases
• Reduce phase: Parallel category matching across nodes
• Results written back to S3 output bucket

Estimated Performance:

• Single m5.xlarge: ~2000 docs/second (40x improvement per node)
• 10 node cluster: ~20,000 docs/second
• Category comparisons: ~45M/second
• Additional gains from:
• Distributed S3 access (vs single Oracle DB)
• Modern CPU architecture
• More RAM (16GB vs 512MB)
• SSD storage
• Network throughput

This represents roughly:

• 400x improvement in document processing
• 40x improvement in category comparisons
• Better scalability and reliability
• Lower operational overhead

How long would it take to process the 3B CommonCrawl web pages as a document. Estimate cost.

Let me break this down systematically:

CommonCrawl Size Analysis:

• 3B web pages
• Average page size ~50KB
• Total data approximately 150TB uncompressed

Processing Requirements:

• At 20,000 docs/second (from previous calculation)
• Time calculation: 3,000,000,000 / 20,000 = 150,000 seconds
• = 41.67 hours with 10 nodes

Cost Calculation:

EMR Cluster (using current US-East-1 on-demand pricing):

• m5.xlarge: $0.192/hour per node
• EMR cost: $0.048/hour per node
• Total per node/hour: $0.24
• 10 nodes × 42 hours × $0.24 = $100.80

1. S3 Storage:

• Input data: 150TB × $0.023/GB = $3,450/month
• Output data (estimated 20% of input): 30TB × $0.023/GB = $690/month
• S3 API requests: ~$100

Data Transfer

• Internal AWS transfer: ~$0
• Multiple reads/writes: $0.02/GB
• 150TB × $0.02 = $3,000

DynamoDB for category lookup:

• Provisioned capacity: ~$200

Total Estimated Cost:

• Computing: ~$101
• Storage: ~$4,140
• Transfer: ~$3,000
• Database: ~$200
• Total: ~$7,441

Optimization Suggestions:

Storage:

• Use spot instances (70% savings on compute)
• Increase node count to reduce time
• Compress data to reduce storage costs
• Use S3 Intelligent Tiering for longer-term storage

With optimizations:

• Cost could be reduced to ~$4,000-5,000
• Processing time could be reduced to ~8-10 hours with 50 nodes
• Spot instances would reduce compute costs to ~$30-40

What are spot instances.
Spot instances are unused AWS EC2 capacity that you can bid on for significantly lower prices – typically 50-90% cheaper than regular on-demand instance prices. Here’s how they work:

Key Characteristics:

1. Price varies based on supply and demand
2. AWS can reclaim the instance with 2 minutes notice
3. Best for flexible, fault-tolerant workloads

Example Pricing:

• On-demand m5.xlarge: $0.192/hour
• Spot m5.xlarge: Often $0.04-0.06/hour (70-80% savings)

Best Used For:

• Batch processing
• Data analysis
• Background jobs
• Non-time-critical workloads
• Workloads that can handle interruption

Not Suitable For:

• Critical production applications
• Database servers
• Real-time processing
• Systems requiring consistent uptime

Handling Interruptions:

1. Applications should checkpoint progress
2. Use instance groups to maintain capacity
3. Store data externally (S3, EBS)
4. Configure automatic failover

For Our CommonCrawl Example:

• Use spot instances for processing nodes
• Keep master node as on-demand for stability
• Set up automatic bidding with maximum price
• Configure job resume capability
• Could reduce compute costs from $101 to ~$30

Would you like more details about implementing spot instances in an EMR cluster?

If we vectorize the data by tokenizing – no string compares – how much would that reduce cost.

Let’s recalculate with vectorized processing:

Performance Improvement:

• String comparisons replaced with vector operations
• Using CPU/GPU SIMD instructions for parallel processing
• Estimated 20-30x speedup in comparison operations

Original Processing:

• 20,000 docs/second with string comparisons
• New estimate: 400,000-600,000 docs/second
• 3B documents ÷ 500,000 = 6,000 seconds
• ≈ 1.67 hours with 10 nodes

Cost Breakdown:

1. EMR Compute (using spot instances):

• 2 hours × 10 nodes × $0.06/hour = $1.20
• Master node (on-demand) 2 hours × $0.24 = $0.48
• Total compute: ~$1.68

• Initial vectorization storage (compressed): ~50TB
• S3 cost: 50TB × $0.023/GB = $1,150
• Temporary processing storage: ~$100

Data Transfer:

• Single pass read/write
• 50TB × $0.02 = $1,000

Vector Database (optional):

• Using FAISS or similar: ~$50

New Total: ~$2,300
(Compared to previous $4,000-5,000)

Additional Benefits:

• Faster processing
• Lower CPU utilization
• Better scaling
• Simpler comparison logic

Key Considerations:

1. One-time cost to vectorize documents
2. Need for vector similarity threshold tuning
3. Memory requirements for vector operations
4. Potential for GPU acceleration