Day 116: Implement Data Restoration from Archives

Today’s Mission: Building Your Log Time Machine

Today we’re implementing the retrieval half of your archiving system - the ability to seamlessly query historical logs stored in archives. Think of Netflix’s viewing history feature: users can search years of data instantly, even though most of it lives in cold storage.

What You’ll Build:

• Archive query router that automatically detects historical queries

• Streaming decompression engine for large archive files

• Smart caching layer for frequently accessed archives

• Unified API that combines live and archived data seamlessly

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The Archive Retrieval Challenge

When Spotify analyzes user listening patterns over 2+ years, they’re querying petabytes of archived data. The challenge isn’t just storage - it’s making archived data feel as accessible as live data while managing costs and performance.

Most systems fail here because they treat archived data as a separate concern, forcing users to know whether their query hits live or historical data. Production systems hide this complexity entirely.

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Core Architecture Components

Query Intelligence Router

The router analyzes incoming queries to determine the optimal data sources. A query for “errors in the last hour” hits live data, while “monthly error trends” triggers archive retrieval. Smart routing prevents unnecessary archive access.

Archive Locator Service

Maps time ranges to specific archive files across storage tiers. Uses metadata indexes to quickly identify relevant archives without scanning entire storage systems. Critical for sub-second query response times.

Streaming Decompression Engine

Processes compressed archives in chunks rather than loading entire files into memory. Supports multiple compression formats (gzip, lz4, zstd) and can decompress while searching, reducing I/O overhead.

Result Merger

Combines data from multiple sources (live streams, recent archives, cold storage) into unified result sets. Handles deduplication, sorting, and pagination across heterogeneous data sources.

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Implementation Flow

Query Processing States

1. Query Analysis - Parse time ranges and determine data sources

2. Archive Selection - Identify relevant archive files using metadata

3. Parallel Retrieval - Fetch archives concurrently with decompression

4. Stream Processing - Process data chunks without full materialization

5. Result Assembly - Merge and format final response

Smart Caching Strategy

Recently accessed archives stay in warm cache (SSD), while metadata remains in memory. This three-tier approach (memory → SSD → object storage) balances performance with cost.

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Real-World Production Patterns

Airbnb’s Booking Analytics

When analyzing seasonal booking patterns, Airbnb queries 3+ years of historical data. Their restoration system streams terabytes of archived logs while users see sub-10-second response times through intelligent prefetching and compression.

GitHub’s Repository Insights

GitHub’s contribution graphs query years of commit history. They pre-aggregate common queries during archival, making historical analysis nearly as fast as real-time queries.

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Key Technical Insights

Compression-Aware Querying

Instead of decompressing entire archives, the system searches within compressed streams using specialized algorithms. This reduces bandwidth by 80% for large historical queries.

Temporal Locality Optimization

Users querying last month’s data often need last week’s data next. The cache prefetches adjacent time periods, improving hit rates from 20% to 65% in production systems.

Progressive Query Execution

For large time ranges, return initial results immediately while continuing archive processing in background. Users see progress rather than waiting for complete results.

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Integration with Previous Components

Your Day 115 archiving system created the storage structure - today’s restoration system reads from it. The archive metadata created during storage becomes the index for fast retrieval.

The query API you’ll build integrates with existing log processing endpoints, making archived data transparent to client applications. Users query the same endpoints regardless of data age.

Lesson Video

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Hands-On Implementation Guide

Github Link:

https://github.com/sysdr/course/tree/main/day116/day116-archive-restoration

Let’s build this step by step, focusing on the core concepts that make archive restoration work in production systems.

Phase 1: Understanding Archive Metadata (15 minutes)

Concept Deep Dive: Archive restoration begins with intelligent metadata management. Unlike simple file systems, production archives maintain rich metadata about time ranges, compression ratios, and content statistics.

Key Implementation Pattern:

python

# Archive Locator - Core Pattern
class ArchiveLocator:
    async def find_relevant_archives(self, query):
        # 1. Load metadata index
        # 2. Apply time range filtering
        # 3. Check additional filters
        # 4. Return sorted archive list

Building the Foundation:

1. Create project structure following microservices patterns

2. Set up virtual environment with Python 3.11 for modern async features

3. Install minimal dependencies - FastAPI, aiofiles, compression libraries

Expected Output:

bash

✅ Virtual environment created
✅ Dependencies installed successfully
✅ Project structure matches production patterns

Phase 2: Streaming Decompression Engine (20 minutes)

Production Insight: Netflix’s restoration system processes terabytes without loading entire files into memory. The secret? Streaming decompression that processes chunks while reading.

Implementation Strategy:

python

# Streaming Pattern - Memory Efficient
async def decompress_stream(file_path, compression_type):
    # Process in small chunks (8KB typical)
    # Yield control to event loop frequently
    # Support multiple compression formats

Key Optimizations:

• Chunk size tuning (8KB optimal for most systems)

• Async yielding prevents blocking other operations

• Format detection supports gzip, lz4, zstd automatically

Verification Commands:

bash

# Test compression formats
python -c “from restoration.decompressor import StreamingDecompressor; print(’✅ Decompressor ready’)”

# Memory usage test
python -m pytest tests/test_decompressor.py -v

Phase 3: Smart Cache Implementation (20 minutes)

Enterprise Pattern: Multi-tier caching (memory → SSD → cold storage) reduces archive access by 80% in production systems. The cache must handle both small metadata and large result sets.

Caching Strategy:

• Memory cache for frequently accessed metadata

• Disk persistence for session survival

• LRU eviction with access frequency weighting

• Cache key generation based on query fingerprints

Performance Testing:

bash

# Cache hit rate verification
curl -s localhost:8000/api/stats | jq ‘.cache.hit_rate’
# Expected: >0.6 (60% hit rate minimum)

# Memory efficiency test
python -c “
import psutil
import requests
# Generate cache load
for i in range(100):
    requests.get(’http://localhost:8000/api/archives’)
print(f’Memory usage: {psutil.virtual_memory().percent}%’)
“

Phase 4: Query Intelligence Router (25 minutes)

System Design Principle: The router determines optimal query execution paths. Simple time-based queries hit live data, while historical analysis triggers archive processing.

Router Decision Tree:

1. Analyze query time range - Live vs archived data

2. Estimate result size - Streaming vs batch processing

3. Check cache availability - Cached vs fresh computation

4. Resource allocation - Concurrent vs sequential processing

API Design Pattern:

python

# Unified Query Interface
@app.post(”/api/query”)
async def execute_query(query: QueryRequest):
    # 1. Validate query parameters
    # 2. Route to appropriate processor
    # 3. Merge results from multiple sources
    # 4. Apply pagination and filtering

Phase 5: React Dashboard Integration (20 minutes)

Modern UI Patterns: Production dashboards show real-time restoration progress, cache performance, and query optimization suggestions.

Component Architecture:

• QueryForm - Time range selection with validation

• ResultsTable - Streaming results with virtual scrolling

• StatsPanel - Real-time performance metrics

• ArchivesList - Available data sources overview

Build Process:

bash

# Frontend build (if Node.js available)
cd frontend && npm install && npm run build

# Fallback: Backend-served static files
# React components compiled and served by FastAPI

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Build, Test & Verification Guide

Quick Setup Commands

bash

# Install dependencies
pip install -r requirements.txt

# Run comprehensive tests
python -m pytest tests/ -v

# Start infrastructure
docker-compose up -d

# Run demonstration
python run_system.py

Integration Testing

Test Scenarios:

bash

# 1. Basic functionality
python -m pytest backend/tests/ -v

# 2. API integration  
curl -X POST localhost:8000/api/query \
  -H “Content-Type: application/json” \
  -d ‘{”start_time”: “2024-01-01T00:00:00”, “end_time”: “2024-01-02T00:00:00”}’

# 3. Performance verification
python verify.py  # Custom verification script

# 4. Load testing
for i in {1..50}; do
  curl -s localhost:8000/api/stats > /dev/null &
done
wait  # All requests should complete under 5 seconds

Expected Performance Metrics:

• Query latency: <100ms for metadata queries

• Throughput: 50+ concurrent queries/second

• Memory usage: <500MB for typical workloads

• Cache hit rate: >60% after warmup

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Docker Deployment Verification

Container Build

bash

# Multi-stage build for optimization
docker-compose build
# Expected: Clean build with no errors

# Size verification
docker images | grep archive-restoration
# Expected: <500MB image size

Service Health Checks

bash

# Start all services
docker-compose up -d

# Verify connectivity
docker-compose ps
# Expected: All services “Up” status

# API health check
curl localhost:8000/api/stats
# Expected: JSON response with cache/archive statistics

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Troubleshooting Common Issues

Import Errors

bash

# Fix Python path issues
export PYTHONPATH=”$(pwd)/backend/src:$PYTHONPATH”

# Verify imports work
python -c “from restoration.models import QueryRequest; print(’✅ Imports working’)”

Performance Issues

bash

# Check memory usage
python -c “
import psutil
print(f’Memory: {psutil.virtual_memory().percent}%’)
print(f’CPU: {psutil.cpu_percent(interval=1)}%’)
“

# Optimize chunk size if needed
# Edit decompressor.py chunk_size parameter

Cache Problems

bash

# Clear cache and restart
rm -rf data/cache/*
curl -X DELETE localhost:8000/api/cache

# Verify cache recreation
curl localhost:8000/api/stats | jq ‘.cache’

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Assignment: Historical Error Analysis

Objective: Build a restoration query that analyzes error patterns across 6 months of archived logs:

1. Multi-Archive Query - Search across monthly archive files

2. Pattern Detection - Identify recurring error types

3. Trend Visualization - Display error rates over time

4. Performance Optimization - Cache intermediate results

Success Metrics: Query 6 months of data in under 30 seconds, with results streaming as archives load.

Assignment Verification

bash

# 1. Create 6 months of sample data
curl -X POST localhost:8000/api/demo/create-sample-archives

# 2. Execute error pattern query
curl -X POST localhost:8000/api/query \
  -H “Content-Type: application/json” \
  -d ‘{
    “start_time”: “2024-01-01T00:00:00”,
    “end_time”: “2024-06-30T23:59:59”, 
    “filters”: {”level”: “ERROR”},
    “page_size”: 1000
  }’

# 3. Verify streaming response
# Should see progressive results, not all at once

# 4. Check cache performance
curl localhost:8000/api/stats | jq ‘.cache.hit_rate’
# Expected: Improving hit rate with repeated queries

Success Criteria Checklist

• Query completes in <30 seconds

• Results stream as archives load

• Cache hit rate >60% on repeated queries

• Memory usage remains stable

• Error patterns clearly identified

Solution Hints:

• Use metadata indexes to quickly identify relevant archives

• Process archives in parallel, not sequentially

• Stream results to UI as they become available

• Cache aggregated error counts for common time ranges

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Success Criteria

By lesson end, you’ll have:

• ✅ Unified query API handling live and archived data

• ✅ Sub-second response times for archive metadata queries

• ✅ Streaming restoration of multi-GB archive files

• ✅ Smart caching reducing archive access by 60%+

• ✅ Dashboard showing restoration performance metrics

Working Code Demo:

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Key Takeaways

Technical Mastery Gained

✅ Streaming I/O patterns for large file processing
✅ Multi-tier caching strategies for performance
✅ Query routing intelligence for optimal execution paths
✅ Microservices integration patterns
✅ Production monitoring and observability

Real-World Applications

This implementation mirrors systems used by:

• Netflix - User viewing history analysis

• GitHub - Repository activity restoration

• Airbnb - Booking pattern analysis

• Slack - Message history search

Tomorrow’s Foundation

Day 117 builds on today’s restoration system to implement:

• Cost optimization based on access patterns

• Intelligent tiering decisions

• Compression analysis for storage efficiency

The query patterns and performance metrics you capture today become the input for tomorrow’s optimization algorithms.

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Tomorrow’s Preview

Day 117 focuses on storage optimization - intelligent tiering, compression analysis, and cost reduction strategies. Today’s restoration system provides the usage patterns needed for optimization decisions.

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Your archive restoration system now provides the “time machine” capability essential for production log analytics. Users can seamlessly query years of historical data as easily as current logs.