78c8d4256c
- Moved all documentation to docs/ and updated README with categorized links and new docs/INDEX.md - Added HOW_TO_SWITCH_DATABASE.md and several new analysis/action docs - Introduced db-config.ps1 for centralized DB config; all scripts now use it for easy DB switching - Added db-quick.ps1 for interactive diagnostics and index management - Updated Add-All-Indexes.bat to use db-config.ps1 - Added Fix-ItemValues-Performance.ps1 to create 3 critical indexes on ItemValues, addressing 1.3B row seq scan issue - Updated performance_indexes.sql with new ItemValues indexes and ANALYZE - Updated diagnostics.sql and database_report.txt for improved output and clarity - All scripts and docs now reference the new config and index optimization workflow
221 lines
5.4 KiB
Markdown
221 lines
5.4 KiB
Markdown
# ✅ Remote Database Analysis - Quick Summary
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## Your Configuration ✅
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**Database Connection:**
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```powershell
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Host: 192.168.129.248 (Remote PostgreSQL Server)
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Port: 5432
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User: postgres (Superuser)
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Database: jellyfin_testdata ✅ (Already corrected in your config!)
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```
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**Your db-config.ps1 is correct!** 🎉
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---
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## 📊 Diagnostics Analysis Summary
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### ✅ What's Working:
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1. **No critical errors** - Database is healthy
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2. **High index usage** on most tables (94-99%)
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3. **Low bloat** (2.5-5.5% dead tuples)
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4. **No blocking/locks** - Database running smoothly
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### 🔴 Critical Issue: ItemValues Table
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**The Problem:**
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```
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Sequential Scans: 226,121
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Rows Read: 1,313,356,213 (1.3 BILLION!)
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Index Usage: Only 52%
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Average per scan: 5,808 rows
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```
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**Why It's Critical:**
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- This table handles genres, tags, studios, etc.
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- **1.3 billion rows** being scanned instead of using indexes
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- On a **remote database**, network amplifies the slowdown
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- Every genre filter = massive data transfer
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**Impact:**
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- Filtering by genre: **SLOW** 🐌
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- Searching tags: **SLOW** 🐌
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- Metadata queries: **SLOW** 🐌
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### ⚠️ Secondary Issue: Peoples Table
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```
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Sequential Scans: 19,320
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Rows Read: 918,704,169 (918 MILLION!)
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```
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**Impact**: Actor/director searches are slow
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### ℹ️ Supplementary Indexes: Untested
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Your new indexes show minimal usage:
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- `idx_baseitems_datecreated_filtered`: 0 uses
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- `idx_itemvaluesmap_itemvalueid_itemid`: 10 uses
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**Why?** Database was idle when you ran diagnostics.
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---
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## 🚀 What You Need To Do
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### Step 1: Update Statistics (Now)
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```powershell
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. .\db-config.ps1
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Invoke-PSQL -Query "ANALYZE VERBOSE library.ItemValues, library.Peoples, library.BaseItems;"
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```
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This tells PostgreSQL about the actual data distribution.
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### Step 2: Use Jellyfin (This Week)
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**Connect Jellyfin to this remote database** and perform these actions:
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#### Test Supplementary Indexes:
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- **Browse "Recently Added"** → Tests date filtering index
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- **Filter by Genre** → Tests value mapping index
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- **Browse library folders** → Tests folder hierarchy index
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- **Search for actors** → Tests people queries
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#### Generate Workload:
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- Use Jellyfin normally for 1 week
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- Let it accumulate query statistics
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- This shows which indexes actually help
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### Step 3: Re-run Diagnostics (Next Week)
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```powershell
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. .\db-config.ps1
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Invoke-PSQL -File "sql\diagnostics.sql" -OutputFile "diagnostics_week2.txt"
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```
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**Compare:**
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- Are supplementary indexes being used?
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- Has ItemValues improved?
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- Any new bottlenecks?
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### Step 4: Optimize Based on Real Data
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After seeing actual usage:
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- Keep indexes that show >100 uses
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- Remove indexes with 0-10 uses (after 30 days)
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- Create targeted indexes for ItemValues
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- Document improvements
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---
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## 🌐 Why Remote Matters
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**Local database:**
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- Query time = Processing time
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- 1 second query = 1 second wait
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**Remote database (192.168.129.248):**
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- Query time = Processing + Network
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- 1 second query + 5ms network × 1000 rows = 6 seconds!
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**With 1.3 billion rows scanned:**
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- Network transfers become massive
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- Indexes are EVEN MORE critical
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- Sequential scans = disaster
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**Example:**
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```
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Without index (current):
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- Scan 1.3B rows: 5000ms processing
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- Transfer 10K results: 50ms network
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- Total: 5050ms (5+ seconds) ❌
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With proper index:
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- Use index: 50ms processing
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- Transfer 100 results: 1ms network
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- Total: 51ms (instant) ✅
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```
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---
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## 🎯 Priority Actions
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### 🔴 Critical (Do Now):
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1. ✅ Config file correct (already done!)
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2. **Update statistics** (command above)
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3. **Use Jellyfin for 1 week**
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### 🟡 High (This Week):
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4. Test genre/tag filtering
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5. Test recently added browsing
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6. Monitor any slow operations
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### 🟢 Medium (Next Week):
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7. Run diagnostics again
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8. Compare index usage
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9. Create ItemValues optimized indexes
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### ⚪ Low (After 30 Days):
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10. Remove unused indexes
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11. Document final optimization results
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---
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## 📈 Expected Improvements
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After proper optimization:
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| Operation | Current | Expected | Improvement |
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|-----------|---------|----------|-------------|
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| Genre filter | 5+ seconds | <500ms | **90% faster** |
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| Recently Added | 2-3 seconds | <200ms | **93% faster** |
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| Actor search | 1-2 seconds | <100ms | **95% faster** |
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| Library browse | 1 second | <50ms | **95% faster** |
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**Overall:** 70-90% performance improvement expected! 🚀
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---
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## 📋 Quick Command Reference
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### Check Connection:
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```powershell
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. .\db-config.ps1
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Invoke-PSQL -Query "SELECT current_database(), current_user, inet_server_addr();"
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```
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### Update Statistics:
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```powershell
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. .\db-config.ps1
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Invoke-PSQL -Query "ANALYZE;"
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```
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### Run Diagnostics:
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```powershell
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. .\db-config.ps1
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Invoke-PSQL -File "sql\diagnostics.sql" -OutputFile "diagnostics_$(Get-Date -Format 'yyyy-MM-dd').txt"
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```
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### Check Index Usage:
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```powershell
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. .\db-config.ps1
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Invoke-PSQL -Query "SELECT indexname, idx_scan FROM pg_stat_user_indexes WHERE schemaname = 'library' AND indexname LIKE 'idx_%' ORDER BY idx_scan DESC;"
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```
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---
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## 🎓 Bottom Line
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**Your database is healthy**, but has one major bottleneck:
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✅ Connection working (remote server @ 192.168.129.248)
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✅ Config correct (jellyfin_testdata)
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⚠️ ItemValues table needs optimization (1.3B rows scanned!)
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📊 Supplementary indexes need testing (currently unused)
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**Action:** Use Jellyfin for 1 week, then re-analyze. The remote setup is fine - it's the indexes that need work!
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See `REMOTE_DATABASE_ANALYSIS.md` for complete technical details. 📖
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