vending_data_intelligence_report/PERFORMANCE_ANALYSIS.md

# Performance Analysis - Data Intelligence Report Generator

**Date:** 2024-12-11
**Total Runtime:** ~33 minutes (1971 seconds)

## Executive Summary

| Category | Time | Percentage |
|----------|------|------------|
| **SQL Queries** | ~31 min | **94%** |
| Excel Generation | ~12 sec | 0.6% |
| PDF Generation | ~3 sec | 0.2% |
| Other (consolidation, recommendations) | <1 sec | <0.1% |

**Conclusion:** The bottleneck is 100% in Oracle SQL queries. Excel and PDF generation are negligible.

---

## Top 20 Slowest Operations

| Rank | Operation | Duration | Rows | Notes |
|------|-----------|----------|------|-------|
| 1 | `QUERY: clienti_sub_medie` | 130.63s | 100 | Complex aggregation |
| 2 | `QUERY: vanzari_lunare` | 129.90s | 25 | Monthly aggregation over 12 months |
| 3 | `QUERY: indicatori_agregati_venituri_yoy` | 129.31s | 3 | YoY comparison - 24 month scan |
| 4 | `QUERY: sumar_executiv_yoy` | 129.05s | 5 | YoY comparison - 24 month scan |
| 5 | `QUERY: sumar_executiv` | 129.84s | 6 | Basic KPIs |
| 6 | `QUERY: dispersie_preturi` | 97.11s | 50 | Price variance analysis |
| 7 | `QUERY: trending_clienti` | 69.84s | 12514 | Large result set |
| 8 | `QUERY: marja_per_client` | 68.58s | 7760 | Large result set |
| 9 | `QUERY: concentrare_risc_yoy` | 66.33s | 3 | YoY comparison |
| 10 | `QUERY: concentrare_risc` | 66.19s | 3 | Risk concentration |
| 11 | `QUERY: dso_dpo_yoy` | 65.88s | 2 | YoY comparison |
| 12 | `QUERY: clienti_marja_mica` | 65.93s | 7 | Low margin clients |
| 13 | `QUERY: sezonalitate_lunara` | 65.93s | 12 | Seasonality |
| 14 | `QUERY: concentrare_clienti` | 65.76s | 31 | Client concentration |
| 15 | `QUERY: indicatori_agregati_venituri` | 65.59s | 3 | Revenue indicators |
| 16 | `QUERY: marja_client_categorie` | 65.27s | 2622 | Client-category margins |
| 17 | `QUERY: top_produse` | 65.26s | 50 | Top products |
| 18 | `QUERY: clienti_ranking_profit` | 65.03s | 2463 | Client profit ranking |
| 19 | `QUERY: marja_per_categorie` | 64.85s | 4 | Margin by category |
| 20 | `QUERY: productie_vs_revanzare` | 64.86s | 3 | Production vs resale |

---

## Fast Queries (<5 seconds)

| Query | Duration | Rows |
|-------|----------|------|
| `stoc_lent` | 0.06s | 100 |
| `solduri_furnizori` | 0.08s | 172 |
| `pozitia_cash` | 0.10s | 4 |
| `indicatori_lichiditate` | 0.13s | 4 |
| `analiza_prajitorie` | 0.15s | 39 |
| `stoc_curent` | 0.16s | 28 |
| `solduri_clienti` | 0.29s | 825 |
| `facturi_restante_furnizori` | 0.55s | 100 |
| `dso_dpo` | 0.65s | 2 |
| `ciclu_conversie_cash` | 0.95s | 4 |
| `clasificare_datorii` | 0.99s | 5 |
| `facturi_restante` | 1.24s | 100 |
| `aging_datorii` | 1.43s | 305 |
| `portofoliu_clienti` | 1.60s | 5 |
| `rotatie_stocuri` | 1.70s | 100 |
| `grad_acoperire_datorii` | 2.17s | 5 |
| `proiectie_lichiditate` | 2.17s | 4 |
| `aging_creante` | 4.37s | 5281 |

---

## Excel Generation Breakdown

| Operation | Duration | Rows |
|-----------|----------|------|
| Save workbook | 4.12s | - |
| trending_clienti sheet | 2.43s | 12514 |
| marja_per_client sheet | 2.56s | 7760 |
| aging_creante sheet | 1.57s | 5281 |
| clienti_ranking_profit sheet | 0.78s | 2463 |
| marja_client_categorie sheet | 0.56s | 2622 |
| All other sheets | <0.2s each | - |

**Total Excel:** ~12 seconds

---

## PDF Generation Breakdown

| Operation | Duration |
|-----------|----------|
| Chart: vanzari_lunare | 0.80s |
| Chart: concentrare_clienti | 0.61s |
| Chart: ciclu_conversie_cash | 0.33s |
| Chart: productie_vs_revanzare | 0.21s |
| Save document | 0.49s |
| All pages | <0.01s each |

**Total PDF:** ~3 seconds

---

## Root Cause Analysis

### Why are queries slow?

1. **Full table scans on `fact_vfacturi2`**
   - Most queries filter by `data >= ADD_MONTHS(SYSDATE, -12)` or `-24`
   - Without an index on `data`, Oracle scans the entire table

2. **YoY queries scan 24 months**
   - `sumar_executiv_yoy`, `indicatori_agregati_venituri_yoy`, etc.
   - These compare current 12 months vs previous 12 months
   - Double the data scanned

3. **Complex JOINs without indexes**
   - Joins between `fact_vfacturi2`, `fact_vfacturi_detalii`, `vnom_articole`, `vnom_parteneri`
   - Missing indexes on foreign keys

4. **Repeated aggregations**
   - Multiple queries calculate similar sums (vânzări, marjă)
   - Each query re-scans the same data

---

## Optimization Recommendations

### Priority 1: Add Indexes (Immediate Impact)

```sql
-- Index on date column (most critical)
CREATE INDEX idx_vfacturi2_data ON fact_vfacturi2(data);

-- Composite index for common filters
CREATE INDEX idx_vfacturi2_filter ON fact_vfacturi2(sters, tip, data);

-- Index on detail table join column
CREATE INDEX idx_vfacturi_det_nrfac ON fact_vfacturi_detalii(nrfactura);
```

### Priority 2: Materialized Views (Medium-term)

```sql
-- Pre-aggregated monthly sales
CREATE MATERIALIZED VIEW mv_vanzari_lunare
BUILD IMMEDIATE
REFRESH COMPLETE ON DEMAND
AS
SELECT
    TRUNC(data, 'MM') as luna,
    SUM(valoare) as vanzari,
    SUM(marja) as marja
FROM fact_vfacturi2
WHERE sters = 0 AND tip NOT IN (7,8,9,24)
GROUP BY TRUNC(data, 'MM');
```

### Priority 3: Query Consolidation (Long-term)

- Combine related queries into single CTEs
- Calculate base metrics once, derive others
- Use window functions instead of self-joins for YoY

---

## Monitoring

Run with performance logging enabled:
```bash
python main.py --months 12
# Check output/performance_log.txt for detailed breakdown
```

---

## Version History

| Date | Change |
|------|--------|
| 2024-12-11 | Initial performance analysis with PerformanceLogger |