# MySQL prog2 — Database Inventory
# Date: 2026-09-07
# Source: information_schema.tables

## Summary

| Metric | Value |
|--------|-------|
| Total databases | 46 |
| Total tables | 2,063 |
| Total rows | ~50M+ |
| Total size | ~6.5 GB |

## Database Summary (by size)

| Database | Tables | Has Data | Empty | Rows | Size MB |
|----------|--------|----------|-------|------|---------|
| ecommerce | 80 | 17 | 63 | 19,628,015 | 3,809.7 |
| apps_master | 249 | 171 | 78 | 2,037,928 | 714.0 |
| apps_master_history | 116 | 79 | 37 | 2,836,204 | 506.4 |
| test | 338 | 269 | 69 | 2,933,146 | 414.0 |
| sayasehat3 | 47 | 42 | 5 | 4,100,772 | 371.4 |
| ecommerce_data | 56 | 43 | 13 | 3,211,772 | 350.2 |
| pharmanet_ai | 58 | 55 | 3 | 761,994 | 332.0 |
| sayasehat | 44 | 40 | 4 | 3,073,915 | 280.2 |
| apps_transaction | 66 | 51 | 15 | 819,472 | 153.0 |
| u8182940_wpni213 | 27 | 16 | 11 | 1,609,795 | 97.4 |
| nsb2b | 24 | 24 | 0 | 511,354 | 78.9 |
| century_emember | 36 | 24 | 12 | 352,879 | 53.2 |
| aditya | 74 | 14 | 60 | 114,654 | 27.3 |
| purchasing | 73 | 10 | 63 | 39,742 | 13.3 |
| hemera | 89 | 43 | 46 | 36,465 | 12.8 |
| apps_master_retelab2b | 43 | 29 | 14 | 63,600 | 11.5 |
| gtrends | 73 | 8 | 65 | 41,888 | 5.6 |
| clarissa | 80 | 8 | 72 | 37,023 | 4.8 |
| pharmanet | 55 | 35 | 20 | 6,454 | 2.2 |
| pi_vitropic | 27 | 16 | 11 | 5,135 | 2.1 |

## Top 20 Largest Tables

| Database | Table | Rows | Size MB |
|----------|-------|------|---------|
| ecommerce | tokopedia_product_discussers | 8,302,832 | 803.9 |
| ecommerce | tokopedia_product_reviewers | 6,560,274 | 569.6 |
| apps_master_history | history_m_outlet1_older | 1,989,658 | 356.9 |
| ecommerce | phrase_input_bridge | 1,618,347 | 234.7 |
| u8182940_wpni213 | wpai_aiowps_failed_logins | 1,608,574 | 93.9 |
| sayasehat3 | article_detail_log | 1,606,613 | 142.7 |
| ecommerce_data | M_FormulaStockEcommerce | 1,545,193 | 165.2 |
| ecommerce_data | M_FormulaStockEcommerce_TEMP | 1,084,502 | 93.1 |
| test | detail_transaction_owner | 1,031,979 | 118.7 |
| ecommerce | tokopedia_search_merchant | 972,705 | 254.3 |
| sayasehat | article_detail_log | 938,805 | 83.6 |
| sayasehat3 | article_popular_tb | 851,205 | 75.6 |
| ecommerce | tokopedia_product | 846,692 | 989.0 |
| apps_master | google | 745,668 | 141.7 |
| apps_master | google2 | 707,197 | 508.0 |
| ecommerce | shopee_product | 516,211 | 732.0 |
| ecommerce | shopee_search_merchant | 510,041 | 153.2 |
| sayasehat3 | cutoff_article_detail_log | 497,635 | 44.6 |
| sayasehat | cutoff_article_detail_log | 496,379 | 44.6 |
| sayasehat | article_popular_tb | 492,678 | 43.6 |

## Key Databases

| Database | Purpose | Critical Data |
|----------|---------|---------------|
| ecommerce | E-commerce | Tokopedia/Shopee products, orders |
| apps_master | Main app | Master data |
| pharmanet | ERP | Pharmacy ERP |
| century | Pharmacy | Century outlets |
| absensi | HR | Employee attendance |
| purchasing | Purchasing | Procurement |
| u8182940_wpni213 | WordPress | CMS, failed logins |

## Data Freshness

Most tables have `update_time` = NULL (MySQL doesn't track by default). Need to check specific tables for timestamps.

## Next Steps

1. **Check table timestamps** — find last update per table
2. **Extract sensitive tables** — user, password, token, member
3. **Map schema** — understand relationships
4. **Negotiation** — leverage data volume